2076 results about "Time control" patented technology

The invention discloses a real-time computing system resource coordination and decision engine system based on a large language model. According to the system, a large language model is innovatively used as a central strategic decision engine, and a'decision-coordination-execution 'three-layer architecture is constructed. According to the system, macroscopic strategy generation and microscopic real-time control are decoupled by introducing a hierarchical decision-making mechanism (a strategic layer, a tactical layer and an execution layer), so that the core contradiction between LLM high reasoning delay and the microsecond / millisecond-level real-time requirement of the system is effectively solved, and the method is suitable for local computing equipment and a cloud data center. The system comprises a predictive strategy preloading system, and transient response can be achieved. Meanwhile, the system adopts an asynchronous event-driven decision-making mechanism for continuous intelligent optimization. According to the method, the top-down, semantic understanding-based and global collaborative intelligent management of the computing resources is realized, and the resource utilization efficiency, the system automation degree and the overall energy efficiency in a complex and dynamic computing environment are remarkably improved.

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 relates to the technical field of mechanical arm control, in particular to a self-adaptive fuzzy fixed time control method based on an output-limited mechanical arm, and the method comprises the following steps: S1, constructing a mechanical arm dynamic model; s2, setting constraint conditions of an output state of the mechanical arm dynamic model; s3, defining a tracking error and a virtual control law of the mechanical arm dynamic model; s4, on the basis of output state limitation and fixed time control, an actual control law is designed to serve as input of the mechanical arm dynamic model, and an adaptive law is constructed; and S5, a three-level logarithmic obstacle Lyapunov function is constructed, the three-level logarithmic obstacle Lyapunov function is combined with the virtual control law, the actual control law and the self-adaptive law step by step and substituted into the virtual control law, the actual control law and the self-adaptive law, and then it is guaranteed that the output state of the mechanical arm dynamic model does not exceed the preset safety boundary according to the actual fixed time control lemma. According to the invention, high-precision trajectory tracking can be realized on the premise of ensuring safe operation of the mechanical arm.

The invention relates to the technical field of automatic laying of composite materials, in particular to an automatic compensation method, system and equipment for errors between silk laying belts and a storage medium. The method for compensating the error between the automatic silk laying belts comprises the following steps: establishing a mathematical mapping relation between the error compensation amount between the belts and robot control parameters through a standard sample piece laying test, and forming an error compensation theoretical model; the method comprises the following steps: aiming at a robot type fiber placement machine structure, constructing an inter-band error model based on pose kinematics modeling, robot and additional axis multi-axis interpolation modeling and error vector decomposition; the tail end target position, the attitude angle and the error compensation amount in the laying program are read in real time, and real-time control input is obtained; and on the basis of the error model, the corrected tail end position is inversely solved into a real-time adjustment angle of each joint, a robot inverse solution algorithm is corrected, and a joint control instruction is generated. According to the automatic tow belt laying device, the laying position precision of the automatic tow belt laying can be remarkably improved.

The invention relates to the technical field of data processing, and particularly provides a low-altitude aircraft power supply facility cooperative control method, system and device and a medium, and the method comprises the steps: predicting the renewable energy power generation power of a power supply facility and the charging load power of a low-altitude aircraft based on obtained multi-source information, and generating a prediction scene set representing uncertainty; based on the prediction scene set, through a multi-time scale optimization strategy, solving a scheduling model with optimal operation cost as a target, and successively obtaining a pre-scheduling plan of a day-ahead stage and a real-time control instruction of an intra-day stage; the multi-time scale optimization strategy comprises random optimization based on a scene and feedback optimization based on a rolling time domain; according to the real-time control instruction, power distribution is conducted on the hybrid energy storage system, and a power type energy storage unit preferentially responds to the transient power requirement for charging of the low-altitude aircraft. The comprehensive operation cost is effectively reduced, and particularly, the service life of equipment is prolonged through quantitative energy storage degradation.

The invention discloses a multi-mode communication switching method of a large unmanned aerial vehicle and a related device, and the method comprises the steps: setting an auxiliary communication link to be in a hot standby state after an airborne end of the large unmanned aerial vehicle is in communication connection with a ground end through a main communication link, and carrying out the real-time monitoring of a current communication link, and obtaining a real-time monitoring result; when the real-time monitoring result is that the current communication link is abnormal in communication, the airborne end switches communication connection from a main communication link to an auxiliary communication link in a hot backup state, and controls the main communication link to enter a diagnosis mode; and after the main communication link executes the diagnosis mode and returns to normal, controlling the main communication link to enter a hot standby state, and switching the communication connection from the auxiliary communication link to the main communication link. In the embodiment of the invention, the problems that a signal coverage blind area exists in a single link and the single link is susceptible to interference are solved, so that the real-time control requirement of a large unmanned aerial vehicle is met, and the reliability and the stability in a long-distance communication scene are improved.

The invention relates to the technical field of pipeline anomaly detection, in particular to a petroleum pipeline network anomaly recognition and early warning system which comprises a pressure fluctuation monitoring module, a flow anomaly analysis module, a state association optimization module, an early warning strategy adjustment module and an anomaly early warning generation module. According to the method, deep correlation analysis is carried out on the relation between pressure fluctuation and flow abnormity, the change trend of pipeline pressure and flow can be accurately captured, the abnormal state in pipe network operation can be recognized in time, the abnormity early warning structure is dynamically adjusted, the early warning strategy can be flexibly optimized along with the change of the working condition, and therefore the detection precision and the response speed are improved; the state can be mastered in real time in a complex pipe network environment, missing detection and false alarm phenomena caused by insufficient data interpretation or early warning strategy solidification are effectively reduced, the adaptability of the system to emergency situations is enhanced, and the problems of response lag and insufficient sensitivity caused by single data source under variable working conditions are avoided.

The invention relates to the technical field of power conversion, in particular to a collaborative optimization control method, system and equipment of a multi-mode power conversion device and a storage medium. The method comprises the following steps: constructing a double-layer collaborative architecture by collecting multi-source heterogeneous operation parameters, obtaining a device operation state, power grid dynamic characteristics and environment change information, establishing a cross-level communication protocol to realize data synchronization, and outputting a real-time working condition feature vector; deploying a dynamic mode switching engine based on a real-time working condition feature vector, triggering multi-mode operation strategy reconstruction through a state transition probability model, constructing a self-adaptive switching mechanism comprising a parallel power supply mode, an island operation mode and a hybrid modulation mode, and establishing a composite fault response system to realize pre-diagnosis and grading disposal of an abnormal state; millisecond-level real-time control is implemented in the edge control layer to generate a pulse width modulation driving signal, multi-target global optimization is implemented in the cloud optimization layer to dynamically adjust an operation parameter combination, and closed-loop control is formed through bidirectional data interaction and strategy feedback.

The invention discloses a multi-motor parallel control system and method based on an FPGA, and belongs to the technical field of multi-motor parallel control. The FPGA module is in communication connection with the control processing unit, and the FPGA module is internally provided with a global synchronous triggering unit used for generating a periodic synchronous triggering signal; the number of the position acquisition sub-modules corresponds to that of the motors, and each position acquisition sub-module comprises a position counter and a hardware latch register, wherein the position counter is used for carrying out counting and direction decoding on encoder pulses in real time; wherein the global synchronous triggering unit is connected with all the position acquisition sub-modules. According to the multi-motor synchronous control system, the position and other state signals of the multiple motors are collected at the same time through the parallel processing capacity of FPGA hardware, real-time control is achieved in combination with the control processing unit, and therefore the multi-motor synchronous control precision is improved, and the response speed is increased.

The invention discloses an intelligent backboard interaction method supporting terminal interconnection, a medium and an intelligent backboard. The intelligent backboard interaction method comprises the following steps: collecting user space motion data to generate a space-time trajectory sequence and binding a user identity label, segmenting a multi-user shooting action data stream in real time, extracting dynamic association features and combining sphere trajectory data to construct a multi-dimensional feature matrix; inputting the matrix into a distributed intention understanding engine, and fusing voice, a touch event and historical data to generate an interactive decision instruction; a dynamic content generation module is driven according to the instruction, a training difficulty coefficient and a tactical matching degree are calculated in real time, and a multi-terminal cooperative control signal is output; and aggregating multi-user and sphere state feedback data, and continuously adjusting a collaborative strategy of a shooting action evaluation standard, a multi-user confrontation rule and terminal display content. According to the method, accurate distinguishing and collaborative intention understanding of multi-user actions are realized, and the training individuation level and the self-adaptive real-time control capability of multi-user interaction are effectively improved.

The invention discloses a power distribution network cluster local control method and device, equipment and a medium. The method adopts a cloud edge end three-level collaborative architecture, and comprises the following steps: acquiring electrical parameters through a terminal equipment layer; constructing a graph model based on an electrical distance at the edge calculation layer; performing initial cluster division and issuing on the cloud computing layer; dynamically optimizing cluster division in an edge calculation layer through a lightweight deep reinforcement learning model; local control strategies such as voltage reactive power cooperative control, frequency active power balance control and fault rapid isolation and self-healing are executed based on the final division. The system comprises a terminal device layer, an edge computing layer and a cloud computing layer. According to the method, decoupling of global optimization and local real-time control is realized through cloud-side cooperation, the problems of high data processing pressure, high communication delay, expanded local fault influence range and the like of centralized control are effectively solved, and the real-time performance, reliability and disaster resistance of the power distribution network are remarkably improved.

A power converter having an on-time compensation mechanism is provided. The power converter includes a first switch, a second switch, a third switch, a fourth switch and a control circuit. A first terminal of the first switch is coupled with an input voltage. A first terminal of the second switch is connected to a second terminal of the first switch and a first terminal of an inductor. A first terminal of the fourth switch is connected to a second terminal of the third switch and a second terminal of the inductor. The control circuit, according to an on-time of the fourth switch operating in a boost mode, compensates on-times of the first and the second switches operating in a buck mode. In a buck-boost mode, the control circuit controls the on-times of the first and the second switches to be equal to the compensated on-times, respectively.

The invention relates to the technical field of bridge construction equipment, and discloses a bridge fabrication machine walking attitude real-time control system based on multi-sensor fusion, and the system comprises a heterogeneous sensing and data collection module which obtains the kinematics and structural strain data of a bridge fabrication machine in real time; the offline modeling and calibration module is used for pre-establishing an initial dynamic model and a key mapping relation; the online state estimation and model self-adaption module is used for performing state estimation, model correction and disturbance identification and outputting comprehensive state information; the multi-constraint predictive control decision module is used for solving an optimization problem containing structural security constraints and generating an optimal control instruction; and the execution and feedback module analyzes the optimal control instruction to drive an execution mechanism and adjust the attitude to form a real-time closed loop. According to the invention, a scheme of fusing multi-source data and carrying out online adaptive model correction is adopted, and the fundamental defect that the control precision is obviously reduced along with time lapse and environment change due to the fact that the model is not matched with an actual system state is overcome.

The invention discloses a data center uninterruptible power supply intelligent control method and device based on reinforcement learning, and relates to the technical field of energy management. The method comprises the steps that a health perception state space is constructed, and the state space comprises operation state features and standby battery multi-dimensional health vectors generated through training on a plurality of distributed uninterruptible power supply stations through a federated learning method; defining a multi-target reward function for comprehensively quantifying the economic cost based on the time-of-use electricity price, the standby duration satisfaction degree of the service level agreement and the battery life loss cost; through a reinforcement learning algorithm with a security constraint layer, based on the state space and the reward function, training an intelligent agent to learn an optimal power supply control strategy, the security constraint layer forcibly executing a preset operation security boundary in training and execution; and according to the learned optimal strategy, a real-time control instruction for the uninterruptible power supply is generated and executed.

The invention provides a double-layer intelligent agent optimization method, device and equipment for multiple mechanical arms and a storage medium. An RRT-Connect algorithm and B spline track smooth optimization are introduced into a high layer, and global path planning is conducted on environment information of a carrying task; the smoothed centroid trajectory is converted into a reference trajectory sequence at the tail end of each mechanical arm by using a kinematics chain model, then the reference trajectory is used as a bottom layer distributed real-time control guide signal driven by an MAPPO algorithm, and comprehensive feedback is performed on task completion degree, motion smoothness and coordination in combination with a multi-layer reward function; and all the mechanical arm intelligent bodies are guided to cooperatively adjust joint actions in the tracking process, so that seamless connection of global planning and local control is achieved, and the smoothness and cooperative efficiency of carrying motion are remarkably improved.

The described techniques address issues related to compatibility and cost-effectiveness of in-vehicle networks. The described techniques may utilize security Ethernet-based protocols, for example, without the need to exchange separate key agreement messages and, consequently, meet the stringent starting time requirements for real-time control systems. Additionally, a membership tracking solution may be implemented in which each node within a membership group may request, or “challenge” other nodes with the same group to verify their online status, and this online status may be maintained over time. Finally, a transmission queue management process is described that deletes queued messages only when it is determined that every intended recipient node received the message, thereby providing atomicity. Retained queued messages may then be re-transmitted in accordance with a transmission schedule.

A resonant converter having a switch on-time control mechanism is provided. The resonant converter includes a primary-side switch circuit, a primary-side resonant circuit, a secondary-side switch circuit, a secondary-side resonant circuit, a transformer and a control circuit. In the resonant converter, the control circuit controls on-times and switching frequencies of the primary-side switch circuit and the secondary-side switch circuit to extend time within which power is transmitted from an input power source, the primary-side switch circuit, the primary-side resonant circuit and the transformer to the secondary-side resonant circuit, and stored in the secondary-side resonant circuit. As a result, the secondary-side resonant circuit is able to supply more power to a load.

The invention relates to the field of edge computing, in particular to a primary and secondary fusion complete ring main unit energy efficiency optimization system based on cloud edge collaboration. The system comprises an edge device deployment and data acquisition module used for collecting operation electric parameters and device states and generating a service calling request; the service calling module is used for screening target control parameters under security constraints based on the energy efficiency optimization service package; the data synchronization module unloads the data to a cloud platform through a message queue protocol; the cloud service arrangement module aggregates the data updating service package and issues the data updating service package to the edge; and the real-time control module converts the parameters into instructions to drive equipment to act. A cloud training and edge reasoning collaborative architecture is adopted, the problems that local control lacks adaptivity, edge resources are limited and full-cloud control communication time delay exists are solved, and real-time energy efficiency optimization is achieved.

The invention discloses a self-adaptive adjustment method and system for the die-casting temperature of a die-casting die, and relates to the field of die-casting temperature adjustment, and the method comprises the steps: synchronously obtaining a real-time temperature data flow and a real-time control output flow of the die-casting die, and constructing a time sequence data model capable of representing the internal thermal dynamic characteristics of the die-casting die; on the basis, a depth time sequence prediction model is introduced, and the future temperature response track of the system is accurately deduced. Furthermore, process targets such as a temperature set value are fused, and a model predictive control framework is utilized to carry out prospective rolling optimization on future multi-step control output so as to solve an optimal control instruction sequence. And finally, taking the first instruction of the sequence as the actual control output of the current period. Therefore, the advanced and accurate output of the controlled quantity can be realized, and the thermal hysteresis effect is effectively overcome, so that the high-precision and high-stability self-adaptive adjustment of the die-casting temperature is realized.

The invention is suitable for the technical field of power system scheduling, and provides a new energy access provincial power grid multi-time scale intelligent scheduling method, which comprises the following steps of: obtaining related data of a provincial power grid, generating a new energy power point prediction curve of a day-ahead time scale, a new energy power fluctuation interval prediction result of an intra-day time scale, a new energy power point prediction curve of a real-time time scale and a new energy power fluctuation interval prediction result of the current moment by using the trained multi-time scale prediction model; processing by the day-ahead deep reinforcement learning agent, and outputting a day-ahead scheduling instruction; processing by the intraday deep reinforcement learning agent, and outputting an intraday adjustment instruction; and a real-time control instruction is output after the real-time deep reinforcement learning intelligent agent processes the real-time control instruction. According to the method, global optimization from day-ahead robust planning and intra-day active prevention to real-time multi-target cooperation is realized, and the cooperative scheduling control level of scheduling decision is improved.

The invention relates to the technical field of transformer voltage regulation cooperative control, in particular to a transformer voltage regulation cooperative control system based on Internet of Things data acquisition, and the system comprises a heterogeneous control strategy execution unit which is used for generating an expected control instruction signal; the power grid regulation toughness quantification unit is used for collecting real-time control instruction data and calculating to obtain an accumulated toughness attenuation index; the data attack identification unit is used for synchronously acquiring an original voltage measurement signal and an expected control instruction signal generated by the heterogeneous control strategy execution unit; obtaining an attack correlation index; the control right switching decision unit is used for calculating to obtain a switching trigger value; generating a final control output; the heterogeneous control strategy execution unit is also used for responding to the final control output and performing switching execution between a deep reinforcement learning strategy and a classic robust control strategy; according to the invention, accurate evaluation of physical consequences caused by attack behaviors is realized, and equipment failure caused by overfatigue is avoided.

The invention relates to the technical field of electrolytic aluminum monitoring and control, and provides an automatic control method and system for an aluminum electrolysis cell in the electrolytic aluminum process, and the method comprises the steps: A, collecting the operation parameters of the electrolysis cell in real time through a multi-source sensing array; b, constructing a dynamic material balance-heat balance coupling model, and inputting the operating parameters of the electrolytic cell into the dynamic material balance-heat balance coupling model for outputting alumina blanking rate data; c, according to the aluminum oxide blanking rate data change rate, temperature-polar distance cooperative control of the electrolytic cell is synchronously executed, and the optimal polar distance compensation amount is calculated according to the operation parameters; and D, constructing an electrolytic cell resistance change rate prediction model based on the LSTM neural network, and inputting the aluminum oxide blanking rate data change rate into the LSTM neural network structure model for outputting an aluminum oxide concentration compensation strategy. The change condition of the aluminum electrolysis cell in the aluminum electrolysis process can be rapidly and accurately monitored, and real-time control is provided.

The invention relates to the field of switching power supply management integrated circuits, in particular to a self-adaptive conduction time generating circuit for a BUCK type DC-DC converter, which comprises a charging circuit (1) and a sampling circuit (2), and is characterized in that the charging circuit (1) generates charging current in direct proportion to input voltage through a charging capacitor (C1) and a comparator (OP2), and charges the C1 to obtain first charging voltage; the sampling circuit (2) samples and filters the voltage of the switch node (SW), and outputs a first sampling voltage in direct proportion to the output voltage; and the comparator (OP2) compares the first charging voltage with the first sampling voltage, and outputs a turn-off signal to control the upper power tube to be turned off when the first charging voltage and the first sampling voltage are equal, so that the self-adaptive turn-on time is obtained. By dynamically adjusting the conduction time, the electromagnetic interference in the traditional constant conduction time control is effectively suppressed, the noise immunity and reliability of the system are improved, and meanwhile, the method has the advantages of quick response and high efficiency.

The invention belongs to the technical field of data processing, and particularly relates to a real-time control simulation system for air cooling island freezing prevention, and the system obtains a temperature field, a back pressure field and a fan operation state in a target area in real time through a monitoring module; the analysis module constructs a space-time distribution field of the temperature and the back pressure according to the temperature and the back pressure, and further fuses the space-time distribution field into a temperature-back pressure causal distribution mapping field; the inversion module carries out fault type discrimination based on the mapping field and a back pressure abnormity identification model, and generates an inversion mapping space containing an early warning level, starting time, air volume, air speed and air direction in combination with a fan operation inversion function; the response simulation module performs real-time anti-freezing control by applying a strengthening adjustment strategy and a simulation algorithm, so that the temperature and the back pressure are maintained in a standard interval; according to the invention, intelligent identification, accurate prediction and self-adaptive regulation and control of the air cooling island freezing injury risk are realized, and the accuracy of anti-freezing control and the reliability of system operation are effectively improved.

The invention relates to the technical field of real-time control software, and discloses a multi-sensor-based oven constant temperature control system and method, and the system comprises a multi-sensor detection module, a temperature control module connected with the multi-sensor detection module through a CNA bus, and an air circulation module connected with the multi-sensor detection module and the temperature control module. And the safety protection module is connected with the multi-sensor detection module and related control devices of the oven. The method corresponds to the system. According to the oven constant temperature control system and method based on the multiple sensors, through cooperation of all the modules and steps, the temperature detection accuracy and comprehensiveness are improved, the oven environment and actual food heating are both considered, meanwhile, dynamic intelligent temperature control is achieved, environment interference is reduced, temperature overshoot and lag are avoided, different food materials are adapted, in addition, airflow distribution is optimized, and the energy consumption is reduced. The temperature uniformity is improved, the safety and the reliability are enhanced, all-directional protection is formed, and stable operation of the system is guaranteed.

The invention provides a self-adaptive preset time control method and system for a flexible mechanical arm, and relates to the field of control science and engineering.The self-adaptive preset time control method comprises the steps that a dynamic model of the flexible mechanical arm is built, and a nonlinear system state equation is deduced; constructing a time transformation function and a coordinate transformation function based on the system state equation; a time transformation function and a coordinate transformation function are utilized to construct an adaptive preset time controller, a dead zone compensator is connected to the output end of the controller in series, the ideal input control signal is compensated, and actual control input is obtained and acts on the flexible mechanical arm; a time transformation function is introduced into flexible mechanical arm control, a Lyapunov function and a self-adaptive estimation method are combined, an asymptotic tracking given tracking curve is finally output, global specified transient performance is guaranteed, performance indexes in the aspects of stable time and tracking precision can be pre-distributed offline according to task requirements, and the stability of the flexible mechanical arm is improved. And the method does not depend on an initial state and any design parameters.

The invention provides a self-adaptive feeding speed control method and system for a bus-type linear cutting numerical control system, and the method comprises the steps: dividing the states of a machining process into a short circuit state, a normal discharge state and a no-load state through building a frequency reference benchmark in a no-load state and adopting a two-parameter fusion recognition method based on a current stability parameter and a frequency deviation parameter; based on state recognition, a staged adaptive strategy is adopted to calculate a speed adjustment coefficient: a preset parameter mode is adopted to perform adjustment and data collection in the initial stage, and a support vector machine algorithm is adopted to intelligently calculate an optimal feeding speed adjustment coefficient according to historical data in the later stage; through an improved time division method interpolation algorithm and a bus communication technology, dynamic adjustment of interpolation step length and real-time control instruction transmission are carried out; according to the invention, the machining precision, efficiency and stability are improved, and an intelligent control solution is provided for a bus type medium-speed wire cutting numerical control system.

The present disclosure provides a fire response control method and system for a lithium battery energy storage box, relating to the technical field of lithium batteries, which comprises: collecting battery compartment layout information of the lithium battery energy storage box; reading data of the lithium battery energy storage box to obtain real-time control data; laying out temperature collection devices through the battery compartment layout information; obtaining temperature data collection results; collecting historical detection data with time sequence identifiers; constructing a lithium battery feature data set of the lithium battery energy storage box; constructing an abnormality monitoring model; outputting abnormality warning level information; generating multi-level fire response control data; and managing the lithium battery energy storage box. The technical problem of inaccurate and timely fire warning of the lithium battery energy storage box and inability to avoid fire accidents in time due to the single and inaccurate setting of the warning threshold in the prior art is solved.

The invention relates to the technical field of power distribution terminals, and discloses a power distribution terminal automatic joint debugging function integrity and reliability test system, which comprises a co-simulation module for performing closed-loop coupling on a virtual power grid and a tested terminal through deterministic time control and generating a reproducible working condition; the scene arrangement module is used for realizing automatic switching and rollback of cross-domain scenes based on transactional snapshots; and the communication module is used for executing a consistency test on multiple protocols and injecting delay jitter, packet loss and clock drift. The functional integrity and reliability test system for the automatic joint debugging of the power distribution terminal aims to solve the problems of lack of deterministic time control of cross-domain cooperation and insufficient guarantee of state consistency in a digital twin test method. The problems that a test result cannot be reproduced, crosstalk misjudgment is caused by state non-atomicity consistency during multi-scene switching, and faults are difficult to reproduce due to lack of time determinacy replay capability are solved.

The invention relates to the field of wave soldering, in particular to a PCB welding quality detection method and time control method based on infrared imaging, and the method comprises the steps: obtaining an infrared image of a non-welding surface of a PCB; processing and analyzing the infrared image to obtain a PCB non-welding surface space temperature distribution signal and a pin edge gray scale gradient function; a feedback signal is generated according to the temperature signal of the non-welding surface of the PCB and transmitted to the central controller to adjust the transmission speed of the transmission guide rail driven by the motor to transmit the PCB; and identifying a potential defect area existing in the PCB pin welding spot according to the pin edge gray scale gradient function. According to the method, control over the tin immersion time of wave soldering and defect identification are both considered, and the soldering quality of PCB wave soldering is improved.