Device collaborative processing optimization method applied to multi-fabric garment

Abstract

The application discloses a device collaborative processing optimization method applied to multi-fabric clothes, relates to the technical field of fabric collaborative processing, and solves the technical problem that in the prior art, the correlation analysis of the equipment running state and the running environment parameters is insufficient when the equipment is collaboratively processed, and it is difficult to accurately distinguish state self-abnormality and state interference abnormality.The application discloses a device collaborative processing optimization method applied to multi-fabric clothes, which realizes the deep collaboration and optimization of the multi-fabric clothes processing full-process equipment through three core steps of equipment digitalization transformation, pretreatment collaborative control and needle trace dynamic matching; accurately distinguishes state self-abnormality and state interference abnormality, realizes accurate maintenance and fault prediction, determines reasonable adjustment trends for different types of fabric conversion, optimizes parameters in combination with historical data and real-time humidity feedback, improves ironing effect consistency, establishes process parameter abnormality standards and matching adjustment standards, and provides a basis for subsequent process parameter setting and real-time monitoring.

Description

Technical Field

[0001] This invention relates to the field of fabric collaborative processing technology, specifically to an optimized method for collaborative processing of equipment used in multi-fabric garments. Background Technology

[0002] In the multi-fabric garment processing industry, as consumers demand more variety in clothing styles and materials, single-fabric processing equipment and traditional collaborative models are no longer suitable for the complex scenarios of multi-fabric mixed processing. The industry as a whole faces many pain points such as low equipment collaboration efficiency, unstable processing quality, and difficulty in tracing the source of failures.

[0003] Currently, there is insufficient correlation analysis between equipment operating status and operating environment parameters, making it difficult to accurately distinguish between abnormal status itself and abnormal status interference. Furthermore, there is a lack of a linkage analysis mechanism based on abnormality type and production efficiency parameters, resulting in difficulties in fault tracing, poor maintenance targeting, and the inability to achieve predictive maintenance to avoid production interruptions.

[0004] Secondly, in the pre-treatment stage, existing technologies mostly adopt control methods with fixed parameters or simple partition settings, which do not fully consider the differences in thickness, moisture absorption and environmental adaptability of different fabrics (such as cotton, polyester fiber, wool, etc.). Especially in the scenario of fluctuating ambient humidity and alternating ironing of multiple fabrics, problems such as uneven ironing effect and fabric damage are likely to occur, making it difficult to meet the requirements of pre-treatment precision for multi-fabric garments.

[0005] Finally, in the cutting and sewing process, the sudden change in the coefficient of friction of the fabrics at the seams of multi-fabric garments can easily cause a jump in the tension of the sewing thread. Existing equipment lacks a dynamic matching mechanism for the cutting and sewing processes and relies only on constant sewing parameters or simple adjustments, which often results in problems such as uneven stitch tightness, thread breakage, and low sewing efficiency, seriously affecting the sewing quality and production efficiency of multi-fabric garments.

[0006] To address the aforementioned technical deficiencies, a collaborative system for multi-fabric garment processing equipment is proposed. This system features a robust fault tracing and anomaly analysis mechanism, accurately distinguishing between equipment malfunctions and environmental interference. It can quickly pinpoint the core factors affecting production efficiency, improve the targeted nature of equipment maintenance, shorten the troubleshooting cycle, and further enhance production continuity and stability. Summary of the Invention

[0007] The purpose of this invention is to solve the problems mentioned above by proposing a method for optimizing the collaborative processing of equipment for multi-fabric garments.

[0008] The objective of this invention can be achieved through the following technical solutions:

[0009] An optimization method for collaborative processing of equipment applied to multi-fabric garments is as follows:

[0010] Step 1: Digital transformation of equipment;

[0011] Connect the equipment to the Internet of Things (IoT) to link the corresponding equipment in each process flow and collect and transmit data in real time. The real-time data collected includes equipment operating status and production efficiency. Analyze the collected data in real time to perform equipment coordination anomaly analysis at each processing stage.

[0012] Step 2: Preprocessing and collaborative control;

[0013] After completing the digital transformation, pretreatment collaborative control is carried out in the pretreatment process of multi-fabric garment processing, and the steam injection volume and injection position are adjusted in real time according to the thickness and moisture absorption of different fabrics.

[0014] Step 3: Dynamic matching of machine needle stitches;

[0015] After the cutting and sewing equipment is integrated, data on real-time needle and stitch are collected and matched appropriately.

[0016] Furthermore, the equipment digital transformation process in step one is as follows:

[0017] To implement network control of all equipment within the production line, data collection and analysis are conducted after the network control of the equipment is confirmed.

[0018] Each piece of equipment within the process flow is marked as a process execution end, and data is collected from the process execution end to obtain the quantitative parameters corresponding to the equipment operating status of the process execution end. The quantitative parameters are selected and determined based on the corresponding equipment type and process type. Based on the monitoring time of the quantitative parameters of the equipment operating status of the process execution end, the operating environment parameters are identified simultaneously.

[0019] Based on the fluctuation time of any quantitative parameter of the operating status of the process execution terminal equipment, record the operating environment parameters at the fluctuation time. Based on the influence probability of the operating environment parameters and the fluctuation time, mark the current operating time of the process execution terminal as an abnormal time of the status itself or an abnormal time of status interference.

[0020] Production efficiency analysis is performed on each process execution end, using quantitative production indicators as production efficiency parameters; process execution time periods are selected, and based on the ratio of the number of times the state itself is abnormal to the number of times the state is disturbed, the process execution time periods are divided into high-impact periods and low-impact periods.

[0021] Furthermore, the duration of abnormal fluctuations in production quantitative indicators at the process execution end during the high-impact period of the equipment is obtained, and the peak frequency of fluctuations in production quantitative indicators at the process execution end during the low-impact period of the equipment is also obtained.

[0022] If the duration of abnormal fluctuations in the production quantification indicators of the process execution end during the high-impact period of the equipment exceeds the duration percentage threshold, or if the peak value of the fluctuation frequency of the production quantification indicators of the process execution end during the low-impact period of the equipment exceeds the peak value threshold, then the corresponding process execution end will be marked as an efficiency abnormal end.

[0023] If the duration of abnormal fluctuations in the production quantification indicators of the process execution end during the high-impact period of the equipment does not exceed the duration percentage threshold, and the peak value of the fluctuation frequency of the production quantification indicators of the process execution end during the low-impact period of the equipment does not exceed the peak value threshold of the fluctuation frequency, then the corresponding process execution end will be marked as the efficiency normal end.

[0024] Furthermore, when the process execution end is an efficiency abnormal end, the overlap time between the abnormal fluctuation time of the production quantitative index and the abnormal time of the state itself is recorded and marked as the state qualitative change time; the overlap time between the abnormal fluctuation time of the production quantitative index and the abnormal time of the state disturbance is recorded and marked as the environmental qualitative change time.

[0025] When the process execution end is at the normal efficiency end, record the overlap between the abnormal fluctuation time of the production quantitative index and the abnormal time of the state itself, and mark it as the state elimination time; record the overlap between the abnormal fluctuation time of the production quantitative index and the abnormal time of the state interference, and mark it as the environment elimination time.

[0026] Furthermore, based on the corresponding operating time type of the process execution end, a traceability analysis is performed. Specifically, when the state undergoes a qualitative change, the impact of the state on efficiency and progress is recorded; when the environment undergoes a qualitative change, the impact of the environment on efficiency and progress is recorded. Targeted maintenance is carried out based on the corresponding time point and the data collected at the corresponding time point. When the state is eliminated, the progress is recorded if the state has no impact; when the environment is eliminated, the progress is recorded if the environment has no impact. Parameter elimination is carried out based on the data collected within the corresponding progress, thus narrowing the scope of maintenance.

[0027] Furthermore, the preprocessing and collaborative control process in step two is as follows:

[0028] When pre-treating different types of fabrics for multi-fabric garments, the processing is adjusted according to the fabric type. A visual sensor identifies fabric areas, a humidity sensor collects real-time humidity of the fabric surface, and a thickness sensor collects fabric thickness. The fabric surface humidity fluctuation trend is recorded when the ambient humidity fluctuates. If the fluctuation range within the trend is consistent with the ambient humidity fluctuation, the corresponding fabric is marked as an easily controllable fabric. Based on the comparison of different thickness values ​​within different fabric areas, the thickness of easily controllable fabrics is divided into those that meet the easily controllable thickness range and those that do not. If the fluctuation range within the trend is inconsistent with the ambient humidity fluctuation, the corresponding fabric is marked as a difficult-to-control fabric. Based on the comparison of different thickness values ​​within different fabric areas, the thickness of easily controllable fabrics is divided into those that meet the difficult-to-control thickness range and those that do not.

[0029] Furthermore, during the pre-processing, the fabric type at each position in the ironing trajectory is determined based on the ironing trajectory set by the ironing device, the starting area of ​​the ironing trajectory is determined, and the ironing setting parameters for the starting area type are determined, specifically the steam jet volume and jet position.

[0030] After matching the ironing settings parameters for the starting area of ​​the ironing trajectory, the fabric type of the starting area is determined. Based on the comparison of adjacent ironing areas, if the fabrics are of the same type, the fabric thickness is identified and the steam jet volume adjustment trend is determined according to the fabric thickness, and the adjustment amount is set. If the fabrics are not of the same type, the steam jet volume adjustment trend is determined according to the fabric conversion trend. After determining the steam jet volume adjustment trend, the steam jet volume adjustment is set based on the thickness value deviation and historical operation adjustment. It should be noted that if the humidity of the current fabric area after ironing does not reach the set threshold after adjustment, the steam jet volume adjustment amount is immediately increased.

[0031] Furthermore, the dynamic matching process of the needle stitch in step three is as follows:

[0032] Extract the cutting and sewing processes from the multi-fabric garment processing flow, and collect the distance span of the change in the texture trajectory of the corresponding fabric boundary when the cutting process is executed;

[0033] If the change in the distance span of the fabric boundary texture trajectory exceeds the change in distance span threshold, the corresponding cutting process will be marked as a deformation execution process; if the change in the distance span of the fabric boundary texture trajectory does not exceed the change in distance span threshold, the corresponding cutting process will be marked as an unchanged execution process.

[0034] Extract the adjacent sewing processes of the current cutting process, and when sewing different fabrics, collect the average deviation of the sewing thread tightness on the fabric surface after the sewing process is executed;

[0035] If the average deviation of the sewing thread tension on the fabric surface exceeds the average deviation threshold, the corresponding sewing process will be marked as an inefficient sewing process; if the average deviation of the sewing thread tension on the fabric surface does not exceed the average deviation threshold, the corresponding sewing process will be marked as an efficient sewing process.

[0036] Furthermore, the executed cutting and sewing processes are analyzed. If the deformation execution process is adjacent to the efficient sewing process, the pressure of the corresponding cutting process is adjusted, and the original pressure value is set as the cutting abnormality standard. If the deformation execution process is adjacent to the inefficient sewing process, the pressure of the corresponding cutting process is adjusted, and the sewing process is dynamically fine-tuned for the take-up spring stroke or the shuttle speed.

[0037] If the unchanged execution process is adjacent to a high-efficiency sewing process, record the pressure value of the current cutting process and the take-up spring stroke or shuttle speed of the sewing process, and use them as matching adjustment standards; if the unchanged execution process is adjacent to a low-efficiency sewing process, record the take-up spring stroke and shuttle speed of the sewing process, mark them as sewing abnormality standards, and adjust the take-up spring stroke and shuttle speed at the same time.

[0038] Furthermore, matching settings are made for subsequent processes to be executed, and the parameters of the cutting and sewing processes corresponding to the completed fabric types are kept constant. When the parameters are constant, the cutting abnormality standard and the sewing abnormality standard are used as real-time detection indicators, and matching control is performed after an abnormality occurs, with the matching adjustment standard as the control standard.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. The beneficial effects of step one, equipment digital transformation: By analyzing the correlation between quantitative parameters of equipment operating status and parameters of the operating environment, it is possible to accurately distinguish between abnormal status itself and abnormal status interference. Combined with production efficiency parameters, it is possible to divide the high / low impact periods of equipment, and achieve accurate marking of the efficiency abnormal end and the normal end. Then, through the traceability analysis of the administrator terminal, it is possible to accurately locate the time of status quality change, environmental quality change, status elimination and environmental elimination. This facilitates targeted maintenance and fault tracing, narrows the scope of maintenance, and enables accurate fault prediction and large-scale fault investigation, improving the predictive maintenance capability of equipment, effectively avoiding production interruption, and ensuring production continuity and stability.

[0041] 2. Beneficial effects of pretreatment collaborative control in step two: During the execution of the ironing trajectory, the steam jet volume adjustment trend and adjustment amount are determined based on the thickness difference for the same type of fabric. For different types of fabrics (easy to control → difficult to control / difficult to control → easy to control), a reasonable steam jet volume adjustment trend is determined. The parameter settings are optimized by combining the thickness value deviation and historical operation adjustment amount. At the same time, the fabric humidity after ironing is monitored in real time and dynamically adjusted. This significantly improves the accuracy of steam jet volume and jet position adjustment, effectively solves the problem of uneven ironing effect in scenarios of alternating ironing of multiple fabrics and fluctuation of environmental humidity, and ensures the consistency of pretreatment (ironing) quality of different fabrics. This collaborative control method is suitable for dynamic scenarios of multi-fabric garment processing. It can achieve real-time dynamic optimization of parameters without manual intervention, improve the automation level and processing efficiency of the pretreatment stage, and reduce the risk of fabric damage caused by improper parameters.

[0042] 3. The beneficial effects of dynamic matching of needle and thread in step three: By collecting the change in distance span of the texture trajectory of the cut fabric boundary, the type of cutting process (deformed / unchanged execution process) is accurately determined. Combined with the average deviation of the sewing thread tension on the fabric surface after sewing (with the sewing thread input length under the same thickness as the evaluation parameter), the type of sewing process (high-efficiency / low-efficiency process) is determined, realizing accurate identification of the cutting and sewing process status. Differentiated parameter adjustment strategies are formulated for different process type combinations (deformed / unchanged execution process and high-efficiency / low-efficiency sewing process adjacent). For example, when the deformed execution process is adjacent to the high-efficiency sewing process, the cutting pressure is adjusted and an abnormal standard is set. When the deformed execution process is adjacent to the low-efficiency sewing process, the cutting pressure and sewing parameters (thread take-up spring stroke, shuttle speed) are adjusted simultaneously. When the unchanged execution process is adjacent to the low-efficiency sewing process, the sewing parameters are optimized and an abnormal standard is marked. This effectively solves the problem of uneven stitch tension and thread breakage caused by sudden changes in the friction coefficient at the seams of multiple fabrics, improving sewing quality and sewing efficiency. Attached Figure Description

[0043] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0044] Figure 1 This is a flowchart illustrating the overall principle of the method of this invention.

[0045] Figure 2 This is a flowchart of the data acquisition and analysis method in step one of the present invention. Detailed Implementation

[0046] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0047] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0048] Please see Figure 1 As shown, an equipment collaborative processing optimization method is applied to multi-fabric garments. The specific equipment collaborative processing optimization method is as follows:

[0049] Step 1: Digital transformation of equipment;

[0050] Connecting equipment to the network via IoT technology enables real-time data acquisition and transmission, including equipment operating status and production efficiency. Real-time data analysis is used to identify equipment coordination anomalies at each processing stage and to perform predictive maintenance to prevent production interruptions.

[0051] Step 2: Preprocessing and collaborative control;

[0052] After completing the digital transformation, pretreatment collaborative control is implemented in the pretreatment process of multi-fabric garment processing. The amount and position of steam jet are adjusted in real time according to the thickness and moisture absorption of different fabrics to improve the ironing effect.

[0053] Step 3: Dynamic matching of machine needle stitches;

[0054] After the cutting and sewing equipment is integrated, real-time data on the needle and stitch are collected and matched reasonably. When crossing the seam between two fabrics, the sewing thread tension will jump due to the sudden change in the coefficient of friction of the fabric, resulting in uneven stitch tightness or even thread breakage. Existing equipment uses constant sewing parameters or simple partition settings.

[0055] Step one involves the digital transformation of equipment, which involves networking and controlling various devices within the production line. However, the execution speed of each device's corresponding process is not uniform. When evaluating at the same time, the amount of real-time processing data collected by each device will be inconsistent. Faster devices will require data storage, while slower devices will experience network control lag. Therefore, the following improvements are made:

[0056] Establish a tiered storage mechanism of "edge caching + cloud synchronization" for high-speed devices to avoid data accumulation or loss, while reducing the pressure on the network and central server; specifically: memory buffer: millisecond-level writing, no IO latency, adapted to real-time data acquisition of high-speed devices; local database: lightweight and embedded, avoiding lag caused by frequent network transmissions; asynchronous synchronization thread: periodically synchronizes local data to the cloud without blocking device data acquisition; circular buffer: set maximum length to prevent memory overflow and adapt to the resource limitations of production line equipment.

[0057] For slow-moving equipment, an asynchronous control mode of "instruction queue + status confirmation + dynamic timeout" is adopted to avoid overall control blockage caused by slow equipment response. Specifically: Instruction queue: Control instructions are queued first, and the equipment executes at its own pace to avoid "instruction backlog → lag"; Dynamic timeout: The timeout time is automatically calculated based on the equipment's process speed to adapt to the response capabilities of different equipment; Asynchronous execution: Control threads run independently, and the main thread (production line master control) is not blocked to ensure overall control efficiency; Status feedback: The equipment execution status is updated in real time, and the master control terminal can query it at any time to avoid lag caused by "blindly issuing instructions".

[0058] In the production line control system, the real-time data of each device is aligned with the time window, and the control strategy is dynamically adjusted according to the device speed.

[0059] The specific hardware is as follows: Edge computing gateway: Deploy edge gateways on the production line to handle local data caching and command scheduling for each device, reducing cloud dependence and lowering network latency; Industrial-grade network: Utilize 5G / industrial Ethernet to allocate independent communication channels to each device, avoiding bandwidth contention between control commands and data transmission; Device status acquisition: Add sensors (such as encoders and photoelectric switches) to each device to collect "busy / idle" status in real time, and the central control system dynamically issues commands based on the status;

[0060] After confirming the network control of the equipment, perform data acquisition and analysis; please refer to [link / reference]. Figure 2 As shown;

[0061] Each piece of equipment within the process flow is labeled as a process execution end, and data is collected from these ends to obtain quantified parameters corresponding to the equipment's operating status. These quantified parameters are selected and determined based on the corresponding equipment type and process type, such as equipment operating temperature. Based on the monitoring time of the quantified parameters of the equipment's operating status at the process execution end, operating environment parameters are simultaneously identified. These operating environment parameters are represented as the associated operating environment parameters corresponding to the equipment's operating status quantified parameter type, such as the relationship between operating environment temperature and equipment operating temperature; they also include the equipment's operating environment.

[0062] Based on the floating time of any quantitative parameter of the operating status of the process execution terminal equipment, the operating environment parameters at the floating time are recorded. Based on the comparison between the influence probability of the operating environment parameters and the floating time, the current operating time of the process execution terminal is marked as an abnormal time of the status itself or an abnormal time of status interference. The influence probability of the operating environment parameters is obtained through historical fault factor probability analysis, and the floating time comparison is expressed as the order of floating times or the interval duration.

[0063] Production efficiency analysis is performed on each process execution end, using quantitative production indicators as production efficiency parameters, including parameters such as product defect rate. Process execution periods are selected, and based on the ratio of the number of times the state itself is abnormal to the number of times the state interference is abnormal within that period, the process execution period is divided into high-impact periods and low-impact periods. Specifically, a high-impact period is defined as a ratio exceeding a certain threshold, and a low-impact period is defined as a ratio below a certain threshold.

[0064] Obtain the percentage of time during which abnormal fluctuations in production quantitative indicators at the process execution end occur during periods of high equipment impact, and simultaneously obtain the peak frequency of fluctuations in production quantitative indicators at the process execution end during periods of low equipment impact.

[0065] If the duration of abnormal fluctuations in the production quantification indicators of the process execution end during the high-impact period of the equipment exceeds the duration percentage threshold, or if the peak value of the fluctuation frequency of the production quantification indicators of the process execution end during the low-impact period of the equipment exceeds the peak value threshold, then the corresponding process execution end will be marked as an efficiency abnormal end.

[0066] If the duration of abnormal fluctuations in the production quantification indicators of the process execution end during the high-impact period of the equipment does not exceed the duration percentage threshold, and the peak value of the fluctuation frequency of the production quantification indicators of the process execution end during the low-impact period of the equipment does not exceed the peak value threshold of the fluctuation frequency, then the corresponding process execution end will be marked as the efficiency normal end.

[0067] Send the abnormal moment of the corresponding state of the process execution end or the abnormal moment of state interference, as well as the efficiency abnormal moment or efficiency normal moment of the corresponding runtime segment, to the administrator terminal together.

[0068] The administrator terminal processes the data:

[0069] When the process execution end is at the efficiency abnormal end, record the overlap between the abnormal fluctuation time of the production quantitative index and the abnormal time of the state itself, and mark it as the state qualitative change time; record the overlap between the abnormal fluctuation time of the production quantitative index and the abnormal time of the state disturbance, and mark it as the environmental qualitative change time.

[0070] When the process execution end is at the normal efficiency end, record the overlap between the abnormal fluctuation time of the production quantitative index and the abnormal time of the state itself, and mark it as the state elimination time; record the overlap between the abnormal fluctuation time of the production quantitative index and the abnormal time of the state interference, and mark it as the environmental elimination time.

[0071] The administrator terminal performs source analysis based on the corresponding running time type of the process execution terminal. Specifically, it records the impact of state on efficiency and progress when the state undergoes a qualitative change, or records the impact of environment on efficiency and progress when the environment undergoes a qualitative change. Targeted maintenance is carried out based on the data collected at the corresponding time point. It should be noted that the quantitative parameters of the running state and the quantitative indicators of production efficiency are not unique and are applicable to the current system based on the real-time running scenario.

[0072] The system records the progress when the status is unaffected or the progress when the environment is unaffected. Based on the data collected within the corresponding progress, parameters are eliminated to narrow down the scope of maintenance. This facilitates effective fault tracing when both the status itself and the status interference are abnormal. It also enables accurate fault diagnosis when the corresponding time periods of normal efficiency and abnormal efficiency alternate. As a result, it can accurately identify and predict faults at specific moments and also perform fault diagnosis over a wide range of time periods.

[0073] Step two, the preprocessing and collaborative control process, is as follows:

[0074] When pre-treating multi-fabric garments for different types of processed fabrics, the treatment is adjusted according to the fabric type; this application uses ironing as a pre-treatment.

[0075] The fabric area is identified by a visual sensor, the real-time humidity of the fabric surface is collected by a humidity sensor, and the fabric thickness is collected by a thickness sensor. When the ambient humidity fluctuates, the trend of humidity fluctuation on the fabric surface is recorded. If the fluctuation span within the trend is consistent with the ambient humidity fluctuation, the corresponding fabric is marked as an easy-to-control fabric. Based on the comparison of different thickness values ​​in different fabric areas, the thickness of easy-to-control fabrics is divided into those that meet the thickness range of easy-to-control fabrics and those that do not meet the thickness range of easy-to-control fabrics. The scenario of consistent fluctuation also includes the fluctuation span deviation being lower than the set deviation threshold.

[0076] If the fluctuation span within the fluctuation trend is inconsistent with the fluctuation of environmental humidity, the corresponding fabric will be marked as a difficult-to-control fabric; and based on the comparison of different thickness values ​​in different fabric areas, the thickness of easy-to-control fabrics will be divided into those that meet the thickness range of difficult-to-control fabrics and those that do not meet the thickness range of difficult-to-control fabrics; the scenario of inconsistent fluctuations also includes situations where the fluctuation span deviation is not lower than the set deviation threshold.

[0077] During pre-processing, the fabric type at each position in the ironing trajectory is determined based on the ironing trajectory set by the ironing device, the starting area of ​​the ironing trajectory is determined, and the ironing setting parameters for the starting area type are determined, specifically the steam jet volume and jet position.

[0078] After matching the ironing setting parameters for the starting area of ​​the ironing trajectory, the fabric type of the starting area is determined. Based on the comparison of the types of adjacent ironing areas, if they are the same type of fabric, the fabric thickness is identified and the steam jet volume adjustment trend is determined according to the fabric thickness, and the adjustment amount is set. If they are not the same type of fabric, the steam jet volume adjustment trend is determined according to the fabric transformation trend. That is, when an easy-to-control fabric transforms into a difficult-to-control fabric, the steam jet volume is increased, and vice versa. After determining the steam jet volume adjustment trend, the steam jet volume adjustment setting is made based on the thickness value deviation and the historical operation adjustment amount. It should be noted that if the humidity of the current fabric area after ironing does not reach the set threshold after adjustment, the steam jet volume adjustment amount is immediately increased.

[0079] The dynamic matching process of the needle stitch in step three is as follows:

[0080] The cutting and sewing processes of multi-fabric garment processing are extracted, and the distance span of the change in the texture trajectory of the fabric boundary is collected when the cutting process is executed. It should be noted that due to different fabric materials, the fabric boundary will produce different stretching after cutting, resulting in changes in the texture boundary trajectory.

[0081] If the change in the distance span of the fabric boundary texture trajectory exceeds the change in distance span threshold, the corresponding cutting process will be marked as a deformation execution process; if the change in the distance span of the fabric boundary texture trajectory does not exceed the change in distance span threshold, the corresponding cutting process will be marked as an unchanged execution process.

[0082] Extract the adjacent sewing processes of the current cutting process. When sewing different fabrics, collect the average value of the tension deviation of the sewing thread on the fabric surface after the sewing process is executed. It should be noted that the tension deviation value is based on the different lengths of sewing thread used under the same thickness after the sewing is completed, which is why the tension of the sewing thread on the fabric surface is inconsistent.

[0083] If the average deviation of the tension of the sewing thread on the fabric surface exceeds the average deviation threshold, the corresponding sewing process will be marked as an inefficient sewing process; if the average deviation of the tension of the sewing thread on the fabric surface does not exceed the average deviation threshold, the corresponding sewing process will be marked as an efficient sewing process.

[0084] Analyze the executed cutting and sewing processes. If the deformation execution process is adjacent to the efficient sewing process, adjust the pressure of the corresponding cutting process and set the original pressure value as the cutting abnormality standard; reduce the amount of change in the fabric boundary texture trajectory; if the deformation execution process is adjacent to the inefficient sewing process, adjust the pressure of the corresponding cutting process and dynamically fine-tune the take-up spring stroke or shuttle speed of the sewing process.

[0085] If the unchanged execution process is adjacent to a high-efficiency sewing process, record the pressure value of the current cutting process and the take-up spring stroke or shuttle speed of the sewing process, and use them as matching adjustment standards; if the unchanged execution process is adjacent to a low-efficiency sewing process, record the take-up spring stroke and shuttle speed of the sewing process, mark them as sewing abnormality standards, and adjust the take-up spring stroke and shuttle speed at the same time.

[0086] The system matches and sets the subsequent processes to be executed, keeps the parameters of the cutting and sewing processes corresponding to the completed fabric types constant, and uses the cutting and sewing anomaly standards as real-time detection indicators when the parameters are constant. After an anomaly occurs, the system performs matching control, and uses the matching adjustment standard as the control standard. As the processing volume of multi-fabric garments continues to increase, the corresponding process parameter types and standard parameters become more comprehensive, and the accuracy of collaborative processing optimization is higher.

[0087] This invention discloses an equipment collaborative processing optimization method for multi-fabric garments, aiming to solve technical problems such as low equipment collaboration efficiency, unstable processing quality, and difficulty in fault tracing in existing multi-fabric garment processing. Through three core steps, namely, equipment digital transformation, pre-processing collaborative control, and dynamic matching of needle and thread, it realizes deep collaboration and optimization of equipment throughout the entire process of multi-fabric garment processing.

[0088] Thresholds, preset values, preset ranges, etc. are set for result comparison and analysis to determine whether they are good or bad. The value of these thresholds is determined by a combination of large-scale model analysis of sample data and human experience. They can also be adjusted appropriately based on seasonal or common-sense influences.

[0089] 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. A method for optimizing the cooperative processing of a multi-fabric garment with equipment, characterized in that, The optimization methods for equipment collaborative processing are as follows: Step 1: Digital transformation of equipment; Connect the equipment to the Internet of Things (IoT) to link the corresponding equipment in each process flow and collect and transmit data in real time. The real-time data collected includes equipment operating status and production efficiency. Analyze the collected data in real time to perform equipment coordination anomaly analysis at each processing stage. Step 2: Preprocessing and collaborative control; After completing the digital transformation, pretreatment collaborative control is carried out in the pretreatment process of multi-fabric garment processing, and the steam injection volume and injection position are adjusted in real time according to the thickness and moisture absorption of different fabrics. Step 3: Dynamic matching of machine needle stitches; After the cutting and sewing equipment is integrated, data on real-time needle and stitch are collected and matched appropriately. Extract the cutting and sewing processes from the multi-fabric garment processing flow, and collect the distance span of the change in the texture trajectory of the corresponding fabric boundary when the cutting process is executed; If the distance span of the fabric boundary texture trajectory changes exceeds the threshold for changing the distance span, the corresponding cutting process will be marked as a deformation execution process. If the change in the fabric boundary texture trajectory does not exceed the change in distance span threshold, the corresponding cutting process will be marked as unchanged execution process. Extract the adjacent sewing processes of the current cutting process, and when sewing different fabrics, collect the average deviation of the sewing thread tightness on the fabric surface after the sewing process is executed; If the average deviation of the tightness of the sewing thread on the fabric surface exceeds the average deviation threshold, the corresponding sewing process will be marked as an inefficient sewing process. If the average deviation of the tightness of the sewing thread on the fabric surface does not exceed the average deviation threshold, the corresponding sewing process will be marked as a high-efficiency sewing process. Analyze the executed cutting and sewing processes. If the deformation execution process is adjacent to the high-efficiency sewing process, adjust the pressure of the corresponding cutting process and set the original pressure value as the cutting abnormality standard. If the deformation process is adjacent to an inefficient sewing process, the pressure of the corresponding cutting process will be adjusted, and the sewing process will be dynamically fine-tuned at the same time, with the take-up spring stroke or the shuttle speed being adjusted. If the unchanged execution process is adjacent to the high-efficiency sewing process, record the pressure value of the current cutting process and the take-up spring stroke or shuttle speed of the sewing process, and use them as the matching adjustment standard. If the unchanging process is adjacent to an inefficient sewing process, record the take-up spring stroke and hook speed of the sewing process and mark it as a sewing abnormality standard, and adjust the take-up spring stroke and hook speed at the same time. The subsequent processes to be executed are matched and set. The parameters of the cutting and sewing processes corresponding to the completed fabric types are kept constant. When the parameters are constant, the cutting abnormality standard and the sewing abnormality standard are used as real-time detection indicators. After the abnormality occurs, matching control is performed, and the matching adjustment standard is used as the control standard.

2. The method for optimizing the cooperative processing of a device applied to a multi-fabric garment according to claim 1, wherein, The process of digital transformation of equipment in step one is as follows: To implement network control of all equipment within the production line, data collection and analysis are conducted after the network control of the equipment is confirmed. Each piece of equipment in the process flow is marked as a process execution end, and data is collected from the process execution end to obtain the quantitative parameters corresponding to the equipment operation status of the process execution end. The quantitative parameters are selected and determined based on the corresponding equipment type and process type. Based on the monitoring time of the equipment operating status at the process execution end, quantitative parameters are monitored, and operating environment parameters are identified simultaneously. Based on the fluctuation time of any quantitative parameter of the operating status of the process execution terminal equipment, record the operating environment parameters at the fluctuation time. Based on the influence probability of the operating environment parameters and the fluctuation time, mark the current operating time of the process execution terminal as an abnormal time of the status itself or an abnormal time of status interference. Perform production efficiency analysis on each process execution end, using quantitative production indicators as production efficiency parameters; Select the process execution period and divide the process execution period into high-impact periods and low-impact periods based on the ratio of the number of times the state itself is abnormal to the number of times the state is disturbed.

3. The equipment collaborative processing optimization method for multi-fabric garments according to claim 2, characterized in that, Obtain the percentage of time during which abnormal fluctuations in production quantitative indicators at the process execution end occur during periods of high equipment impact, and simultaneously obtain the peak frequency of fluctuations in production quantitative indicators at the process execution end during periods of low equipment impact. If the duration of abnormal fluctuations in the production quantification indicators of the process execution end during the high-impact period of the equipment exceeds the duration percentage threshold, or if the peak value of the fluctuation frequency of the production quantification indicators of the process execution end during the low-impact period of the equipment exceeds the peak value threshold, then the corresponding process execution end will be marked as an efficiency abnormal end. If the duration of abnormal fluctuations in the production quantification indicators of the process execution end during the high-impact period of the equipment does not exceed the duration percentage threshold, and the peak value of the fluctuation frequency of the production quantification indicators of the process execution end during the low-impact period of the equipment does not exceed the peak value threshold of the fluctuation frequency, then the corresponding process execution end will be marked as the efficiency normal end.

4. The equipment collaborative processing optimization method for multi-fabric garments according to claim 3, characterized in that, When the process execution end is at the efficiency abnormal end, record the overlap between the abnormal fluctuation time of the production quantitative index and the abnormal time of the state itself, and mark it as the state qualitative change time; record the overlap between the abnormal fluctuation time of the production quantitative index and the abnormal time of the state disturbance, and mark it as the environmental qualitative change time. When the process execution end is at the normal efficiency end, record the overlap between the abnormal fluctuation time of the production quantitative index and the abnormal time of the state itself, and mark it as the state elimination time; record the overlap between the abnormal fluctuation time of the production quantitative index and the abnormal time of the state interference, and mark it as the environment elimination time.

5. The equipment collaborative processing optimization method for multi-fabric garments according to claim 4, characterized in that, Based on the corresponding operating time type of the process execution terminal, traceability analysis is performed. Specifically, when the state changes, the impact of the state on efficiency and progress is recorded; when the environment changes, the impact of the environment on efficiency and progress is recorded. Targeted maintenance is carried out based on the corresponding time point and the data collected at the corresponding time point. When the state is eliminated, the progress is recorded if the state has no impact; when the environment is eliminated, the progress is recorded if the environment has no impact. Parameter elimination is carried out based on the data collected within the corresponding progress, narrowing the scope of maintenance.

6. The equipment collaborative processing optimization method for multi-fabric garments according to claim 1, characterized in that, Step two, the preprocessing and collaborative control process, is as follows: When pre-treating different types of fabrics for multi-fabric garments, the processing is adjusted according to the fabric type. A visual sensor identifies fabric areas, a humidity sensor collects real-time humidity data of the fabric surface, and a thickness sensor collects fabric thickness data. The fabric surface humidity fluctuation trend is recorded when the ambient humidity fluctuates. If the fluctuation range within the trend is consistent with the ambient humidity fluctuation, the corresponding fabric is marked as an easily controllable fabric. Based on the comparison of different thickness values ​​within different fabric areas, the thickness of easily controllable fabrics is divided into those that meet the easily controllable thickness range and those that do not. If the fluctuation range within the trend is inconsistent with the ambient humidity fluctuation, the corresponding fabric is marked as a difficult-to-control fabric. Based on the comparison of different thickness values ​​within different fabric areas, the thickness of difficult-to-control fabrics is divided into those that meet the difficult-to-control thickness range and those that do not.

7. The equipment collaborative processing optimization method for multi-fabric garments according to claim 6, characterized in that, During pre-processing, the fabric type at each position in the ironing trajectory is determined based on the ironing trajectory set by the ironing device, the starting area of ​​the ironing trajectory is determined, and the ironing setting parameters for the starting area type are determined, specifically the steam jet volume and jet position. After matching the ironing settings parameters for the starting area of ​​the ironing trajectory, the fabric type of the starting area is determined. Based on the comparison of adjacent ironing areas, if the fabrics are of the same type, the fabric thickness is identified and the steam jet volume adjustment trend is determined according to the fabric thickness, and the adjustment amount is set. If the fabrics are not of the same type, the steam jet volume adjustment trend is determined according to the fabric conversion trend. After determining the steam jet volume adjustment trend, the steam jet volume is adjusted and set according to the thickness value deviation and historical operation adjustment. If the humidity of the current fabric area after ironing does not reach the set threshold after adjustment, the steam jet volume adjustment amount is immediately increased.

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