189 results about "Deviation vector" patented technology

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention relates to the technical field of precision manufacturing, and discloses a multi-mode laser galvanometer calibration method and device. The method comprises the steps of obtaining a smooth angle sequence, comparing the smooth angle sequence with a calibration model, extracting an abnormal component if a comparison deviation is abnormal, and performing smooth processing to obtain a deviation vector; fusing the statistical characteristics of the light beam position and the deviation vector, predicting the light beam position, and generating a calibration parameter set if the light beam position exceeds a deviation range; extracting a key compensation item and optimizing a driving sequence, fusing frequency domain interference to generate a simulation track, and determining a stability index; and comparing the environment feature set, adjusting parameters to obtain an enhanced path model, and determining a final calibration scheme in combination with real-time feedback. According to the method, accurate calibration of the galvanometer in a multi-mode dynamic scene can be realized.

The invention discloses a ground wire multi-machine synchronous traction pay-off scheduling method and system, relates to the technical field of high-voltage transmission line construction, and is used for solving the problem of insufficient multi-machine real-time tension synchronization precision in the existing method. A tension sensor and a speed sensor which are arranged on each traction device collect a tension value, a traction speed value and position coordinate data of a ground wire in real time, and transmit the data to a central scheduling controller; establishing a multi-machine unified time synchronization reference and tension reference model, calibrating a timestamp through a GPS module, and initializing a threshold value based on historical parameters; comparing tension values one by one to calculate a deviation vector sequence; activating a compensation algorithm to execute a speed fine adjustment and tension compensation instruction; iteratively calculating the feedback adjustment value until the deviation is stable; and integrating the synchronous data to the overall paying-off scheduling model to dynamically update the path planning and speed curve. According to the method and the system, the problem of insufficient multi-machine real-time tension synchronization precision in the prior art is effectively solved.

The invention discloses a control method and system of a servo press, and relates to the field of press control, a force displacement curve is obtained in real time from a current punching period, a key feature vector of the force displacement curve is extracted, the key feature vector is compared with an ideal golden feature vector, and a multi-dimensional deviation vector is obtained. Particularly, a deviation decoupling method based on morphological analysis is introduced, and the total deviation can be accurately decomposed into a thermal drift compensation amount caused by the thermal effect of equipment and a material characteristic compensation coefficient caused by plate performance fluctuation by analyzing translation and zooming differences of a current curve and a gold curve in form. Therefore, independent and accurate compensation can be carried out for a physical source, the problems of information aliasing and compensation failure caused by the fact that disturbance sources cannot be distinguished in the prior art are effectively solved, intelligent and feed-forward type cooperative compensation for the next stamping period is achieved, and long-term stability and high precision of the production process are ensured.

The invention relates to a motor energy-saving control method for adaptive parameter adjustment. The method comprises the following steps: constructing an operation parameter sequence and comparing the operation parameter sequence with a preset reference model by collecting the rotating speed, the torque and the current signal of a motor in real time to generate a parameter deviation vector; based on a deviation threshold comparison result, carrying out noise processing on the overrun deviation to obtain a filtered sequence, and calculating a time delay degree in combination with historical parameters; calculating a compensation gain according to the time delay, fusing the compensation gain with the filtered deviation to generate a control instruction sequence, correcting the compensation gain through fuzzy control to obtain a stabilization parameter, and further generating a correction control instruction sequence; and finally, acquiring load data, fusing the load data with the correction instruction to obtain a system operation characteristic value, and generating an optimization state identifier when a judgment standard is met. By adopting the method, adaptive adjustment of the control parameters can be realized, the dynamic working condition is accurately responded, and the energy-saving efficiency of the motor under the variable-load working condition is improved.

The invention relates to the technical field of automatic control, and discloses a thermal power generating unit cooperative control method based on digital twinning, which comprises the following steps: firstly, off-line executing benchmark construction, and obtaining a benchmark digital twinning model and a benchmark cooperative control parameter set which represent the ideal state of a unit; a twinborn deviation vector is obtained by comparing real-time output of a physical unit with theoretical output of a model, a deviation characteristic vector of quantitative characteristic deviation is obtained through processing of a deconstruction algorithm, then historical deviation characteristics are analyzed by adopting a time sequence prediction model, a prediction output vector representing a future evolution trend is calculated, and then a prediction result is obtained. And calculating a prospective control parameter correction vector based on the prediction vector, and combining the prospective control parameter correction vector with a reference parameter set to generate a dynamic cooperative control parameter set. According to the method, the slow time-varying drift of the dynamic characteristics of the unit can be actively compensated, the continuous optimization of a control strategy is realized, and the stability and the operation economy of a control system are improved.

The invention relates to the technical field of production process control, in particular to an MES system management and control method and system based on precision mold production and manufacturing, and the method comprises the following steps: obtaining a measurement point cloud and a theoretical model, calculating rigid body transformation matrix registration to generate a registration point cloud, searching a nearest projection point, measuring geometric deviation, and constructing a three-dimensional deviation vector; and calculating the actual residual material thickness, screening and generating margin insufficient risk points, analyzing a numerical control code to extract a coordinate instruction, retrieving a nearest risk point, calculating a compensation distance, and adjusting coordinates to generate a self-adaptive finish machining program. According to the method, the three-dimensional deviation vector is constructed to represent the entity state, the risk area is screened in combination with the machining allowance, the compensation distance is calculated according to the actual residual thickness, and the instruction is dynamically adjusted to generate the self-adaptive program, so that the path is automatically corrected according to the shape of the workpiece, and the overcut risk caused by insufficient allowance is effectively avoided; and accurate matching between the machining process and the workpiece state is ensured.

The invention relates to a puncture deviation correction method, device and equipment based on dynamic vision and a storage medium, and the method comprises the steps: collecting image data and spatial position data of a to-be-punctured object, extracting target positioning information from the image data, and constructing a corresponding three-dimensional deformation model; determining corresponding offset data according to the three-dimensional deformation model, and fusing the target positioning information with the offset data to generate a deviation vector; on the basis of the deviation vector, fuzzy control rule calculation is executed, and a control output quantity is generated in combination with historical control data; the deviation direction and amplitude of a preset time period are predicted by analyzing the change trend of the deviation vector at multiple time points, a feed-forward compensation amount is generated, and then a compensation control instruction is generated; and third-order track information of the puncture path is generated according to the compensation control instruction, and dynamic deviation correction control is completed according to the third-order track information. The method has the effect of improving the accuracy of the automatic puncture system.

The invention relates to the technical field of vehicle control, and discloses a vehicle positioning and control method and system based on machine vision, and the method comprises the steps: building a vehicle kinematics prediction model, and generating a reference pose sequence according to a driving instruction through the vehicle kinematics prediction model; recording a first reference pose during image acquisition; calculating a deviation vector between a positioning result and the first reference pose when positioning calculation is completed; determining a dynamic damping coefficient according to a direction included angle between the velocity vector and the deviation vector, generating a compensation gain sequence, and converting the compensation gain sequence into a compensation torque of a current loop given value; according to the method, the effective decoupling of the sensing time delay of the sensor and the dynamic response of the carrier is realized, the phase lag is reduced on the premise of ensuring the momentum continuity of the controlled object, the instantaneous control oscillation of positioning correction is eliminated, and the mechanical stress impact of an execution mechanism is inhibited.

The invention relates to the technical field of surface mounting, and discloses an SMT (Surface Mount Technology)-based patch vision correction method, which comprises the following steps: S1, a calibration stage: constructing a double-coordinate system mapping model based on a PCB (Printed Circuit Board) plane, and establishing a nonlinear distortion compensation matrix according to the patch thickness; s2, a pickup stage: a CCD shooting camera is arranged at the top of the suction nozzle, a top CCD is triggered to shoot when the suction nozzle picks up an element, and pin coplanarity data is extracted; s3, a positioning stage: setting a reference point at the top of the PCB, scanning the PCB reference point through a CCD, and calculating a deviation between an actual position and a theoretical coordinate by using an algorithm processor; and S4, compensation execution: the motion shaft system dynamically adjusts the mounting path according to the deviation vector. The visual correction method based on SMT pasting has the advantages of accurate pasting and the like, and solves the problems of pasting position offset, low efficiency and unstable precision caused by factors such as vibration of an element feeder, mechanical transmission errors and thermal deformation of a PCB (Printed Circuit Board) due to dependence on manual calibration in the existing SMT pasting process.

The invention relates to the technical field of motion control, in particular to a motion trail real-time optimization analysis method and system based on multi-modal perception, and the method comprises the following steps: a multi-sensor array obtains position and speed visual features, carries out the coordinate transformation, extracts a direction amplitude, and builds a multi-modal perception data set; the method comprises the following steps: inputting Kalman filtering prediction trajectory comparison deviation to generate a trajectory deviation vector field, calculating a deviation gradient, screening an over-threshold point marking time sequence, generating a dynamic weight correction parameter through weighted least square distribution normalization, and outputting a real-time optimization trajectory scheme through weighted correction fusion coordinates. The position information and the velocity vector of a moving target are obtained in real time by fusing multi-modal sensing data, a state estimation model is combined to predict a trajectory, a trajectory deviation vector field is generated, and a deviation point is subjected to weighted correction, so that the trajectory optimization precision is effectively improved, the weight is dynamically allocated to optimize the priority, and the trajectory optimization precision and the system stability are improved. And the self-adaption and robustness in a complex environment are enhanced.

The present application relates to the technical field of sheet stamping forming, and provides a stamping part springback compensation method and device based on a machine learning model, which comprises the following steps: setting process parameter conditions for a stamping part, setting initial values for the set process parameter conditions, and obtaining initial process parameter conditions; performing finite element sampling on the stamping part, performing CAE forming simulation springback prediction and compensation calculation, and obtaining sample data under the initial process parameter conditions; traversing parameters in the initial process parameter conditions, cyclically adjusting parameter values of each parameter, and obtaining a sample data set; preprocessing the sample data set, training a machine learning model by using the preprocessed sample data set, and obtaining a springback compensation big data model; and taking measured deviation vector values of the stamping part as input, and obtaining predicted springback compensation vector values by the springback compensation big data model. The present application can efficiently, accurately and at low cost complete stamping die surface compensation.

The invention relates to the technical field of data processing, discloses a structure perpendicularity self-adaptive control method and system based on autonomous advanced correction, and is applied to intelligent regulation and control of an operation platform, concrete pouring and sliding and lifting speed in slip form construction. A deviation vector is constructed by collecting structural attitude and environment data and carrying out dynamic confidence weighted fusion, and three-stage progressive training is carried out based on the vector to obtain an advanced correction strategy network; after the dominant offset mode is recognized, a strategy network is input, and a construction correction instruction sequence including an operation platform adjustment scheme, a concrete pouring position adjustment scheme and a slip-up speed adjustment scheme is output; and the lifting stroke difference of all lifting points of the sliding formwork operation platform is adjusted according to the instruction to generate platform inclination, the concrete pouring amount is increased on the opposite side of the deviation direction, the sliding and lifting speed is controlled to execute perpendicularity correction, and structural response is monitored in real time and dynamic adjustment is conducted. The precision of perpendicularity control in the slip-form construction stage and the adaptive capacity of the system to the complex environment are improved.

The invention discloses a drilling track real-time monitoring and intelligent deviation correction control method, device and equipment, and aims to construct a multi-sensor fusion drill attitude measurement technology to realize high-precision track deviation detection and error compensation; performing potential energy accumulation on the negative deviation component to generate a deviation energy pool by adopting a positive and negative deviation decomposition technology of a track deviation vector; establishing a chaos control band framework, analyzing and identifying a stable boundary and an unstable boundary through energy fluctuation characteristics, and extracting an optimal response interval to guide deviation correction control; determining an active deviation rectification vector, and controlling a hydraulic cylinder to generate deflection torque through a composite deviation rectification instruction; an oscillation feature extraction technology is introduced, a stability index is generated based on system oscillation parameters and position mapping of a chaos control band, a chaos edge control field is constructed, and precise regulation and control of complex nonlinear drilling dynamics are achieved; the energy conversion efficiency is obtained and the chaos edge control field is combined to generate a real-time deviation correction control quantity, so that a closed-loop intelligent deviation correction control loop is formed.

According to the self-adaption-based automatic control method and system for the numerical control typesetting machine, the dynamic feature tensor of the raw material is obtained, the defect that single data is prone to being interfered is overcome through the method of obtaining the multivariate data set, more comprehensive working condition input is provided for self-adaption control, a real-time machining track is obtained, and the machining efficiency is improved. The method comprises the steps of obtaining a track deviation vector through a real-time processing track, constructing a reinforcement learning model, inputting a dynamic feature tensor and the track deviation vector into the reinforcement learning model, obtaining an optimal control set, constructing a deformation self-adaptive model according to the optimal control set, and obtaining deformation quantity prediction data through the deformation self-adaptive model. According to the intelligent linkage method, deformation quantity prediction data is obtained, a composite control instruction is obtained according to the deformation quantity prediction data, the composite control instruction is input into a control module for real-time regulation and control, control is conducted through cutting data feedback typesetting paths, a reinforcement learning model and self-adaptive lifting are integrated, and typesetting precision is improved.

The invention relates to the technical field of numerical control machining and automatic control, in particular to a numerical control machining self-adaptive control system and method based on multi-source data, and the method comprises the steps that FPGA hardware synchronously collects main shaft current, servo current of each feed shaft, cutting vibration and grating ruler pulse, and time bases are aligned and packaged into a machining multi-dimensional state matrix; the multi-physical field error is decoupled through a three-layer cascade machine tool dynamics model, a tool nose contour deviation vector is solved, and servo lag and cutting force deformation factors are fused to construct a dynamic constraint boundary. When the deviation amplitude exceeds the limit, generating a speed feed-forward gain compensation instruction based on an inverse control model to perform instant suppression; when it is monitored that oscillation is eliminated and returns to a steady state, full-time-domain data stream interception logic is triggered, and a process optimization instruction is generated through backtracking. According to the method, the problems of nonlinear time-varying error decoupling and real-time compensation lag under multi-physics field coupling are solved, and the dynamic precision and process stability of numerical control machining under complex working conditions are remarkably improved.

The invention discloses a tea making monitoring method and system based on a knowledge graph, and the method comprises the steps: integrating variety characteristics, processing technology parameters, equipment operation states and environmental factors into a structured data set in a unified format through a data fusion technology, and constructing a processing technology knowledge graph through a graph representation learning model; the method comprises the following steps: representing correlation between process links and parameters by nodes and edges, calculating a parameter deviation vector in real time, triggering cross-link influence propagation simulation, identifying a potential influence path, extracting an equipment related node sequence, analyzing an abnormal fluctuation mode, generating an abnormal index set, tracing the contribution degree of a preorder link parameter to abnormity, and generating a parameter adjustment vector; and finally, the optimized sequence output of the machining process parameters is realized. According to the invention, through fusion of the knowledge graph and the abnormity traceability, the problem of dynamic optimization of the processing parameters is solved, and the precise control capability of tea processing and the efficiency of equipment abnormity processing are significantly improved.

The invention relates to the technical field of precast concrete proportioning, and discloses a precast concrete proportioning optimization method and system based on artificial intelligence. The system comprises a mix proportion design scheme obtaining module which is used for obtaining a first mix proportion scheme according to material proportion hierarchy according to component engineering requirements; the environmental parameter acquisition module is used for acquiring real-time environmental data of a production area; the performance reference prediction module is used for calculating reference parameters according to the material performance characteristic matrix in the first scheme and determining a first performance reference vector; the environment evolution prediction module is used for obtaining a multi-stage production environment parameter sequence based on a real-time data prediction trend; the performance deviation evaluation module is used for obtaining multi-stage performance deviation vectors through a prediction model in combination with the reference vector; the optimization decision generation module optimizes the allocation ratio parameters according to the allocation ratio parameters and generates an optimization strategy; and the proportioning control execution module is used for proportioning equipment parameters according to a strategy. The system adapts to a dynamic production environment, and accurate matching and stable quality are guaranteed.

The invention relates to the technical field of data model construction, in particular to an electric power spot transaction data model construction method based on data analysis, and the method comprises the steps: collecting key data in real time; preprocessing data to generate a training set; constructing a model to be incremented; determining the stability of training data; identifying an abnormal conduction type; generating a deviation vector adjustment parameter; and incrementally training and updating the model. According to the method, in the clearing process of the high-fluctuation limited electric power spot market, multi-dimensional parameters such as the node marginal electricity price change rate, the main output unit climbing gradient, the real-time order book fractal dimension and the satellite remote sensing cloud layer moving speed are introduced at the same time for data analysis; the problems of risk early warning delay and poor market regulation and control effect caused by poor model adaptability and insufficient prediction accuracy due to lack of timely identification and response to market supply and demand fluctuation and abnormal behaviors are effectively solved.

The invention relates to a loose-leaf notebook inner page hole site processing self-adaptive positioning method and system and electronic equipment, and the method comprises the steps: obtaining the morphological feature information of the edge of a to-be-processed paper pile, and determining a datum reference line and an actual edge contour curve according to the morphological feature information; and based on a deviation mapping model, determining a pose deviation vector according to the datum reference line and the actual edge contour curve, generating a positioning adjustment instruction according to the pose deviation vector by utilizing a self-adaptive compensation algorithm, and driving a positioning mechanism to carry out pose correction on the to-be-processed paper pile. And aligning the hole site machining center with the actual edge profile curve. Accurate sensing and self-adaptive compensation of the shape of the edge of the inner page of the loose-leaf notebook are achieved, the problem that traditional mechanical positioning is poor in adaptability is effectively solved, high precision and high quality of hole site machining are guaranteed, and the yield is increased.

The invention discloses an ambiguity deviation vector classification and probability decoupling collaborative protection level construction method and device, and belongs to the technical field of integrity monitoring. The method comprises the following steps: firstly, constructing a model of a positioning error under a fixed solution, then constructing an initial protection level model based on a Bayesian formula, then performing protection level parameter decoupling by utilizing an ambiguity deviation vector probability, classifying deviation vectors according to a modulus length, calculating a modulus length boundary value, and finally obtaining a protection level parameter; and finally, calculating a final protection level based on decoupling and classification results. According to the method, the influence of ambiguity error fixation on the protection level can be quantified, and the requirements of safety critical applications on reliability and interpretability are met.

The invention discloses an intelligent customer service question and answer optimization method and system based on large model driving, and relates to the technical field of data processing.The method comprises the steps that a semantic flow self-supervision state machine is used for generating a semantic attribution graph and extracting core semantic factors, reverse semantic generation and cross-factor compensation are executed, and a structured target semantic set is constructed; a multi-branch task state diagram is constructed through behavior path inversion, and after a main task state is recognized, two-dimensional semantic projection is carried out, and a strategy tensor is generated; generating a polymorphic answer and constructing a reply text based on the strategy tensor, the main task state and the semantic set; and calculating a deviation vector through a user feedback path and updating the large model. The technical problem that an existing intelligent customer service system is low in semantic understanding precision, single in answer and lack of individuation, and consequently the question and answer response speed and precision are low is solved, and the technical effect that the semantic understanding precision and the answer diversity are improved through a dynamic optimization mechanism driven by a large model, so that the question and answer response speed and precision are improved is achieved.

The invention discloses a tunnel mining engineering blasting effect evaluation and analysis method based on the Internet, and relates to the technical field of tunnel mining engineering, and the method comprises the steps: carrying out the synchronous fusion of a multi-modal feature set, and generating a multi-dimensional blasting feature representation; performing multi-scale time sequence decomposition and spectrum energy clustering on the multi-dimensional blasting characteristic representation to generate a blasting response mode, and performing statistics and spectrum analysis to generate a blasting effect evaluation index set; comparing the blasting effect evaluation index set with a standard template in a historical blasting engineering database to generate a deviation reference, and calculating a current blasting effect deviation degree by adopting a Bayesian dynamic inference method to generate a deviation vector; performing dynamic weight adjustment and feature contribution correction on the deviation vector to generate a comprehensive blasting effect scoring result; and analyzing and summarizing the comprehensive blasting effect scoring results to generate a blasting effect evaluation report. The reliability of blasting quality control and safety early warning is improved.

The invention discloses a tunnel engineering blasting effect evaluation and analysis method based on big data, and relates to the technical field of geotechnical engineering monitoring, and the method comprises the steps: constructing a blasting digital twinborn body based on a blasting energy thermodynamic diagram, reconstructing a rock mass fracture extension three-dimensional characteristic field according to space energy density distribution, and generating a triangular mesh model for simulating a blasting contour; aligning the real spot cloud data of the tunnel blasting section with the triangular mesh model of the blasting contour to obtain a contour deviation value and a three-dimensional contour deviation field, identifying a contour deviation sagittal plane domain and marking a stake mark interval position; and based on the space energy density distribution and the contour deviation value, predicting a blasting effect comprehensive score, and generating a blasting effect analysis report. According to the method, multi-dimensional intelligent evaluation of the blasting effect is realized through learnable weight parameters and a Sigmoid gating mechanism, the overall effect can be reflected, the cause of a specific problem can be positioned, and a continuously optimized evaluation system is formed.

The invention discloses a personality adaptive training and dynamic evolution method of a role large model and a related product. The method comprises the steps of obtaining interaction log data when a self-adaptive training triggering condition is met; scoring the interaction log data to obtain an index score vector, comparing the index score vector with a target score vector to obtain a deviation vector, and summarizing the deviation vector to form a deviation report; constructing a training sample based on the deviation report; on the premise of freezing trunk parameters of the basic language generation model, executing small-step increment updating on an evolvable personality vector and / or a scene sub personality increment module set in the personality configuration file, and applying core personality attribute anchoring constraint, distribution difference constraint and rule constraint to obtain a target personality configuration file; and when the target personality configuration file passes the security verification, writing back and storing in a versioning manner, otherwise, rolling back to update the current personality configuration file. According to the method, personality controllable evolution is realized in multi-scene continuous interaction, and the risk of personality drift and cross-scene inconsistency is reduced.

The invention relates to the technical field of control and adjustment, and discloses a real-time control system and method for the production progress of an intelligent manufacturing platform, and the system comprises an information extraction module, a data packaging module, a state sequence module, a progress association module, a progress deviation module and a cooperative control module, and is used for obtaining multi-source heterogeneous data and a scheduling resource set of the intelligent manufacturing platform; packaging the multi-source heterogeneous data to obtain a production live set; mapping the production live set to a production element sequence of an intelligent manufacturing platform to obtain a production state sequence; extracting an evolution vector and a scheduling vector in the production state sequence to obtain a progress association vector pair; forward iteration is carried out on the progress offset between the progress association vector pairs based on process parameters in the scheduling vector, and a deviation vector of the intelligent manufacturing platform is obtained; performing collaborative optimization on the scheduling resource set based on the deviation vector to obtain a collaborative control sequence of the intelligent manufacturing platform; according to the invention, the accuracy and efficiency of real-time control of the production progress of the intelligent manufacturing platform can be improved.

The invention relates to the technical field of robot control, and discloses a power station narrow space inspection robot control method. According to the method, when a preset inspection triggering condition is met, a historical operation state of a target inspection robot at a specified historical moment is obtained, the historical operation state comprises historical accumulated operation duration and historical environment parameters, and meanwhile, a real-time operation state is obtained, and the real-time operation state comprises real-time accumulated operation duration and real-time environment parameters. Through analysis of historical and real-time operation states, an operation state deviation vector is constructed, and a corresponding operation deviation measurement set is retrieved. When the set distribution dispersion is larger than or equal to a dispersion threshold value, the ratio of the historical duration from the storage starting moment to the specified historical moment to the current duration from the storage starting moment to the current moment is calculated and serves as a control parameter adjusting proportion, and then control parameters of the target inspection robot are updated according to the proportion.

The invention discloses an attitude control method and system for continuously locking a ground target by an unmanned aerial vehicle, and the method comprises the steps: obtaining a target position and an unmanned aerial vehicle attitude data stream, carrying out the coordinate transformation to generate a target attitude relation identifier, and carrying out the deviation vector decomposition to form a visual axis deviation feature set; disassembling the image into a fast and slow response chain through attitude mapping processing, repairing a fracture node, constructing an attitude correction track, extracting a control feature mode, generating a response feature factor, and constructing an attitude compensation constraint matrix; frequency domain decomposition and cross coupling calculation are carried out on the optical axis deviation feature set to determine the coupling correlation degree, and a superposition disturbance sequence is identified and dynamically reconstructed to generate a compensation gain sequence; executing stability evaluation to determine time sequence distribution, identifying abnormal disturbance characteristics and extracting key moment control characteristics; the constraint matrix and the gain sequence are fused to generate a fusion control law, a dynamic priority control coefficient is determined, a target continuous locking attitude control instruction is output, and continuous and stable locking of the moving target is achieved.

The invention discloses a wire harness processing control method and system based on artificial intelligence, and the method comprises the steps: collecting sensor array data, carrying out the normalization processing, forming a standardized parameter set, precisely recognizing an abnormal mode through a classification algorithm, marking a potential fault point, extracting a time sequence feature sequence, and generating a prediction deviation vector through a prediction algorithm, analyzing dominant influence factors, grouping similar deviation modes through a clustering algorithm, and judging process adjustment requirements; and an optimization instruction sequence is generated, a production scene is matched based on historical data, the adjusted parameter configuration is dynamically injected into a control system, and the parameter model is continuously optimized through real-time monitoring and feedback circulation. According to the method, through data-driven anomaly detection and prediction adjustment, the stability and efficiency of the manufacturing process are remarkably improved, the fault rate is reduced, and intelligent machining state optimization is achieved.

The invention relates to the technical field of industrial process control, and discloses a plastic product large-scale production multi-parameter cooperative control system and method. A deviation calculation module; an adaptive compensation module; and a set value updating module. The method comprises the following steps: injecting a probe signal in a preset non-sensitive stage, synchronously acquiring a response signal, and calculating a process response feature vector; calculating the deviation between the measurement vector and a preset reference vector to generate a measurement deviation vector; determining a target compensation vector through deviation attribution in combination with prediction drift, updating a process response Jacobian matrix online, and calculating a multi-parameter cooperative adjustment amount; and updating the machine set value of the next period according to the adjustment amount. According to the method, the problems of control lag and parameter coupling are solved through prospective compensation and self-adaptive updating, and the control precision and robustness of multiple parameters in large-scale production of plastic products are improved.