17310 results about "Automation" patented technology

The invention discloses a multi-mechanical-arm space-time synchronization control method for coiled pipe welding, and belongs to the technical field of automation control, and the method comprises the steps: in a primary laser and TIG hybrid welding execution stage, carrying out real-time data acquisition on a welding area of a workpiece, carrying out real-time deviation detection, and once real-time welding deviation is detected, carrying out time-space synchronization on the welding area of the workpiece; a local correction mechanism is triggered immediately, and deviation is prevented from being accumulated in the single welding process; after one-time welding is completed, the workpiece enters a transition area, global contour reconstruction is conducted on a welded section of the workpiece through three-dimensional scanning equipment, global deviation recognition is conducted, and unprocessed hysteresis deformation and accumulative errors are covered and corrected in real time; on the basis of the analysis result of the global deviation recognition, double correction is conducted on secondary welding, and cooperative control over space track correction and technological parameter adaptation is achieved; and after secondary welding is completed, the correction effect is evaluated through a double-layer evaluation mechanism, and the stability of the correction process is ensured.

The invention discloses a modular reconfigurable production line control system integration method, which relates to the technical field of industrial automation, and comprises the following steps: establishing virtual mapping based on physical attribute parameters to form a production line digital twin basic model framework; collecting data in real time based on an on-site sensor, and establishing a digital twinborn dynamic mapping mechanism synchronous with a physical production line state; a reconstruction scheme is imported into a virtual environment, and key performance indexes are analyzed through a production line digital twin model rehearsal module combination process. A virtual production line model is constructed through a digital twin technology, a rehearsal and verification reconstruction scheme in a virtual environment is supported, trial and error time and cost required by traditional physical debugging are remarkably reduced, a reconstruction strategy is further optimized through a cloud AI algorithm, closed-loop optimization from virtual verification to physical execution is achieved in combination with edge end real-time control, and the real-time performance of the virtual production line is improved. The production line can quickly complete local or overall reconstruction according to production requirements, and the response speed and flexibility of the production line are greatly improved.

The invention discloses a dynamic path planning and self-adaptive control method and system for a coating robot, and relates to the technical field of coating automation. The method comprises the following steps: acquiring point cloud data through three-dimensional scanning equipment, constructing a dynamically updated workpiece curved surface model, and extracting curvature, edge and high-curvature mutation region features; a spraying path is generated based on a reinforcement learning algorithm, and path density, speed and coating supply are dynamically adjusted for a high-curvature area; distance, force feedback and environment parameters are fused, and mechanical arm and spray gun parameters are dynamically adjusted; dividing operation sub-areas and distributing tasks based on robot capability characteristics to realize multi-machine cooperation; and fault redundancy control and multispectral imaging are added to optimize the coating quality. The system comprises a sensing module, a decision-making module, an execution module, a redundancy control module and a communication module. According to the invention, the uniformity of the complex curved surface coating, the multi-machine cooperation efficiency and the system anti-interference capability are improved, and the method is suitable for spraying large workpieces such as aircraft fuselages and high-speed rail vehicle bodies.

The invention discloses a CAD automatic generation system and method based on intelligent model selection and application, and aims at achieving automatic modeling under the multi-modal design requirement. The system comprises a user interaction module for receiving multi-modal input such as natural language, sketch and voice; the intelligent demand analysis module is used for combining an industrial large language model and a product knowledge graph, combining semantic analysis and generating a structured demand; the intelligent model selection calculation module is used for matching the optimal parameter combination and the component list based on a multi-objective optimization algorithm; the CAD automatic generation module calls a parametric modeling engine to generate an editable three-dimensional model; the constraint solving module is used for processing hard constraints and soft constraints in real time and dynamically adjusting model parameters; and the model output and interaction module feeds back a design state and supports user iteration. The system realizes full-process automation from the design intention to the CAD model, improves the design efficiency and accuracy, and is suitable for the fields of mechanical design, intelligent manufacturing and the like.

The present disclosure relates to configured artificial intelligence methods and systems and related transportation systems and methods, including software-defined vehicles, for transportation systems using sensor and other data, and the integration of a transportation system with an AI convergence system of systems, providing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system.

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

The invention provides an intelligent boxing method and system. The intelligent boxing method comprises the steps of obtaining boxing task information and generating a boxing scheme. The boxing task information comprises the size, the weight, the stacking limitation, the number and the priority of each cargo, and the size, the bearing capacity and the occupation state of each loading unit. And under the condition that the size, bearing and stacking constraint conditions are met, a plurality of candidate boxing combinations are generated, the loading utilization rate, the residual space distribution and the shape matching degree are evaluated based on a preset optimization target, and an optimal scheme is selected. The system comprises an input terminal, a task information receiving module, a data storage module, a task and scheme storage module, an operation processing module and a final boxing scheme generation module. According to the method, multi-dimensional constraint matching and weight balance are considered, and space waste and transportation risks are avoided; the maximum greedy algorithm and the adaptive large neighborhood search optimization are combined to realize the combination of automation and experience. The method can be expanded to batch task scheduling and grouping sequencing, so that the calculation complexity is reduced, and the overall boxing and transportation efficiency is improved.

The invention discloses an integrated verification and test system and method for complex rules of a substation monitoring system, and the system comprises a rule editing module which provides a visual interface to support a user to edit the complex rules containing various types of variables and time sequence constraints; the analysis and verification module is used for performing grammar, variable conflict and business compliance multi-level verification on the rules; the intelligent test case generation module is used for automatically generating test cases covering normal, boundary and abnormal conditions based on the rule tree structure; the simulation test interface module converts the use case into a standard test script, submits the standard test script to a simulation tool for execution, and outputs the standard test script to a monitoring system through protocol conversion; the result analysis module is used for comparing an execution result with an expected result and generating a structured test report; according to the invention, full-process automation of rule verification and testing is realized, the verification efficiency is remarkably improved, and the test requirements of various transformer substation business scenes are met.

The invention discloses a metal grinding machine tool grinding wheel wear compensation type intelligent control system, and relates to the technical field of mechanical manufacturing automation, and the system comprises a contact type reference detection module which is used for obtaining the initial contour and reference wear loss of a grinding wheel through discrete point measurement in a non-machining state; the multi-parameter non-contact real-time detection module is used for collecting acoustic emission, vibration and spindle current signals in the grinding process through three types of sensors; the grinding environment sensing module collects the temperature of a grinding area and the cutting fluid concentration; the intelligent control unit receives reference data and an environment signal, performs deep learning on a wear loss prediction model after multi-source data fusion, performs periodic reference calibration, analyzes the dynamic wear loss, and synchronously transmits the data to the data storage and tracing module; the executing mechanism adjusts grinding wheel feed amount compensation according to the dynamic abrasion loss; and the data storage and tracing module stores full-process data in real time. According to the system, high-precision and real-time wear compensation is achieved, the machining precision and consistency are improved, and high-end manufacturing requirements are met.

The invention relates to an active splitting and isolated island operation method based on load importance under a disaster condition, and belongs to the technical field of power systems and automation thereof. On the basis of disaster types and power supply area characteristics, constructing a load importance dynamic grading system, optimizing the weight through an analytic hierarchy process-index weight optimization collaborative algorithm, and combining with real-time updating to generate a grading result; the method comprises the following steps: deploying wide-area and local monitoring devices to collect power grid data, setting a safety threshold, triggering a splitting decision when the safety threshold exceeds the limit and the trend deteriorates, calculating an instability rate by using a multi-parameter collaborative instability pre-judgment algorithm, and starting pre-splitting preparation; and the main control center calls a target splitting section optimization algorithm to determine a section in combination with the grading result and the power grid data, generates a splitting instruction after verifying the stability of the island through load flow calculation, and performs classified stability control on the split island. The method can realize accurate load grading, early instability pre-judgment, guarantee of important load power supply such as medical treatment and the like, reduces catastrophe loss, and is suitable for power grid management and control under various disasters.

The invention discloses a welding robot path planning system cooperating with industrial vision and collision detection, and relates to the technical field of robot automatic welding, the welding robot path planning system comprises an upper computer, the upper computer is in communication connection with the following modules: an environment sensing module used for collecting welding environment information in real time, comprising three-dimensional point cloud data of a workpiece and obstacle information; and the data processing and fusion module is used for fusing the visual identification result and the obstacle detection data. By integrating industrial visual recognition and collision detection technologies, the dynamic change of the welding environment can be sensed in real time, an accurate environment model is constructed based on three-dimensional point cloud data, compared with a traditional welding robot with a preset path, the welding track can be dynamically adjusted, welding defects caused by environment interference are avoided, and meanwhile the welding efficiency is improved. In combination with an assembly error compensation mechanism, the deviation between a theoretical model and an actual workpiece can be corrected, the welding position precision is ensured, and the high-precision ship manufacturing requirement is met.

The invention provides a marine equipment drawing recognition method and system based on a large model, and is applied to the field of intelligent analysis and knowledge management of ship engineering. The method comprises the following steps: receiving a marine equipment engineering drawing image, extracting global features through character and line detection after preprocessing so as to identify a drawing type, and combining symbol classification and topological graph construction to determine an equipment connection relationship, performing multi-source information reasoning on the target component by fusing a rule engine and a large language model, and outputting a high-confidence identification result; according to the scheme, the accuracy and the automation level of recognition of the equipment parts in the complex marine drawing can be remarkably improved, and the technical bottlenecks of a traditional method in the aspects of insufficient cross-modal information fusion, limited semantic understanding depth, poor heterogeneous drawing adaptability and the like are effectively solved.

The invention relates to the technical field of data analysis, provides an intelligent factory automatic monitoring method and system based on knowledge base enhancement, and realizes more accurate anomaly analysis and more effective process adjustment of an intelligent factory. The method comprises the steps of performing knowledge enhancement fusion processing on an obtained real-time monitoring data set of an intelligent factory through a pre-constructed process knowledge base and a pre-constructed monitoring rule base, and generating a process knowledge graph; performing abnormal mode recognition processing on the process knowledge graph based on a semantic matching strategy, extracting feature description of an abnormal event and a semantic association path with a historical monitoring text, and generating an abnormal mode analysis result containing abnormal root cause inference; according to the abnormal mode analysis result and the dynamic incidence relation in the process knowledge graph, an automatic monitoring report containing root cause priority ranking and optimization operation guidance is generated, and the automatic monitoring report is fed back to the intelligent factory control terminal to trigger process adjustment operation.

The invention relates to the field of metallurgical automation control, and discloses an intelligent metallurgical automation control method and system, and the method comprises the steps: collecting the real-time operation data of multiple working procedures of metallurgical production, carrying out the time alignment processing, and quantifying the correlation features between the working procedures, thereby obtaining an initial quantification feature set; dynamically simulating the feature set, acquiring intensity and direction parameters of process variable fluctuation conduction, and constructing a cross-process influence model; a global coordination instruction set is generated by combining downstream constraint feedback optimization distributed controller coordination parameters; updating a multi-process state vector, and if a local optimal risk exists, correcting a conduction path; and generating an optimal control scheme based on downstream real-time feedback iterative optimization, and distributing adjustment parameters to realize multi-process cooperative control. The process coupling relation can be accurately quantified, and the metallurgical production efficiency and the quality stability are improved.

The invention discloses a high-precision laser positioning unpacking path planning system, and belongs to the technical field of robot automatic control and industrial automation. The system comprises a data synchronization module used for multi-sensor hardware synchronization and data alignment; the high-precision positioning module is used for outputting a precise pose based on environment skeleton characteristics and sliding window optimization; the semantic map construction module is used for fusing vision and laser data to generate a dynamic semantic grid map; the global path planning module is used for planning a smooth path in the skeleton channel by utilizing a mixed potential field improved A * algorithm; the motion control module is used for realizing closed-loop motion control and safety monitoring through model prediction and tracking; and the operation execution module is used for finishing millimeter-level precise stopping of an operation point by adopting visual servo and triggering unpacking operation. According to the method, the positioning robustness under dynamic shielding is improved through the environmental skeleton features, the safety and the high efficiency of the path are ensured by utilizing semantic understanding and intelligent planning, and the full-process automation from navigation to precise operation is realized.

The invention belongs to the technical field of coupling alignment, and particularly relates to an intelligent auxiliary system for coupling alignment based on dual-laser displacement and an MEMS gyroscope, which adopts a mode of cooperative work of an active stable attitude measurement module and a space reference beacon module to establish a dynamic stable measurement basis which is not influenced by barring operation and environmental disturbance. The micro attitude change of the measuring device can be sensed and actively compensated in real time in the measuring process, it is ensured that laser displacement data at all angles are collected in the same absolute space coordinate system, and the high consistency and accuracy of multi-point measurement data are ensured. And then fusing the attitude feedback signal and the attitude reference signal by using an intelligent resolving module to generate an attitude control instruction, and processing displacement measurement data acquired by a double-laser displacement sensor after the attitude is stable to resolve a centering deviation parameter, thereby being beneficial to improving the efficiency, precision and automation level of coupling alignment work.

The invention discloses a ship mobile energy storage system CAN communication protocol automatic test system and method, and the system comprises a protocol modeling and test configuration module, a simulation node behavior simulation module, a CAN bus interaction and power closed loop module, and a test analysis and result evaluation module. A CAN communication protocol specification of the ship mobile energy storage system and parameters of tested equipment can be obtained, a protocol analysis model and a test configuration file are constructed for format verification and logic consistency verification, meanwhile, a corresponding test case sequence is automatically generated, and the message sending time is controlled to generate a time sequence simulation message flow; and sending the time sequence simulation message flow to the tested equipment, simulating energy interaction behavior acquisition to generate a power feedback data set, testing to obtain a signal consistency deviation and a power execution error to construct a protocol conformity evaluation matrix, and generating a visual test report. According to the invention, the high coverage rate and automatic closed-loop test of the CAN communication protocol can be realized.

The invention discloses a motion error dynamic compensation method applied to a surgical mechanical arm, and belongs to the technical field of surgical mechanical arm control and medical automation. The method comprises the following steps: firstly, acquiring position data of each joint encoder of the surgical mechanical arm, a motor current signal, a position feedback signal of an end effector, load change information and accumulative operation duration in real time; processing the data, fusing the data through adaptive Kalman filtering to estimate a joint dynamic error, inputting a load change rate and an accumulated operation time length into a pre-training error prediction model to output a compensation basic quantity, and fusing the load change rate and the accumulated operation time length to generate a real-time error compensation quantity; and then the compensation amount is superposed to a joint original control instruction to correct motion parameters, and finally the compensated tail end position deviation is analyzed, and a precision identifier or an adjustment prompt is generated and fed back to a control system. According to the method, real-time error compensation is achieved, the compensation precision and working condition adaptability are improved, it is guaranteed that the mechanical arm is stably positioned, and the surgical operation risk is reduced.

The invention relates to the field of industrial automation control, and discloses an intelligent control system and method based on multi-parameter coupling analysis and predictive maintenance. According to the method, flow, temperature and water quality parameters are collected in real time through a sensor network, and after data cleaning and standardization processing, a parameter coupling relation model is constructed; an LSTM neural network is adopted to predict a system energy consumption trend, and a time sequence regression model is combined to analyze equipment performance attenuation characteristics; and when the predicted value exceeds a threshold value, dynamically adjusting operation parameters and optimizing load distribution to form closed-loop control. Accurate energy-saving control under complex working conditions is achieved, energy consumption can be reduced by 8%-12% through tests, the equipment maintenance cost is reduced by 15%-20%, and the system operation efficiency is remarkably improved.

The invention discloses a backup disaster recovery optimization method, and relates to the technical field of IT operation and maintenance and disaster recovery, and the method comprises the steps: analyzing a statistical dependency relationship between basic performance indexes through employing a causal discovery algorithm based on a standardized monitoring data flow, and constructing a directed weighted graph; when a basic performance index in the directed weighted graph deviates from a normal performance baseline, triggering a reverse traceability positioning root cause component, identifying a service influence path through forward path analysis, and generating a backup enhancement instruction; in a digital twin environment, according to historical drill records and a strategy library, multiple potential disaster recovery switching schemes are generated, parallel deduction is carried out, the performance influence of each potential disaster recovery switching scheme on a virtual service link is quantitatively evaluated in the deduction process, and the optimal disaster recovery switching scheme is selected by comparing evaluation results. According to the invention, the prospective optimization of the disaster recovery decision and the minimization of the business influence are realized, and the automation, intelligence and business adaptation capabilities of the backup disaster recovery system are enhanced.

The invention belongs to the technical field of industrial automation and artificial intelligence crossing, and particularly relates to an AI industrial control intelligent master system and method based on deep learning. The system comprises an intelligent programming module, a real-time monitoring module, a fault management module, an optimization decision module and a man-machine interaction module. The system automatically generates a PLC program code through a deep learning model, monitors an equipment operation state through a multi-dimensional anomaly detection technology, analyzes a fault mode and a propagation path by using a knowledge graph and a graph neural network, optimizes process parameters and a maintenance strategy based on digital twinning and reinforcement learning, provides a 3D virtual interface and an AR remote cooperation function, and improves the reliability of the system. And intelligent management of the whole life cycle of the industrial control system is realized. The PLC programming efficiency is greatly improved, accurate anomaly detection and fault prediction are realized, system operation parameters are optimized, the service life of equipment is prolonged, the man-machine interaction experience is improved, and the problem of unified management of multiple brands of PLCs is solved.

The invention relates to a simple and reliable touch control switch. The simple and reliable touch control switch consists of a 12-V direct-current power supply, a one-way silicon controlled rectifier 1 (SCR1) trigger circuit, a one-way silicon controlled rectifier 2 (SCR2) trigger circuit, a relay control circuit and a load RL circuit. A conventional mechanical self-locking rocker switch or a toggle switch has the defects that actions are mechanical, operating actions are different because of persons, a contact is easy to jitter, a contact is easy to ablate, abrade and the like. The touch control switch is realized by only using a small number of common components and two touch metal pieces, namely an on-load circuit is switched on by touching a touch electrode once, and an off-load circuit is switched off by touching the touch electrode once. The overall touch control switch is made of discrete components, and strong electricity can be effectively isolated from weak electricity through a relay, so that the power supply of a load can use alternating current and also can use direct current. Therefore, the touch control switch has a certain function in the field of household appliances, industrial automation, and optical, mechanical and electrical integration.

The invention belongs to the technical field of ship navigation and port automation, and particularly discloses a dock ship guiding path planning and dynamic obstacle avoidance system, which is characterized in that a data acquisition module acquires ship parameters, environmental data and obstacle information through a ship end sensing unit and a dock end monitoring unit and standardizes the ship parameters, the environmental data and the obstacle information; the initial path planning module generates an optimal initial ship guiding path based on an improved A * algorithm in combination with ship physical constraints, environmental constraints and operation rules; the dynamic obstacle avoidance module monitors the movement of an obstacle in real time, predicts the trajectory of a ship and the obstacle, and triggers local path re-planning and smoothing processing when a collision risk occurs; and the control output module converts the path into a control instruction, issues and receives ship feedback through two-way communication, and forms closed-loop control according to a deviation fine adjustment instruction or re-planning. The system realizes full-process automation of ship guiding, can dynamically adapt to environmental changes, improve path precision and operation safety, adapt to different docks and ship types, and ensure that ships are accurately and safely parked to target berths.

The invention discloses a furnace body temperature control system, which belongs to the technical field of industrial automation control, and comprises the steps of monitoring and collecting multi-dimensional data of a physical furnace body in real time, performing preprocessing, generating a state vector, setting a working condition judgment method, classifying various working conditions, outputting working condition labels and characteristic parameters, and establishing a health degree evaluation model; the state of the actuator is monitored in real time, and frequent maintenance and short service life caused by overuse of equipment are prevented; constructing a furnace body virtual model, setting a real-time prediction method, and simulating furnace body temperature distribution so as to predict a future temperature trend, judge whether to start a rehearsal mode, output an optimal scheme, and realize advanced intervention of prediction and decision making; a working condition identification and temperature prediction result is read, a decision engine method is set, and a dynamic control instruction is generated and executed; and comparing data of the physical furnace body with data of the digital twinborn body, setting a self-evolution method, and carrying out online calibration and correction on the virtual model.

The invention relates to the technical field of pipeline welding automation, and discloses a pipeline multi-layer and multi-pass welding track self-adaptive planning method. The method comprises the steps that weld joint track poses, welding electrical parameters and temperature information are collected in real time, and a sliding time window data sequence is constructed; establishing an interlayer constraint model and a trajectory prediction model based on the sequence, and generating a plurality of trajectory deviation predictions through short-term recursive prediction and long-term sequence prediction; a residual error reciprocal weighted fusion strategy is combined with a welding seam forming quality evaluation function, fusion track deviation is obtained, and uncertainty is evaluated; the fusion deviation is superposed to an original planned trajectory, and a self-adaptive correction trajectory is generated through a multi-objective optimization model; and the welding robot is controlled to execute track correction and real-time feedback updating. According to the method, through dual-time scale prediction and trajectory-process parameter collaborative optimization, the problem of insufficient welding seam forming precision caused by lack of dynamic correction in traditional static planning is effectively solved, and the welding quality and efficiency are remarkably improved.

The invention relates to the technical field of industrial automation and intelligent control, in particular to a general control system strategy optimization method based on reinforcement learning, and the method comprises the steps: firstly collecting the operation data of a system, constructing a state vector of reinforcement learning, and enabling a reinforcement learning agent to fully understand the current operation condition of the system; then, the state vector is input into a reinforcement learning strategy network, an action for adjusting a control strategy is generated by the network, and the action can be used for modifying the proportion, integral or differential coefficient of PID and can also be used for adjusting the prediction step length, weight coefficient or constraint strength of model prediction control, so that the adaptive capacity of a controller to external changes is enhanced; then, a reward signal is constructed according to a response result of the reference controller; the reward function comprehensively considers the error size, the steady-state characteristic, the system energy consumption, the control smoothness and the stability requirement, so that the reinforcement learning not only pays attention to the error minimization when optimizing the strategy, but also considers the low energy consumption, the smooth action and the anti-interference performance at the same time.

The invention discloses a non-point source pollution emergency bypass decision control method, particularly relates to the technical field of automation and process control, and aims to solve the problems that existing scheduling cannot align multi-source measurement and evaluate credibility in minutes under a unified time base, and is lack of risk measurement integrated with a tide level phase and a moisture regain path and same-layer gating. The bypass rhythm is easy to misjudge, and the oscillation is difficult to audit. A minute-level risk density is constructed through multi-source alignment and credibility grading under a unified time reference, injected tide level phases and moisture regaining paths, bypass rhythms are constrained by same-layer gating and tide windows, and beam search, boundary arbitration and fixed window closed-loop correction are matched, so that the risk density is improved. Therefore, under the superposition of rainstorm and tide, decision making and execution are stably limited in a non-backflow red line, bearing and equipment limit, misjudgment and oscillation are remarkably reduced, the problems of unavailability and difficult auditing caused by multi-source asynchronization and moisture regain coupling are solved, intelligent decision support is provided for non-point source pollution treatment, and it is ensured that the treatment technology is accurate and efficient.

The invention discloses an urban financial service full-link risk prevention and control management system, and belongs to the technical field of risk prevention and control. Comprising a multi-source heterogeneous data acquisition and treatment module, a dynamic risk portrait construction module, a real-time risk monitoring and early warning module, a risk disposal and decision support module, a cross-scene risk linkage analysis module, a compliance and privacy protection module and a system adaptive optimization module. Automation and manual cooperation of risk disposal are realized; a low-risk event automatically executes a preset action through a formula, so that the disposal efficiency is improved; medium and high risk events are pushed to an expert system, a disposal scheme is generated in combination with historical cases, and a block chain records an operation log to ensure transparency and traceability; the module balances machine decision and manual auditing boundaries, and the misjudgment risk is reduced.

The invention discloses an ECU assembling and detecting production line for an automobile and a process of the ECU assembling and detecting production line. The ECU assembling and detecting production line comprises a circuit board feeding mechanism, an implanting mechanism in butt joint with the circuit board feeding mechanism, a detecting mechanism in butt joint with the implanting mechanism and a classifying and packaging mechanism in butt joint with the detecting mechanism. By means of the mode, circuit board feeding, injection molding embedding, injection molding material taking, detecting and classified packaging are integrated, the automation degree is high, secondary positioning and accurate positioning of the circuit board can be achieved, the horizontal position and angle of the circuit board can be automatically adjusted, and the injection mold can be aligned in a self-adaptive mode; each performance of a product can be synchronously detected, and code printing and identification can be carried out without repeatedly carrying and positioning the product and classifying and collecting defective products and non-defective products in different mold cavities, so that subsequent searching for reasons of the defective products is facilitated, and later maintenance of equipment is facilitated.

The invention relates to the technical field of industrial process automation control, and discloses an intelligent adjusting method for an industrial waste liquid low-temperature evaporation process, which comprises the following steps of: superposing pressure step test excitation to a vacuum adjusting valve, synchronously acquiring temperature response data flow, calculating absolute error integral and SIAE and reply time characteristics of a response curve, and calculating the temperature response data flow according to the absolute error integral and SIAE and reply time characteristics of the response curve; substituting into an inverse model to solve an equivalent process gain and a time constant of the controlled object; based on the internal model control principle, PID controller parameters are dynamically calculated and updated through the calculated gain and the time constant, a control law is reconstructed to generate an execution instruction, and the problem of control divergence of a non-linear time-varying object under model mismatch is solved through active energy feature identification and a pole assignment mechanism; decoupling control and self-adaptive adjustment of fluid viscosity sudden change and equipment scaling are achieved, and the stability and control precision of the system are improved.