Crimping process quality monitoring and feedback system

Abstract

The invention relates to the technical field of real-time data processing, in particular to a crimping process quality monitoring and feedback system which comprises a crimping state monitoring module, a quality deviation analysis module, a feedback control module, a safety performance verification module and an operation optimization module. According to the method, dynamic identification is carried out through mechanical distribution and contact characteristic changes in the crimping process, abnormal point information is collected to accurately position connection quality deviation, potential quality fluctuation reflected by deformation trend and load abnormity is analyzed in real time, key factors influencing connection consistency are effectively extracted, self-adaptive adjustment of equipment control instructions is achieved, and the reliability of the equipment control instructions is improved. The operation strategy is dynamically corrected in combination with the load and operation state of the equipment, the integrity evaluation is carried out on the performance state of the equipment, the hidden danger risk caused by temperature rise abnormity and fatigue accumulation is identified, the sequence adjustment and risk avoidance integrated response of the crimping task is completed, and the crimping quality stability and the equipment operation safety are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of real-time data processing technology, and in particular to a quality monitoring and feedback system for the crimping process. Background Technology

[0002] Real-time data processing technology primarily involves processing methods that complete data transmission, analysis, and response with extremely low latency after data generation and acquisition. Core aspects include data acquisition and parsing, time synchronization and scheduling, low-latency communication mechanisms, and efficient data feedback control. This field is widely used in scenarios with extremely high real-time requirements, such as industrial automation, intelligent manufacturing, and network communication. Its systematic technical content covers a complete closed-loop process from edge data perception to central decision control, emphasizing the ability to process data and trigger subsequent actions instantly upon its occurrence. Among these, the traditional crimping process quality monitoring and feedback system refers to the real-time detection and feedback control of the connection quality between terminals and wires during the crimping process. The technical issue addressed is how to dynamically monitor whether the connection status meets quality standards during crimping operations. Traditionally, this involves using pressure sensors to record crimping force curves for initial judgment, then comparing and analyzing the actual curve with a pre-set standard force value, and using preset judgment rules or threshold checks to determine if the crimping is abnormal. Finally, the analysis results are fed back to the operating terminal or used to control the operation process.

[0003] Existing crimping quality monitoring technologies rely on comparing pressure curves with preset thresholds. In dynamic operations, these technologies lack the ability to identify load distribution, contact quality changes, and structural deformation, failing to accurately reveal actual crimping quality deviations. This leads to delayed responses or misjudgments to potential anomalies. In practical applications, if there are slight fluctuations in wire material or terminal shape, the original judgment rules are difficult to adapt, causing qualified crimping to be misjudged as abnormal or vice versa. Furthermore, the lack of a feedback adjustment mechanism for equipment operating load conditions makes it difficult to promptly correct strategies to cope with the impact of load fluctuations, thereby affecting overall operational efficiency and equipment operating safety. The static logic in the task scheduling process also makes it difficult to respond quickly and adjust to abnormal situations. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a quality monitoring and feedback system for the crimping process.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a quality monitoring and feedback system for the crimping process includes: The crimping status monitoring module, based on the operating characteristics of the crimping equipment and combined with the mechanical changes during the connection process between the terminal and the wire, identifies the dynamic load distribution, contact resistance changes and deformation information during the crimping process, determines whether the crimping quality meets the preset standards, identifies potential abnormal points, and obtains information on abnormal crimping quality points. The quality deviation analysis module analyzes the degree of deviation of mechanical distribution and deformation characteristics during the pressing process based on the information of abnormal points in the pressing quality, extracts the key influencing factors of the pressing operation, and generates quality deviation assessment results. Based on the quality deviation assessment results, the feedback control module extracts the current operating parameters of the crimping equipment, combines the equipment's maximum load capacity and real-time load conditions, adjusts the crimping control strategy, and generates optimized crimping control commands. The safety performance verification module calls the optimized crimping control command to check the key performance indicators of the crimping equipment during operation, including the temperature rise and mechanical fatigue level, marks the operating conditions with potential hazards, and generates a safety checklist for the crimping equipment.

[0006] As a further aspect of the present invention, the information on abnormal points in the crimping quality includes abnormal load change characteristics, contact resistance deviation characteristics, and abnormal material deformation parameters; the quality deviation assessment results include mechanical deviation indexes, deformation deviation quantification values, and a set of influencing factors; the optimized crimping control commands include pressure adjustment parameters, speed control parameters, and positioning correction parameters; and the crimping equipment operation safety checklist includes temperature rise warning indicators, fatigue strength assessment values, and operational stability labels.

[0007] As a further aspect of the present invention, the crimping status monitoring module includes: The mechanical distribution capture submodule, based on the dynamic load variation law during crimping and combined with the mechanical distribution characteristics of the contact area between the terminal and the wire, transforms the mechanical changes into a crimping node state sequence, thus obtaining a synchronous state sequence of the crimping node. The deformation feature extraction submodule, based on the synchronous state sequence of the press-fit nodes, matches a preset deformation feature model to extract the deformation difference sequence and obtain the node deformation deviation. The quality anomaly identification submodule calls the node deformation deviation, combines the dynamic load distribution and contact resistance change, and superimposes the deformation characteristics and mechanical distribution fluctuations to generate crimping quality anomaly point information.

[0008] As a further aspect of the present invention, the quality deviation analysis module includes: The mechanical distribution analysis submodule collects the time characteristics of dynamic load distribution during the pressing process based on the information on abnormal points in the pressing quality, analyzes the degree of deviation of mechanical distribution, and generates a mechanical distribution deviation table. The deformation feature detection submodule analyzes the time overlap of deformation features during the pressing process based on the mechanical distribution deviation table, identifies deformation conflict periods, and generates a list of deformation conflict periods. The deviation optimization submodule optimizes the deformation distribution, adjusts the pressing operation sequence and time allocation based on the deformation conflict time period list, combined with the pressing quality requirements and equipment operating characteristics, calculates the deformation optimization value, and generates the quality deviation evaluation result.

[0009] As a further aspect of the present invention, the feedback control module includes: Based on the quality deviation assessment results, the control parameter extraction submodule extracts the operating parameter range corresponding to the crimping equipment, organizes the crimping control strategy adjustment range, and generates a crimping control strategy table. The load-bearing capacity judgment submodule, based on the crimping control strategy table and combined with the real-time load of the crimping equipment, filters the equipment's operating load-bearing capacity, determines whether it exceeds the load-bearing range, marks the limited parameter range, and generates load-bearing capacity analysis results. Based on the load-bearing capacity analysis results, the control parameter adjustment submodule determines the urgency of the crimping operation according to the limited parameter range, and, in conjunction with the original parameters of the equipment and the adjustment range, rearranges the control parameters to generate optimized crimping control commands.

[0010] As a further aspect of the present invention, the security performance verification module includes: The equipment performance inspection submodule monitors the operating status of the crimping equipment based on the optimized crimping control command, checks whether the temperature rise and mechanical fatigue meet safety standards, records the changes in performance indicators, and generates equipment operating performance records. The hidden danger list generation submodule identifies operating states where the temperature rise or mechanical fatigue exceeds the safe range based on the equipment's operating performance records, calculates the risk assessment indicators for temperature rise and mechanical fatigue, marks them as states with hidden dangers, identifies the equipment's operating safety pass rate, and generates a list of safe operating conditions for the crimping equipment.

[0011] As a further aspect of the present invention, the system also includes an operation optimization module: The operation optimization module extracts the operating states with potential hazards based on the operating safety list of the crimping equipment, rearranges the associated crimping tasks and loads, assesses potential operating risks, optimizes the execution order of crimping tasks in combination with real-time conditions, and generates a crimping task tuning table. The crimping task optimization table includes task allocation priority, load refactoring parameters, and execution order optimization items.

[0012] As a further aspect of the present invention, the operation optimization module includes: The status filtering submodule extracts the operating status with potential hazards based on the operating safety list of the crimping equipment, filters the associated crimping tasks, extracts the corresponding load and operating information for each task, analyzes the causes of operating hazards, and generates a list of tasks to be optimized. The task scheduling submodule rearranges associated tasks based on the list of tasks to be optimized, schedules the currently available resources and load distribution, analyzes task performance and running progress, and generates a task and load scheduling plan. The operation optimization submodule, based on the task and load scheduling plan, combined with the current real-time progress and environmental conditions, assesses potential operational risks, optimizes the task execution order and time allocation, and generates a pressing task tuning table.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, dynamic identification is achieved through changes in mechanical distribution and contact characteristics during the crimping process. Abnormal point information is collected to accurately locate connection quality deviations. Potential quality fluctuations reflected by deformation trends and load anomalies are analyzed in real time. Key factors affecting connection consistency are effectively extracted, enabling adaptive adjustment of equipment control commands. The operation strategy is dynamically corrected in conjunction with equipment load and operating status. At the same time, a complete assessment of equipment performance status is conducted to identify potential risks caused by abnormal temperature rise and fatigue accumulation. Ultimately, an integrated response of crimping task sequencing and risk avoidance is achieved. A complete feedback chain is constructed from data identification, deviation diagnosis, control adjustment to operation optimization, effectively improving the stability of crimping quality and the safety of equipment operation. Attached Figure Description

[0014] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart of the crimping status monitoring module in this invention; Figure 3 This is a flowchart of the quality deviation analysis module in this invention; Figure 4 This is a flowchart of the feedback control module in this invention; Figure 5 This is a flowchart of the security performance verification module in this invention; Figure 6 This is a flowchart of the operation optimization module in this invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0017] Please see Figure 1 A quality monitoring and feedback system for the crimping process includes: The crimping status monitoring module, based on the operating characteristics of the crimping equipment and combined with the mechanical changes during the connection process between the terminal and the wire, identifies the dynamic load distribution, contact resistance changes and deformation information during the crimping process, determines whether the crimping quality meets the preset standards, identifies potential abnormal points, and obtains information on abnormal crimping quality points. The quality deviation analysis module analyzes the degree of deviation of mechanical distribution and deformation characteristics during the pressing process based on the information of abnormal points in the pressing quality, extracts the key influencing factors of the pressing operation, and generates quality deviation assessment results. Based on the quality deviation assessment results, the feedback control module extracts the current operating parameters of the crimping equipment, combines the equipment's maximum load capacity and real-time load conditions, adjusts the crimping control strategy, and generates optimized crimping control commands. The safety performance verification module calls the optimized crimping control instructions to check the key performance indicators of the crimping equipment during operation, including the temperature rise and mechanical fatigue level, marks the operating conditions with potential hazards, and generates a safety checklist for the crimping equipment. The operation optimization module extracts potential operational statuses based on the operating safety checklist of the crimping equipment, rearranges associated crimping tasks and loads, assesses potential operational risks, optimizes the execution order of crimping tasks based on real-time conditions, and generates a crimping task tuning table.

[0018] Information on abnormal points in crimping quality includes abnormal load change characteristics, contact resistance deviation characteristics, and abnormal material deformation parameters. Quality deviation assessment results include mechanical deviation indexes, deformation deviation quantification values, and a set of influencing factors. Optimized crimping control commands include pressure adjustment parameters, speed control parameters, and positioning correction parameters. The crimping equipment operation safety checklist includes temperature rise warning indicators, fatigue strength assessment values, and operational stability labels. The crimping task optimization table includes task allocation priority, load reconfiguration parameters, and execution order optimization items.

[0019] Please see Figure 2 The crimping status monitoring module includes: The mechanical distribution capture submodule, based on the dynamic load variation law during crimping and combined with the mechanical distribution characteristics of the contact area between the terminal and the wire, transforms the mechanical changes into a crimping node state sequence, thus obtaining a synchronous state sequence of the crimping node. A high-precision dynamic force sensor array is used to monitor the mechanical distribution characteristics of the contact area between the terminal and the wire in real time during the crimping process. When the crimping equipment starts operation, the force sensor array continuously collects dynamic load data of the crimping die acting on the terminal and the wire at a sampling frequency of 2000 times per second. For example, for a standard terminal crimping process, the sensor continuously increases the peak load from 0N to 2500N within 0ms to 100ms, and then decreases to 100N within 100ms to 120ms. This data is transmitted to the processing unit through a high-speed data acquisition card. The processing unit converts the continuous dynamic load change into a series of discrete crimping node states through a preset mechanical model. For example, the load is defined as "initial contact state" when it is between 0N and 500N, "plastic deformation state" when it is between 500N and 2000N, and "plastic deformation state" when it is greater than 2000N. Furthermore, a duration exceeding 5ms is considered the "final compaction state," and a load below 100N is considered the "die retraction state." For each pressing node (e.g., through 10 discrete monitoring points distributed on the die surface), its mechanical state at a specific time point (e.g., every 5ms) is determined by comparing the current load value with a preset threshold. For example, at the 50ms mark of the pressing process, the load at monitoring point P1 is 1200N, which is higher than the "initial contact state" threshold of 500N and lower than the "final compaction state" threshold of 2000N, and is therefore determined to be in the "plastic deformation state." At the same time point, the load at monitoring point P2 is 1500N, and is also determined to be in the "plastic deformation state." The processing unit synchronously records the state of all monitoring points, forming a time series, and finally obtains a synchronous state sequence of pressing nodes containing the state information of all pressing nodes at each sampling time.

[0020] The deformation feature extraction submodule extracts the deformation difference sequence by matching the preset deformation feature model with the synchronous state sequence of the press-fit nodes, and obtains the node deformation deviation. Based on the synchronous state sequence of the pressing nodes, a preset deformation feature model is invoked. This model stores a series of geometric deformation parameters under ideal pressing conditions, such as an ideal pressing height of 2.30mm ± 0.05mm, an ideal pressing width of 2.80mm ± 0.05mm, and a corresponding cross-sectional area reduction rate of 25% ± 2%. First, the actual geometric deformation data of each pressing node is extracted from the synchronous state sequence of the pressing nodes. For example, through visual inspection, the actual height of the pressing node under the "final compaction state" is measured to be 2.45mm, the actual width is 2.70mm, and the actual cross-sectional area reduction rate is 20%. Subsequently, the extracted actual deformation data is matched and compared one by one with the preset deformation feature model. For example, for the pressing height, the deviation from the ideal height is |2.45mm−2.30mm| = 0.15mm, which exceeds the preset tolerance range of ±0.05mm. For the pressing width, the deviation from the ideal width is |2. 70mm−2.80mm∣=0.10mm, exceeding the preset tolerance range of ±0.05mm. Regarding the cross-sectional area reduction rate, its deviation from the ideal value is ∣20%−25%∣=5%, exceeding the preset tolerance range of ±2%. The process of extracting the deformation difference sequence involves calculating the deviation of various deformation parameters for each crimping node. For example, the deformation height deviation of node N1 is 0.15mm, the deformation width deviation is 0.10mm, and the area reduction rate deviation is 5%. The deformation height deviation of node N2 is 0.03mm, the deformation width deviation is 0.02mm, and the area reduction rate deviation is 1%. The calculation results together constitute the deformation difference sequence. By summing the deformation deviation data of all nodes in the deformation difference sequence, the node deformation deviation is obtained. For example, by weighted summing of the deformation height deviation, width deviation, and area reduction rate deviation of all nodes, a comprehensive node deformation deviation value is obtained. This value represents the overall deviation in deformation during this crimping process.

[0021] The quality anomaly identification submodule calls the node deformation deviation, combines the dynamic load distribution and the change in contact resistance, and superimposes the deformation characteristics and the fluctuation of the mechanical distribution to generate information on the crimping quality anomaly points. The crimping quality is comprehensively evaluated by invoking node deformation deviation and combining it with dynamic load distribution and contact resistance changes provided by force sensors and contact resistance measurement modules. The dynamic load distribution data quantifies its fluctuation by calculating the standard deviation of the force values ​​measured during the crimping process. For example, in a certain crimping cycle, the measured dynamic load sequence is [2000N, 2100N, 1950N, 2050N, 1900N], with an average value of 2000N and a standard deviation of 79.06N. If the preset load fluctuation... If the dynamic threshold is 50N, then the load fluctuation exceeds the threshold range. The change in contact resistance is obtained by measuring the difference in contact resistance before and after crimping. For example, if the contact resistance before crimping is 0.2mΩ and the contact resistance after crimping is 0.8mΩ, then the change in contact resistance is 0.6mΩ. If the preset contact resistance change threshold is 0.3mΩ, then the change in contact resistance also exceeds the threshold range. The fluctuation of deformation characteristics is the deviation value of each item in the aforementioned node deformation deviation. For example, the deformation height deviation is 0.15mm, and the deformation... The width deviation is 0.10 mm, and the area reduction rate deviation is 5%. Next, the comprehensive quality anomaly index is calculated by weighted summation. Specifically, the fluctuation of deformation characteristics, the fluctuation of mechanical distribution, and the change in contact resistance are weighted and summed. For example, the weight of the fluctuation of deformation characteristics is set to 0.4, the weight of the fluctuation of mechanical distribution is set to 0.3, and the weight of the change in contact resistance is set to 0.3. The comprehensive anomaly index I is calculated as: I = 0.4 × (normalized value of deformation height deviation + normalized value of deformation width deviation + area reduction rate deviation) The formula is: (Normalized value of deviation) / 3 + 0.3 × Normalized value of load fluctuation + 0.3 × Normalized value of contact resistance change. Each normalized value is calculated as the ratio of the actual measured value to its corresponding threshold. For example, the normalized value for a deformation height deviation of 0.15mm is 0.15mm / 0.05mm = 3, the normalized value for a load fluctuation of 79.06N is 79.06N / 50N = 1.58, and the normalized value for a contact resistance change of 0.6mΩ is 0.6mΩ / 0.3mΩ = 2. If all abnormal indicators are considered... greater than the preset abnormal threshold (For example, setting) (This threshold is determined through statistical analysis of abnormal samples in historical data). Then, the current crimping point is identified as a quality anomaly. Through the above calculation process, all crimping points with quality problems can be accurately identified, and crimping quality anomaly point information can be generated, such as an anomaly point ID list, the corresponding anomaly type, and the degree of anomaly.

[0022] Please see Figure 3 The quality deviation analysis module includes: The mechanical distribution analysis submodule collects the time characteristics of dynamic load distribution during the pressing process based on the information of abnormal points in the pressing quality, analyzes the degree of deviation of mechanical distribution, and generates a mechanical distribution deviation table. Based on the information on abnormal points in the crimping quality, for the crimping process marked as abnormal, the time characteristics of the dynamic load distribution during the crimping process are collected. For example, for the abnormal crimping task with ID "CRIMP_20230315_001", its real-time load data from 0ms to 120ms is retrieved. The data is represented as a force-time curve that changes with time. Then, the degree of deviation of the mechanical distribution is analyzed. This analysis is completed by comparing the collected actual force-time curve with the preset ideal force-time curve. The preset ideal force-time curve is fitted by the average value of a large number of qualified crimping samples and represents the load characteristics of the standard crimping process. The area difference (integral difference) between the two curves is calculated as a quantitative indicator of the degree of deviation. For example, if the area under the ideal curve is 250 Newton-seconds (N·s). If the area under the actual abnormal curve is 210 N·s, then the deviation area is 40 N·s. If the preset deviation area threshold is 30 N·s, then this deviation exceeds the threshold range. The degree of deviation is divided into three levels: when the deviation area is less than 15 N·s, it is considered "slight deviation"; when the deviation area is between 15 N·s and 30 N·s, it is considered "moderate deviation"; when the deviation area is greater than 30 N·s, it is considered "significant deviation". For example, the deviation area of ​​40 N·s is judged as "significant deviation". By performing this analysis on all abnormal pressing tasks, a mechanical distribution deviation table is generated. This table records the ID of each abnormal pressing task, the corresponding load deviation degree (e.g., "significant deviation") and its quantification value (e.g., 40 N·s), providing basic data for subsequent analysis.

[0023] The deformation feature detection submodule analyzes the temporal overlap of deformation features during the pressing process based on the mechanical distribution deviation table, identifies deformation conflict periods, and generates a list of deformation conflict periods. Based on the mechanical distribution deviation table, and according to the load deviation time periods recorded in the table (e.g., for task ID "CRIMP_20230315_001", the load deviates significantly between 80ms and 110ms), deformation characteristic data of each crimping node within this time period is extracted from the raw data. For example, within this time period, the crimping height is monitored to change from 2.35mm to 2.50mm, and the crimping width changes from 2.75mm to 2.65mm. Deformation conflict periods are identified. The identification process is completed by setting a deformation deviation threshold. When the deformation parameters (e.g., height, width) of a crimping node deviate from their ideal values ​​by more than a preset threshold (e.g., height deviation exceeds 0.08mm or width deviation exceeds 0.07mm) within a specific time period, and this time period overlaps with the mechanical distribution deviation time period, then... The deformation conflict period is identified as follows: For example, if the crimping height measurement is 2.50mm between 85ms and 105ms, the deviation from the ideal height of 2.30mm is 0.20mm, which is greater than the deformation deviation threshold of 0.08mm. This period overlaps with the load deviation period (80ms to 110ms). Therefore, 85ms to 105ms is identified as a deformation conflict period. All identified deformation conflict periods, along with the corresponding crimping task ID and conflict level (e.g., "high", "medium", and "low" conflict levels based on the magnitude of the deviation), are recorded. Finally, a list of deformation conflict periods is generated. This list contains detailed information on all detected deformation conflict events, such as task ID, conflict start time, conflict end time, involved deformation parameters, and conflict level, providing accurate temporal and spatial positioning for deviation optimization.

[0024] The deviation optimization submodule optimizes the deformation distribution, adjusts the pressing operation sequence and time allocation based on the list of deformation conflict periods, combined with the pressing quality requirements and equipment operating characteristics, and generates quality deviation assessment results. Based on the list of deformation conflict periods, the time intervals in which deformation conflicts occur are identified. This list records the deformation during the operation of each device and marks the specific time periods in which conflicts occur. A quality standard range is set according to the crimping quality requirements, assuming that the goal is to control the crimping force F value between 100N and 150N. To optimize deformation distribution, it is first necessary to analyze the equipment's operating characteristics and understand its response characteristics under different loads and speeds, such as maximum pressing force, load speed, and reaction time. These parameters can be obtained from the equipment manual. Match the equipment characteristics with the information in the deformation conflict period list to filter out the time periods that have a significant impact on quality requirements. For each conflict period, calculate the time allocation of the equipment's pressing actions and adjust the sequence of actions in the pressing process. Assuming that the equipment's operating characteristics indicate that the pressing force is prone to being too high when the load is too large, excessive deformation can be avoided by adjusting the sequence of pressing time periods and the force distribution during the pressing process, keeping the pressing force within a suitable range. Combining the pressing quality requirements with the equipment's operating characteristics, the pressing operation sequence and time allocation can be further adjusted. By adjusting the operation sequence, the high-pressure pressing actions are arranged during periods when the equipment is operating relatively stably, and appropriate time is allocated within the pressing cycle. Assuming a crimping cycle is 5 seconds, with 2 seconds of high-pressure application and the remaining 3 seconds as a rest period, this method reduces the impact of deformation on the equipment. The crimping force F and actual time t for each crimping operation are collected and recorded. The deformation D is calculated using the formula D=kF×t (where k is the equipment deformation coefficient), ensuring that the deformation D of each operation is within the target deviation range, for example, D≤0.5mm. By adjusting the operation sequence and time allocation, the deformation D of a certain operation was optimized to 0.45mm, thus meeting the quality requirements. Finally, through a series of adjustments and optimizations, a complete quality deviation assessment result is generated, ensuring that the quality of each crimping operation meets the requirements.

[0025] Please see Figure 4 The feedback control module includes: Based on the quality deviation assessment results, the control parameter extraction submodule extracts the operating parameter range corresponding to the crimping equipment, organizes the crimping control strategy adjustment range, and generates a crimping control strategy table. Based on the quality deviation assessment results, which include the comprehensive deviation value (e.g., D value) of the current crimping task and suggested optimization directions, the operating parameter ranges corresponding to the crimping equipment are first extracted. For example, for a specific model of crimping machine, the crimping force range is 500N to 3000N, the crimping speed range is 10mm / s to 60mm / s, and the holding time range is 0.1s to 0.5s. These parameter ranges are derived from the equipment manufacturer's technical specifications and historical operating data. Then, the adjustment range for the crimping control strategy is compiled. This adjustment range is determined based on the severity of the quality deviation assessment results. For example, if the quality deviation D value (calculated as approximately 8105) exceeds 20% of the normal operating range (e.g., the normal range is set at 8000±800, i.e., 7200-8800), it is considered a "high deviation," allowing an adjustment of ±10% to the crimping force and ±5% to the crimping speed. If the deviation... Between 10% and 20%, a deviation is considered "medium deviation," allowing for adjustments of ±5% to the pressing force and ±2% to the pressing speed. This tiered strategy ensures that the adjustment range matches the degree of deviation, avoiding over-adjustment. For example, if the evaluation results show that the pressing height is too high, the pressing force in the final compaction stage needs to be increased. In this case, the maximum allowable pressing force adjustment range is set to the current value plus 10%, while limiting the adjustment range of parameters. Finally, a pressing control strategy table is generated, which details the current value of each adjustable parameter, the recommended adjustment direction (e.g., increase or decrease), the maximum allowable adjustment range, and the adjustment priority. For example, pressing force: current 2000N, recommended increase, maximum allowable +200N, high priority; pressing speed: current 40mm / s, recommended unchanged, maximum allowable ±2mm / s, medium priority. This provides a preliminary basis for parameter adjustment for the load-bearing capacity judgment submodule.

[0026] The load-bearing capacity judgment submodule is based on the crimping control strategy table and combined with the real-time load of the crimping equipment to screen the equipment's operating load-bearing capacity, determine whether it exceeds the load-bearing range, mark the limited parameter range, and generate load-bearing capacity analysis results. Based on the crimping control strategy table, which details the suggested parameter adjustment ranges and priorities, and considering the real-time load of the crimping equipment, the operating capacity of the equipment is screened. For example, if the real-time load monitoring data of a crimping machine shows a current pressing force of 2100N, a motor current of 10A, and an operating temperature of 45℃, the suggested adjustments in the strategy table are compared with the real-time load data to determine if they exceed the load capacity. For instance, if the strategy table suggests increasing the pressing force by 150N to 2250N, and the maximum rated pressing force of the crimping machine is 2300N, then 2250N is considered within the maximum load capacity. However, if the strategy table suggests increasing the pressing force by 300N to 2400N, this value exceeds the maximum load capacity of 2300N, and the restricted parameter range will be marked. For example, if the pressing force exceeds the maximum load capacity, the pressing force parameter is marked as "restricted," and... The permissible adjustment range is limited to the current value to the maximum rated value (e.g., from 2100N to 2300N), rather than the larger range suggested in the strategy table. Simultaneously, real-time load indicators of the equipment are considered. For example, if the motor current is already close to its maximum permissible value (e.g., 12A), even if the pressure is not at its maximum, further increases in pressure or speed are limited to avoid motor overload. If the operating temperature is close to the warning threshold (e.g., 50°C), any parameter adjustments that lead to temperature rise (e.g., extending the holding time) are limited. Through this meticulous screening and judgment, a load capacity analysis result is generated. This result includes the final adjustable range for each parameter, any adjustment range narrowed due to load limitations, and a preliminary assessment of the equipment's operational risks. For example, pressure: adjustment range limited to +200N to avoid overload; speed: no adjustment limit; temperature: no adjustment limit. This result will guide the final adjustment of the control parameters.

[0027] Based on the load-bearing capacity analysis results, the control parameter adjustment submodule determines the urgency of the crimping operation according to the limited parameter range, and combines the original parameters of the equipment with the adjustment range to rearrange the control parameters and generate optimized crimping control commands. Based on the load-bearing capacity analysis results, this analysis clarifies which parameter adjustments are limited by the load-bearing capacity and the specific ranges of these limitations. The urgency of the crimping operation is determined by these limited parameter ranges. For example, if a core parameter (such as the pressing force) is marked as "severely limited" (adjustment range less than 2%), the urgency of the crimping operation is determined to be "high," requiring immediate parameter adjustment to avoid equipment damage or continued quality degradation. If only minor parameters are slightly limited, the urgency is determined to be "low." Subsequently, combining the original parameters and adjustment ranges, the control parameters are rearranged. The original parameters refer to the initial parameter values ​​set for this crimping task, and the adjustment range is the actual adjustable range given in the load-bearing capacity analysis results. The parameter ranking is based on the urgency and its direct impact on quality anomalies. For example, if the quality anomaly mainly stems from a deviation in mechanical distribution, then the pressing force or crimping... Speed ​​parameters are adjusted first; if the anomaly is related to the temperature rise, the holding time or cooling time will be adjusted first. The highest priority parameter is adjusted first, and fine-tuned within its allowable range to achieve the best quality improvement with the smallest adjustment. For example, if the load-bearing capacity analysis shows that the pressing force can be adjusted from the current value plus 0N to plus 200N, 100N is chosen instead of directly increasing it by 200N to verify its effect and avoid over-adjustment. If adjusting a single parameter is insufficient to solve the problem, other related parameters are adjusted in turn to ensure the coordination of the overall solution. Finally, an optimized crimping control command is generated. This command is a set of all updated parameter values ​​and is sent directly to the crimping equipment for execution. For example, pressing force: 2100N, crimping speed: 38mm / s, holding time: 0.22s, ensuring that the crimping process achieves the best quality within the equipment's safe load-bearing capacity.

[0028] Please see Figure 5 The security performance verification module includes: The equipment performance inspection submodule monitors the operating status of the crimping equipment based on the optimized crimping control commands, checks whether the temperature rise and mechanical fatigue meet safety standards, records the changes in performance indicators, and generates equipment operating performance records. Based on the optimized crimping control commands, the crimping task is initiated, and the operating status of the crimping equipment is monitored in real time, including the temperature rise and mechanical fatigue level. The temperature rise is collected in real time by thermocouples or infrared sensors deployed in key parts of the crimping die, motor, and transmission mechanism. For example, sensor S1 monitors the die surface temperature as 48°C, and sensor S2 monitors the motor housing temperature as 55°C. The real-time temperature is compared with the ambient temperature (e.g., 25°C) or the equipment reference temperature (e.g., 30°C) to calculate the temperature rise. For example, the die temperature rise is 48°C − 25°C = 23K. The mechanical fatigue level is assessed by monitoring the equipment's vibration signals, bearing noise, and the cumulative number of operating cycles of key moving parts. For example, a high-precision accelerometer collects the vibration acceleration data of the crimping head in real time, performs Fourier transform on it to analyze the energy distribution within a specific frequency range, and identifies abnormal vibrations. In dynamic mode, the system records the number of cycles for each pressing operation and estimates the cumulative fatigue damage of key components by combining historical fatigue curves. For example, if the cumulative operating cycle of a bearing has reached 70% of its design life, the system continuously checks whether the temperature rise and mechanical fatigue level meet safety standards. The safety standards are set as specific numerical thresholds, such as the mold temperature rise should be below 30K, the motor temperature rise should be below 60K, and the cumulative fatigue damage index of key components should be below 0.8. If any indicator exceeds the threshold, it is recorded as "abnormal". For example, if the mold temperature rise reaches 35K, it is recorded as an abnormal mold temperature rise. All monitored performance indicator changes, including real-time temperature, vibration data, cumulative cycle count, calculated temperature rise and fatigue damage index, and compliance with safety standards, are accurately recorded to generate an equipment operating performance record. This record provides raw data for generating a hazard list.

[0029] The hazard list generation submodule identifies operating states where the temperature rise or mechanical fatigue exceeds the safe range based on equipment operating performance records, calculates the risk assessment indicators of temperature rise and mechanical fatigue, marks them as states with potential hazards, identifies the equipment operating safety pass rate, and generates a safety list for the pressing equipment. Based on equipment operation performance records, real-time temperature data and stress data of mechanical components are extracted from the equipment's operation records to ensure that temperature changes and stress monitoring are completely recorded throughout the entire operating cycle of the equipment. For the assessment of temperature rise, real-time monitored temperature values ​​are compared with preset safety benchmark temperatures. If the temperature change during a certain period exceeds the safety set value, that period is determined to be a temperature rise exceeding the limit. For mechanical fatigue, data from vibration and stress sensors are combined to calculate the stress level of equipment components, and fatigue accumulation is calculated according to material fatigue strength standards. If the stress data exceeds the fatigue limit, the equipment is marked as mechanically fatigued beyond the limit. Subsequently, risk indicators are comprehensively calculated and generated using risk assessment indices for temperature rise and mechanical fatigue. This process involves a comprehensive assessment of temperature rise and mechanical fatigue, based on historical data of the equipment's workload, temperature, and stress levels, combined with empirical parameters, to calculate the overall risk status of the equipment operation. If the risk value exceeds the preset safety range, the equipment is determined to have potential hazards and further safety assessment is conducted. After calculating the risk assessment indices for temperature rise and mechanical fatigue, the equipment's safety pass rate is assessed according to preset standards. By comparing the equipment's performance across multiple operating cycles and statistically analyzing the ratio of qualified to unqualified cycles, the equipment's safety pass rate is calculated. For example, if the equipment meets safety standards for 90 out of 100 operating cycles, the safety pass rate is 90%. This assessment reflects the long-term operational stability of the equipment, ensuring its reliability and safety under normal operating conditions. Furthermore, the operating status within each cycle is recorded and categorized in detail, generating a safety checklist for the crimping equipment. This checklist lists all cycles that meet standards and those with potential hazards, clearly indicating the specific safety risks. This provides equipment managers with clear safety monitoring data, supporting further maintenance and optimization measures.

[0030] Please see Figure 6 The operation optimization module includes: The status filtering submodule extracts the operating status with potential hazards based on the operating safety checklist of the crimping equipment, filters the associated crimping tasks, extracts the corresponding load and operating information for each task, analyzes the causes of the potential operating hazards, and generates a list of tasks to be optimized. The crimping equipment operation safety checklist details all operational states identified as having potential hazards. First, the operational states with potential hazards are extracted; for example, all those listed in the checklist are identified. For records with values ​​greater than 15, the associated crimping tasks are filtered. For each operational status with potential risks, the corresponding crimping task ID (e.g., task ID "CRIMP_20230315_001") is traced to link the abnormal operating status of the equipment with specific production tasks. For each task, corresponding load and operational information are extracted. For example, for task "CRIMP_20230315_001," detailed load curves, actual crimping speeds, holding times, and the types of terminals and wires processed during the task's execution are extracted from the historical database. This information is crucial for analyzing the causes of potential risks. Subsequently, the causes of operational risks are analyzed. This analysis process involves comparing the operational data of abnormal tasks with those of normal tasks. By comparing operational data, for example, if a potential hazard task is found to have a consistently high dynamic load during the crimping process (e.g., the average load is 15% higher than the standard), or a significantly accelerated temperature rise rate during the holding pressure stage (e.g., temperature rise exceeding 3K per second), and the abnormal characteristics are associated with a specific material batch or combination of crimping parameters, then that specific factor will be identified as the primary cause of the hazard. In this way, the specific operating conditions or environmental factors leading to equipment safety hazards can be clearly identified, generating a list of tasks to be optimized. This list contains all crimping tasks for which specific hazard causes have been identified, such as the task ID, the cause of the hazard (e.g., "high load causes mold overheating"), the parameters involved, and the degree of deviation, providing clear optimization targets for task scheduling.

[0031] The task scheduling submodule rearranges associated tasks based on the list of tasks to be optimized, schedules the distribution of currently available resources and load, analyzes task performance and running progress, and generates task and load scheduling plans. Based on the task list to be optimized, which includes crimping tasks that need improvement and their specific potential causes, the first step is to reschedule related crimping tasks. The scheduling process considers currently available resources and load distribution. For example, if a task in the task list (e.g., a high-force crimping task) is identified as having a potential problem on a certain device, it is assessed whether other crimping devices are currently idle or have low load, and whether that device has the capability to perform such high-force crimping tasks. If a suitable device is available, the task is reassigned to a better device. If not, the task is considered for delay or broken down into smaller subtasks. Simultaneously, the load distribution of all crimping devices is monitored in real time to ensure that task reassignment does not lead to device overload or introduce new potential problems. For example, multiple high-force crimping tasks should not be concentrated on the same device. Subsequently... The analysis examines task performance and progress by comparing the actual execution time, energy consumption, and crimping quality indicators (e.g., the aforementioned D and G values) after task rescheduling with the expected targets. For example, the actual D value of the rescheduled high-force crimping task decreased from 8105 to 7500, and the G value decreased from 22.34 to 10.5. The analysis also records task completion progress and current equipment utilization to ensure the effectiveness of the scheduling plan. Finally, a task and load scheduling plan is generated, detailing the new execution equipment, estimated start / end time, and corresponding resource allocation for each crimping task. For example, task "CRIMP_20230315_001" is transferred from equipment A to equipment B, with an estimated start time of 10:30 and an estimated end time of 10:45, providing a refined execution plan for operational optimization.

[0032] The operation optimization submodule assesses potential operational risks based on task and load scheduling plans, combined with current real-time progress and environmental conditions, optimizes task execution order and time allocation, and generates a task optimization table. Based on task and load scheduling plans, and considering current real-time progress and environmental conditions, potential operational risks are assessed. For example, if a high-pressure crimping task is scheduled for a hot summer period, and the equipment performing the task has just completed multiple heavy-load tasks consecutively, the risk level of performing this high-pressure task is reassessed based on real-time ambient temperature (e.g., 35°C) and equipment internal temperature (e.g., 50°C) data, as well as the equipment's historical overheating records. If the risk level exceeds a preset threshold (e.g., a risk index higher than 0.7), a risk warning is triggered. Subsequently, the task execution order and time allocation are optimized. The optimization process aims to minimize overall operational risk and maximize production efficiency. For example, if the assessment finds that a task is too risky under the current arrangement, it may be postponed to a later period. Tasks are executed during periods of lower temperature, or their priority is reduced to make way for lower-risk but equally important tasks. When multiple devices are working in parallel, the allocation of tasks among different devices is dynamically adjusted. For example, high-stress tasks are assigned to devices in better condition, or long-cycle tasks are split and executed in parallel on multiple devices. During the optimization process, the current real-time progress of the devices is considered. For example, if the actual completion time of a task is earlier than planned, low-risk or high-priority tasks are immediately inserted during this idle time to improve device utilization. Finally, a crimping task tuning table is generated. This table is the finalized task execution plan after risk assessment and resource optimization. For example, it includes task ID, actual start time, actual end time, allocated device, and task status, providing executable and refined instructions for actual production.

[0033] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A crimping process quality monitoring and feedback system, characterized by, The system comprises: The crimping state monitoring module identifies the dynamic load distribution, the contact resistance change and the deformation degree information in the crimping process, judges whether the crimping quality meets the preset standard, identifies the potential abnormal points, and obtains the crimping quality abnormal point information based on the operating characteristics of the crimping equipment and the mechanical changes in the terminal and wire connection process; The quality deviation analysis module analyzes the deviation degree of the mechanical distribution and deformation characteristics in the crimping process, extracts the key influencing factors of the crimping operation, and generates a quality deviation evaluation result according to the crimping quality abnormal point information; The feedback control module extracts the operating parameters of the current crimping equipment, adjusts the crimping control strategy, and generates an optimized crimping control instruction based on the quality deviation evaluation result and in combination with the maximum carrying capacity and real-time load condition of the equipment; The safety performance verification module calls the optimized crimping control instruction, checks the key performance indicators of the crimping equipment in the running process, including the temperature rise amplitude and the mechanical fatigue degree, marks the running state with hidden dangers, and generates a crimping equipment running safety list.

2. The crimping process quality monitoring and feedback system of claim 1, wherein, The crimping quality abnormal point information includes load change abnormal characteristics, contact resistance deviation characteristics and material deformation abnormal parameters, the quality deviation evaluation result includes mechanical deviation indicators, deformation deviation quantitative values and influence factor sets, the optimized crimping control instruction includes pressure adjustment parameters, speed control parameters and positioning correction parameters, and the crimping equipment running safety list includes temperature rise warning indicators, fatigue strength evaluation values and running stability labels.

3. The crimping process quality monitoring and feedback system of claim 1, wherein, The crimping state monitoring module comprises: The mechanical distribution capturing submodule converts the mechanical changes into a crimping node state sequence based on the change law of the dynamic load in the crimping process and in combination with the mechanical distribution characteristics of the terminal and wire contact area, and obtains a crimping node synchronous state sequence; The deformation feature extraction submodule matches a preset deformation feature model according to the crimping node synchronous state sequence, extracts a deformation difference sequence, and obtains a node deformation deviation; The quality abnormality identification submodule calls the node deformation deviation, combines the dynamic load distribution and the contact resistance change, superimposes the fluctuation amount of the deformation feature and the mechanical distribution, and generates crimping quality abnormal point information.

4. The crimping process quality monitoring and feedback system of claim 3, wherein, The quality deviation analysis module comprises: The mechanical distribution analysis submodule collects the time characteristics of the dynamic load distribution in the crimping process according to the crimping quality abnormal point information, analyzes the mechanical distribution deviation degree, and generates a mechanical distribution deviation table; The deformation feature detection submodule analyzes the time overlap of the deformation characteristics in the crimping process based on the mechanical distribution deviation table, identifies the deformation conflict period, and generates a deformation conflict period list; The deviation optimization submodule optimizes the deformation distribution, adjusts the crimping operation sequence and time allocation, calculates a deformation optimization value, and generates a quality deviation evaluation result according to the deformation conflict period list and in combination with the crimping quality requirements and equipment operating characteristics.

5. The crimping process quality monitoring and feedback system of claim 4, wherein, The feedback control module comprises: The control parameter extraction submodule extracts the operating parameter range of the crimping equipment corresponding to the quality deviation evaluation result, arranges the crimping control strategy adjustment interval, and generates a crimping control strategy table; The bearing capacity judgment submodule filters the bearing capacity of the device in operation based on the crimping control strategy table in combination with the real-time load condition of the crimping device, judges whether the bearing capacity exceeds the bearing range, marks the limited parameter interval, and generates a bearing capacity analysis result; The control parameter adjustment submodule judges the emergency degree of the crimping operation according to the limited parameter interval based on the bearing capacity analysis result, re-arranges the control parameter sequence in combination with the original parameters of the device and the adjustment range, and generates an optimized crimping control instruction.

6. The crimping process quality monitoring and feedback system of claim 5, wherein, The safety performance verification module includes: The device performance inspection submodule monitors the running state of the crimping device based on the optimized crimping control instruction, checks whether the temperature rise amplitude and mechanical fatigue degree meet the safety standard, records the performance index change, and generates a device running performance record; The hidden danger list generation submodule identifies the running state in which the temperature rise amplitude or the mechanical fatigue degree exceeds the safety range based on the device running performance record, calculates the risk assessment index of the temperature rise amplitude and the mechanical fatigue, and marks the state as a hidden danger, identifies the device running safety qualified rate, and generates a crimping device running safety list.

7. The crimping process quality monitoring and feedback system of claim 1, wherein, The system further includes a running optimization module: The running optimization module extracts the running state with hidden dangers according to the crimping device running safety list, re-arranges the associated crimping tasks and loads, evaluates the potential running risks, optimizes the crimping task execution sequence in combination with the real-time situation, and generates a crimping task optimization table; The crimping task optimization table includes task allocation priority, load reconstruction parameter, and execution sequence optimization item.

8. The crimping process quality monitoring and feedback system of claim 7, wherein, The running optimization module includes: The state screening submodule extracts the running state with hidden dangers according to the crimping device running safety list, screens the associated crimping tasks, extracts the corresponding load and running information for each task, analyzes the running hidden danger reasons, and generates a to-be-optimized task list; The task scheduling submodule re-arranges the associated crimping tasks according to the to-be-optimized task list, schedules the current available resources and load distribution, analyzes the task performance and running progress, and generates a task and load scheduling plan; The running optimization submodule evaluates the potential running risks based on the task and load scheduling plan in combination with the current real-time progress and environmental condition factors, optimizes the task execution sequence and time allocation, and generates a crimping task optimization table.

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