A laser power remote control system and method

Abstract

The application provides a laser power supply remote control system and method, relates to the technical field of laser power supply, and the system comprises: a task receiving unit that receives an engraving task of a laser power supply; a feature collecting unit that determines an engraving pattern feature; a power supply demand analysis unit that performs power supply demand analysis and outputs a power supply demand performance index; a power supply performance evaluation unit that senses the current remote control circuit element state of the laser power supply to perform power supply performance evaluation and outputs a power supply performance index; and a switching control unit that performs switching control on the first remote control circuit according to the performance index difference and outputs the second remote control circuit. The application solves the technical problems that the power supply of the laser power supply is unstable and the energy efficiency is low due to the lack of a power supply demand modeling and power supply state sensing linkage mechanism based on task features in the prior art, and achieves the technical effects of improving the power supply matching precision and operation stability of the laser power supply.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of laser power supply, in particular to a laser power supply remote control system and method. BACKGROUND

[0002] In the laser engraving process, the stability and dynamic response performance of the laser are key factors to ensure the processing precision and efficiency, and the energy output of the laser is highly dependent on the power supply stability and adjustment capability of the laser power supply. In order to meet the demand of industrial remote operation and centralized control, the existing technology generally adopts a remote control method to manage the laser power supply, and sets the parameters such as power supply switch state, output power and pulse width through remote control to realize basic control. However, such control method usually relies on preset fixed parameters or manual experience adjustment, which is disconnected with the specific engraving task, resulting in that the control strategy is difficult to match with the actual processing load. In addition, the existing method has limited running state sensing capability for the laser power supply body, especially lacks online monitoring mechanism for the performance of key devices in the working process, which cannot identify potential faults or performance degradation problems in time, so that the remote control system cannot make dynamic response adjustment. The above problems are particularly prominent in complex application scenarios with frequent task switching or drastic power supply state fluctuation, directly leading to unstable power supply, system response lag and low energy efficiency. SUMMARY

[0003] The present application provides a laser power supply remote control system and method, which 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 power supply demand modeling based on the characteristics of the engraving task and the linkage mechanism of power supply state sensing in the prior art, resulting in unstable power supply and low energy efficiency, and achieves the technical effects of improving the matching precision of laser power supply and running stability.

[0004] In view of the above problems, on the one hand, the present application provides a laser power supply remote control system, which comprises: a task receiving unit for receiving an engraving task of a laser power supply; a feature acquisition unit for acquiring the features of the engraving task to determine the features of the engraving pattern; a power supply demand analysis unit for analyzing the power supply demand based on the features of the engraving pattern to output power supply demand performance indicators; a power supply performance evaluation unit for sensing the state of each element of the first remote control circuit of the laser power supply through a remote sensing module to output working state sensing data of each element, evaluating the power supply performance based on the working state sensing data, and outputting power supply performance indicators; and a switching control unit for comparing the power supply performance indicators with the power supply demand performance indicators, performing switching control on the first remote control circuit according to the power supply difference performance indicators, and outputting the second remote control circuit after switching.

[0005] In another aspect, the application also provides a laser power supply remote control method, which comprises: receiving an engraving task of the laser power supply; collecting the graphic features of the engraving task to determine the engraving graphic features; analyzing the power supply demand based on the engraving graphic features to output the power supply demand performance index; perceiving the element state of the first remote control circuit of the laser power supply by a remote sensing module to output the working state sensing data of each element, evaluating the power supply performance by the working state sensing data, and outputting the power supply performance index; comparing the power supply performance index with the power supply demand performance index, switching the first remote control circuit according to the power supply difference performance index, and outputting the second remote control circuit after switching.

[0006] The one or more technical solutions provided in the application have at least the following beneficial effects:

[0007] The task receiving unit is responsible for receiving the engraving task issued by the outside, which is the trigger source of the whole control process, ensuring that the system can perform individualized power supply regulation based on specific tasks. The feature collection unit analyzes the graphic features of the received engraving task, providing data support for subsequent power supply demand analysis. The power supply demand analysis unit quantifies the performance demand of the laser power supply based on the graphic features, and outputs the power supply demand performance index. The power supply performance evaluation unit collects and evaluates the state of the key elements of the first remote control circuit currently used by means of the remote sensing module, generates the current power supply performance index, and realizes dynamic control of the power supply capacity. The switching control unit compares the power supply performance index with the demand performance index, intelligently judges whether the current circuit meets the task requirements according to the performance difference, and if not, executes circuit switching to output a more suitable second remote control circuit, thereby realizing task-driven closed-loop control regulation.

[0008] In summary, the application realizes real-time matching control between load demand and power supply capacity by constructing a task feature-driven power supply demand modeling mechanism, combining remote sensing and performance evaluation of power supply element state, improves the intelligence and refinement of laser power supply supply and demand regulation, ensures the stability of power supply and the rational use of resources, thereby significantly improving the quality and efficiency of laser engraving, and meeting the individualized demand of different engraving tasks for laser power supply.

[0009] The above description is only a summary of the technical solutions of the application. In order to more clearly understand the technical means of the application, the application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the application more obvious and easy to understand, the following specific embodiments of the application are described. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1A structural schematic diagram of a laser power supply remote control system provided by an embodiment of the present application.

[0011] Figure 2 A flowchart of a laser power supply remote control method provided by an embodiment of the present application.

[0012] Legend: task receiving unit 10, feature acquisition unit 20, power demand analysis unit 30, power supply performance evaluation unit 40, switching control unit 50. DETAILED DESCRIPTION

[0013] The embodiment of the present application provides a laser power supply remote control system and method, which solves the technical problem that the laser power supply cannot dynamically adjust the control strategy according to the actual engraving task due to the lack of a power supply demand modeling and power supply state perception linkage mechanism based on the characteristics of the engraving task, thereby causing unstable power supply and low energy efficiency, and achieves the technical effects of improving the power supply and demand matching accuracy and operation stability of the laser power supply.

[0014] Embodiment one, as shown in the figure, the embodiment of the present application provides a laser power supply remote control system, the system comprises: Figure 1

[0015] Task receiving unit 10, used for receiving the laser power supply's engraving work task.

[0016] Specifically, the task receiving unit 10 is an information entry module in the laser power supply remote control system, used for receiving the laser engraving work task information issued by the upper system or the user end. The work task information generally includes a graphic file, an engraving material, a processing parameter and the like, and is the starting point of the subsequent control process. After the system starts, 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. This unit supports the input of standard graphic data format (such as SVG, DXF, BMP, etc.) and material identification code, ensures that the task data is not lost and misinterpreted during transmission, and passes the data to the downstream module after structuring.

[0017] By setting the task receiving unit 10, the system can obtain task information in a standardized and structured manner, ensuring that the subsequent processing modules can carry out analysis based on complete and accurate data, and improving the overall compatibility and task response efficiency of the system.

[0018] Feature acquisition unit 20, used for acquiring the graphic features of the engraving work task and determining the engraving graphic features.

[0019] ​Specifically, the feature acquisition unit 20 is a module for image feature analysis of the task graphic file, which is used to extract the engraving structure characteristics of the graphic, such as the contour complexity, line density, gray scale distribution, material category, etc., as the basis for subsequent power supply control personalized analysis. After the task information is received, the feature acquisition unit 20 analyzes the graphic file content through graphic processing algorithms (such as Canny edge detection, gray scale gradient histogram, clustering and hierarchical algorithm, etc.), identifies the graphic contour curvature, line intersection density, and gray scale distribution change range, and outputs a multi-dimensional feature vector in combination with the material type. This multi-dimensional feature vector will be used as the input data of the subsequent power demand analysis unit 30.

[0020] The feature acquisition unit 20 realizes the bridging between the engraving task and the power control demand, so that the system can dynamically adapt the control strategy according to the graphic and material characteristics, and provide a data basis for the formulation of intelligent power supply strategy.

[0021] The power demand analysis unit 30 is used to analyze the power demand based on the engraving graphic features, and outputs the power demand performance index.

[0022] Specifically, the power demand analysis unit 30 is used to construct the performance index of the power parameters required by the task according to the extracted graphic features, including voltage and current error tolerance, fluctuation response speed, power step change, etc., as the target reference for power supply system adjustment. The power demand analysis unit 30 includes a key feature convolution extraction module, a mapping relationship construction module, and an index calculation module. The convolutional neural network and other methods are used to extract the instantaneous power change demand and precision requirement of the engraving process from the graphic features, and then the mapping relationship between the graphic features and the power index is established through sample training or machine learning model, and finally the power performance index required by the task is output.

[0023] The power demand analysis unit 30 realizes the quantitative expression of different task demands, so that the laser power supply system can accurately formulate the power supply target, so as to better adapt to different process requirements and effectively avoid under-supply or over-supply problems.

[0024] The power supply performance evaluation unit 40 is used to perceive the element state of the current first remote control circuit of the laser power supply through the remote sensing module, output the working state perception data of each element, and evaluate the power supply performance with the working state perception data, and output the power supply performance index.

[0025] Specifically, the power supply performance evaluation unit 40 is a module for real-time acquisition of the working state of key elements in the current first remote control circuit and evaluation of the actual power supply capability based on state perception data. The power supply performance evaluation unit 40 accesses the remote perception module to collect data such as temperature rise, voltage offset, current fluctuation, etc. of the operating state of power devices, drive modules, feedback loops, etc. control circuit elements, and 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 discrimination model or a Bayesian state estimation) as an important basis for whether to perform circuit switching.

[0026] By real-time evaluation of the current power supply state, the power supply performance evaluation unit 40 provides dynamic feedback capability for system decision-making, effectively prevents control failure caused by element degradation, aging or abnormality, and enhances the adaptive ability and safety of the remote control system.

[0027] The switching control unit 50 is used to compare the power supply performance index with the power demand performance index, and perform switching control on the first remote control circuit according to the power difference performance index, and output the second remote control circuit after switching.

[0028] Specifically, the switching control unit 50 is used to compare the power supply capability with the demand and automatically switch to a better backup circuit when the performance does not match. 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 difference performance index falls within a tolerable interval. If it exceeds the threshold, the switching control unit 50 calls the multi-relay to analyze the element state of the first control circuit, selects the node to be switched according to the minimum difference principle, working time or failure probability, and switches to the second remote control circuit. At the same time, if the deviation of the engraving execution result collected exceeds the range, the deviation correction module can also be called to adaptively update the circuit control strategy.

[0029] The switching control unit 50 ensures that the power supply system is in the best operating state through intelligent comparison and dynamic node reconstruction mechanism, realizes efficient and stable adjustment of the remote control circuit under multi-task and multi-environment conditions, and greatly improves the control precision and actual operation reliability of the laser power supply.

[0030] Further, the engraving task includes a graphic file and an engraving material type, and the feature acquisition unit 20 is further used to perform the following steps:

[0031] Step P21: The graphic processing module is used to analyze the graphic file and extract engraving contour information, line density and gray scale distribution.

[0032] Step P22: The engraving material type, the engraving contour information, the line density and the gray scale distribution are output as engraving graphic features.

[0033] Specifically, the graphic processing module refers to a core computing module for content analysis of the received graphic file, mainly including image contour extraction algorithm, line density analysis tool, gray area distribution calculation model, etc. The engraving graphic feature is a joint description of graphic structure parameters and material properties, which is the basic reference data for subsequent power demand assessment.

[0034] The feature acquisition unit 20 obtains the engraving task including the graphic file and the engraving material type from the task receiving unit 10, first pre-processes the graphic file, including file format analysis, layer separation, coordinate normalization processing. Subsequently, the graphic processing module performs engraving contour information extraction, line density calculation, and gray scale distribution analysis: the graphic processing module calls the edge detection algorithm to identify the main contour of the graphic, extracts the curvature change of the outer frame, the closed area and other geometric information; then using the grid division method (such as regional pixel block) to count the number of line intersections or intersections in unit area, and obtain the line density distribution of different areas; for gray scale graphics or tasks with gradient content, use histogram statistics or distribution function to analyze the gray scale change range, gradient change trend and other indicators.

[0035] After image processing, the three types of graphic structure parameters (engraving contour information, line density, and gray scale distribution) extracted are fused with the engraving material type received by the task to form a unified engraving graphic feature vector. The feature vector can be used as input for subsequent modules (power demand analysis unit 30) for processing. Among them, the material type is usually associated with its thermal conductivity, light absorption coefficient, melting point and other physical properties through database label, to affect the feature weight calculation.

[0036] Through the joint modeling of graphic structure and material performance, the feature acquisition unit 20 can accurately reflect the actual demand of different graphics on the dynamic response of the power supply, and provide key input support for the subsequent intelligent control strategy.

[0037] Further, the power demand analysis unit 30 includes:

[0038] A convolution module for key feature convolution extraction of the engraving graphic feature, outputting engraving line width variation, graphic engraving granularity, and graphic layering.

[0039] A mapping module for establishing a mapping relationship between key feature samples and power demand performance indicators, wherein the evaluation items of the power demand performance indicators include voltage and current output error, fluctuation suppression rate, and power step size.

[0040] An index calculation module for obtaining the values of the evaluation items corresponding to the key features based on the mapping relationship, calculating the values of the evaluation items, and outputting the power demand performance indicators.

[0041] Specifically, in the power supply demand analysis unit 30, the convolution module first performs multi-dimensional convolution processing on the engraving pattern features to extract a plurality of key feature parameters from the original engraving pattern feature input, including but not limited to engraving line width variation (unit: mm), pattern engraving granularity (unit: dpi), and pattern layering (unit: layer). Among them, the engraving line width variation describes the engraving depth and fineness of each part of the engraving pattern. The pattern engraving granularity describes the complexity of the details in the engraving pattern, including whether there are too many small elements or complex curve structures. The pattern layering describes the different hierarchical structures of the engraving pattern. Taking a specific task as an example, after processing by the convolution module, the following key feature vector is obtained: line width variation is 0.12 mm, engraving granularity is 600 dpi, and pattern layering number is 3 layers.

[0042] Next, the mapping module is used to establish a mapping relationship between key feature samples and power supply demand performance indicators based on a large amount of historical engraving task data and actual power supply performance data of the laser power supply. The mapping relationship is stored in the form of a lookup table constructed by three-dimensional discrete samples, and the coordinate axes correspond to line width variation, engraving granularity, and pattern layering number; each coordinate point corresponds to a power supply demand performance indicator group, including voltage output error, current output error, fluctuation suppression rate, and power step size. The construction method is as follows: after clustering and categorizing the sampling points of the historical tasks, a multi-dimensional interpolation mapping function is established by an interpolation method (such as cubic spline interpolation) to complete the index estimation capability of the continuous space. For example, the input feature (0.10 mm, 600 dpi, 2 layers) corresponds to the index output (voltage output error 0.25 V, current output error 0.12 A, fluctuation suppression rate 95.3%, power step size 8 W); the input feature (0.12 mm, 600 dpi, 3 layers) corresponds to the index output (voltage output error 0.31 V, current output error 0.18 A, fluctuation suppression rate 94.7%, power step size 10 W).

[0043] During task execution, the index calculation module receives the current key feature vector output by the convolution module, calls the mapping relationship established in the mapping module, obtains the voltage and current output error, fluctuation suppression rate, and power step size corresponding to the feature through a three-dimensional space interpolation algorithm, and outputs as the power supply demand performance indicators. The above-mentioned power supply demand performance indicators will be transmitted to the switching control unit 50, compared with the power supply performance indicators of the current laser power supply, and guide the subsequent remote circuit switching control process.

[0044] Through the synergistic effect of the above three modules, stable mapping of key features to performance indicators is realized, providing a reliable basis for subsequent power supply performance evaluation and switching control.

[0045] Further, the power supply performance evaluation unit 40 comprises:

[0046] a data alignment module for timestamp alignment of the working state perception data, and output of processed working state perception data.

[0047] an evaluation module for power supply performance evaluation according to the processed working state perception data, and output of power supply performance indicators, wherein the evaluation items of the power supply performance indicators include voltage and current output error, fluctuation suppression rate and power step size.

[0048] Further, the elements of the first remote control circuit include at least switching devices, multi-way relays, drive modules, voltage feedback loops, current feedback loops and communication control modules.

[0049] 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 the first remote control circuit, which includes several core elements, specifically: switching devices, multi-way relays, drive modules, voltage feedback loops, current feedback loops and communication control modules. Among them, the switching devices are used to control the power supply on-off and adjust the access of different power supply paths; the multi-way relays are used to realize the switching control of multi-node circuits; the drive modules are used to drive the voltage and current output control for normal operation of the laser; the voltage feedback loop and the current feedback loop are used to detect the voltage and current values of the output end in real time; the communication control module is responsible for collecting state information and uploading it to the remote control platform.

[0050] When the laser power supply is running, the remote perception module collects the state data of the above-mentioned elements in real time and transmits them to the power supply performance evaluation unit 40. The power supply performance evaluation unit 40 includes two sub-modules: a data alignment module and an evaluation module. First, the data alignment module receives working state perception data from each element. Due to the diversity of these data sources, different sampling frequencies, and timestamp misalignment problems, in order to ensure evaluation accuracy, the data alignment module uses a timestamp alignment algorithm (such as a weighted 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 to 1000Hz of the drive module from 500Hz, and through interpolation and downsampling operations, the data structure is consistent with other data, and a set of working state perception data with consistent structure is output.

[0051] Next, the evaluation module analyzes the current state of each key evaluation item according to the above processed data set, including voltage output error, current output error, fluctuation suppression rate and power step size. Among them, 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 current in unit time, which is used to evaluate the system anti-interference ability; the power step size is the fine granularity of power adjustment under the dynamic change of load. In order to realize accurate evaluation, the evaluation module adopts a composite algorithm based on Kalman filtering and wavelet analysis to denoise and smooth the voltage and current curves, and extracts local fluctuation characteristics according to the working load time period. The above power supply performance indicators are then transmitted to the switching control unit 50 and compared with the power demand performance indicators of the task end to decide whether to perform remote circuit switching.

[0052] Further, the switching control unit 50 is also used to perform the following steps:

[0053] Step P51: Determine whether the power difference performance indicator is less than the preset difference interval.

[0054] Step P52: If the power difference performance indicator is less than the preset difference interval, do not activate the switching instruction, and use the first remote control circuit to control the laser power supply.

[0055] Step P53: If the power difference performance indicator is greater than or equal to the preset difference interval, activate the switching instruction, connect the multi-way relay to analyze the switching node of the first remote control circuit, output the node to be switched, switch the node to be switched to the standby node, and output the second remote control circuit.

[0056] Specifically, the switching control unit 50 receives the power supply performance indicators and the power demand performance indicators, calculates the power difference performance indicators by setting a unified multi-dimensional comparison mechanism. The difference indicators can be expressed by a weighted function as follows: ΔP = ω1*|ΔU| / U max + ω2*|ΔI| / I max + ω3*(1-R s )+ ω4*S g / S max Where: ΔU is the voltage output error, U max is the maximum voltage deviation allowed by the system; ΔI is the current output error, I max is the maximum current deviation allowed by the system; Rs is the fluctuation suppression rate; S g is the power step size, S max is the maximum power step change allowed by the system. Compare the calculated power difference performance indicators ΔP with the pre-set preset difference interval.

[0057] If the result of the judgment is that the power difference performance index is less than the preset difference interval, that is, the current power supply capability can meet the power demand of the laser engraving task, the original first remote control circuit state is maintained, the switching instruction is not activated, and the first remote control circuit continues to control the power supply of the laser power supply.

[0058] If the result of the judgment is that the power difference performance index is greater than or equal to the preset difference interval, that is, the current power supply performance has a significant deviation and cannot effectively meet the task demand, the switching control unit 50 activates the switching instruction. The specific implementation is as follows: calling the built-in multi-relay state table and historical load feedback record, performing switching node analysis on the internal connection nodes of the first remote control circuit, and screening out a plurality of switching nodes (such as frequently malfunctioning nodes, voltage output fluctuation nodes, etc.); based on the analysis result, selecting a standby node functionally equivalent to the node, connecting the standby path through the control multi-relay, outputting a second remote control circuit to replace the first remote control circuit, and completing the power path switching. Through the above switching mechanism, the reliability of power supply is ensured, and the remote control capability and task execution stability are improved.

[0059] Further, step P53 further includes:

[0060] Step P53-1: According to the working state sensing data of each element, a performance influence relationship between each element and the power supply performance index is established.

[0061] Step P53-2: Using the performance influence relationship, N performance influence indexes corresponding to N elements in the first remote control circuit are identified.

[0062] Step P53-3: Optimize from the N performance influence indexes with the goal of minimizing the power difference performance index, output the element solution set, and the element solution set includes the switching node.

[0063] Specifically, the working state sensing data refers to the real-time element state data collected by sensors or monitoring modules, including current, voltage, temperature, and other key performance data. The performance influence relationship is a mathematical model or empirical formula describing the relationship between element state and power performance index. Through the working sensing module, the real-time working state sensing data of each element of the first remote control circuit is collected, and the influence relationship model between the working state sensing data and the power supply performance index (such as voltage deviation, current error, etc.) is constructed. This model is based on historical data, test results or deep learning training model, and is used to quantify the specific influence of the state change of each element on the power supply performance. For example, through a large amount of experimental data, a linear regression model of the on-off speed of the switching device and the voltage output error is established, and a nonlinear relationship model of the contact resistance of the multi-relay and the current output error is established.

[0064] The established performance influence relationship is used to identify the correlation between each element in the current control circuit and the power performance index. By analyzing the working state awareness data, the performance influence index corresponding to each element in the current state is determined, i.e. the specific influence measure of each element on the power performance in the current state, such as its contribution to voltage, current fluctuation, etc.

[0065] Based on the identified performance influence index, an optimization algorithm (such as genetic algorithm, particle swarm optimization, etc.) is used to optimize the state of each element. The goal is to find a set of elements to be switched, so that after the circuit switching, the performance difference index of the new power supply path is minimized. An example of the objective function is as follows: wherein, is the weight coefficient corresponding to the ith element, reflecting the sensitivity of its state to the overall performance; is the performance influence index value corresponding to the ith element. Taking the particle swarm optimization algorithm as an example, initialize the particle swarm, each particle represents a possible element combination, and assign an initial speed and position; evaluate the fitness of each particle according to the objective function; iteratively optimize through the speed and position update rules between particles, gradually approaching the minimum performance difference solution. When the optimization process reaches the preset convergence condition or the maximum iteration number, the system outputs the element combination represented by the current optimal particle as the target solution set of the nodes to be switched.

[0066] The above steps minimize the power supply performance difference of the laser power supply through accurate element performance monitoring and optimization. By establishing the correlation model between elements and power performance, identifying the influence index and optimizing the selection, the switching of elements can be efficiently controlled, ensuring that the laser power supply always maintains the best performance state under different working conditions, not only improving the stability of the laser engraving process, but also avoiding the problems of unstable power supply or resource waste.

[0067] Further, step P53 further comprises:

[0068] Obtain the continuous working time length of each element; calculate the failure probability of each element according to the continuous working time length, and identify the element whose failure probability meets the expected probability as the node to be switched.

[0069] Specifically, the continuous working time length refers to the time that the element runs continuously in the current working state, which is calculated by monitoring the running period or starting the timer. The failure probability refers to the probability of failure of the element within a given working time length, which is estimated by statistical methods. The node to be switched refers to the element that needs to be replaced or disabled in the switching control process according to the working time length and failure probability.

[0070] First, the continuous working time of each element is obtained through the timer or built-in sensor in the monitoring system. Based on the continuous working time, the failure probability of each element is calculated using a pre-set element failure model. This model is established according to the element's life curve, historical data or statistical rules (such as failure rate, accelerated life test data, etc.). For example, the Weibull distribution can be used to represent the failure rate of the element. After obtaining the failure probability of the element, these probabilities are compared with the pre-set expected probability. If the failure probability of a certain element exceeds the pre-set expected probability threshold, the element will be marked as a switching node, i.e. it needs to be replaced or switched in advance to avoid the impact of element failure on the stability of the power supply.

[0071] By obtaining the continuous working time of the element and calculating the failure probability of the element, the health status of each element can be dynamically evaluated, and the element with high failure probability can be warned and switched. This switching control mechanism based on element life and working state can effectively avoid the impact of device failure on the performance of the laser power supply, improve the reliability and stability of the laser power supply, and avoid production stoppage or quality fluctuation caused by sudden failure.

[0072] Further, the switching control unit 50 further comprises:

[0073] The control execution module is configured to control the laser power supply to perform engraving according to the second remote control circuit after switching, and collect the engraving execution result.

[0074] The deviation correction module is configured to compare the engraving execution result with the pre-set engraving pattern in the graphic file to obtain deviation data, construct an adaptive correction factor based on the deviation data, and update the second remote control circuit based on the adaptive correction factor.

[0075] Specifically, the switching control unit 50 further comprises a control execution module and a deviation correction module. The control execution module is responsible for switching to the second remote control circuit when it is detected that the first remote control circuit cannot meet the power supply demand, continuing to complete the laser engraving job, and continuously tracking the execution progress and result of the engraving. Through integrated sensors (such as image recognition, laser power monitoring, etc.) and feedback mechanisms, the engraving execution result is collected, including the graphic precision, contour clarity, engraving depth, etc.

[0076] The deviation correction module uses image processing techniques (such as edge detection, shape matching, etc.) to compare the engraving execution result with the preset graphic file, identifies the line boundaries in the actual engraved image through an edge detection algorithm, evaluates shape consistency through a shape matching algorithm, and identifies errors such as position offset and rotation of the graphic through a feature point registration algorithm. The output includes deviation data of geometric deviation, position deviation, and shape deviation. The geometric deviation includes line width difference and engraving depth difference; the position deviation includes horizontal and vertical offset and rotation angle; and the shape deviation includes local nonlinear changes between the target shape and the actual image. For the above deviation data, the deviation correction module constructs a mapping model to evaluate the influence relationship between different types of deviation and control parameters, and generates a set of adaptive correction factors for subsequent engraving control correction. Typical correction factors include: a laser power factor for increasing or decreasing laser intensity to correct the engraving depth; an engraving speed factor for adjusting the scanning speed to cooperate with the power control; a focal length adjustment factor for optimizing laser focusing and improving profile accuracy; a path offset factor for compensating for graphic position drift; and a corner correction factor for rotation error compensation. The above factors can be calculated through a causal relationship model trained by a regression model or a neural network model, or can be generated based on empirical formulas or multiple rounds of experimental calibration data. According to the generated correction factors, the key control parameters in the current second remote control circuit are updated in real time. For example: the laser power factor adjusts the PWM duty cycle of the power control module; the path offset factor adjusts the step control amount of the XY platform; and the speed factor adjusts the timing instructions of the engraving path in the drive module. The updated control parameters are transmitted to the control execution module to restart the corrected engraving task, thereby improving the accuracy of subsequent engraving areas and ensuring the consistency of the graphics and the quality of the engraving.

[0077] The control execution module and the deviation correction module in the switching control unit 50 work together to enable the laser power supply to continue the engraving operation after the switching circuit and automatically adjust the subsequent control parameters according to the engraving effect, thereby improving the engraving accuracy and enhancing the intelligence and adaptability of the laser power supply remote control system.

[0078] In summary, the laser power supply remote control system provided by the embodiments of the present application has the following beneficial effects:

[0079] The task receiving unit 10 receives externally issued engraving tasks and serves as the trigger source for the entire control process, ensuring that the system can perform personalized power supply adjustment based on specific tasks. The feature acquisition unit 20 analyzes the graphic features of the received engraving tasks, providing data support for subsequent power demand analysis. The power demand analysis unit 30 quantifies the performance requirements of the laser power supply based on the graphic features and outputs power demand performance indicators. The power supply performance evaluation unit 40, with the help of the remote sensing module, collects and evaluates the status of key components of the currently used first remote control circuit in real time, generating current power supply performance indicators to achieve dynamic control of power supply capabilities. The switching control unit 50 compares the power supply performance indicators with the demand performance indicators, intelligently determines whether the current circuit meets the task requirements based on the performance difference, and if insufficient, executes circuit switching, outputting a more suitable second remote control circuit, thereby realizing task-driven closed-loop control adjustment.

[0080] Overall, the embodiments of this application, by constructing a task-feature-driven power demand modeling mechanism and combining remote sensing and performance evaluation of power component status, realize real-time matching control between load demand and power capacity, improve the intelligence and precision of laser power supply and demand regulation, ensure the stability of power supply and the rational utilization of resources, thereby significantly improving the quality and efficiency of laser engraving and meeting the personalized needs of different engraving tasks for laser power supply.

[0081] Example 2, as Figure 2 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides a method for remote control of a laser power supply, the method comprising:

[0082] Step S1: Receive the engraving task from the laser power supply.

[0083] Step S2: Collect graphic features of the carving task and determine the carving graphic features.

[0084] Step S3: Perform power demand analysis based on the engraved graphic features and output power demand performance indicators.

[0085] Step S4: The remote sensing module senses the current component status of the first remote control circuit of the laser power supply, outputs the working status sensing data of each component, evaluates the power supply performance based on the working status sensing data, and outputs the power supply performance index.

[0086] Step S5: Compare the power supply performance index with the power demand performance index, switch the first remote control circuit according to the power difference performance index, and output the switched second remote control circuit.

[0087] Further, the engraving task is subjected to graphic feature collection to determine the engraving graphic features, including:

[0088] The engraving task includes a graphic file and an engraving material type; a graphic processing module is used to analyze the graphic file to extract engraving contour information, line density and gray scale distribution; the engraving material type, the engraving contour information, the line density and the gray scale distribution are output as the engraving graphic features.

[0089] Further, power supply demand analysis is performed based on the engraving graphic features to output power supply demand performance indicators, including:

[0090] The engraving graphic features are subjected to key feature convolution extraction to output engraving line width variation, graphic engraving granularity and graphic layering; a mapping relationship between key feature samples and power supply demand performance indicators is established, wherein the evaluation items of the power supply demand performance indicators include voltage and current output error, fluctuation suppression rate and power step size; the values of the evaluation items corresponding to the key features are obtained based on the mapping relationship to calculate the values of the evaluation items and output the power supply demand performance indicators.

[0091] Further, power supply performance evaluation is performed based on the working state sensing data to output power supply performance indicators, including:

[0092] The working state sensing data is subjected to timestamp alignment to output processed working state sensing data; power supply performance evaluation is performed based on the processed working state sensing data to output power supply performance indicators, wherein the evaluation items of the power supply performance indicators include voltage and current output error, fluctuation suppression rate and power step size.

[0093] Further, the elements of the first remote control circuit at least include switching devices, multi-way relays, drive modules, voltage feedback loops, current feedback loops and communication control modules.

[0094] Further, the first remote control circuit is subjected to switching control according to the power supply difference performance indicators, including:

[0095] It is determined whether the power supply difference performance indicators are less than a preset difference interval; if the power supply difference performance indicators are less than the preset difference interval, a switching instruction is not activated, and the first remote control circuit is used to control the laser power supply for engraving; if the power supply difference performance indicators are greater than or equal to the preset difference interval, the switching instruction is activated, the multi-way relay is connected to the first remote control circuit for switching node analysis, the output is a node to be switched, the node to be switched is switched to a standby node, and the second remote control circuit is output.

[0096] Further, the node to be switched is output, including:

[0097] According to the working state sensing data of each element, a performance influence relationship between each element and the power supply performance index is established; using the performance influence relationship, N performance influence indexes corresponding to the N elements in the first remote control circuit are identified; optimization is performed from the N performance influence indexes with the minimum power difference performance index as the target, and an element solution set is output, the element solution set including the to-be-switched node.

[0098] Further, outputting the to-be-switched node further includes:

[0099] The continuous working time length of each element is obtained; the failure probability of each element is calculated according to the continuous working time length, and the element whose failure probability meets the expected probability is identified as the to-be-switched node.

[0100] Further, after outputting the second remote control circuit after switching, further includes:

[0101] According to the second remote control circuit after switching, the laser power supply is controlled to perform engraving, and the engraving execution result is collected; according to the deviation comparison between the engraving execution result and the preset engraving pattern in the graphic file, the deviation data is obtained, and the adaptive correction factor is constructed by the deviation data, and the second remote control circuit is updated by the adaptive correction factor.

[0102] Through the foregoing detailed description of the laser power supply remote control system, those skilled in the art can clearly understand the laser power supply remote control method in the embodiment. For the method disclosed in embodiment two, since it corresponds to the system disclosed in embodiment one, it has corresponding execution steps and beneficial effects, and the related parts are described in the system part.

[0103] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A remote control system for a laser power supply, characterized in that, The system includes: The task receiving unit is used to receive engraving tasks from the laser power supply. The feature acquisition unit is used to acquire graphic features of the carving task and determine the carved graphic features. A power demand analysis unit is used to perform power demand analysis based on the engraved graphic features and output power demand performance indicators. The power supply performance evaluation unit is used to sense the component status of the first remote control circuit of the laser power supply through the remote sensing module, output the working status sensing data of each component, perform power supply performance evaluation based on the working status sensing data, and output power supply performance indicators. The switching control unit is used to compare the power supply performance index with the power demand performance index, switch the first remote control circuit according to the power difference performance index, and output the switched second remote control circuit. The feature acquisition unit is also used for: The carving task includes graphic files and carving material types; The graphics file is analyzed using a graphics processing module to extract the sculpted outline information, line density, and grayscale distribution. The carving material type, carving outline information, line density, and grayscale distribution are output as carving graphic features. The power demand analysis unit includes: The convolution module is used to extract key features from the sculpted graphic features through convolution, and output the changes in sculpted line width, graphic sculpting granularity, and graphic layering. The mapping module is used to establish the mapping relationship between key feature samples and power demand performance indicators. The evaluation items of power demand performance indicators include voltage and current output error, fluctuation suppression rate and power step size. The index calculation module is used to obtain the value of the evaluation item corresponding to the key feature based on the mapping relationship, calculate the value of the evaluation item, and output the power demand performance index.

2. The laser power supply remote control system as described in claim 1, characterized in that, The power supply performance evaluation unit includes: The data alignment module is used to align the working status perception data with timestamps and output the processed working status perception data. The evaluation module is used to evaluate the power supply performance based on the processed working status sensing data and output power supply performance indicators. The evaluation items of the power supply performance indicators include voltage and current output error, fluctuation suppression rate and power step size.

3. The laser power supply remote control system as described in claim 1, characterized in that, The components of the first remote control circuit include at least a switching device, a multiplexer, a drive module, a voltage feedback loop, a current feedback loop, and a communication control module.

4. A remote control system for a laser power supply as described in claim 1, characterized in that, The switching control unit is also used for: Determine whether the power supply difference performance index is less than a preset difference range; If the power supply difference performance index is less than the preset difference range, the switching command is not activated, and the laser power supply is engraved and controlled by the first remote control circuit. If the power supply difference performance index is greater than or equal to the preset difference range, activate the switching command, connect multiple relays to perform switching node analysis on the first remote control circuit, output the node to be switched, switch the node to be switched to the backup node, and output the second remote control circuit.

5. A remote control system for a laser power supply as described in claim 4, characterized in that, The switching control unit is also used for: Based on the operating status sensing data of each component, establish the performance impact relationship between each component and the power supply performance indicators. Using the aforementioned performance impact relationships, identify N performance impact indicators corresponding to N components in the first remote control circuit; With the goal of minimizing the power supply difference performance index, an optimization is performed from the N performance impact indices to output a component solution set, which includes the nodes to be switched.

6. A remote control system for a laser power supply as described in claim 4, characterized in that, The switching control unit is also used for: Obtain the continuous operating time of each component; The failure probability of each component is calculated based on the continuous working duration, and the components whose failure probability meets the expected probability are identified as nodes to be switched.

7. A remote control system for a laser power supply as described in claim 1, characterized in that, The switching control unit further includes: The control execution module is used to control the laser power supply to perform engraving according to the switched second remote control circuit, and to collect the engraving execution results; The deviation correction module is used to compare the deviation between the engraving execution result and the preset engraving pattern in the graphic file, obtain deviation data, construct an adaptive correction factor with the deviation data, and update the second remote control circuit with the adaptive correction factor.

8. A method for remote control of a laser power supply, characterized in that, The method is executed by a laser power supply remote control system according to any one of claims 1-7, comprising: The task of engraving using a laser power source; The graphic features of the carving task are collected to determine the carving graphic features; Based on the engraved graphic features, power demand analysis is performed, and power demand performance indicators are output. The remote sensing module senses the current component status of the first remote control circuit of the laser power supply, outputs the working status sensing data of each component, evaluates the power supply performance based on the working status sensing data, and outputs the power supply performance index; compares the power supply performance index with the power demand performance index, switches the first remote control circuit according to the power difference performance index, and outputs the switched second remote control circuit.

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