Injection molding piece laser etching and gold stamping process parameter adaptive matching system and method

Abstract

The application discloses a kind of injection molding piece laser etching and gold stamping process parameter self-adaptive matching system and method, it is related to parameter matching system technical field, analysis module calculates the topological weight value of current process execution equipment relative to other process execution equipment in group, parameter matching module carries out process role election according to topological weight value every process execution equipment, corresponding role process parameter instruction is generated after election is completed, based on process start timing reference, corresponding parameter fragment is extracted, and multiple process execution equipment is synchronously started to cooperate job.The matching system effectively solves the problem of low coordination precision and poor adaptability in traditional injection molding piece laser etching and gold stamping process, not only improves process consistency and product quality stability, but also reduces production cost by reducing debugging time and resource waste.

Description

Technical Field

[0001] This invention relates to the field of parameter matching system technology, specifically to an adaptive matching system and method for laser engraving and hot stamping process parameters of injection molded parts. Background Technology

[0002] In the field of surface decoration processing for injection molded parts, laser engraving and hot stamping are two commonly used precision processes, used to achieve pattern engraving and metallic texture rendering, respectively. The quality of these processes directly affects the product's appearance and market competitiveness. Currently, laser engraving and hot stamping of injection molded parts are often completed using multiple independent process execution devices operating sequentially or in parallel. For example, a dedicated laser engraving machine is responsible for pattern engraving, and then a hot stamping machine completes the metal foil transfer. However, in this traditional processing mode, each process execution device typically relies on preset fixed process parameters, lacking a dynamic perception and response mechanism for real-time differences in the status between process execution devices.

[0003] 1. Due to inherent differences in mechanical performance, sensor sensitivity, and environmental adaptability among different process execution equipment, and the influence of external factors such as temperature, humidity, and material batch fluctuations during processing, the timing and characteristics of the process execution equipment in response to process commands will exhibit slight deviations. If fixed parameters and independent timing control are still used, it is easy to cause problems such as timing misalignment and parameter mismatch when multiple process execution equipment work together, which in turn leads to quality defects such as laser engraving position deviation and uneven hot stamping adhesion.

[0004] 2. Due to the lack of real-time data interaction and global analysis capabilities between process execution equipment, it is impossible to dynamically adjust the division of roles and parameter configurations according to the actual response characteristics of the process execution equipment. This not only increases the time cost of process debugging, but also makes it difficult to adapt to the flexible production needs of small batches and multiple varieties.

[0005] For the decorative processing of injection molded parts involving small quantities, high precision, and multiple processes, there is an urgent need for an intelligent control system that can achieve time synchronization, state perception, and adaptive parameter matching among multiple process execution devices. This system would address the technical bottlenecks of low collaborative accuracy and poor adaptability under traditional fixed parameter modes, thereby improving processing quality and production efficiency. Summary of the Invention

[0006] The purpose of this invention is to provide an adaptive matching system and method for laser engraving and hot stamping process parameters of injection molded parts, so as to solve the problems in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an adaptive matching system for laser engraving and hot stamping process parameters of injection molded parts, comprising a task flow push module, a time difference data generation module, a dataset output module, an analysis module, and a parameter matching module: Task flow push module: Encodes the process requirements of the injection molded parts to be processed into process task flows. After each process execution device detects the process task flow, it generates a process start timing reference based on the physical timestamp. Time difference data generation module: Each process execution device forms a non-overlapping process characteristic signal sequence within the pre-time slot window, and initiates multi-dimensional sensing acquisition to generate response time difference data; Dataset output module: Each process execution device synchronously receives similar data from other process execution devices and summarizes them to obtain a measurement dataset; Analysis module: Calculates the topology weight value of the current process execution device relative to other process execution devices in the group; Parameter matching module: Each process execution device will compete for a process role based on its topology weight value. After the competition is completed, the corresponding role process parameter instructions will be generated. After extracting the corresponding parameter fragments based on the process start timing benchmark, the collaborative operation of multiple process execution devices will be started synchronously.

[0008] Preferably, the analysis module calculates the topology weight value of the current process execution device relative to other process execution devices in the group, including the following steps: In the spatial reachability branch, examine whether there is attenuation in the response time difference between the current process execution equipment and other process execution equipment; In the performance matching branch, in conjunction with the requirements of this process task, examine whether the response characteristics of the current process execution equipment in the current state meet the requirements of this process task. In the energy consumption branch, call the historical energy consumption models of the current process execution equipment and candidate process execution equipment; After quantitatively evaluating the three dimensions of spatial accessibility, performance matching degree and energy consumption, the topology weight value is obtained by comprehensive calculation.

[0009] Preferably, the analysis module is performed independently by each process execution device, enabling the process execution device to complete the role potential assessment within the local computing power range. By comparing with the pre-stored local process execution device performance baseline model, the process execution device uses the performance baseline model as a benchmark to compare the data segments in the measurement dataset related to itself and other process execution devices item by item, and identify abnormal patterns that deviate from the baseline.

[0010] Preferably, the scores for spatial accessibility, performance matching, and energy consumption are all processed using a piecewise mapping method: The original data or feature value is compared with the preset evaluation range. If it falls into the optimal range, the highest score is given; if it falls into the allowable range, a medium score is given; if it exceeds the allowable range, the score tends to zero or a negative value. Weighting coefficients are applied to the dimensions based on the emphasis of the process task. The scores of each branch are multiplied by the corresponding weighting coefficient and then summed to obtain the topological weight value of the process execution equipment relative to other process execution equipment in the group.

[0011] Preferably, after the dataset output module collects the time difference data of each process execution device, it broadcasts the response time difference data to other process execution devices in the same group. After receiving the data from other process execution devices, the receiving process execution device merges its own data with the external data, establishes an index according to the process execution device identifier and timestamp, and forms a structured measurement dataset.

[0012] Preferably, the parameter matching module will conduct process role selection for each process execution device based on the topology weight value, and generate corresponding role process parameter instructions after the selection is completed, including the following steps: The process execution equipment will compare its own topology weight value with that of other process execution equipment in the same group and arrange them from high to low scores. The process execution equipment checks its own weight characteristics according to the preset role priority table. If the topology weight value not only leads in the total score, but also meets the necessary conditions for the role in the dimension, it immediately initiates a request to compete for that role. The process execution equipment retrieves the built-in process parameter knowledge base, combines its current status with the product specification requirements in the task flow, instantiates and adjusts the template, and generates executable role process parameter instructions.

[0013] Preferably, after the parameter matching module extracts the corresponding parameter fragments based on the process start-up timing reference, it synchronously starts the collaborative operation of multiple process execution equipment, including the following steps: The process execution equipment retrieves the built-in process parameter knowledge base, which stores parameter generation rules and experience templates according to different roles. The process execution equipment combines its current status with the product specification requirements in the task flow to instantiate and adjust the experience template, generating executable role process parameter instructions. The instructions include numerical parameters, timing control details, and tolerance monitoring strategies. After the role process parameter instructions are generated, the process execution equipment continuously monitors the updates of the process task flow and extracts parameter fragments that match its own role. When the start-up time specified by the process start-up timing benchmark is reached, all process execution equipment enters the execution state synchronously, forming a multi-process composite flow.

[0014] Preferably, after the time difference data generation module enters the time slot window of each process execution device, the process execution device performs a process probing action. Simultaneously, a multi-dimensional sensor acquisition network deployed in the surrounding environment of the workstation is activated to acquire spatial propagation information of different physical effects, locate feature points triggered by the probing action in the time domain, compare the time scales of the same event feature points measured by different sensors, calculate the arrival time difference between each pair, and for layouts containing multiple sensors, traverse all sensor pairings to generate original time difference records. Outliers caused by sudden environmental interference are removed from the original time difference records, and consistency verification is performed. The filtered time difference data is classified according to the probing process execution device and the sensing process execution device to form a structured response time difference data set.

[0015] Preferably, the task flow push module initiates the process task release mechanism, which integrates all process requirements of the injection molded parts to be processed in a structured manner. The process requirements include product specifications, marking patterns, hot stamping areas, and appearance quality standards.

[0016] This application also provides an adaptive matching method for laser engraving and hot stamping process parameters of injection molded parts, the matching method including the following steps: S1: The process requirements of the injection molded part to be processed are encoded into a process task flow. After each process execution device detects the process task flow, it generates a process start timing reference based on the physical timestamp. Each process execution device forms a non-overlapping process characteristic signal sequence within the pre-time slot window and starts multi-dimensional sensing acquisition to generate response time difference data. S2: Each process execution device synchronously receives similar data from other process execution devices, summarizes them to obtain a measurement dataset, and calculates the topology weight value of the current process execution device relative to other process execution devices in the group; S3: Each process execution device will compete for a process role based on its topology weight value. After the competition is completed, the corresponding role process parameter instructions will be generated. After extracting the corresponding parameter fragments based on the process start timing benchmark, the collaborative operation of multiple process execution devices will be started synchronously.

[0017] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This application uses topology weight values ​​to conduct process role selection and generate role process parameter instructions. After extracting parameter fragments based on the process start timing benchmark, it synchronously starts collaborative operations. This effectively solves the problems of fixed parameters of multiple process execution equipment, low collaborative accuracy, and poor adaptability in traditional laser engraving and hot stamping processes for injection molded parts. It not only improves process consistency and product quality stability, but also reduces production costs by reducing debugging time and resource waste, while enhancing the system's dynamic response capability to complex process requirements.

[0018] 2. This application quantifies the relative importance and response characteristics of each process execution device in the process execution network by calculating the topology weight value of the current process execution device relative to other process execution devices in the group. This weighting mechanism replaces the traditional fixed role allocation mode, enabling parameter matching to better fit the real-time status and actual capabilities of the process execution devices, thereby improving the system's adaptability. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 This is a framework diagram of the matching system of the present invention.

[0021] Figure 2 This is a flowchart of the parameter matching module of the present invention.

[0022] Figure 3 This is a flowchart illustrating the calculation of the topology weight values ​​in this invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example: This example provides an adaptive matching system for laser engraving and hot stamping process parameters of injection molded parts. Please refer to [link / reference]. Figure 1 As shown, it includes a task flow push module, a time difference data generation module, a dataset output module, an analysis module, and a parameter matching module: Task Flow Push Module: This module initiates a process task publishing mechanism, encoding the complete process requirements of the injection molded parts (including product specifications, marking patterns, hot stamping areas, and appearance quality standards) into a unified process task flow. This process task flow is pushed to all process execution equipment in the workshop (including heterogeneous injection molding machines, laser engraving devices, and hot stamping units) at fixed intervals, ensuring that each process execution device can capture the basic framework and key indicators of the task. After detecting the process task flow, each process execution device records the physical timestamp of the task data packet arriving locally and generates a unified process start-up timing benchmark based on these timestamps. This benchmark serves as the starting line for all process execution devices to conduct collaborative operations, eliminating action misalignment caused by clock drift or communication delays between process execution devices.

[0025] Time difference data generation module: Based on the process start-up timing reference, each process execution device actively runs a process probe action within a pre-planned, non-interfering time slot window, forming a set of non-overlapping process characteristic signal sequences. For example, the laser engraving process execution equipment drives the laser to emit a set of low-energy pulses (simulating the energy release characteristics of actual processing), the hot stamping process execution equipment drives the pressure head to perform a micro-pressure (simulating the pressure loading process of real hot stamping), and the injection molding machine can fine-tune the material temperature or clamping force and collect the response curve. These trial actions are strictly staggered on the time axis to avoid mutual interference caused by signal superposition.

[0026] At this point, multi-dimensional sensor acquisition is initiated simultaneously to generate response time difference data: Microphone arrays (or vibration sensors, infrared thermometers, etc.) deployed around the workstation are used to capture the physical effects generated by the probing action (such as the minute vibrations caused by laser engraving pulses, the contact noise of hot stamping heads, and the radiant heat signals of material temperature changes in injection molding machines). The time difference of these physical effects reaching different sensors is recorded, thereby generating response time difference data. This data directly reflects the relative orientation, distance, and media propagation characteristics (such as the influence of workshop airflow on sound / vibration propagation) between the probing process execution equipment and the sensing process execution equipment.

[0027] Dataset Output Module: Each process execution device sends its generated response time difference data to other process execution devices in the group, and simultaneously receives similar data from other process execution devices, summarizing them into a measurement dataset. This dataset not only contains the geometric topological relationships between process execution devices (e.g., laser engraving machine A and hot stamping machine B are 3.2 meters apart at an azimuth angle of 45°), but also implicitly contains clues about the actual performance status of each process execution device.

[0028] Analysis Module: Each process execution device independently performs in-depth analysis of the measurement dataset. Combined with pre-stored performance baseline models of the process execution devices (such as the pulse consistency threshold of a standard laser engraving machine and the pressure response linearity of an ideal hot stamping machine), the topology weight value of the current process execution device relative to other process execution devices in the group is calculated. The topology weight value integrates a comprehensive quantitative indicator of spatial accessibility (e.g., whether a process execution device is obstructed by obstacles causing severe signal attenuation), performance matching degree (e.g., whether the pressure accuracy of a hot stamping machine meets the miniaturized hot stamping requirements of the current task), and energy consumption (e.g., whether using a low-power injection molding machine for long-cycle molding is more optimal), intuitively representing the competence of the process execution device within the overall process topology.

[0029] Parameter matching module: Based on the calculated topology weight values, each process execution device autonomously conducts process role selection. Process execution equipment with high topology weight values ​​is prioritized for core roles requiring stringent spatial accuracy or performance stability. If a laser engraving machine is close to the injection molding part handling station (good spatial accessibility) and possesses high pulse consistency (high performance matching), it will be selected and locked as the main positioning laser engraving unit, responsible for generating the reference marks for hot stamping alignment. Process execution equipment with lower topology weight values ​​will assume auxiliary or supplementary roles. For example, a hot stamping machine, although spatially located further away, can be locked as a large-area uniform hot stamping unit due to stable pressure control and low energy consumption. After the selection process, each process execution equipment generates specific role-specific process parameter instructions (such as the laser power and scanning path accuracy of the main positioning laser engraving unit, and the temperature-pressure-holding time combination of the auxiliary hot stamping unit). Each process execution equipment strictly follows a unified process startup timing benchmark, extracting parameter fragments corresponding to its own role from the continuously updated process task flow, and synchronously starting multi-process execution equipment collaborative operation.

[0030] This embodiment also provides an adaptive matching method for laser engraving and hot stamping process parameters of injection molded parts. The matching method includes the following steps: S1: The main control unit initiates the process task release mechanism, encoding the complete process requirements of the injection molded part (including product specifications, marking patterns, hot stamping areas, and appearance quality standards) into a unified process task flow. This process task flow is pushed to all process execution equipment in the workshop (including heterogeneous injection molding machines, laser engraving devices, and hot stamping units) at fixed intervals, ensuring that each process execution equipment can capture the basic framework and key indicators of the task. After detecting the process task flow, each process execution equipment records the physical timestamp of the task data packet arriving locally and generates a unified process start timing benchmark based on these timestamps. This benchmark is equivalent to the starting line for all process execution equipment to carry out collaborative operations, eliminating action misalignment caused by clock drift or communication delay between process execution equipment.

[0031] S2: Based on the process start-up timing reference, each process execution device actively runs a process probe action within a pre-planned, non-interfering time slot window, forming a set of non-overlapping process characteristic signal sequences: For example, the laser engraving process execution equipment drives the laser to emit a set of low-energy pulses (simulating the energy release characteristics of actual processing), the hot stamping process execution equipment drives the pressure head to perform a micro-pressure (simulating the pressure loading process of real hot stamping), and the injection molding machine can fine-tune the material temperature or clamping force and collect the response curve. These trial actions are strictly staggered on the time axis to avoid mutual interference caused by signal superposition.

[0032] At this point, multi-dimensional sensor acquisition is initiated simultaneously to generate response time difference data: Microphone arrays (or vibration sensors, infrared thermometers, etc.) deployed around the workstation are used to capture the physical effects generated by the probing action (such as the minute vibrations caused by laser engraving pulses, the contact noise of hot stamping heads, and the radiant heat signals of material temperature changes in injection molding machines). The time difference of these physical effects reaching different sensors is recorded, thereby generating response time difference data. This data directly reflects the relative orientation, distance, and media propagation characteristics (such as the influence of workshop airflow on sound / vibration propagation) between the probing process execution equipment and the sensing process execution equipment.

[0033] Each process execution device sends its generated response time difference data to other process execution devices in the group, and simultaneously receives similar data from other process execution devices, summarizing them into a measurement dataset. This dataset not only contains the geometric topological relationships between process execution devices (e.g., laser engraving machine A and hot stamping machine B are 3.2 meters apart at an azimuth angle of 45°), but also implicitly contains clues about the actual performance status of each process execution device.

[0034] S3: Each process execution device independently performs in-depth analysis of the measurement dataset. Combined with pre-stored performance baseline models of the process execution devices (such as the pulse consistency threshold of a standard laser engraving machine and the pressure response linearity of an ideal hot stamping machine), the topology weight value of the current process execution device relative to other process execution devices in the group is calculated. The topology weight value integrates a comprehensive quantitative indicator of spatial accessibility (e.g., whether a process execution device is obstructed by obstacles causing severe signal attenuation), performance matching degree (e.g., whether the pressure accuracy of a hot stamping machine meets the miniaturized hot stamping requirements of the current task), and energy consumption (e.g., whether using a low-power injection molding machine for long-cycle molding is more optimal). This intuitively represents the competence of the process execution device within the overall process topology.

[0035] Based on the calculated topology weight values, each process execution device autonomously conducts a process role election: Process execution equipment with high topology weight values ​​is prioritized for core roles requiring stringent spatial accuracy or performance stability. If a laser engraving machine is close to the injection molding part handling station (good spatial accessibility) and has high pulse consistency (high performance matching), it will be selected and locked as the main positioning laser engraving unit, responsible for generating the reference marks for hot stamping alignment. Process execution equipment with lower topology weight values ​​will assume auxiliary or supplementary roles. For example, a hot stamping machine, although spatially located slightly further away, can be locked as a large-area uniform hot stamping unit due to stable pressure control and low energy consumption. After the selection process, each process execution equipment generates specific role-specific process parameter instructions (such as the laser power and scanning path accuracy of the main positioning laser engraving unit, and the temperature-pressure-holding time combination of the auxiliary hot stamping unit).

[0036] Once the execution phase begins, each process execution device strictly follows a unified process start-up timing benchmark, extracts parameter fragments corresponding to its own role from the continuously updated process task flow, and synchronously starts collaborative operation of multiple process execution devices.

[0037] Specifically, the implementation steps for each functional module of this application are as follows: The task flow push module aims to establish a unified, perceptible, and timely mechanism for distributing process tasks and generating collaborative time sequences. This enables different types of process execution equipment in the workshop to obtain complete processing requirements within the same semantic framework and form a consistent start-up benchmark in the time dimension, thereby overcoming the problem of misaligned actions that easily occur when heterogeneous process execution equipment operates under independent clock and communication conditions.

[0038] A process task release mechanism is initiated, operated by a central task orchestration node or a higher-level system with task management capabilities. Upon activation, all process requirements for the injection molded part are structurally integrated, including product specifications (such as dimensions, material type, and surface pretreatment requirements), marking patterns (graphic outline, line width, position coordinates, and layer order), hot stamping areas (boundary definition, coverage area ratio, foil type, and color requirements), and appearance quality standards (such as color difference tolerance, adhesion grade, and defect tolerance range). This information is encoded into a unified process task flow, using a structured format that can be parsed across process execution devices, for example: Based on a lightweight binary protocol or JSON extended fields, process execution equipment from different manufacturers and control systems can all read task content through a common parsing interface. The task flow is not pushed all at once, but rather broadcast cyclically at fixed intervals. The interval length is comprehensively set based on the workshop's collaborative rhythm and the preparation time of the process execution equipment, ensuring that even if a process execution device misses a task in the previous cycle due to a malfunction, it can catch up in the next cycle, thus improving robustness. The push scope covers all heterogeneous process execution equipment in the workshop involved in processing the product, including injection molding machines, laser engraving devices, and hot stamping units, ensuring that all types of process execution equipment receive the same version of the task framework and key indicators.

[0039] During the task flow push process, each process execution device continuously listens to the designated task release channel. Once a new task data packet is detected, the device immediately records the physical timestamp of the data packet locally. The physical timestamp is provided by the high-precision real-time clock inside the process execution device, typically with nanosecond or microsecond resolution, and is periodically calibrated with the shop floor time synchronization service to suppress clock drift caused by long-term operation.

[0040] The process execution equipment embeds a receiving time marker in the header identification field of the data frame and stores the marker in a local buffer queue. At the same time, it maintains a timestamp list in memory to collect the arrival times of all similar process execution equipment within the current cycle (for process execution equipment that supports peer-to-peer communication, the data can also be enriched by exchanging receiving times with each other).

[0041] Once the task flow push cycle ends or the preset sampling quantity is reached, the process execution equipment enters the stage of generating a unified process start-up timing baseline. Logically, the collected timestamp list is sorted and statistically analyzed. Outliers with obvious anomalies are removed, such as extreme early or late arrival times caused by transient communication interference. The median or mean of the remaining timestamps is calculated as the reference arrival time for the process execution device in this task flow. Each process execution device exchanges its reference arrival time with time information from other process execution devices, and all reference times are globally sorted and centralized again to determine a common time point that can be as close as possible to the reception time of all process execution devices. After necessary corrections (such as subtracting the estimated average propagation delay of the task packet in the channel), this common time point becomes the unified process startup timing reference.

[0042] The time difference data generation module uses the generated process start-up timing reference as a basis to define non-overlapping pre-time slot windows for each process execution device. The planning of this window follows the principle of "time axis staggered and signal non-interference" to ensure that only one type of process execution device performs the trial action at the same time, thereby avoiding the superposition and masking of physical effects emitted by different process execution devices at the sensing end.

[0043] After entering their respective time slot windows, each process execution device will perform a low-intensity, non-destructive process trial to simulate the key features of the actual processing, but without causing any substantial impact on the workpiece or production line.

[0044] The laser engraving device drives the laser to emit a set of low-energy pulses. The pulse width, repetition frequency and energy distribution are all proportionally reduced according to the set values ​​of the actual processing. This can preserve the waveform characteristics of the energy release process and avoid burning or etching the workpiece. The hot stamping unit's pressure head performs a micro-pressure stroke, with a pressure curve similar to that of actual hot stamping, but the displacement amplitude is controlled within a safe threshold to avoid damaging the foil or mold. Without actually performing injection molding, the injection molding machine fine-tunes the barrel temperature or clamping force and records the corresponding response curves, such as the temperature rise rate and pressure stabilization time, to reflect its dynamic operating characteristics. These probing actions are strictly separated in the time domain, forming a non-overlapping sequence of process characteristic signals, which constitutes the target event chain for subsequent sensing and capture.

[0045] While the trial action is being performed, a multi-dimensional sensor network deployed around the workstation is simultaneously activated. This network consists of various types of sensors, such as microphone arrays, vibration sensors, and infrared thermometers, distributed at reasonable locations that cover the entire operating range of the process equipment to acquire spatial propagation information of different physical effects.

[0046] The goal of sensing and data acquisition is to record the time difference between the arrival of these physical effects at sensors in different spatial locations. Event detection is performed on the sampled data stream of each sensing channel, and the processing logic is as follows: In the time domain, locate significant feature points triggered by probing actions, such as the initial transition edge of an acoustic signal, the arrival time of the first peak of a vibration waveform, and the inflection point of a temperature curve. Compare the time scales of the same event feature points measured by different sensors and calculate the time difference between each pair. For a layout containing multiple sensors, iterate through all sensor pairings to generate a set of raw time difference records.

[0047] Next, the data is cleaned and its reliability is screened to remove outliers caused by sudden environmental disturbances (such as personnel movement or short-term malfunctions of process execution equipment), and the remaining time differences are verified for consistency. The screened time difference data is then categorized into test process execution equipment and sensing process execution equipment to form a structured response time difference data set. This set implicitly includes the relative location of the test process execution equipment in the workshop space, its distance from the sensing sensors, and the influence characteristics of media such as airflow and structural conduction on the propagation speed of sound, vibration, and heat.

[0048] In one embodiment of this application, after the time difference data generation module completes the pre-time slot window division and performs the trial action, it synchronously collects physical effect signals by a sensor network composed of a microphone array, vibration sensor and infrared thermometer. It processes the logic to locate the initial transition edge of the sound signal, the arrival time of the first peak of the vibration waveform and the inflection point of the temperature curve, and performs time scale comparison between sensors to generate the original time difference record. Then, after data cleaning and consistency verification, outliers caused by environmental interference are removed and the data is classified according to the trial device and the sensing device to form a structured response time difference data set.

[0049] Taking sound signal acquisition as an example, let microphone A be located at coordinates x1 y1 z1, and microphone B be located at coordinates x2 y2 z2. The distance L between the two microphones is calculated using Euclidean distance, which is equal to the square root of x2 minus x1 squared plus y2 minus y1 squared plus z2 minus z1 squared. When a laser engraving device emits a low-energy pulse, the initial transition time recorded by microphone A is tA, which is equal to 1025 milliseconds, and the initial transition time recorded by microphone B is tB, which is equal to 1037 milliseconds. Then the arrival time difference between the two sensors, ΔtAB, is equal to tB minus tA and 1037 minus 1025, resulting in 12 milliseconds. This time difference value, combined with the known speed of sound v at normal temperature and pressure is approximately 340 meters per second, can be used to infer the projected distance and orientation of the sound source relative to the line connecting the two microphones. If the measured L is 4.08 meters, then the theoretical propagation time tL is equal to L Dividing by v, tL equals 4.08. Dividing by 340, it is approximately 12 milliseconds, consistent with ΔtAB. This indicates that the time difference data is reliable and accurately reflects the location of the sound source and the state of the propagation medium, thus forming response time difference data that can be used for subsequent topology analysis.

[0050] The dataset output module integrates the response time difference data scattered in various process execution devices into a measurement dataset that can be analyzed globally. Its key is to build a unified data view across process execution devices, so that each process execution device can not only grasp its own trial response characteristics, but also obtain similar information from other process execution devices, thereby forming a complete spatiotemporal and state correlation map between process execution devices.

[0051] In actual operation, after each process execution device completes its own time difference data collection, it will broadcast the response time difference data to other process execution devices in the same group according to the agreed communication protocol and data encapsulation format.

[0052] After receiving data from other process execution devices, the receiving end device merges its own data with the external data, indexes it according to the process execution device identifier and timestamp, and forms a structured measurement dataset. This dataset not only saves the original time difference records, but also adds environmental labels at the time of acquisition (such as workshop temperature and humidity, background noise level) and sensor deployment coordinates to facilitate environmental compensation during subsequent analysis. Furthermore, by performing spatial propagation inversion on the time differences between multiple process execution devices, the geometric topological relationships between the process execution devices can be calculated. The geometric topological relationships are directly mapped to the physical layout of the workshop, giving the system spatial awareness capabilities. At the same time, the numerical characteristics of the response time difference can also reflect the actual performance status of the process execution devices.

[0053] In one embodiment of this application, after broadcasting and merging cross-device response time difference data, the dataset output module forms a structured measurement dataset, and calculates the geometric topological relationship between devices by spatial propagation inversion of the time difference, while reflecting the actual performance status of the devices by the time difference numerical characteristics.

[0054] Taking the time difference of sound signals from two laser engraving devices A and B as an example to estimate the distance, let's assume that the starting time for sensor M1 to receive the low-energy pulse sound signal from A is tM1A = 1205 ms, and the starting time for sensor M2 to receive the same signal is tM2A = 1211 ms. Then the time difference ΔtM1M2A = tM2A - tM1A = 1211 - 1205, which is 6 ms. Given that the speed of sound at normal temperature and pressure is v = 340 m / s, and the distance between sensors M1 and M2 is LMS = 2.04 m, according to the triangulation logic, the distance difference from the sound source A to the two sensors is ΔdM1M2A = v × ΔtM1M2A = 340 × 0.006, which gives 2.04 m. The fact that the distance difference is equal to the LMS indicates that the sound source A is located at the intersection of the hyperbola with M1 and M2 as foci and satisfying the distance difference condition. Combining multiple sets of sensor data, the spatial coordinates of A can be uniquely determined.

[0055] Similarly, the measured time difference for device B is 5 ms, so ΔdM1M2B = 340 × 0.005, which gives 1.70 m. Therefore, the geometric distance between device A and device B can be calculated as the modulus of the difference in coordinates between the two points. If the coordinates of A are xA, yA, zA = 3.0 m, 2.0 m, 0 m, and the coordinates of B are xB, yB, zB = 4.2 m, 2.5 m, 0 m, then the distance dAB = √[(xB - xA)]. 2 + (yB - yA) 2 + (zB - zA) 2 ] = √[1.2 2 + 0.5 2 + 0 2 = √[1.44 + 0.25] = √1.69, resulting in 1.3 m. This geometric topological relationship directly maps to the physical layout of the workshop.

[0056] Regarding performance status, the standard deviation of the time difference for multiple measurements of device A is σA = 0.08 ms, while that of device B is σB = 0.25 ms. The smaller standard deviation indicates that the consistency of pulse excitation timing of device A is better than that of device B, reflecting that the performance status of device A is more stable. This numerical characteristic of the time difference, along with the geometric relationship, is stored in the measurement dataset to provide a basis for spatial awareness and status judgment for subsequent analysis modules.

[0057] After the analysis module takes over the measurement dataset, each process execution device will independently conduct in-depth analysis. The purpose of this is to allow the process execution device to complete the role potential assessment within its local computing power range, avoiding the communication bottlenecks and single point of failure risks caused by centralized computing.

[0058] By comparing the data against pre-existing local performance baseline models for process execution equipment—reference standards established based on factory specifications or long-term operational statistics—the process execution equipment uses these baselines as benchmarks to compare each data segment in the measurement dataset related to itself and other process execution equipment, identifying abnormal patterns that deviate from the baseline.

[0059] like Figure 3 As shown, the next step is to calculate the topology weight value. Logically, the process can be divided into three parallel branches, which correspond to the quantitative evaluation of three dimensions: spatial reachability, performance matching degree, and energy consumption. These are then combined into a comparable weight value.

[0060] In one embodiment of this application, the spatial accessibility score is calculated by first obtaining the standard deviation of the time difference of the acoustic signal generated during the trial operation between a certain process execution device and multiple sensors distributed in the workshop. This value reflects the fluctuation and attenuation of the signal during propagation. Then, the mean and standard deviation of the time difference under unobstructed conditions recorded in the baseline model for similar devices are retrieved. The measured value is subtracted from the baseline mean and divided by the baseline standard deviation to obtain a normalized deviation value. The larger the value, the better the signal propagation conditions of the device, the higher the spatial accessibility, the smaller the fluctuation and attenuation of the response time difference between the device and other devices, and the easier it is to obtain stable and accurate perception data in collaborative operations. The smaller the value, the more obvious the fluctuation and attenuation during signal propagation, which may lead to a decrease in perception reliability due to obstacles or environmental influences, and poor spatial cooperation conditions. Extremely low values ​​or negative values ​​represent severely deteriorated propagation conditions.

[0061] The performance matching score is calculated based on the specific requirements of the current process task for a certain performance index, such as the limit requirement for pressure linearity error in the miniature hot stamping area, combined with the allowable upper limit of this index given by the baseline model. A normalized margin value is obtained by subtracting the actual measured value from the baseline allowable upper limit and then dividing by the difference between the allowable upper limit and the task requirement limit. A larger value indicates that the actual measured performance of the equipment better meets the specific requirements of the current process task and is better than the allowable range in the baseline model, meaning it has higher stability and accuracy in key performance indicators. A smaller value indicates that the actual measured performance is closer to or exceeds the allowable limit, reducing the fit with task requirements; extremely low values ​​indicate that the performance is difficult to meet the process requirements.

[0062] The energy consumption score is calculated based on an objective comparison of the equipment's energy consumption per unit time among candidate equipment in the same group. The system first collects long-cycle operating energy consumption data for this equipment and all equipment in the same group, identifies the minimum and maximum values, and then subtracts the equipment's energy consumption from the maximum value and divides it by the difference between the maximum and minimum values ​​to obtain a normalized energy consumption position value. The larger the value, the lower the equipment's energy consumption per unit time, and the higher its energy utilization efficiency among equipment in the same group, which helps to reduce overall operating costs and improve energy efficiency advantages; the smaller the value, the higher the energy consumption, and the worse the economic performance in the group comparison. Extremely low values ​​mean that the equipment is at a significant disadvantage in terms of energy consumption.

[0063] The topology weight value is obtained by summing the obtained normalized deviation value, normalized margin value and normalized energy consumption location value.

[0064] In the spatial accessibility branch, examine whether there is attenuation in the response time difference between the current process execution equipment and other process execution equipment. In the performance matching branch, considering the specific requirements of this process task (such as the high-precision pressure control requirements of the miniature hot stamping area), examine whether the response characteristics of the process execution equipment in the current state meet the requirements. If the linearity and repeatability of the pressure response curve are better than the requirements of the baseline model, the matching score will increase; otherwise, it will decrease. In the energy consumption branch, call the historical energy consumption models of the current process execution equipment and candidate process execution equipment to determine whether selecting this process execution equipment in a specific process can reduce overall energy consumption while ensuring performance. For example, if long-cycle injection molding is handled by a low-power model, the energy consumption score will increase.

[0065] The scoring for each branch uses a segmented mapping method: The system compares the raw data or feature values ​​with preset evaluation intervals. Data falling within the optimal interval receives the highest score, data falling within the allowable interval receives a medium score, and data exceeding the allowable range receives a score approaching zero or a negative value. Subsequently, the system applies weighting coefficients based on the emphasis placed on that dimension by the process task. For example, for the precision-priority hot stamping process, the performance matching coefficient will be higher than the energy consumption coefficient. The scores of each branch are multiplied by their corresponding weighting coefficients and then summed to obtain the topological weight value of the process execution equipment relative to other process execution equipment within the group. This weight value is essentially a set of quantitative indicators integrating spatial conditions, performance status, and energy efficiency; it directly reflects the competence of the process execution equipment in fulfilling a certain role within the overall process topology.

[0066] In one embodiment disclosed in this application, taking a hot stamping machine X as an example, in the spatial accessibility branch, the standard deviation of the acoustic signal time difference between X and multiple sensors is obtained through measurement datasets, which is 0.10 milliseconds. The baseline model for the standard deviation of the time difference of similar devices under unobstructed conditions is 0.15 milliseconds. The measured value is less than the baseline, indicating that the signal attenuation is small. The processing logic of this branch compares the original value of 0.10 with the evaluation interval. 0.10 falls within the optimal interval of 0 to 0.12 milliseconds and is assigned the highest score of 10 points. In the performance matching degree branch, the current task requires that the pressure linearity error of the miniature hot stamping area does not exceed 3%. The linearity error of X's pressure response curve is 1.8%, and the baseline model allows an upper limit of 3%. The measured value is better than the requirement, and this value falls within the optimal interval and is assigned a score of 10 points. In the energy consumption branch, X's historical energy consumption model has a unit time energy consumption of 180 W under long-cycle operation, while another candidate hot stamping machine Y in the same group has an energy consumption of 220 W. W, the task focuses on energy economy, and X's energy consumption score is assigned 9 points in the optimal interval after segmentation mapping.

[0067] The scores for each of the three branches are multiplied by the weighting coefficients set for the process task. For the precision-priority hot stamping step, the performance matching coefficient is set to 0.5, the spatial accessibility coefficient to 0.3, and the energy consumption coefficient to 0.2. Therefore, the spatial accessibility score (10 multiplied by 0.3) equals 3.0, the performance matching score (10 multiplied by 0.5) equals 5.0, and the energy consumption score (9 multiplied by 0.2) equals 1.8. Summing these values ​​yields the topology weight value WX, which equals 3.0 + 5.0 + 1.8, resulting in 9.8. This weight value integrates spatial conditions, performance status, and energy efficiency, directly reflecting that equipment X's competence in playing the core role of high-precision hot stamping within the overall process topology is higher than other equipment with lower weighting values. This provides a basis for the subsequent role selection and parameter generation in the parameter matching module.

[0068] like Figure 2 As shown, the parameter matching module receives the topology weight values ​​output by the analysis module, allowing each process execution device to complete the selection and locking of process roles under the autonomous decision-making framework, and generates role process parameter instructions that can directly drive the machine operation, thereby ensuring that multiple process execution devices can achieve high-precision and high-stability collaborative operation when performing laser engraving and hot stamping composite processes.

[0069] The election process is initiated autonomously by each process execution device based on its locally stored topology weight value, and consists of priority ranking and role matching: The process execution equipment compares its own topology weight value with other process execution equipment in the same group, ranking them from highest to lowest score. A preset role priority table clarifies the dependence of different process stages on indicators such as spatial accuracy, performance stability, and energy consumption, and assigns them to specific roles. For example, the main positioning laser engraving unit requires high spatial accessibility and excellent pulse consistency because this role is responsible for engraving the reference marks for hot stamping alignment on the injection molded parts; any positional deviation will be amplified in subsequent hot stamping processes. The large-area uniform hot stamping unit prioritizes the stability of pressure control and energy economy to maintain consistent quality and reduce operating costs over longer work cycles. The process execution equipment checks its own weight characteristics according to this table. If its topology weight value not only leads in the overall score but also meets the necessary conditions for a core role in key dimensions, it immediately initiates a request to compete for that role.

[0070] The application for selection includes the identification of the process execution equipment, weight details, and evidence data demonstrating that key conditions are met (such as measured pulse consistency curves and calculated spatial distances to pick-up and drop-off stations). To avoid conflicts, a first-come, first-served mechanism combined with weight verification is adopted. When two or more process execution devices compete for the same role, the one with the higher weight value wins. If the scores are the same, the performance of the secondary key dimension is taken into account. If necessary, random delay weighting is introduced to reduce the probability of collision.

[0071] In one embodiment disclosed in this application, the parameter matching module receives the topology weight value output by the analysis module and completes the process role selection and locking under the autonomous decision-making framework. The selection process is based on a preset role priority table, which arranges the role priorities according to the degree of dependence of the process link on spatial accuracy, performance stability and energy consumption. The main positioning laser engraving unit has a priority of 1, requiring high spatial accessibility and excellent pulse consistency. The large-area uniform hot stamping unit has a priority of 2, focusing on pressure control stability and energy economy. The auxiliary laser engraving unit has a priority of 3, requiring basic spatial accessibility and pulse stability. The backup hot stamping unit has a priority of 4, only requiring that the minimum performance and energy consumption thresholds are met.

[0072] Each device compares its own topology weight value with other devices in the same group and sorts them in descending order of score. It checks whether its own weight characteristics meet the necessary conditions listed in the role priority table. If its total score is leading and its key dimensions meet the requirements, it initiates a campaign request, which includes the device identifier, weight details, and supporting data. For example, consider three devices A, B, and C participating in hot stamping alignment processing of a certain injection molded part. A is the main positioning laser engraving machine with a topology weight value WA of 9.8, a spatial accessibility score of 10, and a pulse consistency score of 10; B is a large-area uniform hot stamping machine with a WB of 8.5, a pressure stability score of 9, and an energy consumption score of 8; C is an auxiliary laser engraving machine with a WC of 7.2, a spatial accessibility score of 7, and a pulse consistency score of 8. The role priority table is shown in Table 1. Table 1: Role Priority Table

[0073] The role priority table stipulates that the necessary conditions for the primary positioning laser engraving unit are a spatial accessibility score ≥9 and a pulse consistency score ≥9. Unit A meets these conditions and has the highest WA score, thus initiating the competition and declaring supporting evidence data. Units B and C do not meet these conditions and do not participate in the competition for this role. The necessary conditions for the large-area uniform hot stamping unit are a pressure stability score ≥8 and an energy consumption score ≥7. Unit B meets these conditions and has the highest WB score among the remaining units, thus locking in this role.

[0074] When competition exists, a first-come, first-served approach combined with weighted verification is adopted. If machine B and another hot stamping machine D both have a weight WD of 8.5, then the energy consumption score of the secondary key dimension is compared. B's energy consumption score of 8 is higher than D's 7, so B wins. This mechanism ensures that key dimensions and weight values ​​jointly determine role allocation. The calculation formula is: the device that meets the role locking conditions and has the highest weight value wins; when weight values ​​are equal, the device with the higher secondary key dimension score wins, thus achieving high-precision and high-stability collaborative role allocation across multiple devices.

[0075] Once the selected process execution equipment is assigned a specific role, the process transitions to the generation phase of the role's process parameter instructions. The process execution equipment retrieves a built-in process parameter knowledge base, which stores parameter generation rules and experience templates categorized by different roles. For example, the knowledge base entries for the main positioning laser engraving unit specify that the laser power should be selected within the upper-middle range of the usable range based on the material reflectivity and marking precision, the scanning path accuracy must match the tolerance requirements of the reference mark, and additional fine-tuning strategies are added to cope with the thermal deformation of the actual workpiece; the knowledge base for the auxiliary hot stamping unit lists the combination logic of temperature, pressure, and holding time to ensure uniform heat transfer and firm foil adhesion during large-area hot stamping.

[0076] The process execution equipment, based on its current state (such as laser cooling efficiency and pressure head wear) and the specific requirements of the product specification in the task flow, instantiates and adjusts the template to generate a complete and executable set of role process parameter instructions. The instructions include numerical parameters (power, temperature, pressure, time, etc.), timing control details (such as preheating time and pressurization slope), and tolerance monitoring strategies (such as real-time detection of pulse energy fluctuations and fine-tuning when they exceed limits).

[0077] After the parameter instructions are generated, each process execution device strictly follows the unified process start-up timing benchmark established by the previous task flow push module to perform parameter slicing and synchronous start-up: The process execution equipment continuously monitors updates to the process task flow, extracting parameter segments that match its own role. The task flow is segmented and labeled according to process nodes during design, with each segment carrying a timestamp and role identifier. The process execution equipment only needs to retrieve and extract the segment required for the current collaborative cycle based on the identifier. This ensures that even if the task flow is adjusted in real-time due to order changes, the process execution equipment can only adopt the portion corresponding to its current role and time sequence, without being affected by irrelevant parameters.

[0078] When the start-up time specified by the process start-up timing benchmark is reached, all process execution equipment enters the execution state synchronously. The laser engraving unit ablates the mark according to the high-precision path, the hot stamping unit completes the foil transfer according to the predetermined temperature and pressure curve, and the injection molding machine completes the supply of plastic parts or auxiliary temperature control within the matching timing. All actions are closely connected on the time axis to form a seamless multi-process composite process.

[0079] In one embodiment of this application, after the successfully selected equipment locks the corresponding role, it enters the role process parameter instruction generation stage. The equipment retrieves the built-in process parameter knowledge base classified by role, and adjusts the template instantiation in combination with the current status of the machine and the product specification requirements in the task flow to generate executable instructions containing numerical parameters, timing control details and tolerance monitoring strategies. Parameter slicing and synchronous startup are performed under a unified process start timing benchmark.

[0080] Taking the main positioning laser engraving unit A as an example, its knowledge base entry stipulates that when the material reflectivity is 60% and the marking precision level is 0.1 mm, the laser power should be selected from the upper middle range of 18 W to 24 W, i.e., biased towards the 22 W to 24 W range. The scanning path accuracy needs to match the reference marking tolerance of 0.05 mm, and the additional thermal deformation fine-tuning strategy is to compensate for a displacement of 0.002 mm every 50 consecutive marking points based on the average temperature rise of the workpiece in the previous cycle. Currently, the laser A is operating at a cooling efficiency of 95%. The product specification in the task flow requires a marking line width of 0.08 mm. Therefore, based on the template, the power is selected as 23 W, the scanning path accuracy is set to 0.045 mm, and thermal deformation compensation is enabled.

[0081] The generated parameter instructions contain the following numerical parameters: laser power 23 W, scanning speed 1200 mm / s; timing control details: warm-up time 3 s, pulse interval 0.5 ms; and a tolerance monitoring strategy: real-time detection of pulse energy fluctuations. If the standard deviation of the energy of 10 adjacent pulses exceeds 0.4 mJ, the power is reduced by 0.5 W and the pulse interval is extended by 0.1 ms. After the parameter instructions are generated, A continuously monitors the task flow. The task flow is segmented and labeled at process nodes. The role identifier for the 5th segment of the current collaborative cycle is primary positioning laser engraving, and the timestamp is Tstart equal to 500 ms. The device retrieves the parameters for this segment based on the identifier. The unified process start-up timing benchmark Tbase is 1000 ms. The start-up time Tlaunch is equal to Tbase plus Tstart. Tlaunch is equal to 1000 plus 500, resulting in 1500 ms. At this time, A and other equipment enter the execution state synchronously. The laser engraving unit ablates the mark according to the high-precision path. The hot stamping unit completes the foil transfer according to the curve of temperature 130℃, pressure 2.5 bar, and holding time 1.2 s. The injection molding machine starts feeding parts and maintains the mold temperature at 85℃ at Tbase plus 200 ms, i.e., 1200 ms. All actions are closely connected on the time axis to form a seamless multi-process composite process.

[0082] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0083] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An adaptive matching system for laser engraving and hot stamping process parameters of injection molded parts, characterized in that: It includes a task flow push module, a time difference data generation module, a dataset output module, an analysis module, and a parameter matching module: Task flow push module: Encodes the process requirements of the injection molded parts to be processed into process task flows. After each process execution device detects the process task flow, it generates a process start timing reference based on the physical timestamp. Time difference data generation module: Each process execution device forms a non-overlapping process characteristic signal sequence within the pre-time slot window, and initiates multi-dimensional sensing acquisition to generate response time difference data; Dataset output module: Each process execution device synchronously receives similar data from other process execution devices and summarizes them to obtain a measurement dataset; Analysis module: Calculates the topology weight value of the current process execution device relative to other process execution devices in the group; Parameter matching module: Each process execution device will compete for a process role based on its topology weight value. After the competition is completed, the corresponding role process parameter instructions will be generated. After extracting the corresponding parameter fragments based on the process start timing benchmark, the collaborative operation of multiple process execution devices will be started synchronously.

2. The adaptive matching system for laser engraving and hot stamping process parameters of injection molded parts according to claim 1, characterized in that: The analysis module calculates the topology weight value of the current process execution device relative to other process execution devices in the group, including the following steps: In the spatial reachability branch, examine whether there is attenuation in the response time difference between the current process execution equipment and other process execution equipment; In the performance matching branch, in conjunction with the requirements of this process task, examine whether the response characteristics of the current process execution equipment in the current state meet the requirements of this process task. In the energy consumption branch, call the historical energy consumption models of the current process execution equipment and candidate process execution equipment; After quantitatively evaluating the three dimensions of spatial accessibility, performance matching degree and energy consumption, the topology weight value is obtained by comprehensive calculation.

3. The adaptive matching system for laser engraving and hot stamping process parameters of injection molded parts according to claim 2, characterized in that: The analysis module is independently performed by each process execution device, enabling the process execution device to complete the role potential assessment within its local computing power range. By comparing with the pre-stored local process execution device performance baseline model, the process execution device uses the performance baseline model as a benchmark to compare the data segments in the measurement dataset related to itself and other process execution devices item by item, and identifies abnormal patterns that deviate from the baseline.

4. The adaptive matching system for laser engraving and hot stamping process parameters of injection molded parts according to claim 3, characterized in that: The scores for spatial accessibility, performance matching, and energy consumption are all processed using a piecewise mapping method: The original data or feature value is compared with the preset evaluation range. If it falls into the optimal range, the highest score is given; if it falls into the allowable range, a medium score is given; if it exceeds the allowable range, the score tends to zero or a negative value. Weighting coefficients are applied to the dimensions based on the emphasis of the process task. The scores of each branch are multiplied by the corresponding weighting coefficient and then summed to obtain the topological weight value of the process execution equipment relative to other process execution equipment in the group.

5. The adaptive matching system for laser engraving and hot stamping process parameters of injection molded parts according to claim 1, characterized in that: After the dataset output module collects the time difference data of each process execution device, it broadcasts the response time difference data to other process execution devices in the same group. After receiving the data from other process execution devices, the receiving process execution device merges its own data with the external data, establishes an index according to the process execution device identifier and timestamp, and forms a structured measurement dataset.

6. The adaptive matching system for laser engraving and hot stamping process parameters of injection molded parts according to claim 1, characterized in that: The parameter matching module will conduct process role selection for each process execution device based on the topology weight value. After the selection is completed, the corresponding role process parameter instructions will be generated, including the following steps: The process execution equipment will compare its own topology weight value with that of other process execution equipment in the same group and arrange them from high to low scores. The process execution equipment checks its own weight characteristics according to the preset role priority table. If the topology weight value not only leads in the total score, but also meets the necessary conditions for the role in the dimension, it immediately initiates a request to compete for that role. The process execution equipment retrieves the built-in process parameter knowledge base, combines its current status with the product specification requirements in the task flow, instantiates and adjusts the template, and generates executable role process parameter instructions.

7. The adaptive matching system for laser engraving and hot stamping process parameters of injection molded parts according to claim 6, characterized in that: The parameter matching module extracts the corresponding parameter fragments based on the process start-up timing benchmark, and then synchronously starts the collaborative operation of multiple process execution equipment, including the following steps: The process execution equipment retrieves the built-in process parameter knowledge base, which stores parameter generation rules and experience templates according to different roles. The process execution equipment combines its current status with the product specification requirements in the task flow to instantiate and adjust the experience template, generating executable role process parameter instructions. The instructions include numerical parameters, timing control details, and tolerance monitoring strategies. After the role process parameter instructions are generated, the process execution equipment continuously monitors the updates of the process task flow and extracts parameter fragments that match its own role. When the start-up time specified by the process start-up timing benchmark is reached, all process execution equipment enters the execution state synchronously, forming a multi-process composite flow.

8. The adaptive matching system for laser engraving and hot stamping process parameters of injection molded parts according to claim 1, characterized in that: After the time difference data generation module enters the time slot window of each process execution device, the process execution device performs a process probing action. Simultaneously, a multi-dimensional sensor acquisition network deployed in the surrounding environment of the workstation is activated to acquire spatial propagation information of different physical effects, locate feature points triggered by the probing action in the time domain, compare the time scales of the same event feature points measured by different sensors, calculate the arrival time difference between each pair, and for layouts containing multiple sensors, traverse all sensor pairings to generate original time difference records. Outliers caused by sudden environmental interference are removed from the original time difference records, and consistency verification is performed. The filtered time difference data is classified into probing process execution devices and sensing process execution devices to form a structured response time difference data set.

9. The adaptive matching system for laser engraving and hot stamping process parameters of injection molded parts according to claim 8, characterized in that: The task flow push module initiates the process task release mechanism, which integrates all the process requirements of the injection molded parts to be processed in a structured manner. The process requirements include product specifications, marking patterns, hot stamping areas, and appearance quality standards.

10. An adaptive matching method for laser engraving and hot stamping process parameters of injection molded parts, implemented by the matching system described in any one of claims 1-9, characterized in that: The matching method includes the following steps: S1: The process requirements of the injection molded part to be processed are encoded into a process task flow. After each process execution device detects the process task flow, it generates a process start timing reference based on the physical timestamp. Each process execution device forms a non-overlapping process characteristic signal sequence within the pre-time slot window and starts multi-dimensional sensing acquisition to generate response time difference data. S2: Each process execution device synchronously receives similar data from other process execution devices, summarizes them to obtain a measurement dataset, and calculates the topology weight value of the current process execution device relative to other process execution devices in the group; S3: Each process execution device will compete for a process role based on its topology weight value. After the competition is completed, the corresponding role process parameter instructions will be generated. After extracting the corresponding parameter fragments based on the process start timing benchmark, the collaborative operation of multiple process execution devices will be started synchronously.

Images