CN120566706A - Laser power supply remote control system and method - Google Patents
Laser power supply remote control system and methodInfo
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- CN120566706A CN120566706A CN202510933437.9A CN202510933437A CN120566706A CN 120566706 A CN120566706 A CN 120566706A CN 202510933437 A CN202510933437 A CN 202510933437A CN 120566706 A CN120566706 A CN 120566706A
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- Prior art keywords
- power supply
- remote control
- engraving
- performance
- laser power
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
- H02J13/00026—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission involving a local wireless network, e.g. Wi-Fi, ZigBee or Bluetooth
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Computer Networks & Wireless Communication (AREA)
- Human Computer Interaction (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
本申请提供了一种激光电源远程控制系统及方法，涉及激光电源技术领域，系统包括：任务接收单元，接收激光电源的雕刻作业任务；特征采集单元，确定雕刻图形特征；电源需求分析单元，进行电源需求分析，输出电源需求性能指标；供电性能评估单元，感知激光电源当前的远程控制电路元件状态以进行供电性能评估，输出电源供电性能指标；切换控制单元，按照性能指标差异对第一远程控制电路进行切换控制，输出第二远程控制电路。本申请解决了现有技术由于缺乏基于任务特征的电源需求建模与电源状态感知联动机制，导致激光电源供电不稳定，能效低下的技术问题，达到了提升激光电源供需匹配精度和运行稳定性的技术效果。
The present application provides a laser power supply remote control system and method, which relates to the field of laser power supply technology. The system includes: a task receiving unit, which receives the engraving task of the laser power supply; a feature acquisition unit, which determines the engraving graphic features; a power demand analysis unit, which performs power demand analysis and outputs power demand performance indicators; a power supply performance evaluation unit, which senses the current state of the remote control circuit components of the laser power supply to perform power supply performance evaluation and outputs power supply performance indicators; and a switching control unit, which switches and controls the first remote control circuit according to the performance indicator difference and outputs the second remote control circuit. The present application solves the technical problem of unstable power supply and low energy efficiency of the laser power supply due to the lack of a power demand modeling and power state perception linkage mechanism based on task characteristics in the prior art, thereby achieving the technical effect of improving the supply and demand matching accuracy and operational stability of the laser power supply.
Description
Technical Field
The application relates to the technical field of laser power supplies, in particular to a laser power supply remote control system and a laser power supply remote control method.
Background
In the laser engraving process, the stability and dynamic response performance of the laser are key factors for guaranteeing the machining precision and efficiency, and the energy output of the laser is highly dependent on the power supply stability and the adjusting capability of a laser power supply. In order to meet the requirements of industrial remote operation and centralized control, the prior art generally adopts a remote control mode to manage a laser power supply, and basic control is realized by remotely setting parameters such as a power supply switch state, output power, pulse width and the like. However, such control schemes often rely on preset fixed parameters or manual empirical adjustments, disjointing from a particular engraving task, resulting in control strategies that are difficult to effectively match with actual processing loads. In addition, the existing method has limited capability of sensing the running state of the laser power supply body, especially lacks an on-line monitoring mechanism for the performance of key devices in the working process, and can not timely identify potential faults or performance degradation problems, so that a remote control system can not make dynamic response adjustment. The problems are particularly prominent in complex application scenes of frequent task switching or severe fluctuation of power states, and the problems of unstable power supply, lag system response, low energy efficiency utilization rate and the like are directly caused.
Disclosure of Invention
The application provides a laser power supply remote control system and a method, which solve the technical problems that the laser power supply cannot dynamically adjust a control strategy according to an actual engraving task due to the lack of a power supply demand modeling and power supply state sensing linkage mechanism based on the engraving task characteristics in the prior art, so that unstable power supply and low energy efficiency are caused, and achieve the technical effects of improving the accuracy of matching the supply and the demand of the laser power supply and the running stability.
In view of the above problems, in one aspect, the application provides a laser power supply remote control system, which comprises a task receiving unit for receiving an engraving operation task of a laser power supply, a characteristic acquisition unit for carrying out graph characteristic acquisition on the engraving operation task to determine engraving graph characteristics, a power supply demand analysis unit for carrying out power supply demand analysis based on the engraving graph characteristics to output a power supply demand performance index, a power supply performance evaluation unit for carrying out element state sensing on a first remote control circuit of the laser power supply through a remote sensing module to output working state sensing data of each element, carrying out power supply performance evaluation through the working state sensing data to output a power supply performance index, and a switching control unit for comparing the power supply performance index with the power supply demand performance index, carrying out switching control on the first remote control circuit according to a power supply difference performance index and outputting a switched second remote control circuit.
On the other hand, the application also provides a laser power supply remote control method, which comprises the steps of receiving an engraving operation task of a laser power supply, carrying out graph feature collection on the engraving operation task, determining engraving graph features, carrying out power demand analysis based on the engraving graph features, outputting power demand performance indexes, carrying out element state sensing on a first remote control circuit of the laser power supply currently through a remote sensing module, outputting working state sensing data of each element, carrying out power supply performance assessment through the working state sensing data, outputting power supply performance indexes, comparing the power supply performance indexes with the power demand performance indexes, carrying out switching control on the first remote control circuit according to power difference performance indexes, and outputting a second remote control circuit after switching.
The one or more technical schemes provided by the application have at least the following beneficial effects:
The task receiving unit is responsible for receiving the externally issued engraving job task and is a trigger source of the whole control flow, so that the system can perform personalized power supply regulation and control based on the specific task. And the feature acquisition unit analyzes the graphic features of the received engraving task and provides data support for subsequent power demand analysis. The power supply demand analysis unit quantifies the performance demand of the task on the laser power supply based on the graph characteristics and outputs a power supply demand performance index. The power supply performance evaluation unit acquires and evaluates the state of key elements of the first remote control circuit which is currently used in real time by means of the remote sensing module, and generates the power supply performance index of the current power supply, so that the dynamic control of the power supply capacity is realized. The switching control unit compares the power supply performance index with the demand performance index, intelligently judges whether the current circuit meets the task requirement according to the performance difference value, executes circuit switching if the current circuit is insufficient, and outputs a second remote control circuit which is more adaptive, so that closed-loop control adjustment of task driving is realized.
In summary, by constructing the power demand modeling mechanism driven by task features and combining remote sensing and performance evaluation of the states of the power elements, the application realizes real-time matching control between load demands and power supply capacity, improves the intelligent and fine degree of laser power supply and demand regulation, ensures the stability of power supply and reasonable utilization of resources, thereby remarkably improving the quality and efficiency of laser engraving and meeting the individual demands of different engraving tasks on the laser power supply.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
Fig. 1 is a schematic structural diagram of a remote control system for a laser power supply according to an embodiment of the present application.
Fig. 2 is a schematic flow chart of a remote control method for a laser power supply according to an embodiment of the present application.
Reference numerals describe a task receiving unit 10, a feature collecting unit 20, a power supply demand analyzing unit 30, a power supply performance evaluating unit 40, and a switching control unit 50.
Detailed Description
The embodiment of the application solves the technical problems that the laser power supply cannot dynamically adjust the control strategy according to the actual engraving task due to the lack of a power demand modeling and power state sensing linkage mechanism based on the engraving task characteristics in the prior art, so that unstable power supply and low energy efficiency are caused, and achieves the technical effects of improving the accuracy of matching the supply and the demand of the laser power supply and the running stability.
First embodiment as shown in fig. 1, an embodiment of the present application provides a laser power supply remote control system, which includes:
and a task receiving unit 10 for receiving the engraving job task of the laser power supply.
Specifically, the task receiving unit 10 is an information entry module in the laser power supply remote control system, and is configured to receive task information of a laser engraving job issued by a host system or a user side. The job task information typically includes graphic files, engraving materials, processing parameters, etc., which are the starting points for subsequent control flows. After the system is started, the task receiving unit 10 receives the engraving task data through a wired or wireless communication interface (such as ethernet, RS485, wi-Fi, etc.), and performs basic format analysis and buffering. The unit supports the input of standard graphic data formats (such as SVG, DXF, BMP and the like) and material identification codes, ensures that task data is not lost and misunderstood in the transmission process, and transmits the data after being structured to a downstream module for use.
By arranging the task receiving unit 10, the system can acquire task information in a standardized and structured manner, so that the follow-up processing module can be ensured to be capable of unfolding and analyzing based on complete and accurate data, and the overall compatibility and task response efficiency of the system are improved.
And the feature collection unit 20 is used for collecting the graphic features of the engraving job task and determining the engraving graphic features.
Specifically, the feature collection unit 20 is a module for performing image feature analysis on the task graphic file, and is used for extracting carving structure characteristics of the graphic, such as contour complexity, line density, gray distribution, material category, and the like, as a basis for the subsequent power control personalized analysis. After the job task information is received, the feature collection unit 20 analyzes the content of the graphic file through a graphic processing algorithm (such as Canny edge detection, gray gradient histogram, clustering and layering algorithm, etc.), identifies the curvature of the graphic profile, the line intersection density, the gray distribution variation range, and outputs a multidimensional feature vector in combination with the material type. This multidimensional feature vector will serve as input data for the subsequent power demand analysis unit 30.
The feature collection unit 20 implements bridging between the engraving task and the power control requirements, so that the system can dynamically adapt to the control strategy according to the graphics and material characteristics, and a data base is provided for the formulation of the intelligent power supply strategy.
And the power demand analysis unit 30 is configured to perform power demand analysis based on the engraved pattern feature, and output a power demand performance index.
Specifically, the power demand analysis unit 30 is configured to construct, according to the extracted graphic features, performance indexes of power supply parameters required by the task, including voltage-current error margin, fluctuation response speed, power step change, and the like, as target references for power supply system adjustment. The power demand analysis unit 30 includes a key feature convolution extraction module, a mapping relation construction module, and an index calculation module. The instantaneous power change requirement and the accuracy requirement required by the engraving process are extracted from the graph characteristics by a convolutional neural network and other methods, then the mapping relation between the graph characteristics and the power indexes is established by a sample training or machine learning model, and finally the power performance indexes required by the task are output.
The power demand analysis unit 30 realizes quantitative expression of different task demands, so that a laser power supply system can accurately formulate a power supply target, thereby being more suitable for different process requirements and effectively avoiding the problems of undersupply or oversupply and the like.
The power supply performance evaluation unit 40 is configured to perform component state sensing on the current first remote control circuit of the laser power supply through the remote sensing module, output working state sensing data of each component, perform power supply performance evaluation according to the working state sensing data, and output a power supply performance index.
Specifically, the power supply performance evaluation unit 40 is a module for acquiring the current operating state of the key element in the first remote control circuit in real time, and evaluating its actual power supply capability based on the state-aware data. The power supply performance evaluation unit 40 performs data acquisition, such as temperature rise, voltage offset, current fluctuation and the like, on the running states of control circuit elements, such as a power device, a driving module, a feedback loop and the like, through accessing a remote sensing module, performs time synchronization processing, and then generates a comprehensive power supply performance index of the current control circuit through an evaluation model (such as a threshold judgment model or Bayesian state estimation) as an important basis for whether to execute circuit switching.
By evaluating the current power supply state in real time, the power supply performance evaluation unit 40 provides dynamic feedback capability for system decision, effectively prevents control failure caused by element degradation, aging or abnormality, and enhances the self-adaptation capability and safety of the remote control system.
And the switching control unit 50 is configured to compare the power supply performance index with the power demand performance index, perform switching control on the first remote control circuit according to the power supply difference performance index, and output a switched second remote control circuit.
Specifically, the switching control unit 50 is configured to compare the power supply capability with the demand, and automatically switch to a better standby circuit when the performance is not matched. When there is a difference between the power supply performance index and the power demand performance index, the switching control unit 50 first determines whether the power supply difference performance index falls within a tolerable interval. And if the threshold value is exceeded, calling a multi-path relay to analyze the element state of the first control circuit, selecting a node to be switched according to the minimum difference principle, the working time length or the fault probability, and switching to the second remote control circuit. Meanwhile, if the deviation of the acquired carving execution result exceeds the range, the deviation correction module can be also called to carry out self-adaptive updating on the circuit control strategy.
The switching control unit 50 ensures that the power supply system is continuously in an optimal running state through an intelligent comparison and dynamic node reconstruction mechanism, so that the high-efficiency stable adjustment of the remote control circuit under the conditions of multiple tasks and multiple environments is realized, and the control precision and the actual running reliability of the laser power supply are greatly improved.
Further, the engraving job task includes a graphic file and an engraving material type, and the feature collection unit 20 is further configured to perform the following steps:
and step P21, analyzing the graphic file by utilizing a graphic processing module, and extracting carving contour information, line density and gray level distribution.
And step P22, outputting the type of the carving material, the carving profile information, the line density and the gray scale distribution as carving pattern features.
Specifically, the graphics processing module is a core computing module for analyzing the content of the received graphics file, and mainly comprises an image contour extraction algorithm, a line density analysis tool, a gray area distribution computing model and the like. The engraved pattern features are joint descriptions of pattern structural parameters and material properties, which serve as basic reference data for subsequent power demand assessment.
The feature collection unit 20 obtains an engraving job task including a graphic file and an engraving material type from the task receiving unit 10, and first performs preprocessing on the graphic file, including file format analysis, layer separation, and coordinate normalization processing. The method comprises the steps of carrying out carving outline information extraction, line density calculation and gray level distribution analysis by using a graphic processing module, wherein the graphic processing module calls an edge detection algorithm to identify a main outline of a graphic, extracts geometric information such as curvature change of an outer frame, a closed area and the like, then uses a grid dividing method (such as regional pixel block) to count the number of lines which are staggered or intersected in a unit area to obtain different regional line density distribution conditions, and uses a histogram statistics or distribution function to analyze indexes such as gray level change range, gradient change trend and the like aiming at a gray level graphic or a task with gradual change content.
After the image processing is completed, the three extracted graphic structure parameters (carving outline information, line density and gray distribution) and the carving material types received by the task are fused to form a unified carving graphic feature vector. The feature vector may be used as an input for processing by a subsequent module (power demand analysis unit 30). Wherein the material type is typically associated with its physical properties of thermal conductivity, light absorption coefficient, melting point, etc. by database labeling to affect the feature weight calculation.
The feature acquisition unit 20 can accurately reflect the actual requirements of different graphs on the dynamic response of the power supply through joint modeling of the graph structure and the material performance, and provides key input support for the follow-up intelligent control strategy.
Further, the power demand analysis unit 30 includes:
and the convolution module is used for carrying out key feature convolution extraction on the carving pattern features and outputting carving line width change, pattern carving granularity and pattern layering.
The mapping module is used for establishing a mapping relation between the key characteristic sample and the power supply demand performance index, wherein the evaluation items of the power supply demand performance index comprise voltage and current output errors, fluctuation suppression rate and power step length.
And the index calculation module is used for obtaining the value of the evaluation item corresponding to the key feature based on the mapping relation, and calculating the value of the evaluation item to output the power supply demand performance index.
Specifically, in the power demand analysis unit 30, the convolution module first performs multidimensional convolution processing on the engraved pattern feature, and extracts a plurality of key feature parameters from the original engraved pattern feature input, including, but not limited to, engraved line width variation (in mm), pattern engraving granularity (in dpi), and pattern layering (in layers). Wherein the engraving linewidth variation describes the engraving depth and the fineness of each part of the engraving pattern. The pattern engraving granularity describes the complexity of the details in engraving the pattern, including whether there are too many tiny elements or complex curve structures. The graphic hierarchy describes different hierarchies of engraved graphics. Taking a specific task as an example, the key feature vector is obtained after the processing of a convolution module, wherein the line width change is 0.12mm, the engraving granularity is 600dpi, and the number of layers of the graph is 3.
And the mapping module is used for establishing a mapping relation between the key characteristic sample and the power demand performance index based on a large amount of previous historical engraving task data and the actual power supply performance data of the laser power supply. The mapping relation is stored in a lookup table constructed by three-dimensional discrete samples, coordinate axes respectively correspond to line width change, engraving granularity and number of layers of patterns, and each coordinate point corresponds to a power demand performance index group and comprises voltage output error, current output error, fluctuation suppression rate and power step length. The construction method comprises the following steps of clustering and classifying sampling points of historical tasks, and then establishing a multidimensional interpolation mapping function through an interpolation method (such as cubic spline interpolation) to complete the index estimation capability of continuous space. Examples of map entries are input features (0.10 mm,600dpi,2 layers), corresponding index outputs (voltage output error 0.25V, current output error 0.12A, ripple rejection 95.3%, power step size 8W), input features (0.12 mm,600dpi,3 layers), corresponding index outputs (voltage output error 0.31V, current output error 0.18A, ripple rejection 94.7%, power step size 10W).
In the task execution process, an index calculation module receives the current key feature vector output by the convolution module, invokes the mapping relation established in the mapping module, acquires voltage and current output errors, fluctuation suppression rate and power step length corresponding to the feature through a three-dimensional spatial interpolation algorithm, and outputs the voltage and current output errors, fluctuation suppression rate and power step length as a power supply requirement performance index. The power demand performance index is transmitted to the switching control unit 50, and compared with the current power supply performance index of the laser power supply, and the subsequent remote circuit switching control process is guided.
Through the synergistic effect of the three modules, the stable mapping from key characteristics to performance indexes is realized, and a reliable basis is provided for subsequent power supply performance evaluation and switching control.
Further, the power supply performance evaluation unit 40 includes:
And the data alignment module is used for performing time stamp alignment on the working state sensing data and outputting the processed working state sensing data.
The evaluation module is used for evaluating the power supply performance according to the processed working state sensing data and outputting power supply performance indexes of the power supply, wherein evaluation items of the power supply performance indexes of the power supply comprise voltage and current output errors, fluctuation suppression rate and power step length.
Further, the elements of the first remote control circuit at least comprise a switching device, a multi-path relay, a driving module, a voltage feedback loop, a current feedback loop and a communication control module.
Specifically, the power supply performance evaluation unit 40 evaluates the current power supply capability of the laser power supply in real time based on the actual working state of a first remote control circuit, where the first remote control circuit includes several core elements, specifically, a switching device, a multi-path relay, a driving module, a voltage feedback loop, a current feedback loop, and a communication control module. The switching device is used for controlling the on-off of a power supply and adjusting the connection of different power supply paths, the multi-path relay is used for realizing the switching control of multiple nodes of a circuit, the driving module is used for driving the voltage and current output control of the laser to work normally, the voltage feedback loop and the current feedback loop are used for detecting the voltage and current values of an output end in real time, and the communication control module is responsible for collecting state information and uploading the state information to the remote control platform.
When the laser power supply operates, the remote sensing module collects the state data of the above elements in real time and transmits the state data to the power supply performance evaluation unit 40 in a unified manner. The power performance evaluation unit 40 includes two sub-modules, namely a data alignment module and an evaluation module, and first, the data alignment module receives operation state sensing data from each element. Because the data sources are various, the sampling frequencies are different, the problems of time stamp dislocation and the like exist, and in order to ensure the evaluation precision, a data alignment module adopts a time stamp alignment algorithm (such as a weighting alignment method based on a sliding window) to perform unified time sequence correction on different data streams. For example, the sampling frequency of the current feedback loop is unified from 500Hz to 1000Hz of the driving module, the data structure of the current feedback loop is kept consistent with other data through interpolation and downsampling operation, and the working state sensing data set with consistent structure is output.
And then, the evaluation module analyzes the current state of each key evaluation item according to the processed data set, wherein the current state comprises voltage output errors, current output errors, fluctuation suppression rate and power step length. The voltage output error is the difference between the current output voltage and the target set voltage, the current output error is the difference between the current output current and the target set current, the fluctuation suppression rate is the stability proportion of the output voltage or the current in unit time, the fluctuation suppression rate is used for evaluating the disturbance rejection capability of the system, and the power step size is the fine granularity of power adjustment under the dynamic change of the load. In order to achieve accurate assessment, the assessment module adopts a composite algorithm based on Kalman filtering and wavelet analysis to conduct denoising and smoothing processing on the voltage-current curve, and meanwhile local fluctuation characteristics are extracted according to a work load time period. The power performance index is then transmitted to the switching control unit 50, and compared with the power requirement performance index of the task end to determine whether to perform remote circuit switching.
Further, the switching control unit 50 is further configured to perform the following steps:
And step P51, judging whether the power supply difference performance index is smaller than a preset difference interval.
And step P52, if the power supply difference performance index is smaller than the preset difference interval, not activating a switching instruction, and performing engraving control on the laser power supply by using the first remote control circuit.
And step P53, if the power supply difference performance index is greater than or equal to the preset difference interval, activating a switching instruction, connecting a multi-path relay to perform switching node analysis on the first remote control circuit, outputting a node to be switched, switching the node to be switched into a standby node, and outputting a second remote control circuit.
Specifically, the switching control unit 50 receives the power supply performance index and the power demand performance index, and calculates the power supply difference performance index by setting a unified multidimensional comparison mechanism. The difference index can be expressed as ：ΔP=ω1*∣ΔU∣/Umax+ω2*∣ΔI∣/Imax+ω3*(1-Rs)+ω4*Sg/Smax in a weighted function mode, wherein DeltaU is a voltage output error, U max is a maximum voltage deviation allowed by a system, deltaI is a current output error, I max is a maximum current deviation allowed by the system, rs is a fluctuation suppression rate, S g is a power step size, and S max is a maximum power step change amplitude allowed by the system. And comparing the calculated power supply difference performance index delta P with a preset difference interval.
If the judging result is that the power supply difference performance index is smaller than the preset difference interval, namely the current power supply capacity can meet the power supply requirement of the laser engraving task, the state of the original first remote control circuit is maintained, the switching instruction is not activated, and the first remote control circuit continues to supply power to the laser power supply.
If the judging result is that the power supply difference performance index is greater than or equal to the preset difference interval, that is, the current power supply performance has obvious deviation and cannot effectively meet the task requirement, the switching control unit 50 activates the switching instruction. The method specifically comprises the following steps of calling a built-in multi-path relay state table and a historical load feedback record, carrying out switching node analysis on internal connection nodes of a first remote control circuit, screening out a plurality of nodes to be switched (such as fault frequent nodes, voltage output fluctuation nodes and the like), selecting a standby node which is functionally equivalent to the nodes based on analysis results, switching on a standby path through controlling a multi-path relay, outputting to form a second remote control circuit which replaces the first remote control circuit, and completing power path switching. Through the switching mechanism, the remote control capability and the task execution stability are improved while the power supply reliability is ensured.
Further, step P53 further includes:
and step P53-1, establishing a performance influence relation between each element and the power supply performance index of the power supply according to the working state sensing data of each element.
And step P53-2, identifying N performance influence indexes corresponding to N elements in the first remote control circuit by utilizing the performance influence relation.
And step P53-3, optimizing the N performance influence indexes with the aim of minimizing the power supply difference performance indexes, and outputting an element solution set, wherein the element solution set comprises nodes to be switched.
Specifically, the operating state sensing data refers to real-time element state data collected by a sensor or a monitoring module, and the operating state sensing data comprises key performance data such as current, voltage, temperature and the like. The performance impact relationship is a mathematical model or empirical formula describing the relationship between the state of the component and the power performance indicator. And collecting real-time working state sensing data of each element of the first remote control circuit through a path sensing module, and constructing an influence relation model between the working state sensing data and power supply performance indexes (such as voltage deviation, current error and the like) of the power supply. This model is based on historical data, test results, or deep learning training models to quantify the specific impact of each element's state change on the power supply performance of the power supply. For example, a linear regression model of the on-off speed and the voltage output error of the switching device is established through a large amount of experimental data, and a nonlinear relation model of the contact resistance and the current output error of the multi-path relay is established.
And identifying the association between each element in the current control circuit and the power performance index by using the established performance influence relation. And determining a corresponding performance influence index of each element in the current state by analyzing the working state sensing data, namely, a specific influence measure of each element on the power supply performance in the current state, such as the contribution of each element to voltage, current fluctuation and the like.
Based on the identified performance impact index, optimizing the states of all the elements by using an optimizing algorithm (such as a genetic algorithm, particle swarm optimization and the like), and the aim is to find a group of elements to be switched, so that the performance difference index under a new power supply path is minimum after circuit switching is performed. The objective function is exemplified as follows: , wherein, The sensitivity of the state of the weight coefficient corresponding to the ith element to the overall performance is reflected; The performance impact index value corresponding to the i-th element. Taking a particle swarm optimization algorithm as an example, initializing a particle swarm, wherein each particle represents a possible element combination, endowing an initial speed and a position, evaluating the fitness of each particle according to an objective function, and iteratively optimizing through a speed and position updating rule among particles to gradually approach a minimum performance difference solution. And when the optimization process reaches a preset convergence condition or maximum iteration times, the system outputs the element combination represented by the current optimal particle as a target solution set of the node to be switched.
The steps are monitored and optimized through accurate element performance, and power supply performance difference of the laser power supply is minimized. By establishing an association model of the element and the power performance, identifying the influence index and performing optimization selection, the switching of the element can be efficiently controlled, the laser power supply is ensured to always maintain the optimal performance state under different working conditions, the stability in the laser engraving process is improved, and the problems of unstable power supply or resource waste are avoided.
Further, step P53 further includes:
and calculating the fault probability of each element according to the continuous working time, and identifying the element with the fault probability meeting the expected probability as the node to be switched.
Specifically, the continuous operation time period refers to the time that the element is continuously operated in the current operation state, and is calculated by monitoring the operation cycle of the element or starting a timer. The failure probability refers to the probability of failure of an element in a given operating time period, and is estimated by a statistical method. The node to be switched refers to an element which is selected to be replaced or deactivated according to the working time and the fault probability in the switching control process.
Firstly, the continuous working time length of each element is obtained through a timer or a built-in sensor in the monitoring system. Based on the continuous operation time length, the fault probability of each element is calculated by using a preset element fault model. The model is built based on life curves, historical data or statistical rules (such as failure rate, accelerated life test data, etc.) of the component. For example, a weibull distribution may be used to represent failure rates of elements. After obtaining the failure probabilities of the components, these probabilities are compared with preset expected probabilities. If the failure probability of a certain element exceeds a preset expected probability threshold, the element is marked as a node to be switched, that is, the node needs to be replaced in advance or is subjected to switching operation, so that the influence on the stability of the power supply due to the failure of the element is avoided.
By acquiring the continuous working time of the elements and calculating the fault probability of the elements, the health state of each element can be dynamically estimated, and the elements with high fault probability can be early warned and switched. The switching control mechanism based on the service life and the working state of the element can effectively avoid the influence of equipment faults on the performance of a laser power supply, improve the reliability and stability of the laser power supply and avoid production pause or quality fluctuation caused by sudden faults.
Further, the switching control unit 50 further includes:
And the control execution module is used for controlling the laser power supply to carry out carving according to the switched second remote control circuit and collecting carving execution results.
And the deviation correction module is used for carrying out deviation comparison on the carving execution result and a preset carving pattern in the pattern file, obtaining deviation data, constructing an adaptive correction factor according to the deviation data, and updating the second remote control circuit by the adaptive correction factor.
Specifically, the switching control unit 50 further includes a control execution module and a deviation correction module, where the control execution module is responsible for switching to the second remote control circuit to continue to complete the laser engraving operation when it is detected that the first remote control circuit cannot meet the power requirement, continuously tracking the execution progress and result of the engraving, and collecting the engraving execution result including graphic accuracy, contour definition, engraving depth, and the like through an integrated sensor (such as image recognition, laser power monitoring, and the like) and a feedback mechanism.
The deviation correction module compares the carving execution result with a preset graphic file by utilizing an image processing technology (such as edge detection, shape matching and the like), identifies line boundaries in an actual carving image through an edge detection algorithm, evaluates shape consistency through a shape matching algorithm, and identifies errors such as deviation, rotation and the like of the graphic position through a feature point registration algorithm. The output includes deviation data including geometric deviation, positional deviation, shape deviation. The geometric deviation comprises a line width difference value and an engraving depth difference value, the position deviation comprises a horizontal offset, a vertical offset and a rotation angle which are wholly or partially, and the shape deviation comprises a local nonlinear change between a target shape and an actual image. And aiming at the deviation data, the deviation correction module builds a mapping model, evaluates the influence relation between different types of deviation and control parameters, and generates an adaptive correction factor set for subsequent engraving control correction. Typical correction factors include laser power factors for increasing or decreasing laser intensity to correct engraving depth, engraving speed factors for adjusting scanning speed to match power control, focal length adjustment factors for optimizing laser focusing to improve profile accuracy, path offset factors for compensating pattern position drift, and corner correction factors for rotational error compensation. The factors can be calculated through a causal relation model obtained through regression model or neural network model training, and can also be generated based on an empirical formula or multiple experimental calibration data. And updating the key control parameters in the current second remote control circuit in real time according to the generated correction factors. For example, the PWM duty ratio of the power control module is adjusted by the laser power factor, the stepping control amount of the XY platform is adjusted by the path offset factor, and the timing order of the engraving path in the driving module is adjusted by the speed factor. The updated control parameters are transmitted to the control execution module, and the corrected engraving task is restarted, so that the accuracy of the subsequent engraving area is improved, and the consistency of the graph and the engraving quality are ensured.
The control execution module and the deviation correction module in the switching control unit 50 are in cooperative work, so that the laser power supply can continue to carry out engraving operation after switching the circuit, and subsequent control parameters are automatically adjusted according to the engraving effect, so that the engraving precision is improved, and the intellectualization and adaptability of the remote control system of the laser power supply are enhanced.
In summary, the laser power supply remote control system provided by the embodiment of the application has the following beneficial effects:
the task receiving unit 10 is responsible for receiving the external issued engraving job task and is a trigger source of the whole control flow, so that the system can perform personalized power supply regulation and control based on the specific task. The feature collection unit 20 performs graphic feature analysis on the received engraving task to provide data support for subsequent power demand analysis. The power demand analysis unit 30 quantifies the performance demand of the task on the laser power supply based on the graphic features, and outputs a power demand performance index. The power supply performance evaluation unit 40 acquires and evaluates the state of the key element of the first remote control circuit currently used in real time by means of the remote sensing module, and generates the current power supply performance index of the power supply, thereby realizing the dynamic control of the power supply capability. The switching control unit 50 compares the power supply performance index with the demand performance index, intelligently judges whether the current circuit meets the task requirement according to the performance difference value, executes circuit switching if the current circuit is insufficient, and outputs a second remote control circuit which is more adaptive, thereby realizing closed-loop control adjustment of task driving.
Overall, the embodiment of the application realizes real-time matching control between load demand and power supply capacity by constructing a power supply demand modeling mechanism driven by task characteristics and combining remote perception and performance evaluation of power supply element states, improves the intelligentization and refinement degree of laser power supply demand regulation, ensures the stability of power supply and reasonable utilization of resources, thereby remarkably improving the quality and efficiency of laser engraving and meeting the personalized demands of different engraving tasks on the laser power supply.
In a second embodiment, as shown in fig. 2, based on the same inventive concept as the previous embodiment, the embodiment of the present application provides a laser power supply remote control method, which includes:
And S1, receiving an engraving job task of a laser power supply.
And S2, collecting the graphic features of the engraving job task, and determining the engraving graphic features.
And step S3, carrying out power demand analysis based on the engraved pattern features, and outputting power demand performance indexes.
And S4, performing element state sensing on a first remote control circuit of the laser power supply through a remote sensing module, outputting working state sensing data of each element, performing power supply performance evaluation according to the working state sensing data, and outputting power supply performance indexes of the power supply.
And S5, comparing the power supply performance index of the power supply with the power demand performance index, performing switching control on the first remote control circuit according to the power supply difference performance index, and outputting a switched second remote control circuit.
Further, collecting the graphic features of the engraving job task, determining the engraving graphic features, including:
The engraving operation task comprises a graphic file and engraving material types, wherein the graphic file is analyzed by a graphic processing module, engraving contour information, line density and gray distribution are extracted, and the engraving material types, the engraving contour information, the line density and the gray distribution are output as engraving graphic features.
Further, performing power demand analysis based on the engraved graphic features, outputting a power demand performance index, including:
The method comprises the steps of carrying out key feature convolution extraction on the engraved graph features, outputting engraved line width change, graph engraving granularity and graph layering, establishing a mapping relation between key feature samples and power demand performance indexes, wherein evaluation items of the power demand performance indexes comprise voltage and current output errors, fluctuation suppression rates and power step steps, obtaining values of evaluation items corresponding to the key features based on the mapping relation, and calculating the values of the evaluation items to output the power demand performance indexes.
Further, performing power supply performance evaluation according to the working state sensing data, and outputting power supply performance indexes of a power supply, including:
And performing power supply performance evaluation according to the processed working state sensing data, and outputting power supply performance indexes of a power supply, wherein evaluation items of the power supply performance indexes comprise voltage and current output errors, fluctuation suppression rate and power step length.
Further, the elements of the first remote control circuit at least comprise a switching device, a multi-path relay, a driving module, a voltage feedback loop, a current feedback loop and a communication control module.
Further, the switching control of the first remote control circuit according to the performance index of the power supply difference value includes:
the method comprises the steps of judging whether a power supply difference performance index is smaller than a preset difference interval, if the power supply difference performance index is smaller than the preset difference interval, not activating a switching instruction, carrying out engraving control on a laser power supply by using a first remote control circuit, if the power supply difference performance index is larger than or equal to the preset difference interval, activating the switching instruction, connecting a multi-path relay to carry out switching node analysis on the first remote control circuit, outputting a node to be switched, switching the node to be switched into a standby node, and outputting a second remote control circuit.
Further, outputting the node to be switched includes:
The method comprises the steps of establishing performance influence relation between each element and power supply performance indexes according to working state sensing data of each element, utilizing the performance influence relation to identify N performance influence indexes corresponding to N elements in a first remote control circuit, optimizing the N performance influence indexes with the performance indexes of the power supply difference as targets, and outputting element solution sets, wherein the element solution sets comprise nodes to be switched.
Further, outputting the node to be switched, further includes:
and calculating the fault probability of each element according to the continuous working time, and identifying the element with the fault probability meeting the expected probability as the node to be switched.
Further, after outputting the switched second remote control circuit, the method further includes:
And performing deviation comparison between the engraving execution result and a preset engraving pattern in the pattern file to obtain deviation data, constructing an adaptive correction factor according to the deviation data, and updating the second remote control circuit by the adaptive correction factor.
The foregoing detailed description of a laser power remote control system will be apparent to those skilled in the art, and the method disclosed in the second embodiment has corresponding execution steps and advantages for the method disclosed in the first embodiment, and the relevant points refer to the system part.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
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| CN202510933437.9A CN120566706B (en) | 2025-07-08 | A remote control system and method for laser power supply |
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| CN202510933437.9A CN120566706B (en) | 2025-07-08 | A remote control system and method for laser power supply |
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