A method for conveying control during the production of flexible wire light strings and flexible wire light strings.

Abstract

This invention relates to a conveying control method and a string of flexible wire lamps in the production process of flexible wire lamps, comprising: acquiring specification data and deformation data of the flexible wire material through a sensor array, fusing multimodal signal processing to obtain initial material state parameters; acquiring force feedback signals and dynamically adjusting them through the clamping adaptability index to obtain optimized control parameters; simulating the conveying trajectory and performing congestion prediction analysis based on the optimized control parameters to determine a stable conveying path scheme; acquiring load data and combining it with a dynamic adjustment mechanism through the stable conveying path scheme to obtain a real-time optimized flow path configuration; integrating deformation and congestion constraints based on the flow path configuration to verify path stability and determine production execution instructions; acquiring flow feedback data and embedding closed-loop correction through the production execution instructions to obtain continuously optimized production process parameters.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a conveying control method for the production process of flexible wire light strings and the flexible wire light strings themselves. Background Technology

[0002] String lights, as a highly flexible and widely used lighting decoration product, have irreplaceable value in areas such as holiday decorations and landscape lighting. Their production directly affects product quality and market competitiveness. However, current production methods have revealed many shortcomings in meeting diverse demands, urgently requiring technological innovation to improve the industry's overall efficiency and adaptability.

[0003] Existing production methods often prove inadequate in the face of ever-changing market demands. Many equipment and process designs lack sufficient adaptability, frequently requiring significant time to adjust equipment or reconfigure production lines when product specifications or production requirements change. This adjustment not only leads to production interruptions but also incurs additional costs. A deeper problem lies in the lack of smooth coordination between different stages of the production process, resulting in inefficient material flow between various stages and a high risk of resource waste and time delays.

[0004] Against this backdrop, the core technological challenges gradually became apparent. First, there's the issue of material handling flexibility. As a flexible material, string lights vary considerably in wire diameter and specifications. Traditional fixed clamping methods are ill-suited to the needs of different products, leading to stretching deformation or positional shifts during transport, affecting the accuracy of subsequent processing. Next, this problem extends to production path planning. The lack of real-time monitoring and dynamic adjustment mechanisms for material status makes it difficult to optimize material flow paths based on actual working conditions, resulting in equipment idleness or process congestion. For example, when producing string lights with a fine wire diameter and close spacing, improper clamping force can cause wire deformation during transport. Subsequent processes cannot adjust the path in time to avoid congested areas, ultimately leading to a decrease in the efficiency of the entire production line.

[0005] Therefore, how to achieve flexible clamping and stable conveying of flexible light strings during the production process, and on this basis optimize the dynamic flow path of materials, has become a key issue in improving production efficiency and quality. Summary of the Invention

[0006] This invention provides a conveying control method for the production process of flexible wire light strings, mainly including:

[0007] The specification and deformation data of the flexible wire material are collected by a sensor array, and multimodal signal processing is fused to obtain initial material state parameters. Based on the initial material state parameters, the specification and deformation data are integrated to determine the degree of flexible deformation and identify clamping adaptability indicators. Force feedback signals are obtained and dynamically adjusted using these clamping adaptability indicators to obtain optimized control parameters. Based on these optimized control parameters, the conveying trajectory is simulated and congestion prediction analysis is performed to determine a stable conveying path scheme. Load data is obtained using the stable conveying path scheme and combined with a dynamic adjustment mechanism to obtain a real-time optimized flow path configuration. Based on the flow path configuration, deformation and congestion constraints are integrated to verify path stability and determine production execution instructions. Flow feedback data is obtained using the production execution instructions and closed-loop correction is embedded to obtain continuously optimized production process parameters. Furthermore, the step of acquiring specification data and deformation data of the flexible wire material through a sensor array, fusing multimodal signal processing, and obtaining initial material state parameters includes: acquiring the wire diameter specification value, flexible deformation data, and surface texture information of the flexible wire material through the sensor array; extracting multiple signal data from the sensor array and integrating features to obtain a preliminary data set; if the wire diameter specification value exceeds a preset threshold, adjusting the acquisition frequency and determining the deformation trend index by comparing the differences between the data before and after; based on the deformation trend index, fusing the flexible deformation data using a surface texture scanning method, extracting deformation-related patterns from the surface texture information, and obtaining material deformation prediction parameters; integrating the material deformation prediction parameters with the preliminary data set to determine the overall material state and obtain the initial material state parameters. Furthermore, the step of integrating the specification data and deformation data based on the initial material state parameters, determining the degree of flexible deformation, and identifying the clamping adaptability index includes: extracting wire diameter specifications and flexible deformation data from the initial material state parameters; integrating the data with material length information using a sensor fusion algorithm to obtain an integrated data set; performing noise filtering on the integrated data set, processing interference signals with a Gaussian filter to obtain a filtered data set; extracting features from the filtered data set, fusing the curvature calculation results with the material length information to determine the degree of deformation; and determining the clamping adaptability index based on the degree of deformation. Furthermore, the step of obtaining force feedback signals and dynamically adjusting them through the clamping adaptability index to obtain optimized control parameters includes: obtaining force feedback signals through the clamping adaptability index; extracting deformation risk display from the force feedback signals and integrating it into a real-time monitoring module to obtain a risk judgment mechanism; for the risk judgment mechanism, using dynamic adjustment signals and integrating flexible material processing logic to determine parameter adjustment logic; obtaining the signal processing process from the parameter adjustment logic, combining it with the monitoring integration method, and judging the feedback optimization result; and integrating position adaptation adjustment according to the feedback optimization result to obtain the optimized control parameters.Furthermore, the step of simulating the conveying trajectory and performing congestion prediction analysis based on the optimized control parameters to determine a stable conveying path includes: obtaining a simulated conveying trajectory using a path planning algorithm based on the optimized control parameters; extracting the density distribution of the flexible wire material from the simulation to obtain a material density distribution assessment; performing congestion prediction analysis based on the material density distribution assessment to identify potential congestion areas and determine a congestion risk threshold; fusing a dynamic adjustment signal from the congestion risk threshold to obtain a position adaptation adjustment to obtain a trajectory optimization scheme; and determining the stable conveying path scheme based on the trajectory optimization scheme and the simulated trajectory data. Furthermore, the step of obtaining load data and combining it with a dynamic adjustment mechanism through the stable transport path scheme to obtain a real-time optimized flow path configuration includes: obtaining load data monitoring on the production line through the stable transport path scheme; extracting dynamic adjustment triggers from the load data monitoring to obtain a congestion risk assessment; determining a mechanism response threshold based on the congestion risk assessment; if the mechanism response threshold exceeds a preset threshold, using path calculation iteration to determine production process balance; integrating real-time data synchronization from the production process balance to obtain flow path calibration and obtain transport load balance; and determining the real-time optimized flow path configuration based on the transport load balance and configuration update verification. Furthermore, the step of integrating deformation and congestion constraints based on the flow path configuration, verifying path stability, and determining production execution instructions includes: integrating a deformation avoidance mechanism through the flow path configuration, setting a preset threshold range for material deformation risk, and obtaining constraint integration; extracting congestion risk assessment from the constraint integration, and using an iterative verification process to compare the current path load with the preset threshold multiple times to obtain a stability judgment; determining load balancing allocation based on the stability judgment and real-time data synchronization; obtaining configuration update verification from the load balancing allocation, integrating process flow balancing, determining a risk response threshold, and obtaining the production execution instructions. Furthermore, the step of obtaining continuously optimized production process parameters by acquiring flow feedback data and embedding closed-loop correction through the production execution command includes: acquiring material flow feedback data through the production execution command; extracting equipment status monitoring indicators from the material flow feedback data; embedding them into a closed-loop correction process to obtain a preliminary offset assessment; if the preliminary offset assessment shows a data offset, obtaining a correction adjustment value; integrating the production load distribution; determining update requirements; obtaining a command correction scheme; updating the execution command through the command correction scheme; extracting process parameter changes from the updated execution command; using data synchronization verification; determining the parameter iteration version; obtaining a continuous feedback loop from the parameter iteration version; integrating process response timeliness; determining optimization stability; and obtaining the continuously optimized production process parameters.

[0008] A string of flexible cables includes a flexible cable body with a preset wire diameter along its length; and multiple light-emitting elements spaced apart on the flexible cable body.

[0009] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0010] This invention presents a comprehensive optimization method for addressing the issues of flexible deformation and conveying congestion in the production of flexible LED string materials. It aims to solve business scenarios involving abnormal wire diameter specifications, the risk of flexible deformation, and congestion in the production flow path. The method involves collecting wire diameter and deformation data through a sensor array, dynamically adjusting the frequency through multimodal signal processing, extracting features using a sensor fusion algorithm, and evaluating clamping adaptability. Clamping parameters are then optimized through force feedback signals and real-time monitoring. Simultaneously, a stable conveying path is determined based on path planning and congestion prediction, incorporating a dynamic adjustment mechanism and load data analysis to iteratively verify path stability, ultimately forming a closed-loop correction production execution command. By integrating deformation and congestion constraints, this invention achieves high efficiency and stability in material flow, significantly improving the adaptability and reliability of the production process, reducing deformation risk and congestion probability, and providing an intelligent solution for flexible material production. Attached Figure Description

[0011] Figure 1 This is a flowchart of a conveying control method for the production process of flexible wire light strings according to the present invention;

[0012] Figure 2 This is a module structure diagram of step S102 in the conveying control method for the production process of a flexible wire light string according to the present invention.

[0013] Figure 3 This is a module structure diagram of step S103 in the conveying control method for the production process of a flexible wire light string according to the present invention.

[0014] Figure 4 This is a module structure diagram of step S104 in the conveying control method for the production process of a flexible wire light string according to the present invention.

[0015] Figure 5 This is a module structure diagram of step S105 in the conveying control method for the production process of a flexible wire light string according to the present invention. Detailed Implementation

[0016] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0017] like Figures 1-5 This embodiment of a conveying control method for the production process of flexible wire light strings may specifically include:

[0018] Step S101: Collect wire diameter and flexible deformation data of the flexible light string material through a sensor array. In the process of collection, integrate multi-modal signal processing. If the wire diameter exceeds the preset threshold, adjust the collection frequency to obtain the initial material state parameters.

[0019] The wire diameter, flexibility deformation data, and surface texture information of the flexible LED string material are collected using a sensor array. During the acquisition process, multimodal signal processing is integrated to extract and combine features from the various signal data acquired by the sensor array, resulting in a preliminary data set. The wire diameter value in the preliminary data set is then obtained. If the wire diameter value exceeds a preset threshold, the acquisition frequency is adjusted, and the deformation trend index is determined by comparing the differences between the acquired and unacquired data. Based on the deformation trend index, a surface texture scanning method is used to fuse the flexibility deformation data. Deformation-related patterns are extracted from the surface texture information and combined with the flexibility deformation data to obtain material deformation prediction parameters. These material deformation prediction parameters are then integrated with the preliminary data set to determine the overall material state and obtain initial material state parameters.

[0020] In one implementation, the wire diameter and flexibility deformation data of the flexible light string material are collected by a sensor array. Multiple sensors, such as optical sensors and pressure sensors, are first deployed to form an array structure for real-time monitoring of the material status.

[0021] Specifically, the sensor array is deployed on the production line. For the wire portion of the flexible LED string, optical sensors capture wire diameter image data, while pressure sensors record stress changes during flexible deformation. This arrangement ensures comprehensive data acquisition, covering various forms of the LED string material, from straight to curved. Furthermore, multimodal signal processing is integrated during the acquisition process to consolidate the signals collected by different sensors.

[0022] For example, optical signals provide visual measurements of wire diameter, while pressure signals provide mechanical feedback during deformation. Signal synchronization algorithms align these multimodal data points, avoiding timing discrepancies. Specifically, each modal signal is preprocessed, such as through filtering to remove noise, and then a weighted fusion method is used to calculate a comprehensive feature vector. This fusion process improves data accuracy, enabling better identification of subtle dimensional deviations in the inspection of flexible wire light strings.

[0023] It should be noted that the specific principle of multimodal signal processing lies in transforming visual and mechanical signals into a unified feature space.

[0024] For example, in the processing stage, the visual signal is used to extract the wire diameter profile through an edge detection algorithm, and the mechanical signal is used to calculate the deformation coefficient through strain calculation. These are then fused into a multi-dimensional vector for subsequent analysis. This method is applicable in string light production environments, especially when inspecting flexible string lights, and can handle data variations under different bending angles. If the wire diameter exceeds a preset threshold, the acquisition frequency is adjusted.

[0025] Specifically, the preset threshold is set based on the standard specifications of the LED string materials; for example, a wire diameter exceeding 2 mm is considered abnormal. In this case, the system automatically increases the sampling frequency from once per second to five times per second to capture more detailed data. This adjustment mechanism is implemented through a threshold comparison module, which first compares the current wire diameter measurement with the threshold. If the measurement exceeds the threshold, the frequency controller is triggered to update the sampling rate. In actual LED string material inspection, this dynamic adjustment helps to promptly detect production defects.

[0026] Preferably, the initial material state parameters can be further applied to quality control. For example...

[0027] In one possible implementation, initial parameters include the average wire diameter and deformation modulus, calculated from the fused data to assess the overall flexibility of the light string. On the light string assembly line, these parameters support automated screening of defective materials. In another embodiment, for different types of flexible wire light strings, such as LED strings or decorative strings, the sensor array can be configured to be mobile, adapting to various wire lengths. During data acquisition, if the wire diameter exceeds a threshold, the frequency is adjusted while an anomaly log is recorded for easy traceability. This scenario demonstrates the versatility of the technology, making it equally applicable in material storage environments.

[0028] For example, during flexible deformation data acquisition, multimodal fusion can incorporate temperature sensor signals, integrating temperature effects into deformation calculations during processing to ensure parameter accuracy. This extension is effective under high-temperature production conditions, improving the durability assessment of string light materials.

[0029] Understandably, the initial material state parameters obtained through the above steps enable precise monitoring of the flexible wire LED strings. In implementation, these parameters support subsequent material optimization processes, such as adjusting production parameters to meet specifications, thereby improving overall product quality.

[0030] Specifically, the detailed threshold adjustment process involves a real-time feedback loop: Each time the sensor array collects a set of data, it calculates the wire diameter. If the threshold is exceeded, the frequency adjustment module immediately responds, determining a new frequency value based on a preset algorithm such as proportional-integral control. This mechanism ensures the adaptability of data acquisition and reduces human intervention in the continuous monitoring of light string materials. Furthermore, in various implementation scenarios, such as small light strings or large decorative strings, this technical solution demonstrates flexibility through parametric configuration.

[0031] For example, the threshold can be customized according to the material type, and the fusion processing supports expansion to more modal signals, thereby covering different stages of light string production.

[0032] Step S102: Based on the initial material state parameters, a sensor fusion algorithm is used to integrate wire diameter specifications and flexible deformation data. Noise filtering and feature extraction are performed on the integrated data to determine the degree of flexible deformation and to determine the clamping adaptability index of the flexible material.

[0033] Initial material state parameters are obtained, and wire diameter and flexible deformation data are extracted from these parameters. A sensor fusion algorithm is used to integrate these data with the material length information from the parameters, resulting in an integrated data set. Noise filtering is applied to this integrated data set using a Gaussian filter to process interference signals, resulting in a filtered data set. Features are extracted from this filtered data set, and the flexible deformation analysis obtained through curvature calculation is fused with the material length information to determine the degree of deformation. The clamping adaptability index of the flexible material is determined based on the degree of deformation.

[0034] In one implementation, a sensor fusion algorithm is used to integrate wire diameter specifications and flexible deformation data based on initial material state parameters.

[0035] Specifically, the initial material state parameters are derived from the aforementioned acquisition process, including wire diameter measurements and deformation data vectors. The sensor fusion algorithm integrates these data using a weighted averaging method.

[0036] For example, wire diameter specification data and flexibility deformation data are mapped to a unified coordinate system to achieve data alignment. This integration process first normalizes each data source and then calculates the fusion weight to ensure the dominant role of wire diameter specification in the overall evaluation. On the production line of flexible wire light string materials, this algorithm is suitable for real-time processing, ensuring data consistency. Furthermore, noise filtering and feature extraction are performed on the integrated data. Noise filtering uses a median filter to remove random interference.

[0037] For example, a filter with a window size of 5 is applied to the fused data sequence to preserve the main signal trend. Feature extraction utilizes principal component analysis to extract key features, such as the coefficient of variation of wire diameter and deformation curvature value. These steps ensure data purity in the detection of light string materials, facilitating subsequent judgment.

[0038] In one possible implementation, the feature extraction process involves calculating the correlation matrix between data vectors, thereby reducing dimensionality and highlighting flexibly related features.

[0039] It should be noted that the determination of the degree of flexible deformation is based on the extracted features.

[0040] Specifically, the degree of flexible deformation is determined by comparing the characteristic value with a preset deformation threshold.

[0041] For example, if the deformation curvature value exceeds 0.5, it is considered a high degree of deformation. This judgment mechanism is applied in the bending test of light string materials, taking into account the material's response under different tensile conditions.

[0042] Preferably, a classification rule is introduced during the judgment process to divide the degree of deformation into three levels: low, medium, and high, to support automated decision-making.

[0043] For example, in the assembly of flexible light strings, after assessing the degree of flexible deformation, the clamping adaptability index of the flexible material is further determined. The clamping adaptability index is obtained by calculating the ratio of the degree of deformation to the wire diameter specification.

[0044] For example, the index value = deformation curvature / average wire diameter is used to assess the stability of materials in a clamping device. This determination process ensures that the index quantifies the flexibility of the material, avoiding clamping failures in a production environment.

[0045] Understandably, the principle behind sensor fusion algorithms lies in the complementarity of multi-source data. For example, wire diameter specifications provide static dimensional information, while flexible deformation data reflects dynamic response. By fusing these data, a comprehensive vector is generated, improving the accuracy of judgments. In the storage of LED string materials, this algorithm handles the influence of environmental variables to ensure the reliability of indicators.

[0046] Specifically, the noise filtering implementation includes a signal smoothing step. First, a low-pass filter is applied to the integrated data, with a cutoff frequency set to 10Hz, to remove high-frequency noise. Then, the filtering effect is verified by calculating the signal-to-noise ratio to ensure data quality. In feature extraction, the extraction process is broken down into the following steps: calculating the data covariance matrix and selecting the first two principal components as features. This method handles large data streams and supports real-time judgment in the continuous monitoring of LED string materials. Furthermore, in another embodiment, the threshold parameter is adjusted when judging the degree of flexible deformation for different LED string types, such as RGB flexible wire LED strings.

[0047] For example, the threshold is dynamically set based on the length of the material, with the threshold for long light strings being relaxed to 0.6. This flexibility in adjusting the display technology is also applicable to the production of decorative light strings.

[0048] Preferably, after determining the clamping adaptability index, it can be recorded as a log parameter for traceability.

[0049] For example, an index below 1 indicates high adaptability, supporting material screening. This application enables automated optimization in string light quality control. In one implementation, the entire process is integrated into the control system. After initial parameter input, the fusion algorithm runs, then filters and extracts data, determines the degree of deformation, and finally outputs the index. This integration ensures logical coherence and effective operation in various scenarios involving string light materials, such as bending assembly or straight stretching.

[0050] Step S103: Obtain the force feedback signal of the clamping device through the clamping adaptability index of the flexible material. A real-time monitoring module is integrated into the acquisition process. If the clamping adaptability index shows a risk of deformation, the force feedback signal is dynamically adjusted to obtain optimized clamping control parameters.

[0051] Force feedback signals are acquired by using clamping adaptation indicators. Deformation risk indicators are extracted from these signals and integrated into a real-time monitoring module to obtain a risk assessment mechanism. For this risk assessment mechanism, dynamic signal adjustment is employed, combined with flexible material processing, to determine the parameter adjustment logic. The signal acquisition process is obtained from this parameter adjustment logic, and combined with the monitoring integration method, the feedback optimization result is judged. Based on the feedback optimization result, position adaptation adjustment is integrated to obtain optimized control parameters.

[0052] In one embodiment, the force feedback signal of the clamping device is obtained through the clamping adaptability index of the flexible material.

[0053] Specifically, the clamping adaptability index, derived from the aforementioned process, reflects the flexibility and stability of the material. This index value is first read on the production line for flexible wire LED string materials.

[0054] For example, when the index value indicates that the material is highly flexible, the sensors of the clamping device are activated to collect force feedback signals. These signals include pressure data applied to the material, which is measured in real time by a built-in force sensor to ensure that the acquisition process matches the material's condition.

[0055] Understandably, the principle behind this acquisition mechanism is to use indicators as trigger conditions to achieve targeted signal acquisition, avoiding blind force application in light string assembly scenarios. Furthermore, a real-time monitoring module is integrated into the acquisition process. This module is a component integrated into the control system used to continuously track changes in the force feedback signal.

[0056] Specifically, the module processes the signal flow through an embedded processor.

[0057] For example, data is sampled 10 times per second to monitor pressure fluctuations. If the signal exceeds the normal range, the module immediately records the deviation value. This integrated approach ensures process continuity and supports instantaneous response to material deformation in scenarios involving the bending and clamping of flexible light strings.

[0058] Preferably, if the clamping adaptability index indicates a risk of deformation, the force feedback signal is dynamically adjusted. The determination of deformation risk is based on a comparison of the index value with a preset threshold.

[0059] For example, indicators exceeding a certain limit are considered high-risk. The principle of dynamic adjustment lies in the feedback loop mechanism, which reduces the applied force by adjusting the motor parameters of the clamping device.

[0060] Specifically, the adjustment process is broken down into steps: first, the signal deviation is analyzed; then, the adjustment coefficient is calculated, and the force value is reduced to a safe level. This mechanism is applied in the tensile testing of light string materials to prevent excessive deformation that could lead to breakage.

[0061] In one possible implementation, the optimized clamping control parameters are obtained by integrating the adjusted signals.

[0062] For example, combining adjustment signals with initial parameters generates a parameter vector to guide device operation. These parameters include upper force limits and speed settings, ensuring material integrity in scenarios involving the storage and clamping of flexible LED strings.

[0063] It should be noted that the integration of the real-time monitoring module improves the system's responsiveness.

[0064] Specifically, the module operates on the principle of data stream analysis, involving signal filtering to remove noise, and then comparing the signal with metrics to generate alarms. In a string light production environment, this module handles the parallel clamping of multiple string lights, supporting batch optimization.

[0065] For example, in another embodiment, the color differences of the wires are taken into account when dynamically adjusting the force feedback signal for RGB flexible LED strings.

[0066] Specifically, if the indicators show a high risk, the adjustment process prioritizes reducing the force on the flexible parts, obtaining control parameters for fine assembly. This adjustment demonstrates the adaptability of the technology and is equally effective in clamping decorative light strings. Furthermore, the optimized clamping control parameters can be stored as a configuration file for subsequent operations.

[0067] For example, parameter-guided automatic calibration enables efficient and damage-free clamping of flexible LED strings on continuous production lines. This application ensures process stability in quality control.

[0068] Step S104: Based on the optimized clamping control parameters, a path planning algorithm is used to simulate the conveying trajectory of the soft wire light string material, and a congestion prediction analysis is performed on the simulated trajectory to identify potential congestion areas and determine a stable conveying path scheme.

[0069] By using clamping control parameters and a path planning algorithm, a simulated conveying trajectory is obtained. The density distribution of the flexible LED string material is extracted from the simulation to obtain a material density distribution assessment. Based on this assessment, congestion prediction analysis is performed to identify potential congestion areas and determine a congestion risk threshold. From this risk threshold, a dynamic adjustment signal is integrated to obtain a positional adaptation adjustment, resulting in a trajectory optimization scheme. Based on this optimization scheme and the simulated trajectory data, a stable conveying path is determined, and the final path scheme is finalized.

[0070] In one implementation, a path planning algorithm is used to simulate the conveying trajectory of the flexible light string material based on optimized clamping control parameters.

[0071] Specifically, path planning algorithms are computational methods used to calculate the movement path of materials. Their principle is based on a gridded spatial representation, dividing the conveying area on the production line into multiple grid cells and using search algorithms such as A* algorithm to find feasible paths from the starting point to the ending point. In the production environment of flexible string LED lights, optimized parameters, such as the upper limit of force and speed settings, are first read. These parameters serve as input constraints to simulate the movement trajectory of materials on the conveyor belt.

[0072] For example, on a string light assembly line, the algorithm considers the flexibility of the materials, avoiding sharp turns in the path that could cause deformation, and generates a simulated trajectory from the clamping point to the storage area. This simulation process ensures that the trajectory matches actual production, supporting lossless material transport. Furthermore, congestion prediction analysis is performed on the simulated trajectory. Congestion prediction analysis is a method for assessing potential path congestion through data processing. Its principle lies in analyzing the flow density and velocity changes in the trajectory, and using statistical models to calculate the load probability of each grid cell.

[0073] In one possible implementation, the simulated trajectory is broken down into multiple segments, historical production data such as material throughput rates are collected, and then a predictive model is applied to assess the likelihood of congestion during peak periods.

[0074] Specifically, in the production scenario of RGB flexible LED strings, if the trajectory shows that multiple materials enter a narrow passage simultaneously, the analysis will quantify the congestion risk value to ensure that the prediction covers peak periods of the production line. This analysis mechanism improves the reliability of the path and avoids blind planning.

[0075] Preferably, if the analysis shows a high risk, a potential congestion area is identified. The identification of potential congestion areas is based on a comparison of the prediction results with a threshold.

[0076] For example, when the load probability exceeds a preset value, the corresponding grid is marked as a congested area.

[0077] Understandably, this judgment process involves region mapping, mapping points on the simulated trajectory to the actual production line layout, and identifying potential areas such as bends or intersections in the transport environment of decorative light strings. This method provides clear region localization, supporting subsequent optimization.

[0078] In one embodiment, a stable delivery path is determined by replanning the path.

[0079] Specifically, based on the identified congested areas, the algorithm adjusts the original trajectory to bypass high-risk areas and generate new paths. The principle is to optimize the function to minimize the congestion probability while incorporating clamping parameter constraints.

[0080] For example, on a mass production line for string LED lights, if a bend is identified as a congested area, the solution will either select a straight path or add a buffer zone, outputting a path plan that includes coordinate sequences and speed adjustments. This deterministic process ensures stable transport and reduces material delays in quality control.

[0081] It should be noted that the simulation of the path planning algorithm can be extended to multiple production lines.

[0082] Specifically, in one implementation, for multi-color light string materials, the algorithm simulates multiple trajectories in parallel, incorporating real-time data updates to predict congestion. Congestion prediction analysis improves accuracy by training the model using historical logs. In light string storage scenarios, the versatility of this extended display technology supports the generation of efficient path solutions.

[0083] For example, in another embodiment, for highly flexible LED string materials, the material density distribution is considered when identifying congestion areas, and the scheme is dynamically updated to adapt to production changes. This application on assembly lines enables adaptive path adjustments. Furthermore, the stable conveying path scheme can be output as control commands to guide the operation of the conveying device. In the continuous production of flexible LED strings, this scheme integrates parameters to ensure uninterrupted trajectory, demonstrating the practical value of the technology.

[0084] Step S105: Obtain load data for each process on the production line through a stable conveying path scheme. During the acquisition process, a dynamic path adjustment mechanism is combined. If the load data indicates congestion, the path scheme is recalculated to obtain a real-time optimized flow path configuration.

[0085] By integrating path schemes, load data monitoring on the production line is obtained. Dynamic adjustment triggers are extracted from this load data monitoring to obtain a congestion risk assessment. For this congestion risk assessment, a mechanism response threshold is determined. If the mechanism response threshold exceeds a preset threshold, path calculation iterations are used to determine production process balance. Real-time data synchronization is integrated from this production process balance to obtain flow path calibration, resulting in a transport load balance. Based on this transport load balance and configuration update verification, optimized configuration output is determined, and a real-time optimized flow path configuration is identified.

[0086] In one implementation, load data for each process on the production line is obtained through a stable transport path scheme.

[0087] Specifically, a stable conveying path scheme is a pre-determined material movement route. Its principle is based on coordinate sequences and speed parameters generated by a path planning algorithm, guiding the flow of string LED materials from the assembly area to the packaging area. During load data acquisition, key nodes in the path scheme, such as clamping points and turning sections, are first read. Then, real-time data from each process, such as material quantity and processing time, is collected through a sensor network. This acquisition method ensures data synchronization with the path scheme. On the RGB string LED production line, load data includes material density and waiting time for each process, supporting subsequent analysis. This process demonstrates the fundamental role of the path scheme in data acquisition. Furthermore, a dynamic path adjustment mechanism is incorporated into the acquisition process. The dynamic path adjustment mechanism is a path modification method based on real-time feedback. Its principle is to monitor variable changes and trigger update rules. For example, when a sensor detects a speed deviation, the mechanism calculates an adjustment vector to modify the path curve. In the conveying environment of string LED materials, this mechanism integrates load data, allowing for fine-tuning of the path during operation and avoiding the inefficiency caused by fixed paths.

[0088] In one possible implementation, the mechanism operates through a feedback loop. It first collects the current load, then compares it to a preset threshold. If the deviation exceeds the threshold, path parameters such as speed or direction are adjusted. This combined process enhances the adaptability of the acquired data, ensuring that the data reflects actual dynamic changes on a multi-color LED string assembly line.

[0089] Preferably, if the load data indicates congestion, the route plan is recalculated. Congestion indication is based on the analysis of load data, and its principle involves calculating a load index for each process, such as quantifying the degree of congestion by statistically analyzing the ratio of material backlog to throughput rate. During the judgment process, if the index exceeds a preset value, such as a load density higher than 1.5 times the normal level, recalculation is triggered. The steps of recalculating the route plan include decomposing the original path into segments, identifying congested processes such as narrow passages, and then applying an optimization algorithm to generate alternative routes.

[0090] Specifically, in the mass production scenario of flexible string lights, the algorithm considers the flexibility of the material, avoiding sharp turns that could cause deformation during rerouting, and finding the solution with the minimum congestion probability through iterative search. This recalculation process is detailed as follows: First, the load data and the original path are input; second, a mesh model is constructed to represent the production line layout; then, the search algorithm traverses possible paths, evaluating the load distribution of each; finally, the adjusted path sequence is output. This method provides a rapid response when congestion occurs, reduces delays in the delivery of decorative string lights, and ensures production continuity.

[0091] For example, in another embodiment, for flexible string light materials, the dynamic path adjustment mechanism incorporates predictive elements when acquiring load data.

[0092] Specifically, the mechanism uses historical data to train a simple model to predict future load trends, and then adjusts paths to avoid potential congestion in advance. During peak production periods, this implementation operates through a real-time data update mechanism, supporting the accuracy of load acquisition.

[0093] Understandably, the real-time optimized flow path configuration is achieved by integrating and recalculating the results. Flow path configuration is a complete solution that includes coordinates, speed, and process allocation, and its principle lies in mapping the optimized path to the production line control system. In the continuous production environment of flexible string lights, the configuration generation process includes verifying the stability of the new path, such as checking whether it conforms to clamping control parameters, and then outputting it as a sequence of instructions. This configuration ensures uninterrupted material flow and achieves efficient delivery in quality control. Furthermore, in one implementation, load data acquisition is combined with a dynamic mechanism to extend to multiple production lines.

[0094] Specifically, for light strings of different colors, the system processes multi-path data in parallel, with a mechanism adjusting in real time to balance the load. This extension demonstrates the versatility of the solution when applied in assembly and storage scenarios.

[0095] It's important to note that the detailed process of congestion assessment involves setting data thresholds, based on statistical analysis, such as calculating average load and standard deviation to define indicator values. During recalculation, if congestion is located at an intersection point, the solution prioritizes branch paths. The detailed steps are: identifying the coordinates of the congested node; assessing the load of surrounding grids; generating a detour sequence and verifying material compatibility. This explanation clarifies the business process of assessment and calculation, avoiding the risks of blind adjustments in string light production.

[0096] In one possible implementation, the real-time optimized flow path configuration output can be further used for device control.

[0097] For example, in the packaging process of string lights, a guide is provided to adjust the gripping path of the robotic arm, and load data is used to ensure congestion-free operation.

[0098] Preferably, for scenarios involving changes in the production line, the dynamic path adjustment mechanism considers external factors, such as equipment fault signals, during the acquisition process.

[0099] Specifically, the mechanism integrates a fault detection module and modifies data collection points in real time to ensure the accuracy of congestion indications. This approach maintains stability during the transport of light string materials.

[0100] For example, in the embodiment of the light string storage area, after obtaining the path configuration, the optimization effect is confirmed through simulation verification. This process reduces material delays in business operations and demonstrates the practical value of the technology.

[0101] Step S106: Based on the real-time optimized flow path configuration, integrate the constraints to avoid deformation and congestion, iteratively verify the integrated constraints, determine the stability of the path configuration, and determine the final production execution instruction.

[0102] A deformation avoidance mechanism is integrated through path configuration optimization. This mechanism presets a threshold range for material deformation risk on the production line and integrates constraints. A congestion risk assessment is extracted from this constraint integration, and an iterative verification process is used to verify this assessment. This process compares the current path load with the preset threshold multiple times to obtain a stability judgment. Based on the stability judgment and real-time data synchronization, a load balancing allocation is determined. Configuration update verification is obtained from the load balancing allocation, and the configuration update verification is integrated with the process flow balance to determine the risk response threshold and obtain production execution instructions.

[0103] In one implementation, the method is applied to a manufacturing production line to optimize material flow paths to ensure efficient production.

[0104] Specifically, the first step is to acquire real-time production data, including equipment status, material location, and environmental parameters, and then use this data to construct the initial flow path configuration.

[0105] For example, on an automotive parts assembly line, path configuration considers the movement route from the raw material warehouse to the assembly station, while real-time optimization adjusts path length and speed based on sensor feedback to minimize time consumption. This configuration process emphasizes dynamic adjustment, avoiding the inefficiencies caused by static paths. Furthermore, integrating constraints to avoid deformation and congestion is a core step. Deformation avoidance constraints address the physical changes of materials during flow; for example, in metal parts production, deformation may be caused by temperature or pressure, thus a stress calculation model is introduced to limit path speed within a threshold to prevent material bending. Congestion avoidance constraints examine the capacity of path nodes, such as the upper limit of conveyor belt load, and assess potential congestion points through flow simulation. During integration, these constraints are transformed into a unified optimization function, for example, using a weighted summation method to balance the two, ensuring that the path configuration simultaneously satisfies the deformation threshold and that the congestion probability is below a set value. This integration process is completed through multiple simulation iterations. First, the deformation coefficient is defined as the ratio of the material's elastic modulus to the path curvature; then, the congestion index is calculated as the ratio of node flow rate to capacity; finally, these are merged into a constraint set, providing a basis for subsequent verification. This detailed integration mechanism helps maintain material integrity and flow in complex production environments, resulting in a stable production rhythm.

[0106] Preferably, a cyclic checking mechanism is used for iterative verification of integrated constraints.

[0107] In one possible implementation, the initial path configuration input validation loop is used, and parameters such as path curvature or node assignment are adjusted in each iteration, and constraint satisfaction is recalculated.

[0108] For example, on an electronics assembly line, if the initial validation shows a high risk of congestion, alternative path options are added iteratively until all constraints stabilize. This validation emphasizes convergence conditions, such as stopping when the rate of change is less than one percent for three consecutive iterations.

[0109] It should be noted that determining the stability of path configuration involves multi-dimensional evaluation.

[0110] Specifically, stability is determined through fluctuation analysis, such as calculating the variance of path efficiency. If the variance is below a preset threshold, the path is considered stable. In textile production scenarios, path stability assessment considers changes in fabric tension to avoid quality problems caused by deformation. This process first collects historical data to simulate path behavior, then applies statistical models such as mean-deviation calculations. Detailed steps include: collecting results from multiple rounds of simulation, calculating the average efficiency value, and then calculating the sum of squares of the deviations from each round divided by the number of rounds to obtain the variance. If the result is less than 0.05, the path is considered stable. This assessment ensures that the configuration is not easily affected by interference in actual production, providing a reliable foundation. For example...

[0111] In one embodiment, the entire method is applied to a food processing production line. First, the path configuration is optimized in real-time, and constraints are integrated. Then, iterative verification is performed. If the stability check passes, the final production execution instruction is determined, such as specifying a conveyor speed of 2 meters per minute and avoiding high-load areas. In another embodiment, for a pharmaceutical production line, the integrated constraints focus on preventing deformation of drug containers. Path pressure is monitored in real-time using pressure sensors, and the path slope is adjusted iteratively. Stability is determined based on vibration frequency analysis, and the final instruction includes a precise batch transfer sequence. Furthermore, this method improves production continuity; for example, in the above scenario, stability checks reduce unexpected downtime.

[0112] Understandably, another embodiment is for chemical production. Real-time path optimization takes into account the avoidance of deformation of corrosive materials, constrains are integrated using chemical stability coefficients, the upper limit of the number of iterations for verification is ten, stability is evaluated by path reliability probability, and finally the instruction generates control codes for automated equipment.

[0113] Specifically, in these implementations, modular design decomposes the method into optimization modules, constraint modules, and verification modules, with each module connected through a data interface to ensure smooth overall logic.

[0114] For example, the above process can achieve robustness in path configuration in practical applications, such as reducing delays caused by congestion in heavy machinery manufacturing.

[0115] Step S107: Obtain material flow feedback data after execution through the final production execution instruction. A closed-loop correction step is embedded in the acquisition process. If the feedback data shows an offset, the execution instruction is updated to obtain continuously optimized production process parameters.

[0116] By obtaining the final production execution command, material flow feedback data is acquired after execution. Equipment status monitoring indicators are extracted from this data, and a closed-loop correction process is embedded within these indicators to obtain a preliminary offset assessment. If the preliminary offset assessment shows a data deviation, a correction adjustment value is obtained from it. This value is then integrated with the production load distribution to determine update requirements and generate a command correction scheme. The execution command is updated using this correction scheme, and process parameter changes are extracted from the updated command. These changes are verified using data synchronization to determine the parameter iteration version. A continuous feedback loop is obtained from the parameter iteration version, and its optimization stability is assessed by integrating process response time, resulting in continuously optimized production process parameters.

[0117] In one implementation, the method is applied to a manufacturing production line, whereby the material flow process is initiated by the final production execution instruction, and feedback data is obtained after execution.

[0118] Specifically, production execution instructions include parameters such as path speed and node allocation. After execution, sensors are used to collect feedback data such as material position, speed, and environmental variables.

[0119] For example, on an automotive parts assembly line, instructions specify the movement route from the warehouse to the assembly station, and sensors record the actual trajectory and time consumption of materials in real time. This acquisition process ensures data integrity, providing a basis for subsequent calibration. Furthermore, a closed-loop calibration process is embedded in the process of acquiring feedback data. This process identifies potential problems by comparing actual feedback data with expected parameters.

[0120] Specifically, closed-loop calibration involves a feedback loop mechanism. First, feedback data is input into the analysis module, which calculates the difference between the actual path and the commanded path. If the difference exceeds a threshold, a calibration signal is triggered. In the context of electronic product assembly, feedback data includes conveyor belt load and material temperature. The closed-loop calibration process uses a deviation calculation model to evaluate the degree of matching between these indicators and preset values. The principle behind this mechanism is to create an adaptive system that achieves real-time adjustments through continuous monitoring, thus avoiding production interruptions.

[0121] It's important to note that the embedded closed-loop calibration emphasizes seamless data flow integration. For example, sensor data is directly transmitted to the control center for real-time comparison, thereby maintaining the continuity of the production process. In pharmaceutical production lines, this step can handle positional misalignments of drug containers, ensuring the accuracy of batch transfers.

[0122] Preferably, if the feedback data shows an offset, the execution command is updated. This update process is performed based on the offset detection results, where the offset indicates that the actual data deviates from the expected range, such as material speed being lower than the command value or congestion occurring at path nodes.

[0123] Specifically, offset detection employs a threshold comparison method. First, an offset metric, such as the position deviation rate, is defined. Then, if the rate exceeds a set threshold, such as 5%, an update signal is generated. The update execution command involves adjusting parameters, such as reducing speed or switching to an alternative path. In a food processing production line, if feedback indicates that conveyor load offset is causing delays, the update command will shift node allocation from the main path to an auxiliary channel. This process works by optimizing through iterative adjustments. The details of offset detection include collecting multiple rounds of data samples, calculating the average deviation, and applying statistical methods to determine the degree of offset, thus ensuring the update is targeted. This update mechanism helps reduce uncertainty in production and provides a more reliable command basis.

[0124] For example, in a textile production scenario, feedback data deviations may originate from changes in fabric tension, and update instructions adjust the path curvature to restore balance.

[0125] Understandably, the above steps yield continuously optimized production process parameters. These parameters include optimized path length, speed threshold, and load limit, derived from closed-loop correction and instruction updates.

[0126] Specifically, the optimization process aggregates all feedback and update data to form a parameter set for the next round of production. In chemical production lines, parameter optimization considers the chemical stability of materials, and the updated parameters ensure that corrosive deformation is avoided during the flow process. This optimization leads to continuous improvement in the production process, such as reducing downtime and improving material integrity. In another implementation, this method is applied to heavy machinery manufacturing. After obtaining feedback data and embedding it into a closed-loop correction, if an offset detects node capacity overload, the update instruction increases buffer allocation, resulting in optimized process parameters such as equipment utilization indicators. Furthermore, the logic of the entire process, from instruction execution to parameter optimization, forms a closed loop, ensuring universal applicability in manufacturing scenarios.

[0127] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. The present invention has been described in detail with reference to preferred embodiments. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for controlling the conveying of flexible wire light strings during production, characterized in that, include: The specification and deformation data of the flexible wire material are collected by a sensor array, and multimodal signal processing is fused to obtain initial material state parameters. Based on the initial material state parameters, the specification and deformation data are integrated to determine the degree of flexible deformation and identify clamping adaptability indicators. Force feedback signals are obtained and dynamically adjusted using the clamping adaptability indicators to obtain optimized control parameters. Based on the optimized control parameters, the conveying trajectory is simulated and congestion prediction analysis is performed to determine a stable conveying path scheme. Load data is obtained using the stable conveying path scheme and combined with a dynamic adjustment mechanism to obtain a real-time optimized flow path configuration. Based on the flow path configuration, deformation and congestion constraints are integrated to verify path stability and determine production execution instructions. By obtaining the production execution instructions, flow feedback data is acquired and closed-loop correction is embedded to obtain continuously optimized production process parameters.

2. The conveying control method for the production process of flexible wire light strings as described in claim 1, characterized in that, The process of acquiring specification and deformation data of flexible wire material through a sensor array, fusing multimodal signal processing, and obtaining initial material state parameters includes: acquiring wire diameter specifications, flexible deformation data, and surface texture information of the flexible wire material through a sensor array; extracting multiple signal data from the sensor array and integrating their features to obtain a preliminary data set; if the wire diameter specifications exceed a preset threshold, adjusting the acquisition frequency and determining a deformation trend index by comparing the differences between the data before and after the acquisition; based on the deformation trend index, fusing the flexible deformation data using a surface texture scanning method, extracting deformation-related patterns from the surface texture information, and obtaining material deformation prediction parameters; integrating the material deformation prediction parameters with the preliminary data set to determine the overall material state and obtain the initial material state parameters.

3. The conveying control method for the production process of flexible wire light strings as described in claim 1, characterized in that, The step of integrating the specification data and deformation data based on the initial material state parameters to determine the degree of flexible deformation and establish a clamping adaptability index includes: extracting wire diameter specifications and flexible deformation data from the initial material state parameters; integrating the data with material length information using a sensor fusion algorithm to obtain an integrated data set; performing noise filtering on the integrated data set by processing interference signals with a Gaussian filter to obtain a filtered data set; extracting features from the filtered data set; fusing the curvature calculation results with the material length information to determine the degree of deformation; and determining the clamping adaptability index based on the degree of deformation.

4. The conveying control method for the production process of flexible wire light strings as described in claim 1, characterized in that, The step of obtaining and dynamically adjusting force feedback signals through the clamping adaptability index to obtain optimized control parameters includes: obtaining force feedback signals through the clamping adaptability index; extracting deformation risk display from the force feedback signals and integrating it into a real-time monitoring module to obtain a risk judgment mechanism; using dynamic adjustment signals and integrating flexible material processing logic to determine parameter adjustment logic for the risk judgment mechanism; obtaining the signal processing process from the parameter adjustment logic and judging the feedback optimization result by combining it with the monitoring integration method; and integrating position adaptation adjustment according to the feedback optimization result to obtain the optimized control parameters.

5. The conveying control method for the production process of flexible wire light strings as described in claim 1, characterized in that, The step of simulating the conveying trajectory and performing congestion prediction analysis based on the optimized control parameters to determine a stable conveying path includes: obtaining a simulated conveying trajectory using a path planning algorithm based on the optimized control parameters; extracting the density distribution of the flexible wire material from the simulation to obtain a material density distribution assessment; performing congestion prediction analysis based on the material density distribution assessment to identify potential congestion areas and determine a congestion risk threshold; fusing a dynamic adjustment signal from the congestion risk threshold to obtain a position adaptation adjustment to obtain a trajectory optimization scheme; and determining the stable conveying path scheme based on the trajectory optimization scheme and the simulated trajectory data.

6. The conveying control method for the production process of flexible wire light strings as described in claim 1, characterized in that, The process of obtaining a real-time optimized flow path configuration by acquiring load data and combining it with a dynamic adjustment mechanism through the stable conveying path scheme includes: acquiring load data monitoring on the production line through the stable conveying path scheme; extracting dynamic adjustment triggers from the load data monitoring to obtain a congestion risk assessment; determining a mechanism response threshold based on the congestion risk assessment; if the mechanism response threshold exceeds a preset threshold, using path calculation iteration to determine production process balance; integrating real-time data synchronization from the production process balance to obtain flow path calibration and obtain conveying load balance; and determining the real-time optimized flow path configuration based on the conveying load balance and configuration update verification.

7. The conveying control method for the production process of flexible wire light strings as described in claim 1, characterized in that, The process of integrating deformation and congestion constraints based on the flow path configuration, verifying path stability, and determining production execution instructions includes: integrating a deformation avoidance mechanism through the flow path configuration, setting a preset threshold range for material deformation risk, and obtaining constraint integration; extracting a congestion risk assessment from the constraint integration, employing an iterative verification process, and comparing the current path load with the preset threshold multiple times to obtain a stability judgment; determining load balancing allocation based on the stability judgment and real-time data synchronization; obtaining configuration update verification from the load balancing allocation, integrating process flow balancing, determining a risk response threshold, and obtaining the production execution instructions.

8. The conveying control method for the production process of flexible wire light strings as described in claim 1, characterized in that, The process of obtaining continuously optimized production process parameters by acquiring flow feedback data and embedding closed-loop correction through the production execution command includes: acquiring material flow feedback data through the production execution command; extracting equipment status monitoring indicators from the material flow feedback data; embedding them into a closed-loop correction process to obtain a preliminary offset assessment; if the preliminary offset assessment shows a data offset, obtaining a correction adjustment value; integrating the production load distribution; determining update requirements; obtaining a command correction scheme; updating the execution command through the command correction scheme; extracting process parameter changes from the updated execution command; using data synchronization verification; and determining the parameter iteration version; obtaining a continuous feedback loop from the parameter iteration version; integrating process response timeliness; determining optimization stability; and obtaining the continuously optimized production process parameters.

9. A string of flexible wire lights, characterized in that, The cable is manufactured by any one of claims 1-8, including a flexible cable body having a preset wire diameter specification along its length. Multiple light-emitting elements are spaced apart on the flexible cable body.

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