Wire harness production data management method and system based on Internet of Things
By aligning pressure timing data with IoT gateways and high-frequency piezoelectric sensors, the crimping density energy index is calculated. Combined with equipment wear and temperature changes, the judgment criteria are dynamically adjusted, solving the accuracy and adaptability issues of quality monitoring in wire harness production and achieving efficient quality control and equipment maintenance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-12
- Publication Date
- 2026-04-03
AI Technical Summary
Existing technologies struggle to effectively identify minute defects and equipment status drift in wire harness production, resulting in low quality monitoring accuracy and poor adaptability. This is especially true when equipment ages or the environment changes, making it prone to misjudgment or missed detection.
By continuously collecting crimping data from the terminal block machine through an IoT gateway, aligning the pressure timing data using a high-frequency piezoelectric sensor and the center of gravity method, calculating the crimping density energy index, and dynamically adjusting the judgment criteria in conjunction with equipment wear and temperature changes, the dynamic drift tolerance can be assessed.
This improves the precision and reliability of wire harness production quality assessment, reduces misjudgments, and enables a shift from passive screening to proactive optimization, ensuring product quality consistency and equipment operating efficiency.
Smart Images
Figure CN121785276A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of industrial data management technology. More specifically, this invention relates to a method and system for managing wire harness production data based on the Internet of Things (IoT). Background Technology
[0002] In the modern production process of the wire harness processing industry, fully automatic terminal crimping machines are the core equipment. Their key process involves using the high-speed impact force of a mechanical slider to crimp metal terminals onto wires, causing severe plastic deformation and forming a tight, airtight electrical connection. The quality of this crimping process determines the contact resistance, conductivity, and tensile strength of the finished wire harness. Any slight loosening or overpressure can lead to poor contact or even a short circuit and fire. Therefore, it is a crucial link in ensuring the electrical safety of automotive and electronic equipment.
[0003] Current industry technology primarily monitors production quality through a pressure management system equipped on the crimping machine. This system typically uses piezoelectric or strain gauge sensors to collect real-time data on the pressure changes over time during the crimping process. The conventional approach is to first collect pressure data from a preset number of qualified products to establish a reference baseline, then set an absolute tolerance upper and lower limit or standard envelope for the peak force. When the actual monitored pressure curve exceeds this preset range, the product is judged as defective.
[0004] However, the existing technologies mentioned above have room for improvement when faced with minor defects and environmental changes. On the one hand, for hidden defects such as copper wire oxidation, hard terminal material, or deep wire breakage, the pressure peak is often still within the acceptable range, with only slight differences in the energy distribution or rebound characteristics of the waveform, making it difficult for simple peak detection to distinguish such defects. On the other hand, due to the mechanical thermal expansion effect, the reference pressure will naturally drift when the equipment is in cold start-up and hot operation. Fixed threshold methods are prone to false alarms when the equipment is cold and to missed detections when the equipment is hot. Moreover, the existing system lacks a mechanism to use waveform data to assess the health of the equipment, making it difficult to achieve predictive maintenance for conditions such as die wear. Summary of the Invention
[0005] To address the aforementioned technical problems of low monitoring accuracy and poor adaptability, the present invention provides solutions in the following aspects.
[0006] In a first aspect, the present invention provides a method for managing wire harness production data based on the Internet of Things, comprising: The system continuously collects pressure timing data, mold temperature, ambient temperature, and cumulative equipment operation count for a single crimping operation from the terminal block machine via an IoT gateway. Waveform alignment preprocessing is performed on the pressure timing data of the single crimping operation to obtain valid waveform data. Based on the time distribution characteristics and amplitude differences of the valid waveform data, the crimping density energy index for a single crimping operation is calculated. Wear tolerance is relaxed based on the cumulative equipment operation count, and the dynamic drift tolerance for a single crimping operation is calculated by combining the temperature difference between the mold and ambient temperatures. The crimping density energy index is compared with the dynamic drift tolerance, and the quality status of the wire harness product is determined based on the comparison result. Equipment maintenance warnings or production control are executed based on the changing trend of the crimping density energy index.
[0007] This invention continuously collects crimping production data from the terminal block machine via an IoT gateway and obtains valid waveform data. Based on this, it evaluates the crimping density energy index, which reflects the density of the product. Simultaneously, it considers the mechanical wear caused by the cumulative number of equipment runs and the temperature difference between the mold and the ambient temperature to calculate the dynamic drift tolerance that matches the current equipment state. This incorporates the natural aging law and thermodynamic characteristics of the equipment in the wire harness production process into the quality assessment system, enabling the quality judgment criteria to adaptively adjust with changes in the physical state of the equipment. While ensuring the crimping quality of the product, it reduces misjudgments caused by equipment wear or temperature rise, improves the precision of wire harness production data management and the reliability of judgment results, and uses the index change trend to assist production control to achieve a shift from passive screening to proactive process optimization.
[0008] Preferably, the step of continuously collecting pressure timing data, mold temperature, ambient temperature, and cumulative number of equipment operations for a single crimping operation from the terminal block machine via the IoT gateway includes: A high-frequency piezoelectric pressure sensor is installed at the crimping mold of the terminal crimping machine and connected to an Internet of Things (IoT) gateway. Data is transmitted in real time via Modbus or EtherCAT industrial bus protocol. The IoT gateway collects the crimping production data of the terminal crimping machine, which includes: pressure timing data for a single crimping operation; auxiliary data such as mold temperature, ambient temperature, and cumulative number of equipment runs during a single crimping operation.
[0009] Preferably, the waveform alignment preprocessing of the pressure timing data of a single crimping operation to obtain effective waveform data includes: A preset pressure trigger threshold is set, and each discrete data point in the pressure time series data of a single pressing is traversed. The moment when the instantaneous pressure value corresponding to the discrete data point exceeds the pressure trigger threshold is marked as the valid starting point. The valid starting point and subsequent data are extracted as waveform data segments, and the centroid time point of the waveform data segment is calculated based on the centroid method. All the collected waveform data segments are translated and aligned with the centroid time point as the reference to obtain valid waveform data with consistent time axis of a single pressing.
[0010] This invention uses a preset pressure trigger threshold combined with the centroid method to process the pressure timing data of a single pressing operation. By extracting the effective starting point and subsequent data segments and calculating the centroid time point, all waveform data segments are translated and aligned with the centroid as the reference to obtain effective waveform data. This optimizes the time axis deviation problem caused by minor vibrations in mechanical transmission or sampling frequency limitations, ensuring that subsequent feature extraction is based on the same physical reference. Compared with the method of relying solely on threshold trigger alignment, the centroid method can more robustly represent the overall timing distribution of the waveform and reduce feature calculation deviations caused by phase errors.
[0011] Preferably, the acquisition of the compressive densification energy index includes: Calculate the time-weighted energy distribution; Calculate the weighted shape difference term between the effective waveform data of a single crimping and the standard reference waveform; The energy distribution and the shape difference term are added together to obtain the compression compaction energy index for a single compression.
[0012] This invention calculates the time-weighted energy distribution and the weighted shape difference between the effective waveform data and the standard reference waveform, and adds the two together to obtain the crimping density energy index. This forms a dual evaluation mechanism that includes the energy accumulation of the time axis of the pressure waveform and the microscopic deviation of the waveform geometry. This allows the crimping density energy index to more comprehensively reflect the density and potential defects inside the terminal crimp, thereby improving the ability to identify products with hidden defects and ensuring the stability of the finished wire harness in terms of electrical connection performance and mechanical strength.
[0013] Preferably, the calculation of the time-weighted energy distribution includes: Subtract the start time from the end time of the effective waveform data of a single crimping, and multiply it by the maximum pressure value in the effective waveform data of the single crimping as the denominator; The instantaneous pressure value of the effective waveform data of a single pressing at time t is multiplied by a power of the product of the natural constant e and the time difference between time t and the time when the instantaneous pressure value reaches its peak, with the product as the numerator. Divide the numerator by the denominator and perform integration over the time interval from the start time to the end time to obtain the time-weighted energy distribution.
[0014] Preferably, the weighted shape difference term for calculating the effective waveform data of a single crimping operation and the standard reference waveform includes: The instantaneous pressure value of each discrete sampling point in the effective waveform data of a single pressing is subtracted from the square of the pressure value of the corresponding standard reference waveform, and then summed. This sum is divided by the square of the maximum instantaneous pressure value, and the square root is calculated. Finally, it is multiplied by the weighting coefficient of the shape difference term to obtain the weighted shape difference term.
[0015] Preferably, the dynamic drift tolerance satisfies the following relationship: ; In the formula, Indicates the dynamic drift tolerance of a single crimping operation; This represents the initial basic tolerance value, which is determined by the historical statistical standard deviation of qualified products. This indicates the cumulative number of times the equipment has been used in a single crimping operation; Indicates the recommended rated number of operations for the equipment; Indicates the wear tolerance factor; This represents the absolute value of the difference between the mold temperature and the ambient temperature during a single pressing operation. This represents the coefficient of thermal expansion.
[0016] This invention introduces a calculation model that includes a wear relaxation coefficient and a temperature rise shrinkage coefficient when calculating dynamic drift tolerance. Based on the logarithmic growth law of the cumulative number of equipment runs, the tolerance range is appropriately relaxed. At the same time, the tolerance is tightened according to the difference between the mold temperature and the ambient temperature. This follows the physical law that mechanical parts wear quickly in the early stage and then stabilizes later, as well as the thermodynamic principle that the thermal expansion of materials affects the crimping accuracy. This allows the judgment threshold to dynamically fit the real-time working condition of the terminal block machine. Even in scenarios of equipment aging or continuous operation with rising temperatures, the judgment logic can still maintain its rationality, reducing the risk of false alarms or missed judgments caused by fluctuations in equipment status.
[0017] Preferably, comparing the crimping density energy index with the dynamic drift tolerance, and determining the quality status of the wire harness product based on the comparison result, includes: The calculated crimping density energy index for a single crimping is compared with the dynamic drift tolerance. If the crimping density energy index is less than or equal to the dynamic drift tolerance, the wire harness product is deemed qualified and released. If the crimping density energy index is greater than the dynamic drift tolerance, the wire harness product is deemed unqualified, and a stop or rejection signal is sent to the terminal block control system.
[0018] Preferably, the step of executing equipment maintenance early warning or production control based on the changing trend of the pressing densification energy index includes: The system calculates the moving average of the crimping density energy index of the most recent preset number of single crimpings in the background. If the moving average shows a monotonically increasing trend and the slope exceeds the preset safe slope threshold, it is determined that the mold has the risk of accelerated aging or loosening. Even if the single wire harness product has not exceeded the standard, the system will push a preventive maintenance work order to the MES system.
[0019] This invention utilizes the system backend to calculate the crimping density energy index of the most recent preset number of single crimping operations using a moving average. By monitoring the monotonically rising trend and slope change of the moving average, it can identify the risk of accelerated aging or loosening of the mold before a single wire harness product exceeds the standard. This establishes a preventive maintenance mechanism based on time-series trends, reducing the lag of traditional post-repair or fixed-cycle maintenance. It can promptly push preventive maintenance work orders to the MES system to guide staff to intervene before a failure occurs, reducing sudden downtime and the generation of batch scrap, and ensuring the continuous and stable operation of the wire harness production line.
[0020] Secondly, the present invention provides an IoT-based wire harness production data management system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned IoT-based wire harness production data management method is implemented.
[0021] By adopting the above technical solution, a computer program is generated from the above-mentioned IoT-based wire harness production data management method and stored in a memory so that it can be loaded and executed by a processor. This allows for the creation of terminal devices based on the memory and processor, making them convenient to use.
[0022] The beneficial effects of this invention are as follows: This invention acquires and aligns high-frequency pressure time-series data in real time through an IoT gateway to extract the crimping density energy index, which can capture hidden defects. Based on the nonlinear cumulative law of mechanical wear reflected by the cumulative number of equipment runs and the thermal drift disturbance characteristics caused by the real-time temperature difference of the mold, it calculates the dynamic drift tolerance adapted to the current working conditions. This transforms the quality judgment standard from static numerical comparison to dynamic fitting of the physical state of the production process. While identifying energy anomalies caused by minor defects such as copper wire breakage or material abnormalities, it is compatible with the natural decay of equipment accuracy and thermal fluctuations within a reasonable lifespan. This reduces the misjudgment of good products due to overly strict tolerance settings or the outflow of defective products due to overly lenient tolerance settings. Furthermore, it uses the moving average change trend of the index to identify the risk of mold loosening or aging in advance, realizing the transformation from simple finished product screening to full life-cycle process optimization and preventive maintenance. This ensures the quality consistency of wire harness products and the operating efficiency of production equipment in complex production environments. Attached Figure Description
[0023] Figure 1 The flowchart illustrates a wire harness production data management method based on the Internet of Things in this invention. Figure 2 A schematic diagram illustrating the comparison of pressure timing data for a single crimping operation; Figure 3 This diagram illustrates the effects of production data management and dynamic interception. Detailed Implementation
[0024] This invention discloses a method for managing wire harness production data based on the Internet of Things (IoT), referring to... Figure 1 This includes steps S100-S400: S100 continuously collects the pressure timing data of a single crimping operation, mold temperature, ambient temperature, and cumulative number of equipment runs from the terminal crimping machine via the IoT gateway, and performs waveform alignment preprocessing on the pressure timing data of a single crimping operation to obtain effective waveform data.
[0025] It should be noted that, since the duration of a single pressing process is short, low-frequency sampling is difficult to capture key features, and the microsecond-level jitter in the mechanical transmission system will cause the waveforms acquired each time to not completely overlap on the time axis. If direct comparison and analysis is performed, phase error will be introduced. Therefore, this invention introduces high-frequency acquisition and a centroid-based alignment processing mechanism to reduce the interference of time axis deviation on feature extraction.
[0026] Specifically, a high-frequency piezoelectric pressure sensor is installed at the crimping die of the terminal crimping machine and connected to an IoT gateway. Data is transmitted in real time via Modbus or EtherCAT industrial bus protocols. The IoT gateway collects crimping production data from the terminal crimping machine, including: pressure timing data for a single crimp; auxiliary data such as die temperature, ambient temperature, and cumulative number of machine runs during a single crimp. For example, the sampling frequency of the high-frequency piezoelectric pressure sensor is set to 20kHz to ensure sufficient discrete data points are collected for a single crimping process.
[0027] A preset pressure trigger threshold is established. The discrete data points in the pressure time-series data of a single pressing are traversed, and the moment when the instantaneous pressure value corresponding to the discrete data point exceeds the pressure trigger threshold is marked as a valid starting point. The valid starting point and subsequent data are extracted as waveform data segments, and the centroid time point of the waveform data segment is calculated. All acquired waveform data segments are translated and aligned based on the centroid time point to obtain valid waveform data with a consistent time axis for the single pressing. For example, the pressure trigger threshold is set to 20N, and the centroid time point is obtained based on the centroid method, which is existing technology and will not be elaborated upon here.
[0028] Thus, we obtained the mold temperature, ambient temperature, cumulative number of equipment runs, and valid waveform data.
[0029] S200. Based on the time distribution characteristics and amplitude differences of the effective waveform data, calculate the compression compaction energy index of a single compression.
[0030] It should be noted that latent defects such as copper wire breakage or material abnormalities often do not change the peak pressure, but they do alter the energy distribution and rebound characteristics of the waveform. Furthermore, the standard peak detection method cannot detect subtle differences in the drop in the rebound phase on the right side of the waveform. Additionally, considering that simple energy integration may mask the overall waveform distortion along the time axis, leading to missed detections of waveforms with the same energy but different shapes, this invention constructs a pressing compaction energy index that includes time weighting and shape difference terms. On the one hand, this exponential weighting amplifies the minute fluctuations in the rebound phase; on the other hand, it introduces a comparison with a standard reference waveform to constrain the overall geometric profile, thereby improving the sensitivity and robustness of detecting latent quality problems.
[0031] Specifically, this invention calculates the compaction energy index of a single pressing operation based on the temporal distribution characteristics and amplitude differences of the effective waveform data, including: Set time weighting factor Exemplary This is used to adjust the weight of the effect of time delay on energy integral.
[0032] Set the weighting coefficient for the shape difference term. Exemplary .
[0033] A standard reference waveform is obtained by averaging historical data of qualified products.
[0034] The compressive compaction energy index satisfies the following relationship: ; In the formula, This indicates the compaction energy index for a single press. This indicates the effective waveform data of a single crimping operation at time [time value missing]. The instantaneous pressure value; Represents the natural constant; This represents the maximum instantaneous pressure value in the valid waveform data of a single crimping operation; Indicates the start time of the valid waveform data for a single crimping operation; Indicates the end time of the valid waveform data for a single crimping operation; This indicates the moment when the instantaneous pressure value of the effective waveform data for a single crimping operation reaches its peak. Indicates the time weighting factor; The weighting coefficients for the shape difference term; Indicates the standard reference waveform at The pressure value at any given moment; This indicates the total number of discrete data points contained in the effective waveform data of a single crimping operation; This indicates the effective waveform data of a single crimping operation. The time corresponding to each discrete sampling point; This indicates the effective waveform data of a single crimping operation at time [time value missing]. The instantaneous pressure value.
[0035] In this relation, By utilizing the exponential weighting property, the compression phase signal before the instantaneous pressure value reaches its peak is physically suppressed, and the rebound phase signal after the peak value is amplified, thereby capturing the slight rebound differences caused by copper wire breakage or material hardness. It represents the theoretical maximum envelope impulse of the current crimping action, used for normalization to reduce the scale error introduced by overall pressure fluctuations or crimping speed changes in the equipment, ensuring that the exponent only reflects the degree of waveform distortion. This represents the time-weighted energy distribution. At that time, subtle differences will be amplified exponentially, and the larger the energy distribution, the more the rebound characteristics deviate from the normal value. This indicates the shape difference between the effective waveform data of a single crimping operation and the standard reference waveform. The weighted shape difference term is used to characterize the degree of deviation of the effective waveform data of a single crimping operation from the standard state in terms of the overall geometric profile. The larger the weighted shape difference term, the more obvious the difference between the pressure curve shape generated by the current single crimping operation and the standard qualified product, that is, the greater the overall quality deviation.
[0036] For example, suppose the duration of a certain crimping... ; Set time weighting factor Shape weight coefficient Assuming the integral term is calculated The result is , for The compression compaction energy index for a single compression is: .
[0037] For example, Figure 2This is a schematic diagram comparing the pressure timing data of a single crimping operation. The standard reference waveform and the effective waveform data of a single crimping operation with latent defects, i.e. the latent defect waveform, coincide at the peak. However, the energy characteristic difference area formed by the two in the springback stage reflects the difference in energy distribution. The difference in energy distribution is captured by the crimping density energy index of the present invention.
[0038] Thus, the compressive compaction energy index for a single compressive bonding process was obtained.
[0039] S300. The wear tolerance of the basic tolerance is relaxed according to the cumulative number of times the equipment is run, and the dynamic drift tolerance of a single pressing is calculated in combination with the temperature difference between the mold temperature and the ambient temperature.
[0040] It should be noted that mechanical wear during long-term operation of equipment will naturally lead to a decrease in precision. If the tolerance is too strict, it will cause unnecessary waste. Furthermore, thermal expansion and instability under hot conditions increase the risk of defective products. If the tolerance is too wide, it will cause missed judgments. Therefore, this invention introduces a dynamic adjustment mechanism based on the laws of mechanical wear and thermal effects, which can adaptively adjust the judgment criteria according to the degree of equipment aging and the current temperature.
[0041] Specifically, this invention calculates the dynamic drift tolerance of a single crimping operation based on the cumulative number of equipment runs and temperature difference changes, including: Set wear tolerance factor Exemplary It is used to characterize the extent to which tolerances can be relaxed due to wear.
[0042] Set the temperature rise and contraction coefficient Exemplary It is used to characterize the degree to which tolerances need to be tightened due to increased temperature.
[0043] The dynamic drift tolerance satisfies the following relationship: ; In the formula, Indicates the dynamic drift tolerance of a single crimping operation; This represents the initial basic tolerance value, which is determined by the historical statistical standard deviation of qualified products. This indicates the cumulative number of times the equipment has been used in a single crimping operation; Indicates the recommended rated number of operations for the equipment; Indicates the wear tolerance factor; This represents the absolute value of the difference between the mold temperature and the ambient temperature during a single pressing operation. This represents the coefficient of thermal expansion.
[0044] In this relation, The wear compensation term indicates that as the cumulative number of times the equipment is operated increases, the wear compensation term exhibits logarithmic growth, and the tolerance can be appropriately relaxed to accommodate mechanical fatigue; The thermal stability compensation term is defined as follows: as the difference between the mold temperature and the ambient temperature increases, the thermal stability compensation term becomes less than 1, thereby tightening the tolerance to offset the risks caused by thermal instability.
[0045] For example, setting initial basic tolerance values Recommended rated number of operations for the equipment Second, wear relaxation factor Temperature rise shrinkage coefficient Assume the equipment has been operated a total of [number] times. If the mold temperature for a single pressing is 35℃ and the ambient temperature for a single pressing is 25℃, and the absolute value of the temperature difference between the mold and the ambient temperature for a single pressing is 10, then the dynamic drift tolerance for a single pressing is: .
[0046] It should be noted that, according to the calculation results, although the wear threshold can be relaxed, due to the large temperature difference at present, the system has decided to tighten the threshold after comprehensive judgment, that is, reduce it from 0.05 to 0.036, in order to prevent defective products from being released in harsh environments.
[0047] Thus, the dynamic drift tolerance of a single crimping was obtained.
[0048] S400: Compare the crimping density energy index with the dynamic drift tolerance, and determine the quality status of the wire harness product based on the comparison result; execute equipment maintenance early warning or production control based on the changing trend of the crimping density energy index.
[0049] It should be noted that since the quality judgment of a single product can only intercept immediate defective products, while the long-term trend of the pressing density energy index can reflect the gradual wear or loosening of the mold, this invention combines threshold judgment and trend analysis to not only control the quality of a single product, but also push preventive maintenance instructions to the manufacturing execution system, thereby transforming post-event maintenance into pre-event prevention.
[0050] Specifically, the calculated crimping density energy index for a single crimping is compared with the dynamic drift tolerance. If the crimping density energy index is less than or equal to the dynamic drift tolerance, the wire harness product is deemed qualified and released. If the crimping density energy index is greater than the dynamic drift tolerance, the wire harness product is deemed unqualified, and a stop or rejection signal is sent to the terminal block control system.
[0051] Preferably, the system calculates a moving average of the crimping density energy index of the most recent preset number of single crimping operations in the background. If the moving average shows a monotonically increasing trend and the slope exceeds a preset safety slope threshold, it is determined that the mold has a risk of accelerated aging or loosening. Even if the single wire harness product has not exceeded the standard, the system will push a preventive maintenance work order to the MES system. For example, the preset number of times is 500, and the safety slope threshold is 0.0001.
[0052] For example, Figure 3 This is a schematic diagram of the production data management and dynamic interception effect. Although the traditional peak force does not exceed the traditional judgment range, the single pressing density energy index of the present invention shows an upward trend and exceeds the dynamic drift tolerance, thereby achieving the interception of products with hidden defects.
[0053] This concludes the assessment of the wire harness crimping quality and the maintenance of the equipment's health status.
[0054] This invention also discloses an IoT-based wire harness production data management system, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an IoT-based wire harness production data management method according to this invention.
[0055] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.
[0056] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.
Claims
1. A method for managing wire harness production data based on the Internet of Things, characterized in that, include: The IoT gateway continuously collects the pressure timing data of a single crimping operation, mold temperature, ambient temperature, and cumulative number of equipment runs of the terminal crimping machine; The pressure timing data of a single crimping operation is preprocessed with waveform alignment to obtain effective waveform data. Based on the temporal distribution characteristics and amplitude differences of effective waveform data, the compaction energy index of a single pressing is calculated. The wear tolerance is relaxed based on the cumulative number of times the equipment has been run, and the dynamic drift tolerance of a single pressing is calculated in combination with the temperature difference between the mold temperature and the ambient temperature. The crimping density energy index is compared with the dynamic drift tolerance, and the quality status of the wire harness product is determined based on the comparison result; equipment maintenance warnings or production control are executed based on the changing trend of the crimping density energy index.
2. The method for managing wire harness production data based on the Internet of Things according to claim 1, characterized in that, The continuous collection of pressure timing data, mold temperature, ambient temperature, and cumulative number of equipment operations for a single crimping operation via an IoT gateway includes: A high-frequency piezoelectric pressure sensor is installed at the crimping mold of the terminal crimping machine and connected to an Internet of Things (IoT) gateway. Data is transmitted in real time via Modbus or EtherCAT industrial bus protocol. The IoT gateway collects the crimping production data of the terminal crimping machine, which includes: pressure timing data for a single crimping operation; auxiliary data such as mold temperature, ambient temperature, and cumulative number of equipment runs during a single crimping operation.
3. The method for managing wire harness production data based on the Internet of Things according to claim 1, characterized in that, The waveform alignment preprocessing of the pressure timing data from a single crimping operation to obtain valid waveform data includes: A preset pressure trigger threshold is set, and each discrete data point in the pressure time series data of a single pressing is traversed. The moment when the instantaneous pressure value corresponding to the discrete data point exceeds the pressure trigger threshold is marked as the valid starting point. The valid starting point and subsequent data are extracted as waveform data segments, and the centroid time point of the waveform data segment is calculated based on the centroid method. All the collected waveform data segments are translated and aligned with the centroid time point as the reference to obtain valid waveform data with consistent time axis of a single pressing.
4. The method for managing wire harness production data based on the Internet of Things according to claim 1, characterized in that, The acquisition of the compression compaction energy index includes: Calculate the time-weighted energy distribution; Calculate the weighted shape difference term between the effective waveform data of a single crimping and the standard reference waveform; The energy distribution and the shape difference term are added together to obtain the compression compaction energy index for a single compression.
5. The method for managing wire harness production data based on the Internet of Things according to claim 4, characterized in that, The calculation of the time-weighted energy distribution includes: Subtract the start time from the end time of the effective waveform data of a single crimping, and multiply it by the maximum pressure value in the effective waveform data of the single crimping as the denominator; The instantaneous pressure value of the effective waveform data of a single pressing at time t is multiplied by a power of the product of the natural constant e and the time difference between time t and the time when the instantaneous pressure value reaches its peak, with the product as the numerator. Divide the numerator by the denominator and perform integration over the time interval from the start time to the end time to obtain the time-weighted energy distribution.
6. The method for managing wire harness production data based on the Internet of Things according to claim 4, characterized in that, The weighted shape difference term for calculating the effective waveform data of a single crimping operation and the standard reference waveform includes: The instantaneous pressure value of each discrete sampling point in the effective waveform data of a single pressing is subtracted from the square of the pressure value of the corresponding standard reference waveform, and then summed. This sum is divided by the square of the maximum instantaneous pressure value, and the square root is calculated. Finally, it is multiplied by the weighting coefficient of the shape difference term to obtain the weighted shape difference term.
7. The method for managing wire harness production data based on the Internet of Things according to claim 1, characterized in that, The dynamic drift tolerance satisfies the following relationship: ； In the formula, Indicates the dynamic drift tolerance of a single crimping operation; This represents the initial basic tolerance value, which is determined by the historical statistical standard deviation of qualified products. This indicates the cumulative number of times the equipment has been used in a single crimping operation; Indicates the recommended rated number of operations for the equipment; Indicates the wear tolerance factor; This represents the absolute value of the difference between the mold temperature and the ambient temperature during a single pressing operation. This represents the coefficient of thermal expansion.
8. The method for managing wire harness production data based on the Internet of Things according to claim 1, characterized in that, The step of comparing the crimping density energy index with the dynamic drift tolerance, and determining the quality status of the wire harness product based on the comparison result, includes: The calculated crimping density energy index for a single crimping is compared with the dynamic drift tolerance. If the crimping density energy index is less than or equal to the dynamic drift tolerance, the wire harness product is deemed qualified and released. If the crimping density energy index is greater than the dynamic drift tolerance, the wire harness product is deemed unqualified, and a stop or rejection signal is sent to the terminal block control system.
9. The method for managing wire harness production data based on the Internet of Things according to claim 1, characterized in that, The step of executing equipment maintenance early warning or production control based on the changing trend of the pressing densification energy index includes: The system calculates the moving average of the crimping density energy index of the most recent preset number of single crimpings in the background. If the moving average shows a monotonically increasing trend and the slope exceeds the preset safe slope threshold, it is determined that the mold has the risk of accelerated aging or loosening. Even if the single wire harness product has not exceeded the standard, the system will push a preventive maintenance work order to the MES system.
10. A wire harness production data management system based on the Internet of Things, characterized in that, include: A processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement an Internet of Things-based wire harness production data management method according to any one of claims 1-9.