Energy consumption optimization control system of denim laser washing equipment

Abstract

The application relates to the technical field of laser beam workpiece processing, in particular to an energy consumption optimization control system of a denim fabric laser water washing device, which is used to solve the technical problem of high energy consumption of a laser water washing device in the current denim fabric water washing process. The system comprises: a characteristic acquisition unit, which is used to acquire the heat delay characteristic of a laser head; a distribution generation unit, which is used to generate an energy demand distribution of different positions in a to-be-etched pattern of denim fabric; a region identification unit, which is used to determine a high dynamic energy consumption region in the to-be-etched pattern according to the energy demand distribution, the high dynamic energy consumption region being a continuous region with an energy demand value spatial variation amplitude exceeding a preset threshold; and a path optimization unit, which is used to optimize a scanning path covering the high dynamic energy consumption region based on the heat delay characteristic, so as to reduce the energy consumption of the laser head in the water washing process and generate an optimized scanning path.

Description

Technical Field

[0001] This application relates to the field of laser beam workpiece processing technology, specifically to an energy consumption optimization control system for a laser washing equipment for denim fabric. Background Technology

[0002] Currently, denim fabrics can be processed using laser engraving technology to create washed patterns. This technology utilizes a high-energy laser beam to irradiate the fabric surface, causing a chemical reaction in the dyes or altering the surface texture, resulting in personalized effects such as whiskers, distressed details, and faded finishes. This technology offers advantages such as being environmentally friendly and highly efficient. However, the processing parameters of existing laser washing equipment largely rely on manual settings. For complex washed patterns like whiskers and distressed details, the energy requirements fluctuate drastically across different areas. Manually or partially automated parameters cannot adapt to these fluctuations in real time, leading to frequent power switching and repetitive movement of the laser washing equipment across different regions, resulting in high energy consumption. Summary of the Invention

[0003] To address the high energy consumption of laser washing equipment used in denim fabric washing processes, this application aims to provide an energy consumption optimization and control system for denim fabric laser washing equipment. The specific technical solution adopted is as follows: The characteristic acquisition unit is used to acquire the thermal delay characteristics of the laser head, which are used to characterize the relationship between the actual output power of the laser head and time during power switching. The distribution generation unit is used to generate the energy demand distribution at different locations in the pattern to be etched on the denim fabric; The region identification unit is used to determine the high dynamic energy consumption region in the pattern to be etched based on the energy demand distribution. The high dynamic energy consumption region is a continuous region where the energy demand value changes more than a preset threshold in space. The path optimization unit is used to optimize the scanning path covering high dynamic energy consumption areas based on thermal delay characteristics, so as to reduce energy consumption during the laser head washing process and generate an optimized scanning path.

[0004] In one possible implementation, the characteristic acquisition unit includes: a steady-state establishment subunit, used to control the laser head to operate at a first power level until thermal equilibrium is reached; a transient excitation and acquisition subunit, used to switch the laser head from the first power level to a second power level and continuously acquire the actual output power at multiple sampling moments after the switch; and a curve generation subunit, used to fit and generate a thermal delay characteristic curve to characterize the change law of actual output power over time based on the actual output power at multiple sampling moments.

[0005] In one possible implementation, the energy demand distribution is represented by an energy demand distribution heatmap, where the grayscale value of each pixel in the heatmap represents the required thermal effect intensity at that location. The distribution generation unit includes: an image parsing subunit, used to extract features from the pattern to be etched to obtain texture level information for each region of the pattern; a parameter matching subunit, used to map the texture level information to the target laser power value based on a pre-established process parameter library; the process parameter library is used to store the mapping relationship between texture level information and laser power value; and a heatmap construction subunit, used to determine the energy demand distribution heatmap of the pattern to be etched based on the target laser power value at each location in the pattern.

[0006] In one possible implementation, the region identification unit includes: an energy demand determination subunit, used to convert the energy demand distribution into an energy demand sequence arranged in scanning order according to a preset initial scanning path; an extreme point determination subunit, used to construct a location heat demand curve based on the energy demand sequence and obtain all extreme points by differentiating the location heat demand curve; and a high dynamic energy consumption region determination subunit, used to calculate the minimum distance between adjacent extreme points and determine the pattern region corresponding to the extreme points that continuously satisfy the minimum distance less than or equal to a preset threshold as a high dynamic energy consumption region.

[0007] In one possible implementation, the path optimization unit includes: a loss assessment subunit, used to determine the etching cost factor of each scan path segment in the high dynamic energy consumption region based on thermal delay characteristics; and a path reconstruction subunit, used to replan the scan path segments according to the etching cost factor of each scan path segment to form an optimized scan path.

[0008] In one possible implementation, the loss assessment subunit is specifically used to: determine the change in energy demand between the current scanning position and the next candidate scanning position on the scanning path segment and the estimated travel time of the laser head; query the effective power adjustment capability of the laser head within the estimated travel time in the thermal delay characteristic curve; and calculate the etching cost factor of the scanning path segment between the current scanning position and the next adjacent scanning position based on the matching degree between the change in energy demand and the effective power adjustment capability.

[0009] In one possible implementation, the path reconstruction subunit is specifically used to: select a target next scan position from multiple candidate next scan positions that minimizes the overall cumulative path cost based on the etching cost factor of each scan path segment; and update the scan order in the high dynamic energy consumption region according to the selected target next scan position to form a replanned scan path segment.

[0010] In one possible implementation, the path reconstruction subunit is also used to: identify low-energy processing areas that are adjacent to the replanned scanning path segment and whose energy demand is lower than the merging threshold; merge the scanning path of the low-energy processing area with the replanned scanning path segment to form a merged processing area; and optimize the movement trajectory of the laser head in the merged processing area to reduce non-processing movement across different energy-consuming areas.

[0011] In one possible implementation, the system further includes: a real-time monitoring and feedback unit for acquiring the temperature or actual output power of the laser head in real time during the laser water washing process; and a dynamic compensation subunit for dynamically adjusting the scanning speed or output power setting of the laser head based on the real-time acquired temperature or actual output power and the expected energy requirement corresponding to the current scanning position.

[0012] In one possible implementation, the system further includes: a data management unit for recording the energy demand distribution, optimized scanning path, and actual energy consumption data corresponding to historical processing tasks; and a strategy optimization unit for optimizing preset thresholds in the region identification unit, mapping relationships in the process parameter library, or optimization strategies in the path optimization unit based on the data recorded in historical processing tasks using machine learning algorithms.

[0013] This application offers the following advantages: By acquiring the thermal delay characteristics during laser head power switching, this application determines the time response law of the equipment's energy output. Simultaneously, by combining this with the energy demand distribution generated from the pattern to be etched and identifying high-dynamic-energy-consumption areas, the scanning path optimization becomes more targeted, fundamentally avoiding the ineffective energy consumption caused by power response lag and blind path planning in traditional laser washing processes. By optimizing the scanning path for high-dynamic-energy-consumption areas based on thermal delay characteristics, the frequency of laser head power switching and repetitive area expansion movements can be effectively reduced, thereby lowering the energy consumption of the laser washing equipment during the laser washing of denim fabrics. Attached Figure Description

[0014] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A schematic diagram of the system architecture of an energy consumption optimization control system for a denim laser washing equipment provided in one embodiment of this application; Figure 2A schematic diagram of a characteristic acquisition unit in an energy consumption optimization control system for a denim laser washing equipment according to an embodiment of this application; Figure 3 A schematic diagram of a distributed generation unit in an energy consumption optimization control system for a denim laser washing equipment according to an embodiment of this application; Figure 4 A schematic diagram of an area identification unit in an energy consumption optimization control system for a denim laser washing equipment according to an embodiment of this application; Figure 5 This is a schematic diagram of a path optimization unit in an energy consumption optimization control system for a denim laser washing equipment according to an embodiment of this application. Detailed Implementation

[0016] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an energy consumption optimization control system for a denim laser washing equipment proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0018] Unless otherwise specified, the normalization function Norm() mentioned in this application uses maximum and minimum value normalization. The maximum and minimum values ​​are preset empirical extreme values ​​derived from a large amount of historical experimental data. If the calculation result exceeds the [0,1] interval, a truncation function is used to limit it to the [0,1] range (i.e., if the result is less than 0, it is taken as 0; if it is greater than 1, it is taken as 1) to eliminate the influence of outliers on the evaluation index.

[0019] In the laser washing process of denim fabric, the laser washing equipment automatically retrieves the corresponding laser process parameters from the process parameter library based on the data of the pattern to be etched, and performs automated production to ensure the processing effect of the pattern. However, for complex washing patterns, such as whiskering effects and distressed or faded effects, existing laser washing equipment cannot adjust in real time according to the laser's working mode. The laser often needs to be started and stopped at high frequencies, scanned multiple times, and repeatedly etched, resulting in a large amount of additional energy consumption. This application analyzes the current laser temperature and the process parameters of the engraving area to predict the energy consumption load of the laser at the next position, and then dynamically adjusts the laser's scanning frequency and power to reduce the additional energy consumption caused by multiple scans and repeated etching.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the energy consumption optimization control system for a denim laser washing equipment provided in this application.

[0021] Please see Figure 1 It shows a system architecture diagram of the energy consumption optimization control system of a denim laser washing equipment according to an embodiment of this application, such as... Figure 1 As shown, the system includes: The characteristic acquisition unit 101 is used to acquire the thermal delay characteristics of the laser head. The thermal delay characteristics are used to characterize the relationship between the actual output power of the laser head and time during power switching.

[0022] The distribution generation unit 102 is used to generate the energy demand distribution at different locations in the pattern to be etched on the denim fabric.

[0023] The region identification unit 103 is used to determine the high dynamic energy consumption region in the pattern to be etched based on the energy demand distribution. The high dynamic energy consumption region is a continuous region where the energy demand value changes more than a preset threshold in space.

[0024] The path optimization unit 104 is used to optimize the scanning path covering the high dynamic energy consumption area based on the thermal delay characteristics, so as to reduce the energy consumption during the laser head washing process and generate an optimized scanning path.

[0025] Optionally, the characteristic acquisition unit 101 controls the laser head to first operate at the target power until thermal equilibrium, then performs a transient power switch and collects actual output power data at different times after the switch. Finally, it fits and generates a thermal delay characteristic curve. This curve accurately characterizes the response law of the actual output power of the laser head during power switching (including switching from low power to high power and from high power to low power), providing a quantitative basis for subsequent path optimization. Optionally, this application can pre-determine the target power for common pattern regions and operate at these target powers until thermal equilibrium. As an example, the process of fitting and generating the thermal delay characteristic curve includes: based on the actual output power at multiple sampling times, using the least squares method to perform polynomial fitting on the sampling data points to generate a thermal delay characteristic curve characterizing the change law of the actual output power over time. During the fitting process, with time as the independent variable and actual output power as the dependent variable, the least squares method is used to minimize the sum of squared errors to determine the optimal fitting polynomial coefficients, thereby generating a smooth curve.

[0026] The distribution generation unit 102 first extracts image features from the denim fabric pattern to be etched, and obtains information on texture, brightness, detail, and other levels in each area. Then, based on the pre-established process parameter library, it maps the texture level information to the target laser power value at the corresponding position. Finally, in the form of an energy demand distribution heat map, it intuitively presents the thermal effect demand intensity at different positions in the pattern to be etched, forming complete energy demand distribution data.

[0027] The region identification unit 103 converts the generated energy demand distribution into an energy demand sequence arranged according to a preset initial scanning path. By analyzing the energy demand variation range of adjacent scanning positions, it filters out continuous regions where the spatial variation range of energy demand values ​​continuously exceeds a preset threshold. This region is the high dynamic energy consumption region, thus achieving accurate positioning of key areas of energy waste.

[0028] The path optimization unit 104 takes the thermal delay characteristics obtained by the characteristic acquisition unit 101 as the core basis, first evaluates the energy consumption cost of each scanning path segment in the high dynamic energy consumption area, and then combines the matching degree of energy demand and equipment power response to re-plan the scanning path covering the area. By adjusting the scanning order, reducing invalid switching and non-processing movement, the mechanical movement energy consumption and laser invalid power consumption during the laser head washing process are reduced, and finally the optimized scanning path is generated.

[0029] As an example, the laser water washing equipment in this application embodiment is a carbon dioxide pulsed laser machine, with a water chiller maintaining... The flow rate is constant. During the operation of a carbon dioxide pulsed laser, the power is increased in the high-energy-consumption region, and the laser head energy reaches a stable optical power output within a short period of time. However, in the low-energy-consumption region, as the power is reduced, the residual heat inside the laser cavity is slowly released, and the actual optical power output varies over time. This heat delay characteristic further exacerbates the energy consumption problem of traditional processes in the processing of complex gradient patterns.

[0030] When the energy consumption pattern of the area to be washed contains complex and gradually changing patterns, such as areas with different energies scattered throughout the pattern, traditional line-by-line scanning etching causes frequent jumps in laser power commands. This necessitates frequent temperature adjustments or repeated etching by the laser head during the etching process, resulting in significant energy loss. Therefore, this application identifies high-energy change locations, analyzes the changes in energy consumption indicators around these locations using thermal delay curves, obtains directional energy analysis for multiple high-energy change locations, and constructs etching difficulty difference values ​​to adjust the etching path for frequent start-stop operations.

[0031] Based on the above technical solution, this application determines the time response law of the equipment's energy output by obtaining the thermal delay characteristics during the laser head power switching process. Simultaneously, it combines this with the energy demand distribution for generating the pattern to be etched and identifies high-dynamic-energy-consumption areas, making scanning path optimization more targeted. This fundamentally avoids the ineffective energy consumption caused by power response lag and blind path planning in traditional laser washing processes. By optimizing the scanning path for high-dynamic-energy-consumption areas based on thermal delay characteristics, the frequency of laser head power switching and repetitive area expansion movements can be effectively reduced, thereby lowering the energy consumption of the laser washing equipment during the laser washing of denim fabrics.

[0032] like Figure 2 As shown, in one possible implementation, the characteristic acquisition unit 101 includes: a steady-state establishment subunit 1011, used to control the laser head to operate at a first power level until thermal equilibrium is reached; a transient excitation and acquisition subunit 1012, used to switch the laser head from the first power level to a second power level and continuously acquire the actual output power at multiple sampling moments after the switch; and a curve generation subunit 1013, used to fit and generate a thermal delay characteristic curve that characterizes the change law of actual output power with time based on the actual output power at multiple sampling moments.

[0033] As an example, the first power level is the power required for the laser to operate in the high-energy-consumption region of the target, and the second power level is 0W. Using this example, this application obtains the thermal delay curve of the laser head during use. The process includes: The laser was operated continuously at the first power level for 10 minutes to allow the laser cavity to reach and maintain a thermally stable state. Afterward, the laser power setting was instantaneously adjusted to 0W, and a heat conduction model was established to simulate the cooling physical process of the laser. Simultaneously, the actual output power P(t) of each pulse during the cooling process was recorded.

[0034] A Cartesian coordinate system is established with time t as the horizontal axis and the actual output power P(t) as the vertical axis. Several sampling points in the coordinate system are smoothly connected to obtain the thermal delay curve L of the laser head during use. p Thermal delay curve L p It covers the complete power response characteristics, specifically including: the rise-edge response curve when the laser switches from low power to high power (characterizing the rise-up delay), and the fall-edge response curve when switching from high power to low power (characterizing the residual heat release / cooling delay).

[0035] Based on this, this application, by establishing a thermal equilibrium state, transiently switching power and collecting data, and fitting and generating curves, can accurately capture the rise delay characteristics of the laser head switching from low power to high power and the cooling delay characteristics switching from high power to low power, forming a quantified and reliable thermal delay curve. This provides core data support for subsequent path optimization, avoids the blindness of path optimization caused by unknown thermal delay characteristics of the laser head, and ensures precise matching of subsequent power adjustment and energy requirements.

[0036] like Figure 3 As shown, in one possible implementation, the energy demand distribution is represented by an energy demand distribution heatmap, where the grayscale value of each pixel in the heatmap represents the required thermal effect intensity at that location. The distribution generation unit 102 includes: an image parsing subunit 1021, used to extract features from the pattern to be etched to obtain texture level information for each region of the pattern; a parameter matching subunit 1022, used to map the texture level information to a target laser power value based on a pre-established process parameter library; the process parameter library is used to store the mapping relationship between texture level information and laser power value; and a heatmap construction subunit 1023, used to determine the energy demand distribution heatmap of the pattern to be etched based on the target laser power value at each location in the pattern.

[0037] As one possible implementation, this application utilizes image processing technology to extract the texture information of the pattern to be etched, outputting level information (i.e., pattern level information) in different dimensions, including texture, brightness, and detail. The pattern level information of the denim fabric to be washed is matched with a pre-established database of process parameters. The thermal energy requirements of each pixel location within a specified area for different effects are predicted in the laser processing software, thereby generating a processing heat map. The grayscale intensity of each pixel in this processing heat map corresponds to the required thermal effect intensity at that pixel location during the laser washing process. .

[0038] Optionally, the process of establishing the aforementioned process parameter cards includes: collecting actual patterns of different denim fabrics during the laser washing process, extracting image features from them, identifying texture features in different areas of the patterns, and outputting grade information in different dimensions, including texture, brightness, and detail; simultaneously extracting the laser process parameters used in the laser washing production process of the corresponding patterns, such as laser power, scanning frequency, etching depth, and scanning speed. Integrating the above pattern grade information and corresponding laser process parameters, using them as samples for each set of process parameters, and establishing a process parameter library using a convolutional neural network.

[0039] Based on the above technical solution, this application obtains texture level information by extracting features from the etched pattern and automatically maps the texture level to the target laser power using a pre-established process parameter library. This replaces the traditional method of manually setting parameters, significantly reducing energy waste and processing quality fluctuations caused by human error. Simultaneously, it visually presents the thermal effect intensity at each location in the form of an energy demand distribution heatmap, providing a clear and quantifiable basis for the accurate identification of high-dynamic energy consumption areas. This ensures that area identification is neither missed nor misjudged, and can adapt to the personalized energy requirements of different texture patterns. While ensuring the processing effect of complex patterns such as whiskers and frayed holes, it achieves precise matching of energy supply, balancing the dual goals of processing quality and energy consumption optimization.

[0040] like Figure 4 As shown, in one possible implementation, the region identification unit 103 includes: an energy demand determination subunit 1031, used to convert the energy demand distribution into an energy demand sequence arranged in scanning order according to a preset initial scanning path; an extreme point determination subunit 1032, used to construct a location heat demand curve based on the energy demand sequence, and obtain all extreme points by differentiating the location heat demand curve; and a high dynamic energy consumption region determination subunit 1033, used to calculate the minimum distance between adjacent extreme points, and determine the pattern region corresponding to the extreme points that continuously satisfy the minimum distance less than or equal to a preset threshold as a high dynamic energy consumption region.

[0041] One possible approach is to calculate the change in thermal energy at each pixel location during the laser head's line-by-line scanning process. ( );in This represents the heat energy requirement at the i-th pixel position; This represents the thermal energy requirement at the (i-1)th pixel position during the laser head's line-by-line scanning process, through... The difference reflects the heat change in the area to be washed during the laser scanning process. A traversal analysis of the initial scanning path of the laser head is performed to calculate the change in heat demand along the path as the position changes. This data is then integrated to obtain the ideal laser head working position-heat demand curve. For the ideal location-heat demand curve Perform differentiation to obtain all extreme points of the curve, and record the number of extreme points. Information on the required change in pixel position corresponding to the i-th extreme point .

[0042] Calculate the difference in etching distance between the current laser head at the i-th etching position and the positions of the preceding and following extreme points along the path, which is also the minimum distance between adjacent extreme points. , ,in, Indicates the laser head working position-thermal demand curve In the equation, the distance between the (i+1)th extreme point adjacent to the i-th extreme point is... Indicates the laser head working position-thermal demand curve In the context of the i-th extreme point, the distance between the (i-1)-th extreme point and the i-th extreme point is considered; min represents the minimum distance between extreme points before and after the selection. A smaller distance indicates a higher probability that the laser needs to be started and stopped at high frequencies during operation. This will be used to continuously... Several extreme points are marked as complex areas to be washed (i.e., continuous areas where the energy demand value in the pattern to be etched changes in space beyond a preset threshold, i.e., high dynamic energy consumption areas) with high frequency start-stop. T is a preset value, and the present invention presets T = 10 pixel distances.

[0043] Based on the above technical solution, this application converts the energy demand distribution into an energy demand sequence arranged in scanning order, calculates the energy demand difference between adjacent scanning points, and accurately identifies high dynamic energy consumption areas where the energy demand variation continuously exceeds the standard. This determination method can accurately locate the core energy waste areas that require high-frequency power switching, so that subsequent path optimization does not need to be processed indiscriminately across the entire area, but focuses on the key links of energy waste. This not only improves the efficiency of path optimization, but also avoids resource waste caused by over-optimization of low-energy consumption areas, and prevents high dynamic energy consumption areas from continuously generating a large amount of ineffective energy consumption due to missed optimization, thereby achieving reasonable allocation of optimized resources and maximizing energy-saving effects.

[0044] like Figure 5 As shown, in one possible implementation, the path optimization unit 104 includes: a loss assessment subunit 1041, used to determine the etching cost factor of each scan path segment in the high dynamic energy consumption region based on thermal delay characteristics; and a path reconstruction subunit 1042, used to re-plan the scan path segments according to the etching cost factor of each scan path segment to form an optimized scan path.

[0045] It should be noted that, because traditional laser water washing path planning uses a line-by-line scanning method, for complex, high-dynamic-energy-consumption areas, traditional path planning results in repeated switching of etching energy within short distances. This leads to obvious peaks and sudden drops in the obtained position-heat demand curve, manifesting as a sawtooth curve distribution or even breakage. Therefore, this application uses a laser control system to monitor the laser head temperature or output power feedback in real time. Combined with the heat delay curve established in the aforementioned steps... This predicts whether the actual output energy can reach the set value in a timely and accurate manner if the laser head moves to the next processing position and processes at the required power. The laser's temperature change is combined with a heat conduction model. The power-time curve under ideal conditions is also considered. Adjustments are made to obtain a smooth transition position-heat demand curve, reducing the energy consumption of the etching process.

[0046] Optionally, the loss assessment subunit 1041 is specifically used to: determine the change in energy demand between the current scanning position and the next candidate scanning position on the scanning path segment and the estimated movement time of the laser head; query the effective power adjustment capability of the laser head within the estimated movement time in the thermal delay characteristic curve; and calculate the etching cost factor of the scanning path segment between the current scanning position and the next adjacent scanning position based on the matching degree between the change in energy demand and the effective power adjustment capability.

[0047] As an example, for a high-dynamic-energy-consumption region x with multiple scans, the etching cost factor of the laser head at the current position of the i-th position is used. Satisfy the following formula:

[0048] in, This represents the minimum distance between adjacent extreme points; a smaller value indicates stronger vibration and higher etching costs. It is calculated by taking the reciprocal... And normalize, so that and The relationship is inversely proportional, more accurately reflecting the cost increase caused by frequent start-stop cycles. Let represent the thermal energy requirement at the i-th pixel position, and j represent the next adjacent pixel at the i-th position in the original progressive scan path. This represents the thermal energy requirement at the next adjacent pixel j of the i-th position. This formula reflects the cost of the laser head etching path at the current i-th position by considering the distance difference and heat generated between two adjacent etching positions along the laser head etching path. The longer the path, the greater the etching cost, and the greater the likelihood of path adjustment.

[0049] Optionally, the path reconstruction subunit 1042 is specifically used for: selecting a target next scanning position that minimizes the overall cumulative path cost from multiple candidate next scanning positions based on the etching cost factors of each scanning path segment; and updating the scanning order in the high dynamic energy consumption region according to the selected target next scanning position to form a replanned scanning path segment. Here, the overall cumulative path cost refers to the total energy consumption cost required for the laser head to complete continuous etching along a candidate scanning path within the high dynamic energy consumption region or the merged processing area, calculated from the etching cost factors of all continuous scanning path segments on that path. The accumulated costs are used as a quantitative basis for path selection. By comparing the accumulated path costs of different candidate scan paths, the optimal scan path with the lowest total energy consumption is selected to ensure the global optimality of path optimization.

[0050] Optionally, the path reconstruction subunit 1042 is also used to: identify low-energy processing areas that are adjacent to the replanned scanning path segment and whose energy demand is lower than the merging threshold; merge the scanning path of the low-energy processing area with the replanned scanning path segment to form a merged processing area; and optimize the movement trajectory of the laser head in the merged processing area to reduce non-processing movement across different energy-consuming areas.

[0051] Specifically, this application comprehensively considers the etching cost factors of laser heads at several locations within a high dynamic energy consumption region to determine the etching reconstruction parameters that allow for adjustment of the etching path of the laser head at the i-th position at the current location. : ;in This indicates the number of times the laser head restarts during power switching in the xth high dynamic energy consumption region; This is a very small positive number used to avoid a denominator of zero and ensure the validity of the calculation result. In this formula, the average difference between the etching cost factor of all laser heads and the average etching cost factor during the actual etching process is calculated. This reflects the etching cost of the entire high dynamic energy consumption area. Combined with the ratio of the laser head's dwell time in the xth working area to the overall path etching cost, it reflects the priority of the laser head at the current i-th position for path reconstruction. The larger the ratio, the greater the etching path consumption of the laser head at the current i-th position, and the easier it is to reconstruct and adjust the laser head's path.

[0052] Calculate the etching difficulty of the laser head at the initial etching position of the laser head during the operation. ; ;in, The spatiotemporal reconstruction parameter represents the adjustment of the laser head etching path at the i-th position. The larger the spatiotemporal reconstruction parameter, the less difficult it is to adjust the relative etching at the x-th etching position of the current laser head. This is a very small positive number, used to avoid the denominator being zero and to ensure the calculation result is valid. In this formula, This represents the spatial distance between adjacent positions in the laser head etching path at position i. A larger value indicates a greater spatial and temporal interval between different spray areas within the current etching path, suggesting a relatively less concentrated etching path. This allows for path replanning during the etching process, reducing the etching difficulty. (Ratio) The larger the value, the greater the relative cost pressure of the laser head at that location, the more necessary it is to reduce costs through path reconstruction (the higher the feasibility of adjustment), and the lower the etching difficulty.

[0053] This analysis examines the change in etching difficulty before and after the laser head is moved from the i-th pixel position to the j-th pixel position, determining the degree of influence of path reconstruction on laser head path reconstruction, which is used as the etching difficulty difference value. ; ;in, This indicates the etching difficulty of the path when the laser head is at the j-th pixel position; This represents the difficulty of etching and reconstructing the path of the laser head at position i. The calculation results are normalized to the range [-1, 1].

[0054] In the processing heatmap, a 3×3 sliding window is set, and the pixel position corresponding to a single extreme point is placed in the center of the sliding window. Based on the characteristic of the delay in laser energy adjustment, and combined with the thermal delay curve... Calculate the degree of change in thermal capacity within the calculation window: ;in, This represents the etching thermal energy requirement for the j-th pixel within the sliding window corresponding to the i-th extreme point. This represents the thermal delay between the i-th pixel and the j-th pixel, based on the travel time Δt from pixel i to pixel j, as shown on the curve. Find the corresponding data difference in the middle; This is a very small positive number used to avoid a denominator of zero and ensure the validity of the calculation result. In this formula, the ratio of energy demand to heat delay is used. This reflects the consistency between laser power changes and the etching process.

[0055] After calculating the degree of thermal energy change in each window, the degree of thermal energy change in the eight window positions surrounding the current laser head position is traversed, and the position with the smallest thermal energy change is selected as the water washing path of the laser head in the next moment. This can be understood as taking the position with the smallest thermal energy change as the path point of the water washing path of the laser head in the next moment.

[0056] After the above path optimization is completed, low-energy-consumption areas adjacent to the reconstructed path segment and with energy requirements below the merging threshold are identified. Their scanning paths are then merged with the reconstructed path to optimize the movement trajectory and reduce non-processing movement. For example, the merging threshold is 30% of the target laser power value; that is, adjacent areas with energy requirements lower than 30% of the average target laser power of the current high-dynamic-energy-consumption area are identified as low-energy-consumption processing areas.

[0057] Based on the above technical solution, this application uses a loss assessment subunit combined with thermal delay characteristics to accurately calculate the etching cost factor of each scanning path segment in the high dynamic energy consumption area, providing reliable data support for path adjustment and avoiding the problem of energy consumption increase caused by blind path reconstruction. The path reconstruction subunit selects the optimal scanning position with the goal of minimizing the overall cumulative path cost, dynamically updates the scanning order of the high dynamic energy consumption area, merges adjacent low energy consumption processing areas and optimizes the movement trajectory, which greatly reduces the non-processing movement of the laser head across areas and the number of frequent start-stop cycles, effectively reducing the energy consumption of mechanical movement and the power consumption of ineffective heating and cooling of the laser. The solution clarifies the priority of path adjustment by using etching reconstruction parameters, quantifies the feasibility of route optimization by using etching difficulty, assesses the impact of adjustment by using the difference in etching difficulty, and then uses a 3×3 sliding window to comprehensively analyze the matching degree of energy demand and thermal delay to select the optimal scanning path. This ensures the targetedness and accuracy of path optimization and avoids the problem of repeated etching caused by the mismatch between power adjustment and energy demand. While significantly reducing the overall energy consumption of the laser water washing process, it improves the continuity and processing efficiency of the scanning process, and ensures the texture accuracy and effect stability in the processing of complex patterns. It achieves multiple synergistic benefits of energy consumption optimization, efficiency improvement and processing quality assurance.

[0058] In one possible implementation, the system further includes: a real-time monitoring and feedback unit for acquiring the temperature or actual output power of the laser head in real time during the laser water washing process; and a dynamic compensation subunit for dynamically adjusting the scanning speed or output power setting of the laser head based on the real-time acquired temperature or actual output power and the expected energy requirement corresponding to the current scanning position.

[0059] Optionally, the system may also include: a data management unit for recording the energy demand distribution, optimized scanning path, and actual energy consumption data corresponding to historical processing tasks; and a strategy optimization unit for optimizing preset thresholds in the region identification unit, mapping relationships in the process parameter library, or optimization strategies in the path optimization unit based on the data recorded in historical processing tasks using machine learning algorithms.

[0060] Specifically, during the entire laser water washing process using a carbon dioxide pulsed laser machine, the real-time monitoring and feedback unit continuously collects the temperature or actual output power of the laser head, synchronously correlates it with the current scanning position information, and transmits the collected real-time data to the system main control unit. This data is then matched with the expected energy requirements and optimized scanning path corresponding to the current position, providing data support for dynamic compensation.

[0061] The dynamic compensation subunit obtains the thermal delay curve L established by the unit based on the temperature or actual output power collected by the real-time monitoring and feedback unit, combined with the expected energy demand and characteristics of the current scanning position. pThe scanning speed or output power setting of the laser head is dynamically adjusted to ensure that the actual energy output matches the expected demand, thus avoiding ineffective energy consumption and processing defects.

[0062] The data management unit categorizes and stores relevant data for each processing task, including the energy demand distribution of the pattern to be etched, the optimized scanning path, actual energy consumption data, and processing effect feedback data, forming a structured historical processing database that provides a complete data sample for strategy optimization.

[0063] Based on historical processing data stored in the data management unit, the strategy optimization unit continuously optimizes system parameters and strategies through machine learning algorithms. This includes adjusting the preset threshold of the region identification unit, optimizing the mapping relationship between texture level and laser parameters in the process parameter library, and improving the path planning strategy of the path optimization unit. This forms a closed loop of processing-data accumulation-optimization, continuously improving the system's energy consumption optimization effect and adaptability.

[0064] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0065] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. An energy consumption optimization control system for a denim laser washing equipment, characterized in that, The system includes: The feature acquisition unit is used to acquire the thermal delay characteristics of the laser head, which are used to characterize the relationship between the actual output power of the laser head and time during power switching. The distribution generation unit is used to generate the energy demand distribution at different locations in the pattern to be etched on the denim fabric; The region identification unit is used to determine the high dynamic energy consumption region in the pattern to be etched based on the energy demand distribution. The high dynamic energy consumption region is a continuous region where the energy demand value changes spatially beyond a preset threshold. The path optimization unit is used to optimize the scanning path covering the high dynamic energy consumption area based on the thermal delay characteristics, so as to reduce the energy consumption during the laser head washing process and generate an optimized scanning path.

2. The energy consumption optimization control system for the denim laser washing equipment according to claim 1, characterized in that, The feature acquisition unit includes: A steady-state establishment subunit is used to control the laser head to operate at a first power level until thermal equilibrium is reached. The transient excitation and acquisition subunit is used to switch the laser head from the first power level to the second power level and continuously acquire the actual output power at multiple sampling moments after the switch. The curve generation subunit is used to fit and generate the thermal delay characteristic curve, which characterizes the change of the actual output power over time, based on the actual output power at the multiple sampling times.

3. The energy consumption optimization control system for the denim laser washing equipment according to claim 1, characterized in that, The energy demand distribution is characterized by an energy demand distribution heatmap, where the grayscale value of each pixel in the energy demand distribution heatmap represents the required thermal effect intensity at that location. The distribution generation unit includes: An image analysis subunit is used to extract features from the pattern to be etched and obtain texture level information of each region of the pattern. The parameter matching subunit is used to map the texture level information to the target laser power value based on a pre-established process parameter library; the process parameter library is used to store the mapping relationship between texture level information and laser power value. A heat map construction subunit is used to determine the energy demand distribution heat map of the pattern to be etched based on the target laser power value at each position in the pattern.

4. The energy consumption optimization control system for the denim laser washing equipment according to claim 1, characterized in that, The region identification unit includes: The energy demand determination subunit is used to convert the energy demand distribution into an energy demand sequence arranged in the scanning order according to the preset initial scan path; The extreme point determination subunit is used to construct a location heat demand curve based on the energy demand sequence and obtain all extreme points by taking the derivative of the location heat demand curve. The high dynamic energy consumption region determination sub-unit is used to calculate the minimum distance between adjacent extreme points and determine the pattern region corresponding to the extreme points that continuously satisfy the minimum distance being less than or equal to a preset threshold as the high dynamic energy consumption region.

5. The energy consumption optimization control system for the denim laser washing equipment according to claim 1, characterized in that, The path optimization unit includes: The loss assessment subunit is used to determine the etching cost factor of each scan path segment in the high dynamic energy consumption region based on the thermal delay characteristics. The path reconstruction subunit is used to replan the scan path segments according to the etching cost factor of each scan path segment to form the optimized scan path.

6. The energy consumption optimization control system for the denim laser washing equipment according to claim 5, characterized in that, The loss assessment subunit is specifically used for: Determine the change in energy demand between the current scanning position and the next candidate scanning position on the scanning path segment, as well as the estimated movement time of the laser head; The effective power adjustment capability of the laser head during the estimated movement time is queried from the thermal delay characteristic curve. Based on the matching degree between the change in energy demand and the effective power regulation capability, the etching cost factor of the scan path segment between the current scan position and the next adjacent scan position is calculated.

7. The energy consumption optimization control system for the denim laser washing equipment according to claim 5, characterized in that, The path reconstruction subunit is specifically used for: Based on the etching cost factor of each scanning path segment, a target next scanning position that minimizes the overall cumulative path cost is selected from multiple candidate next scanning positions for the current scanning position. Based on the next scan position of the selected target, the scan order within the high dynamic energy consumption region is updated to form a replanned scan path segment.

8. The energy consumption optimization control system for the denim laser washing equipment according to claim 7, characterized in that, The path reconstruction subunit is also used for: Identify low-energy processing areas that are adjacent to the replanned scan path segment and whose energy demand is below the merging threshold; The scanning path of the low-energy processing area is merged with the replanned scanning path segment to form a merged processing area; The movement trajectory of the laser head within the merged processing area is optimized to reduce non-processing movement across different energy consumption areas.

9. The energy consumption optimization control system for the denim laser washing equipment according to claim 7, characterized in that, The system also includes: The real-time monitoring and feedback unit is used to collect the temperature or actual output power of the laser head in real time during the laser water washing process. The dynamic compensation subunit is used to dynamically adjust the scanning speed or output power setting of the laser head based on the real-time collected temperature or actual output power and the expected energy requirement corresponding to the current scanning position.

10. The energy consumption optimization control system for the denim laser washing equipment according to claim 1, characterized in that, The system also includes: The data management unit is used to record the energy demand distribution, the optimized scanning path, and the actual energy consumption data corresponding to historical processing tasks. The strategy optimization unit is used to optimize the preset threshold in the region identification unit, the mapping relationship in the process parameter library, or the optimization strategy in the path optimization unit based on the data recorded in the historical processing tasks, using a machine learning algorithm.

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