Intelligent plug integrated manufacturing method and system

Abstract

The invention relates to the field of intelligent manufacturing, in particular to an intelligent plug integrated manufacturing method and system of an intelligent integrated manufacturing system.The method comprises the steps that a product obtained after injection molding is obtained and subjected to reliability detection, and the functional integrity of an electronic element is verified through a high-temperature and high-humidity environment simulation test; detecting the contact resistance and the insulation strength of each connection point by adopting an electrical performance tester, and determining that the product quality reaches the standard if all detection parameters meet the preset standard; and establishing an assembly yield statistical model according to a reliability detection result, calculating the qualification rate and defect distribution condition of the current batch of products through a statistical analysis algorithm, and if the assembly yield is lower than a preset target value, adjusting the process parameters and re-executing the optimization process to obtain a continuously improved manufacturing process control strategy.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing, specifically to intelligent integrated manufacturing systems, and more particularly to an integrated manufacturing method and system for intelligent plugs. Background Technology

[0002] As a core technology area of ​​modern electrical safety protection, the integrated manufacturing of smart plugs is directly related to the safe operation of electrical equipment and the prevention of fire accidents. Its technical level determines the reliability and intelligence of the entire electrical system.

[0003] As electrical loads become increasingly complex and sophisticated, the passive protection mode of traditional plugs can no longer meet the diverse overload protection time requirements of different devices. Currently, overload protection plugs on the market mainly employ mechanical thermal protectors or simple electronic protection circuits. These solutions generally suffer from low response accuracy, inability to adjust protection parameters according to load characteristics, and complex manufacturing processes. Especially when integrating electronic protection functions, most products still rely on traditional post-assembly processes, resulting in poor product consistency and high costs. The core challenge in this field stems from the contradiction between the integration processes of electronic components and plastic housings.

[0004] In traditional step-by-step assembly processes, the delicate electronic structures of critical protection components such as temperature sensors and magnetic latching relays are highly susceptible to mechanical stress and environmental factors, resulting in assembly yields consistently hovering below 85%. This process defect further exacerbates the reliability issues of circuit connections. Traditional physical lead connections face high-temperature and high-pressure impacts from the molten metal during injection molding, leading to frequent lead breakage and poor contact, severely restricting the overall reliability of the product. More critically, due to the inability to achieve true integrated manufacturing of electronic protection circuits and connector structures, existing products struggle to integrate high-precision time-limit control functions and cannot provide differentiated overload protection strategies based on different load characteristics such as motor starting and precision equipment operation.

[0005] Therefore, how to achieve integrated manufacturing of plug structure and protection circuit while ensuring the integrity of electronic components, and at the same time solve the reliability problem of traditional lead connection, has become the key issue for breakthrough in smart plug overload protection technology. Summary of the Invention

[0006] This invention provides a method for manufacturing an integrated smart plug, comprising the following steps:

[0007] The system acquires real-time monitoring data from the internal temperature sensor of the smart plug and the status information of the magnetic latching relay. It uses a preset threshold comparison algorithm to determine whether the current load current exceeds the rated protection range. If the temperature sensor detects a value exceeding the preset temperature threshold, it triggers an overload warning signal and records timestamp data.

[0008] The differential protection time limit control module is activated based on the overload warning signal. The load feature recognition algorithm is used to analyze the current waveform feature parameters. The load type is determined by the current rise slope and peak duration. If it is identified as a motor starting load, the delay protection parameter is set to 3 seconds. If it is identified as a precision equipment load, the delay protection parameter is set to 0.5 seconds.

[0009] By controlling the temperature and pressure parameters in the injection molding process through process integration optimization algorithms, adjusting the melt temperature to a preset safe range based on the thermistor characteristics data of electronic components, and adopting a segmented injection molding process to reduce the mechanical stress impact on the temperature sensor and magnetic latching relay, an optimized combination of molding process parameters is obtained.

[0010] The traditional physical lead connection method is replaced by leadless connection technology. The conductive plastic material forms an embedded circuit connection channel during the injection molding process. The geometric size and distribution position of the conductive channel are determined according to the circuit layout design data. If the resistance value of the conductive channel is within the preset range, the connection quality is judged to be qualified.

[0011] After injection molding, the product is subjected to reliability testing. The functional integrity of electronic components is verified by high temperature and high humidity environment simulation test. The contact resistance and insulation strength of each connection point are tested by electrical performance tester. If all test parameters meet the preset standards, the product quality is determined to be up to standard.

[0012] Based on the reliability test results, an assembly yield statistical model is established. The pass rate and defect distribution of the current batch of products are calculated through statistical analysis algorithms. If the assembly yield is lower than the preset target value, the aforementioned process parameters are adjusted and the optimization process is re-executed to obtain a manufacturing process control strategy for continuous improvement.

[0013] By recording the changing trends of key parameters throughout the entire production process through an integrated manufacturing data management system, data mining algorithms are used to analyze the correlation between process parameters and product quality. Based on historical data, a predictive model is established to determine the optimal combination of process parameters and to establish a standardized operating procedure for the integrated manufacturing of smart plugs.

[0014] This invention provides an integrated manufacturing system for smart plugs, mainly comprising:

[0015] The real-time monitoring and overload judgment module is used to obtain real-time monitoring data from the internal temperature sensor of the smart plug and the status information of the magnetic latching relay. It uses a preset threshold comparison algorithm to determine whether the current load current exceeds the rated protection range. If the temperature sensor detects a value exceeding the preset temperature threshold, an overload warning signal is triggered and timestamp data is recorded.

[0016] The differentiated protection control module is used to activate the differentiated protection time limit control module based on the overload warning signal. It uses a load feature recognition algorithm to analyze the current waveform feature parameters and determines the load type by the current rise slope and peak duration. If it is identified as a motor starting load, the delay protection parameter is set to 3 seconds; if it is identified as a precision equipment load, the delay protection parameter is set to 0.5 seconds.

[0017] The process parameter optimization module is used to control the temperature and pressure parameters in the injection molding process through process integration optimization algorithms. It adjusts the melt temperature to a preset safe range based on the thermal characteristics data of electronic components, and adopts a segmented injection molding process to reduce the mechanical stress impact on the temperature sensor and magnetic latching relay, thereby obtaining an optimized combination of molding process parameters.

[0018] The leadless connection molding module is used to replace the traditional physical lead method with leadless connection technology. It forms an embedded circuit connection channel through conductive plastic material during the injection molding process. The geometric size and distribution position of the conductive channel are determined according to the circuit layout design data. If the resistance value of the conductive channel is within the preset range, the connection quality is judged to be qualified.

[0019] The reliability testing module is used to acquire the reliability test results of the injection molded product. It verifies the functional integrity of electronic components through high temperature and high humidity environment simulation test, and uses an electrical performance tester to test the contact resistance and insulation strength of each connection point. If all test parameters meet the preset standards, the product quality is determined to be up to standard.

[0020] The yield statistics and process adjustment module is used to establish an assembly yield statistical model based on the reliability test results. It calculates the pass rate and defect distribution of the current batch of products through statistical analysis algorithms. If the assembly yield is lower than the preset target value, the aforementioned process parameters are adjusted and the optimization process is re-executed to obtain a manufacturing process control strategy for continuous improvement.

[0021] The data management and predictive modeling module is used to record the changing trends of key parameters throughout the production process through an integrated manufacturing data management system. It uses data mining algorithms to analyze the correlation between process parameters and product quality, establishes predictive models based on historical data to determine the optimal combination of process parameters, and determines the standardized operating procedures for integrated manufacturing of smart plugs.

[0022] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0023] This invention discloses an integrated manufacturing method for smart plugs. By real-time monitoring of internal temperature sensor data and the status of the magnetic latching relay, combined with a preset threshold comparison algorithm, it determines whether the load current exceeds the protection range. When an overload is detected, a differentiated protection time-limit control module is activated, employing a load feature recognition algorithm to analyze the current waveform and setting different delay protection parameters according to the load type. During injection molding, this invention uses a process integration optimization algorithm to control temperature and pressure parameters and employs leadless connection technology to form an embedded circuit. Through reliability testing and statistical analysis, the manufacturing process is continuously optimized, and a predictive model is established to determine the optimal parameter combination. This invention achieves highly reliable integrated manufacturing of smart plugs, improving product quality and production efficiency. Attached Figure Description

[0024] Figure 1 This is a flowchart of an integrated manufacturing method for a smart plug according to the present invention.

[0025] Figure 2 This is another schematic diagram of an integrated manufacturing method for a smart plug according to the present invention.

[0026] Figure 3 This is a schematic diagram of the framework of an integrated intelligent plug manufacturing system according to the present invention. Detailed Implementation

[0027] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] like Figure 1-3 This embodiment of an integrated smart plug manufacturing method specifically includes:

[0029] Step S101: Obtain real-time monitoring data from the internal temperature sensor of the smart plug and the status information of the magnetic latching relay. Determine whether the current load current exceeds the rated protection range through a preset threshold comparison algorithm. If the temperature sensor detects a value exceeding the preset temperature threshold, trigger an overload warning signal and record timestamp data.

[0030] The smart plug uses a built-in temperature sensor and magnetic latching relay to acquire real-time temperature data and status information. The temperature data is preliminarily processed to obtain the current operating status parameters of the device. These parameters are compared with preset thresholds. If the temperature exceeds the threshold, an overload warning signal is triggered, determining the time point of the abnormal state. For each abnormal state time point, a timestamp is recorded, and the abnormal data is saved through the system log storage module to obtain an abnormal record. Based on the abnormal record, the status information of the magnetic latching relay is analyzed. If the status information shows that the load current exceeds the rated range, a current anomaly identifier is generated to assess the operational risk of the device. Combining the current anomaly identifier and the continuous monitoring results of the temperature data, if the duration exceeds a preset duration, an emergency power-off command is generated to determine the timing of protective measures. Based on the emergency power-off command, the smart plug's control module is activated to perform a power-off operation. The system feedback mechanism obtains the post-power-off status information to determine whether the protective measures have been completed. Based on the post-power-off status information, the latest data from the temperature sensor and the magnetic latching relay are reviewed. If the data returns to the normal range, a recovery record is generated to determine the stability of the device's status.

[0031] Specifically, in the process of acquiring real-time monitoring data from the internal temperature sensor of the smart plug and obtaining status information from the magnetic latching relay, the built-in temperature sensor module first collects internal temperature data once per second, for example, the current temperature reading is 45.5 degrees Celsius. Simultaneously, the on / off state of the magnetic latching relay is obtained through the relay status detection circuit; assuming the current state is closed, it indicates that the load is running. Next, the collected temperature data is compared with a preset temperature threshold, assuming the threshold is set at 50.0 degrees Celsius. A simple comparison algorithm is used: if the current temperature is greater than or equal to the threshold, it is determined to be an overheating risk; if the current temperature of 45.5 degrees Celsius is less than 50.0 degrees Celsius, no warning is triggered yet, but the system will continuously record each collected data to local storage, forming a temperature change trend chart for subsequent analysis of the device's operational stability. Meanwhile, the system monitors the load current via a current sensor. Assuming the rated protection range is 10.0 amps, the current detected value is 11.2 amps, exceeding the threshold. Combined with temperature data analysis, although the temperature is not exceeded, the excessive current may cause a rapid rise in temperature in the future. Therefore, the system automatically triggers an overload warning signal and records the current event with a timestamp in the format "2023-10-15 14:30:25 Current Overload Warning," and uploads the data to the cloud database for remote monitoring. Furthermore, if the temperature exceeds 50.0 degrees Celsius in subsequent monitoring, for example, reaching 51.3 degrees Celsius, the system will immediately calculate the overheating duration using an internal logic algorithm. Assuming the duration exceeds 5 minutes, it will automatically cut off the relay power to protect the equipment. At the same time, it will generate a detailed log, including the peak temperature of 51.3 degrees Celsius, the duration of 5 minutes and 10 seconds, and the cut-off timestamp. The log is synchronized to the cloud for fault tracing. The entire process is automated through an embedded microcontroller. Combined with dual monitoring of current and temperature, a closed-loop protection mechanism is formed to ensure the safe operation of the equipment. Threshold settings are optimized through data analysis, such as dynamically adjusting the temperature threshold to 49.5 degrees Celsius based on historical data to provide early warning.

[0032] Step S102: Activate the differentiated protection time limit control module based on the overload warning signal, use the load feature recognition algorithm to analyze the current waveform feature parameters, and determine the load type by the current rise slope and peak duration. If it is identified as a motor starting load, set the delay protection parameter to 3 seconds. If it is identified as a precision equipment load, set the delay protection parameter to 0.5 seconds.

[0033] An overload warning signal is acquired, triggering a system response and acquiring current waveform data from a current sensor. A pre-established load feature recognition algorithm is used to extract features from the current waveform data, resulting in a set of feature parameters. Based on this set of feature parameters, the current rise slope and peak duration are analyzed to determine the load type. If the current rise slope is higher than a preset threshold and the peak duration is greater than a preset duration, the load type is determined to be a motor starting load; if the current rise slope is lower than a preset threshold and the peak duration is less than a preset duration, the load type is determined to be a precision equipment load. Based on the load type, the corresponding protection time limit parameter is obtained from a preset delay parameter configuration table, resulting in a delay parameter value. A longer delay parameter value corresponds to a motor starting load, while a shorter delay parameter value corresponds to a precision equipment load. Using the delay parameter value, the protection time limit setting of the control module is adjusted, and the delay parameter value is transmitted to the protection execution unit to complete the dynamic configuration of the protection time limit. Current waveform feedback data after the protection execution unit's execution is acquired, and the feature parameters of the current waveform feedback data are analyzed to determine if they are within a preset safety range. If the characteristic parameters exceed the preset safety range, a secondary feature extraction process is triggered to obtain a new set of characteristic parameters. Based on the new set of characteristic parameters, the load type determination process is repeated, and historical characteristic parameters are compared and analyzed to determine whether the load type has changed. If the load type has changed, the delay parameter configuration is updated to obtain an updated protection time limit parameter value. Using the updated protection time limit parameter value, the operating state of the control module is adjusted to complete the closed-loop control of overload protection.

[0034] Specifically, in the process of implementing the differentiated protection time-limit control module to activate the overload warning signal, the system first automatically collects current signal data. Assuming the collected current signal rapidly rises from 5 amps to 20 amps within 1 second, the system will activate the load characteristic recognition algorithm to analyze the rising slope and peak duration of the current waveform. The calculated rising slope is 15 amps / second, and the peak duration of 20 amps is detected to be 2 seconds. A comparison is then performed using a preset algorithm model. The model defines that if the rising slope is greater than 10 amps / second and the peak duration exceeds 1.5 seconds, it is determined to be a motor starting load; if the rising slope is less than 10 amps / second or the peak duration is less than 0.5 seconds, it is determined to be a precision equipment load. In this example, the system identifies it as a motor starting load and then automatically applies the delay protection parameters. The delay time is set to 3 seconds to avoid false protection due to excessive starting current. The system also records the number of times the load is started. Assuming it is the 5th start, if it exceeds the preset limit of 10 starts, an alarm signal will be triggered, notifying the background management system to perform a load health assessment. If it is identified as a precision equipment load, for example, the current rises from 2 amps to 5 amps, the slope is 3 amps / second, and the peak duration is 0.3 seconds, the system sets the delay protection parameter to 0.5 seconds to quickly respond to possible overload risks and protect the equipment from damage. At the same time, the event data is uploaded to the cloud analysis platform, and the parameter thresholds of the load identification model are further optimized through machine learning algorithms to ensure the accuracy of subsequent identification. All processes are completed automatically by the system, forming a closed-loop control logic from signal acquisition, feature analysis to parameter adjustment.

[0035] Step S103: The temperature and pressure parameters in the injection molding process are controlled by the process integration optimization algorithm. The melt temperature is adjusted to the preset safe range according to the thermal characteristics data of electronic components. The segmented injection molding process is adopted to reduce the mechanical stress impact on the temperature sensor and the magnetic latching relay, and the optimized molding process parameter combination is obtained.

[0036] Real-time temperature and pressure data during the injection molding process are acquired and combined with the thermal sensitivity information of electronic components to construct an initial parameter dataset, obtaining a preliminary distribution of process parameters. Based on this initial parameter dataset, a support vector machine algorithm is used to classify the temperature and pressure parameters, identifying key influencing areas and parameter adjustment priorities. If the classification results indicate that the temperature parameter exceeds a preset safety range, the melt temperature control strategy is adjusted, generating a first adjustment scheme; if the classification results indicate that the pressure parameter causes abnormal mechanical stress, a second adjustment scheme is generated, resulting in a targeted optimized parameter combination. Based on the first and second adjustment schemes, a segmented injection molding process logic is introduced, acquiring stress distribution data for each injection stage to determine the impact of mechanical stress on the temperature sensor and magnetic latching relay. Based on the stress distribution data, the stage switching timing of the segmented injection molding process is dynamically adjusted to generate an optimized process execution sequence, determining the final injection molding parameter combination. Through the optimized process execution sequence, the operating status of the temperature sensor and magnetic latching relay is monitored in real time, feedback data is obtained, and it is determined whether the final injection molding parameter combination meets the preset safety range and stress control requirements. If the feedback data indicates a deviation, the temperature and pressure parameters are readjusted based on the feedback data to generate an updated combination of process parameters, thus completing the closed-loop optimization process.

[0037] Specifically, based on the process integration optimization algorithm, a temperature and pressure control model for the injection molding process is first constructed by combining finite element analysis and machine learning. It is assumed that the thermistor characteristics data of the electronic components indicate that the safe operating temperature range of the temperature sensor is 150℃ to 180℃, and the magnetic latching relay has a maximum pressure tolerance of 10MPa. Using a BP neural network algorithm, historical injection molding data (melt temperature, mold temperature, injection pressure) is input to train the model to predict the impact of temperature and pressure on component stress. The model is iterated 1000 times with a learning rate of 0.001, and the error converges to 0.01. When optimizing the melt temperature, based on the thermistor characteristics data, a target temperature of 165℃ is set with a deviation of ±5℃. The heater power is adjusted in real time using a PID control algorithm, with the formula u(t)=Kp*e(t)+Ki*∫e(t)dt+Kd*de(t) / dt, where Kp=2.5, Ki=0.1, and Kd=0.5, ensuring temperature stability. The segmented injection molding process reduces mechanical stress through multi-stage pressure control. A three-stage injection process is set: Stage 1: Pressure 4 MPa, Speed ​​20 mm / s, Duration 2 seconds; Stage 2: Pressure 6 MPa, Speed ​​15 mm / s, Duration 3 seconds; Stage 3: Holding Pressure 2 MPa, Duration 5 seconds. ANSYS simulation analysis of stress distribution verifies that the peak stress in Stage 1 decreased from 15 MPa to 8 MPa, meeting the relay withstand requirements. The final optimized parameter combination is: melt temperature 165℃, mold temperature 80℃, injection pressure 6 MPa, holding pressure time 5 seconds, and cooling time 10 seconds. Analysis shows that this parameter combination reduces thermal stress on the temperature sensor by 20%, mechanical impact on the relay by 15%, and the molding defect rate from 5% to 2%. Optimized parameters are recorded in the process database and fed back to the PLC system for automated adjustment, logically ensuring parameter consistency and production stability.

[0038] Step S104: The leadless connection technology is used to replace the traditional physical lead method. An embedded circuit connection channel is formed by conductive plastic material during the injection molding process. The geometric size and distribution position of the conductive channel are determined according to the circuit layout design data. If the resistance value of the conductive channel is within the preset range, the connection quality is judged to be qualified.

[0039] Obtain the data of the embedded circuit channels generated by conductive plastic injection molding, determine the initial geometric dimensions and distribution positions of the channels, and construct an initial channel model. Simulate the initial channel model through finite element analysis to obtain the channel resistance value distribution and determine the set of resistance values. If the values in the set of resistance values are within the preset range, determine that the initial channel model is qualified and obtain an optimized channel model. Adjust the injection molding process parameters according to the optimized channel model, obtain the adjusted injection molding process data, and determine the final channel geometric dimensions. During the injection molding process, monitor the actual resistance value corresponding to the final channel geometric dimensions in real time, and determine whether the actual resistance value is within the preset range to obtain the quality inspection result. Use the support vector machine algorithm to classify the quality inspection result, obtain the positions of unqualified channels, and determine the defect distribution map. Update the circuit layout data according to the defect distribution map to obtain the updated layout data and determine the new channel geometric dimensions.

[0040] Specifically, in the process of implementing the leadless connection technology to replace the traditional physical lead method, first, a geometric model of the conductive channel is generated by computer-aided design software according to the circuit layout data. Assuming that the circuit layout requires the total length of the conductive channel to be 50 cm, the width to be 0.5 cm, and the thickness to be 0.2 cm, the system will automatically calculate the volume of the channel to be 5 cubic centimeters, and combine the resistivity of the conductive plastic (such as 0.01 ohm·cm) to estimate the theoretical resistance value to be 0.25 ohms to ensure that the design meets the expectations. Then, use the finite element analysis algorithm to optimize the distribution position of the conductive channels, set the minimum channel spacing to 1 cm to avoid interference, and the software iteratively calculates the heat distribution and current density to ensure that the temperature rise of the channels does not exceed 10 degrees Celsius under high load (current is 5 amperes) to generate the optimal distribution map. Subsequently, during the injection molding process, a precision injection molding device is used to inject the conductive plastic material (such as carbon fiber reinforced polymer) and ordinary plastic into the mold in layers, and the system monitors the injection pressure (controlled within 100 MPa) and temperature (maintained at 200 degrees Celsius) in real time to ensure that the conductive material is evenly distributed in the embedded channels to form a stable circuit connection. Finally, an automated test device is used to measure the actual resistance value of the conductive channel. Assuming that the preset resistance range is 0.2 to 0.3 ohms, if the test value is 0.26 ohms, the system automatically determines that the connection quality is qualified; if it exceeds the range, such as 0.35 ohms, the defect analysis module is triggered to trace the root cause of the problem in combination with the resistance distribution data and injection molding parameters, such as uneven material distribution or abnormal pressure, and generate optimization suggestions to adjust the next production parameters. The above process is seamlessly connected through an information system, forming a closed-loop logic from design to testing to ensure the efficiency and reliability of technology implementation.

[0041] Step S105: Obtain the products after injection molding for reliability testing. Verify the functional integrity of electronic components through high-temperature and high-humidity environment simulation tests. Use an electrical performance tester to detect the contact resistance and insulation strength of each connection point. If all the detected parameters meet the preset standards, it is determined that the product quality is qualified.

[0042] Obtain the data of the injection-molded products in the high-temperature and high-humidity environment simulation test. The data includes environmental parameters and electronic component response signals. After sorting, the original data set of the environmental simulation test is obtained. According to the original data set of the environmental simulation test, use signal processing technology to denoise and extract features from the electronic component response signals, and obtain the processed signal feature set. Through the signal feature set, analyze the functional integrity performance of the electronic components in the high-temperature and high-humidity environment. If the characteristic values deviate from the preset standard range, it is determined that the function is abnormal, and the preliminary judgment result of functional integrity is obtained. Obtain the preliminary judgment result of functional integrity, combine the contact resistance and insulation strength data collected by the electrical performance tester, and perform parameter comparison for each connection point. If the detected value exceeds the preset threshold range, it is marked as an abnormal point, and the electrical performance test result is obtained. According to the electrical performance test result, fuse the preliminary judgment result of functional integrity, and use the support vector machine algorithm to comprehensively classify the abnormal points and functional performance, determine the impact of the abnormal points on the overall quality, and obtain the comprehensive quality evaluation result. Through the comprehensive quality evaluation result, perform a final comparison on the classified abnormal points and functional performance data. If all parameters are within the preset standard range, it is determined that the product quality is qualified, and the final quality determination conclusion is obtained.

[0043] Specifically, during the reliability testing process of the product after injection molding, first, verify the functional integrity of electronic components through high-temperature and high-humidity environmental simulation tests. The environmental test chamber can be set to a temperature of 85 degrees Celsius and a relative humidity of 85%, and the test is continued for 72 hours. During this period, the operation status data of the components are recorded every 1 hour using an automated monitoring system. For example, the voltage fluctuation range is controlled within ±0.1V, and the current is stabilized between 95% and 105% of the rated value. The fluctuation frequency and amplitude are analyzed through algorithms. If the preset threshold is exceeded, it is automatically marked as abnormal, and the cause of the abnormality is analyzed by comparing with historical data to form a preliminary reliability assessment report. Subsequently, an electrical performance tester is used to detect the contact resistance and insulation strength of each connection point. The specific method is to apply a 5V test voltage to each connection point using a high-precision tester, and the measured contact resistance value should be less than 0.01 ohms. At the same time, a 500V DC voltage is applied to test the insulation strength, and the insulation resistance should be greater than 100 megohms. The test data is automatically collected by the system and compared with the standard value to calculate the deviation rate. For example, the resistance deviation rate = (measured value - standard value) / standard value × 100%. If the deviation rate is less than 5%, it is judged as qualified. When the data is abnormal, a secondary detection is automatically triggered and a detailed analysis log is generated. Finally, if all test parameters meet the preset standards, the system will integrate the data of environmental tests and electrical performance tests to generate a product quality compliance report. The report includes the average value, standard deviation, and pass rate statistics of each test. For example, the pass rate needs to reach more than 98%. If it does not meet the standard, the production batch traceability system is automatically associated to analyze potential problems with raw materials or process parameters, forming a closed-loop feedback mechanism to ensure that the quality control logic is rigorous and traceable.

[0044] Step S106, establish an assembly yield statistical model based on the reliability test results, calculate the pass rate and defect distribution of the current batch of products through statistical analysis algorithms. If the assembly yield is lower than the preset target value, adjust the aforementioned process parameters and re-execute the optimization process to obtain a continuously improved manufacturing process control strategy.

[0045] A reliability testing data set is obtained, including assembly yield and defect distribution data for the current batch of products. Descriptive statistical analysis is used to process the reliability testing data set, calculating the pass rate and the proportion of various defects to obtain the quality characteristics of the batch of products. Based on these quality characteristics, a logistic regression model is constructed to predict the assembly yield fluctuation trend and identify the key defect types affecting the yield. If the assembly yield predicted by the logistic regression model is lower than a preset threshold, the main defect types are extracted from the defect distribution data, and corresponding process parameter configurations are obtained to obtain a set of parameters to be adjusted. For the set of parameters to be adjusted, a genetic algorithm is used to optimize the process parameters, generating a new parameter configuration scheme to obtain optimized process parameters. The assembly process is executed using the optimized process parameters to obtain a new reliability testing data set, and the updated assembly yield and defect distribution are calculated to obtain improved quality characteristics. Based on the improved quality characteristics, the logistic regression model is updated to predict a new assembly yield trend and determine whether a preset threshold has been reached. If the updated assembly yield is lower than the preset threshold, the process parameters are repeatedly optimized and the assembly process steps are repeated to obtain stable manufacturing process parameters.

[0046] Specifically, based on reliability testing results, the assembly yield statistical model is first constructed by collecting testing data from 1000 products in the current batch, including dimensional deviations, surface defects, and functional test results. A normal distribution model is used to analyze the yield, assuming that the dimensional deviation of qualified products is within ±0.05mm, calculating the mean μ = 0.02mm, standard deviation σ = 0.01mm, and yield P(X∈[-0.05,0.05]) = 99.73%. Pareto analysis is used for defect distribution, showing that 80% of defects are concentrated in surface scratches (60%) and loose connectors (20%). If the target yield is set at 99.8%, and the current batch does not meet the target, process parameter adjustments are triggered. The gradient descent algorithm is used to optimize the parameters, with an initial learning rate of 0.01. The assembly pressure is increased from 200N to 220N, and the vibration frequency is decreased from 50Hz to 45Hz. After 10 iterations, convergence is achieved, and the predicted yield is improved to 99.82%. The optimized process was re-executed, new batch data was collected, and normal distribution and Pareto analyses were repeated to confirm that the yield was stable above 99.8%, and the proportion of scratches in the defect distribution was reduced to 50%. Time series analysis was used to monitor yield fluctuations over 10 consecutive batches, and an ARIMA(1,1,0) model was used to predict future trends with parameter estimates of p=0.8 and q=0.2, ensuring that the yield remained stable after process parameter adjustments. Finally, an automated control strategy was developed, with the system updating parameters in real time and feeding back to the Manufacturing Execution System (MES) for continuous improvement.

[0047] Step S107: Record the changing trends of key parameters throughout the entire production process through the integrated manufacturing data management system, analyze the correlation between process parameters and product quality using data mining algorithms, establish a prediction model based on historical data to determine the optimal combination of process parameters, and determine the standardized operation process for integrated manufacturing of smart plugs.

[0048] Key parameters in the production process are obtained from the manufacturing data management system, and their changing trends over time are recorded to obtain a key parameter trend dataset. Data mining algorithms are used to analyze this dataset to extract the correlation between process parameters and product quality, and a correlation model is determined. If the significance index of the correlation model is higher than a preset threshold, a random forest algorithm is trained using historical data to construct a product quality prediction model, resulting in the prediction model. Based on the prediction model, candidate combinations of process parameters are obtained, and a grid search algorithm is used to calculate the predicted quality score for each candidate combination to determine the optimal process parameter combination. Using the optimal process parameter combination, a sequence of work instructions for manufacturing smart plugs is generated, resulting in a preliminary standardized work process. If the instruction sequence of the preliminary standardized work process meets preset production efficiency constraints, the stability of the preliminary standardized work process is verified using a process simulation tool to determine the final standardized work process. The execution log of the final standardized work process is obtained, recording the changing trends of key parameters during the production process, and the dataset in the manufacturing data management system is updated.

[0049] Specifically, in the integrated manufacturing process of smart plugs, an integrated manufacturing data management system records the changing trends of key parameters in real time. For example, it records that the injection molding machine temperature is controlled between 230.5℃ and 235.5℃, the pressure parameter is maintained at 1200 bar, and the injection time is controlled at 3.2 seconds. Simultaneously, it collects defect rate data for each batch of products, such as surface scratch rate controlled below 0.5%. Subsequently, data mining algorithms, such as random forest algorithms, are used to analyze the correlation between process parameters and product quality. Specifically, feature importance analysis is performed on 100,000 production data points from the past year, revealing that temperature has a weight of 0.42 on surface quality, and pressure has a weight of 0.28, thus identifying key influencing parameters. Based on this historical data, a predictive model is built using a support vector machine regression algorithm. Inputting parameters such as temperature, pressure, and time, the model predicts the defect rate. The accuracy rate on the training set reaches 92.3%, and the accuracy rate on the test set is 89.7%. Through model optimization, the optimal combination of process parameters is determined to be a temperature of 233.2℃, a pressure of 1210 bar, and a time of 3.1 seconds, reducing the defect rate to 0.3%. Finally, based on the prediction results and parameter combinations, the system automatically generates standardized work process documents, embeds them into the manufacturing execution system, automatically adjusts equipment parameters to ensure parameter consistency for each production line, and links with the quality inspection system. If the defect rate exceeds 0.5%, the system automatically backtracks the parameter adjustment records to form a closed-loop optimization logic to ensure production stability.

[0050] This invention provides an integrated manufacturing system for smart plugs, mainly comprising:

[0051] The real-time monitoring and overload judgment module is used to obtain real-time monitoring data from the internal temperature sensor of the smart plug and the status information of the magnetic latching relay. It uses a preset threshold comparison algorithm to determine whether the current load current exceeds the rated protection range. If the temperature sensor detects a value exceeding the preset temperature threshold, an overload warning signal is triggered and timestamp data is recorded.

[0052] The differentiated protection control module is used to activate the differentiated protection time limit control module based on the overload warning signal. It uses a load feature recognition algorithm to analyze the current waveform feature parameters and determines the load type by the current rise slope and peak duration. If it is identified as a motor starting load, the delay protection parameter is set to 3 seconds; if it is identified as a precision equipment load, the delay protection parameter is set to 0.5 seconds.

[0053] The process parameter optimization module is used to control the temperature and pressure parameters in the injection molding process through process integration optimization algorithms. It adjusts the melt temperature to a preset safe range based on the thermal characteristics data of electronic components, and adopts a segmented injection molding process to reduce the mechanical stress impact on the temperature sensor and magnetic latching relay, thereby obtaining an optimized combination of molding process parameters.

[0054] The leadless connection molding module is used to replace the traditional physical lead method with leadless connection technology. It forms an embedded circuit connection channel through conductive plastic material during the injection molding process. The geometric size and distribution position of the conductive channel are determined according to the circuit layout design data. If the resistance value of the conductive channel is within the preset range, the connection quality is judged to be qualified.

[0055] The reliability testing module is used to acquire the reliability test results of the injection molded product. It verifies the functional integrity of electronic components through high temperature and high humidity environment simulation test, and uses an electrical performance tester to test the contact resistance and insulation strength of each connection point. If all test parameters meet the preset standards, the product quality is determined to be up to standard.

[0056] The yield statistics and process adjustment module is used to establish an assembly yield statistical model based on the reliability test results. It calculates the pass rate and defect distribution of the current batch of products through statistical analysis algorithms. If the assembly yield is lower than the preset target value, the aforementioned process parameters are adjusted and the optimization process is re-executed to obtain a manufacturing process control strategy for continuous improvement.

[0057] The data management and predictive modeling module is used to record the changing trends of key parameters throughout the production process through an integrated manufacturing data management system. It uses data mining algorithms to analyze the correlation between process parameters and product quality, establishes predictive models based on historical data to determine the optimal combination of process parameters, and determines the standardized operating procedures for integrated manufacturing of smart plugs.

[0058] The above description of the embodiments is only for the purpose of helping to understand the technical solutions and core ideas of this application; those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for manufacturing an integrated smart plug, characterized in that, The method includes the following steps: Step S101: Obtain real-time monitoring data from the internal temperature sensor of the smart plug and the status information of the magnetic latching relay. Use a preset threshold comparison algorithm to determine whether the current load current exceeds the rated protection range. If the temperature sensor detects a value exceeding the preset temperature threshold, trigger an overload warning signal and record timestamp data. Step S102: Activate the differentiated protection time limit control module according to the overload warning signal, use the load feature recognition algorithm to analyze the current waveform feature parameters, and determine the load type by the current rise slope and peak duration. If it is identified as a motor starting load, set the delay protection parameter to 3 seconds. If it is identified as a precision equipment load, set the delay protection parameter to 0.5 seconds. Step S103: Control the temperature and pressure parameters in the injection molding process through process integration optimization algorithm, adjust the melt temperature to the preset safe range according to the thermal characteristics data of electronic components, and adopt segmented injection molding process to reduce the mechanical stress impact on temperature sensor and magnetic latching relay, so as to obtain the optimized molding process parameter combination. Step S104: Use leadless connection technology to replace the traditional physical lead method. During the injection molding process, an embedded circuit connection channel is formed by conductive plastic material. The geometric size and distribution position of the conductive channel are determined according to the circuit layout design data. If the resistance value of the conductive channel is within the preset range, the connection quality is judged to be qualified. Step S105: Obtain the injection-molded product for reliability testing. Verify the functional integrity of electronic components through high temperature and high humidity environment simulation test. Use an electrical performance tester to test the contact resistance and insulation strength of each connection point. If all test parameters meet the preset standards, the product quality is determined to be up to standard. Step S106: Establish an assembly yield statistical model based on the reliability test results, calculate the pass rate and defect distribution of the current batch of products through statistical analysis algorithms, and adjust the aforementioned process parameters and re-execute the optimization process to obtain a manufacturing process control strategy for continuous improvement. Step S107: Record the changing trends of key parameters throughout the entire production process through the integrated manufacturing data management system, use data mining algorithms to analyze the correlation between process parameters and product quality, establish a prediction model based on historical data to determine the optimal combination of process parameters, and determine the standardized operation process for integrated manufacturing of smart plugs.

2. The integrated manufacturing method of a smart plug according to claim 1, characterized in that, The obtaining step S101 includes: The smart plug uses a built-in temperature sensor and magnetic latching relay to obtain real-time temperature data and status information. The temperature data is preliminarily processed to obtain the current operating status parameters of the equipment; If the temperature data exceeds the preset threshold, an overload warning signal is triggered to determine the time point of the abnormal state by comparing the operating status parameters with the preset threshold. For the time point of the abnormal state, the corresponding timestamp information is recorded, and the abnormal data is saved through the system log storage module to obtain the abnormal record; Based on the anomaly record, analyze the status information of the magnetic latching relay. If the status information shows that the load current exceeds the rated range, generate a current anomaly flag to determine the equipment operation risk. Based on the continuous monitoring results of the current anomaly indicator and the temperature data, if the duration exceeds a preset time, an emergency power-off command is generated to determine the timing for the execution of protective measures. According to the emergency power-off command, the control module of the smart plug performs a power-off operation, obtains the status information after the power-off through the system feedback mechanism, and determines whether the protection measures have been completed. Based on the status information after the power outage, the latest data of the temperature sensor and the magnetic latching relay are checked. If the data recovers to the normal range, a recovery record is generated to determine the stability of the equipment status.

3. The integrated manufacturing method for a smart plug according to claim 1, characterized in that, Step S102 includes: Obtain an overload warning signal, trigger a system response, and acquire current waveform data from the current sensor; The current waveform data is subjected to feature extraction using a pre-established load feature recognition algorithm to obtain a set of feature parameters; Based on the set of characteristic parameters, the current rise slope and peak duration are analyzed to determine the load type. If the current rise slope is higher than a preset threshold and the peak duration is greater than a preset duration, then the load type is determined to be a motor starting load. If the current rise slope is lower than a preset threshold and the peak duration is less than a preset duration, then the load type is determined to be a precision equipment load. According to the load type, the corresponding protection time limit parameter is obtained from the preset delay parameter configuration table to obtain the delay parameter value. The motor starting load corresponds to a longer delay parameter value, and the precision equipment load corresponds to a shorter delay parameter value. By adjusting the protection time limit setting of the control module through the delay parameter value, the delay parameter value is transmitted to the protection execution unit to complete the dynamic configuration of the protection time limit; Obtain the current waveform feedback data after the protection execution unit is executed, and analyze whether the characteristic parameters of the current waveform feedback data are within a preset safety range; If the feature parameters exceed the preset safety range, a secondary feature extraction process is triggered to obtain a new set of feature parameters; Based on the new set of feature parameters, the process for determining the type of repetitive load is combined with a comparative analysis of historical feature parameters to determine whether the load type has changed. If the load type changes, the delay parameter configuration is updated to obtain the updated protection time limit parameter value; By adjusting the updated protection time limit parameter value, the operating state of the control module is adjusted to complete the closed-loop control of overload protection.

4. A method for manufacturing an integrated smart plug according to any one of claims 1-3, characterized in that, Step S103 includes: Real-time temperature and pressure data during the injection molding process are acquired, and combined with the thermal sensitivity information of electronic components, an initial parameter dataset is constructed to obtain a preliminary distribution of process parameters. Based on the initial parameter dataset, the support vector machine algorithm is used to classify the temperature and pressure parameters to determine the key influence areas and parameter adjustment priorities. If the classification results indicate that the temperature parameter exceeds the preset safety range, the melt temperature control strategy is adjusted to generate a first adjustment scheme. If the classification results indicate that the pressure parameters cause abnormal mechanical stress, a second adjustment scheme is generated to obtain a targeted optimized parameter combination. Based on the first and second adjustment schemes, a segmented injection molding process logic is introduced to obtain stress distribution data for each injection stage and determine the impact of mechanical stress on the temperature sensor and magnetic latching relay. Based on the stress distribution data, the timing of stage switching in the segmented injection molding process is dynamically adjusted to generate an optimized process execution sequence and determine the final injection molding parameter combination. The optimized process execution sequence is used to monitor the operating status of the temperature sensor and magnetic latching relay in real time, obtain feedback data, and determine whether the final injection molding parameter combination meets the preset safety range and stress control requirements. If the feedback data indicates a deviation, the temperature and pressure parameters are readjusted based on the feedback data to generate an updated combination of process parameters, thus completing the closed-loop optimization process.

5. A method for manufacturing an integrated smart plug according to any one of claims 1-3, characterized in that, Step S104 includes: Acquire the embedded circuit channel data generated by conductive plastic injection molding, determine the initial geometric dimensions and distribution positions of the channels, and construct an initial channel model; The initial channel model was simulated using finite element analysis to obtain the channel resistance value distribution and determine the set of resistance values. If each value in the set of resistance values ​​is within a preset range, then the initial channel model is deemed qualified, and an optimized channel model is obtained. Adjust the injection molding process parameters according to the optimized channel model, obtain the adjusted injection molding process data, and determine the final channel geometry. During the injection molding process, the actual resistance value corresponding to the final channel geometry is monitored in real time to determine whether the actual resistance value is within a preset range, thereby obtaining the quality inspection result. The quality inspection results are classified using a support vector machine algorithm to obtain the locations of non-conforming channels and determine the defect distribution map; The circuit layout data is updated based on the defect distribution map to obtain the updated layout data, and the new channel geometry is determined.

6. A method for manufacturing an integrated smart plug according to any one of claims 1-3, characterized in that, Step S105 includes: Data from high temperature and high humidity environment simulation tests of injection molded products are obtained. The data includes environmental parameters and electronic component response signals. After processing, the original dataset of environmental simulation test is obtained. Based on the original dataset of the environmental simulation test, signal processing technology is used to denoise and extract features from the response signals of electronic components to obtain the processed signal feature set. By analyzing the signal feature set, the functional integrity performance of electronic components under high temperature and high humidity environment is analyzed. If the feature value deviates from the preset standard range, it is determined to be a functional abnormality, and a preliminary judgment result of functional integrity is obtained. The preliminary judgment result of the functional integrity is obtained. Combined with the contact resistance and insulation strength data collected by the electrical performance tester, the parameters of each connection point are compared. If the detected value exceeds the preset threshold range, it is marked as an abnormal point, and the electrical performance test result is obtained. Based on the electrical performance test results and the preliminary judgment results of functional integrity, the support vector machine algorithm is used to comprehensively classify the anomalies and functional performance, determine the impact of the anomalies on the overall quality, and obtain the comprehensive quality assessment results. Based on the comprehensive quality assessment results, a final comparison is made between the categorized anomalies and functional performance data. If all parameters are within the preset standard range, the product quality is determined to be qualified, and the final quality judgment conclusion is obtained.

7. A method for manufacturing an integrated smart plug according to any one of claims 1-3, characterized in that, Step S106 includes: Acquire a reliability testing data set, which includes assembly yield and defect distribution data for the current batch of products; The reliability test data set is processed using descriptive statistical analysis methods to calculate the pass rate and the proportion of various defects, thereby obtaining the quality characteristics of the batch of products. Based on the aforementioned quality characteristics, a logistic regression model is constructed to predict the trend of assembly yield fluctuations and identify the key defect types that affect yield. If the assembly yield predicted by the logistic regression model is lower than the preset threshold, the main defect types are extracted from the defect distribution data, the corresponding process parameter configurations are obtained, and a set of parameters to be adjusted is obtained. For the set of parameters to be adjusted, a genetic algorithm is used to optimize the process parameters, generate a new parameter configuration scheme, and obtain the optimized process parameters. The assembly process is executed using the optimized process parameters to obtain a new set of reliability test data, and the updated assembly yield and defect distribution are calculated to obtain the improved quality characteristics. Based on the improved quality characteristics, update the logistic regression model, predict the new assembly yield trend, and determine whether the preset threshold has been reached. If the updated assembly yield is lower than the preset threshold, the process parameters and assembly process steps are repeatedly optimized to obtain stable manufacturing process parameters.

8. A method for manufacturing an integrated smart plug according to any one of claims 1-3, characterized in that, Step S107 includes: Key parameters in the production process are obtained from the manufacturing data management system, and the changing trends of these key parameters over time are recorded to obtain a dataset of key parameter changing trends. Data mining algorithms were used to analyze the dataset of key parameter change trends, extract the correlation between process parameters and product quality, and determine the correlation model. If the significance index of the association model is higher than the preset threshold, then a random forest algorithm is trained using historical data to construct a product quality prediction model, and the prediction model is obtained. Based on the prediction model, candidate combinations of process parameters are obtained, and a grid search algorithm is used to calculate the prediction quality score of each candidate combination to determine the optimal combination of process parameters. By combining the optimal process parameters, a sequence of work instructions for manufacturing smart plugs is generated, resulting in a preliminary standardized work process. If the instruction sequence of the preliminary standardized work process meets the preset production efficiency constraints, the stability of the preliminary standardized work process is verified by a process simulation tool, and the final standardized work process is determined. Obtain the execution log of the final standardized work process, record the changing trends of key parameters during the production process, and update the dataset in the manufacturing data management system.

9. An integrated manufacturing system for a smart plug, characterized in that, This system is used to implement the integrated manufacturing method of a smart plug according to any one of claims 1-8, the system comprising: The real-time monitoring and overload judgment module is used to obtain real-time monitoring data from the internal temperature sensor of the smart plug and the status information of the magnetic latching relay. It uses a preset threshold comparison algorithm to determine whether the current load current exceeds the rated protection range. If the temperature sensor detects a value exceeding the preset temperature threshold, an overload warning signal is triggered and timestamp data is recorded. The differentiated protection control module is used to activate the differentiated protection time limit control module based on the overload warning signal. It uses a load feature recognition algorithm to analyze the current waveform feature parameters and determines the load type by the current rise slope and peak duration. If it is identified as a motor starting load, the delay protection parameter is set to 3 seconds; if it is identified as a precision equipment load, the delay protection parameter is set to 0.5 seconds. The process parameter optimization module is used to control the temperature and pressure parameters in the injection molding process through process integration optimization algorithms. It adjusts the melt temperature to a preset safe range based on the thermal characteristics data of electronic components, and adopts a segmented injection molding process to reduce the mechanical stress impact on the temperature sensor and magnetic latching relay, thereby obtaining an optimized combination of molding process parameters. The leadless connection molding module is used to replace the traditional physical lead method with leadless connection technology. It forms an embedded circuit connection channel through conductive plastic material during the injection molding process. The geometric size and distribution position of the conductive channel are determined according to the circuit layout design data. If the resistance value of the conductive channel is within the preset range, the connection quality is judged to be qualified. The reliability testing module is used to acquire the reliability test results of the injection molded product. It verifies the functional integrity of electronic components through high temperature and high humidity environment simulation test, and uses an electrical performance tester to test the contact resistance and insulation strength of each connection point. If all test parameters meet the preset standards, the product quality is determined to be up to standard. The yield statistics and process adjustment module is used to establish an assembly yield statistical model based on the reliability test results. It calculates the pass rate and defect distribution of the current batch of products through statistical analysis algorithms. If the assembly yield is lower than the preset target value, the aforementioned process parameters are adjusted and the optimization process is re-executed to obtain a manufacturing process control strategy for continuous improvement. The data management and predictive modeling module is used to record the changing trends of key parameters throughout the production process through an integrated manufacturing data management system. It uses data mining algorithms to analyze the correlation between process parameters and product quality, establishes predictive models based on historical data to determine the optimal combination of process parameters, and determines the standardized operating procedures for integrated manufacturing of smart plugs.

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