Electronic component intelligent production control method and system

Abstract

The invention provides an intelligent production control method for electronic components, and belongs to the technical field of intelligent manufacturing. The electronic component intelligent production control method can face different temperature requirements and heat load requirements, when the temperature of a certain station exceeds a threshold value, a heat load balancing request is sent to any station so as to quickly adjust the heat load of each station, the heat load distribution of each station is dynamically adjusted, and the working efficiency is improved. The multi-station temperature control system achieves accurate control of multi-station temperature, thereby effectively dealing with temperature fluctuation in the production process of electronic components, improving stability and product quality in the production process, and providing an innovative solution for temperature management in a parallel production environment.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent manufacturing technology, specifically to an intelligent production control method and system for electronic components. Background Technology

[0002] Currently, electronic component manufacturing is a core pillar of modern manufacturing, widely used in communications, automotive, and medical fields. Its production process demands extremely high precision and stability, directly impacting product quality and corporate competitiveness. Temperature control is a crucial element affecting the quality of processes such as soldering, packaging, and testing. However, many systems typically employ a single temperature control mode, making it difficult to adapt to the diverse temperature requirements of different processes, hindering independent control of multiple areas, and easily affecting component performance due to uneven temperature distribution. More critically, when a control unit fails, the system lacks dynamic adjustment capabilities and cannot quickly redistribute the heat load. This fault sensitivity further exacerbates the stability challenges of the production line, meaning any localized failure can affect the entire line, causing shutdowns or quality degradation. These limitations present significant challenges for companies pursuing efficient and stable production goals.

[0003] The information disclosed in the background section is only for enhancing the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] In view of this, this disclosure provides an intelligent production control method for electronic components, which realizes dynamic adjustment of the heat load distribution of each workstation.

[0005] In a first aspect, embodiments of this application provide an intelligent production control method for electronic components. The method includes: acquiring a preset temperature range and a preset heat load for each workstation in the production of electronic components; determining a temperature control reference value for each workstation based on the preset temperature range and the preset heat load; acquiring the temperature value and heat load of the current workstation; determining whether the temperature deviation between the temperature value of the current workstation and its corresponding temperature control reference value exceeds a deviation threshold; if the deviation threshold is exceeded, sending a heat balance request to any workstation, the heat balance request including the temperature deviation value of the current workstation and the heat load to be transferred; acquiring the temperature value and heat load of the arbitrary workstation; and redistributing the heat load of the current workstation and the heat load of the arbitrary workstation based on the heat balance request, the temperature value of the arbitrary workstation, and the heat load of the arbitrary workstation.

[0006] Secondly, embodiments of this application provide an intelligent production control system for electronic components. This system includes: a first acquisition module, a determination module, a second acquisition module, a judgment module, a sending module, a third acquisition module, and an allocation module. The first acquisition module is used to acquire preset temperature ranges and preset heat loads for each workstation in the production of electronic components. The determination module is used to determine temperature control reference values ​​for each workstation based on the preset temperature ranges and preset heat loads. The second acquisition module is used to acquire the temperature value and heat load of the current workstation. The judgment module is used to determine whether the temperature deviation between the temperature value of the current workstation and its corresponding temperature control reference value exceeds a deviation threshold. The sending module is used to send a heat balance request to any workstation if the deviation threshold is exceeded. The heat balance request includes the temperature deviation value of the current workstation and the heat load to be transferred. The third acquisition module is used to acquire the temperature value and heat load of the arbitrary workstation. The allocation module is used to redistribute the heat load of the current workstation and the heat load of the arbitrary workstation based on the heat balance request, the temperature value of the arbitrary workstation, and the heat load of the arbitrary workstation.

[0007] According to the intelligent production control method for electronic components provided in this disclosure, the method can obtain the preset temperature range and preset heat load of each workstation in the production of electronic components. Based on the preset temperature range and preset heat load, a temperature control reference value for each workstation is determined; the temperature value and heat load of the current workstation are obtained; it is determined whether the temperature deviation between the current workstation's temperature value and its corresponding temperature control reference value exceeds a deviation threshold; if the deviation threshold is exceeded, a heat balance request is sent to any workstation, the heat balance request including the current workstation's temperature deviation value and the heat load to be transferred; the temperature value and heat load of any workstation are obtained; and the heat load of the current workstation and the heat load of any workstation are redistributed based on the heat balance request, the temperature value of any workstation, and the heat load of any workstation. In this way, it can meet different temperature and heat load requirements. When the temperature of a certain workstation exceeds the threshold, it can quickly adjust the heat load of each workstation by sending a heat load balancing request to any workstation. This achieves dynamic adjustment of the heat load distribution of each workstation and precise control of the temperature of multiple workstations. This effectively copes with temperature fluctuations in the production process of electronic components, improves the stability of the production process and the quality of products, and provides an innovative solution for temperature management in parallel production environments. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments or conventional technologies of this disclosure, the accompanying drawings used in the description of the embodiments or conventional technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating an exemplary embodiment of the intelligent production control method for electronic components provided in this application.

[0010] Figure 2 This is a flowchart illustrating an intelligent production control method for electronic components provided in another exemplary embodiment of this application.

[0011] Figure 3 This is a flowchart illustrating a smart production control method for multiple electronic components provided in another exemplary embodiment of this application.

[0012] Figure 4 This is a flowchart illustrating a method for intelligent production control of electronic components, provided in yet another exemplary embodiment of this application.

[0013] Figure 5 This is a flowchart illustrating an intelligent production control method for electronic components provided in another exemplary embodiment of this application.

[0014] Figure 6 This is a flowchart illustrating an intelligent production control method for electronic components provided in another exemplary embodiment of this application.

[0015] Figure 7 This is a flowchart illustrating an intelligent production control method for electronic components provided in another exemplary embodiment of this application.

[0016] Figure 8 This is a flowchart illustrating an intelligent production control method for electronic components provided in another exemplary embodiment of this application. Detailed Implementation

[0017] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are set forth to give a full understanding of embodiments of this disclosure.

[0018] The terms “a,” “one,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended inclusion and that other elements / components / etc. may exist in addition to those listed. The terms “first” and “second” are used only as markers and are not a limitation on the number of objects.

[0019] Currently, electronic component manufacturing is a core pillar of modern manufacturing, widely used in communications, automotive, and medical fields. Its production process demands extremely high precision and stability, directly impacting product quality and corporate competitiveness. Temperature control is a crucial element affecting the quality of processes such as soldering, packaging, and testing. However, many systems typically employ a single temperature control mode, making it difficult to adapt to the diverse temperature requirements of different processes, hindering independent control of multiple areas, and easily affecting component performance due to uneven temperature distribution. More critically, when a control unit fails, the system lacks dynamic adjustment capabilities and cannot quickly redistribute the heat load. This fault sensitivity further exacerbates the stability challenges of the production line, meaning any localized failure can affect the entire line, causing shutdowns or quality degradation. These limitations present significant challenges for companies pursuing efficient and stable production goals.

[0020] This disclosure provides a method for intelligent production control of electronic components, such as... Figure 1 The illustrated method for intelligent production control of electronic components may include the following steps: Step S110: Obtain the preset temperature range and preset heat load of each station in the production of electronic components; Step S120: Determine the temperature control reference value for each workstation based on the preset temperature range and preset heat load; Step S130: Obtain the temperature value and heat load of the current workstation; Step S140: Determine whether the temperature deviation between the current workstation's temperature value and its corresponding temperature control reference value exceeds the deviation threshold. Step S150: If it is determined that the deviation threshold is exceeded, a heat balance request is sent to any workstation. The heat balance request includes the temperature deviation value of the current workstation and the heat load to be transferred. Step S160: Obtain the temperature value and heat load of any workstation; Step S170: Based on the heat balance request, the temperature value of any workstation, and the heat load of any workstation, redistribute the heat load of the current workstation and the heat load of any workstation.

[0021] According to the intelligent production control method for electronic components provided in this disclosure, the method can obtain the preset temperature range and preset heat load of each workstation in the production of electronic components. Based on the preset temperature range and preset heat load, a temperature control reference value for each workstation is determined; the temperature value and heat load of the current workstation are obtained; it is determined whether the temperature deviation between the current workstation's temperature value and its corresponding temperature control reference value exceeds a deviation threshold; if the deviation threshold is exceeded, a heat balance request is sent to any workstation, the heat balance request including the current workstation's temperature deviation value and the heat load to be transferred; the temperature value and heat load of any workstation are obtained; and the heat load of the current workstation and the heat load of any workstation are redistributed based on the heat balance request, the temperature value of any workstation, and the heat load of any workstation. In this way, it can meet different temperature and heat load requirements. When the temperature of a certain workstation exceeds the threshold, it can quickly adjust the heat load of each workstation by sending a heat load balancing request to any workstation. This achieves dynamic adjustment of the heat load distribution of each workstation and precise control of the temperature of multiple workstations. This effectively copes with temperature fluctuations in the production process of electronic components, improves the stability of the production process and the quality of products, and provides an innovative solution for temperature management in parallel production environments.

[0022] The steps of the intelligent production control method for electronic components provided in this disclosure are described in detail below: In one embodiment of this disclosure, step S110 involves obtaining the preset temperature range and preset heat load for each workstation in the production of electronic components. Specifically, in a parallel production environment, the preset temperature range and preset heat load data for each workstation are first obtained through a pre-established multi-workstation temperature requirement database. Assuming the database contains 10 workstations, the preset temperature range for workstation 1 is 20-25℃, and the preset heat load is 500W; the preset temperature range for workstation 2 is 18-22℃, and the preset heat load is 450W. This data can be stored in JSON format and extracted using an SQL query such as "SELECT station_id, temp_min, temp_max, heat_load FROM station_temp_db" to obtain a structured dataset.

[0023] In one embodiment of this disclosure, in step S120, the temperature control reference value for each workstation is determined based on a preset temperature range and a preset heat load. The calculation formula for determining the temperature control reference value is as follows: Where T is the temperature control reference value, A is the midpoint value of the preset temperature range, and W is the preset heat load.

[0024] Specifically, for example, the midpoint of the preset temperature range for station 1 is (20+25) / 2=22.5℃, and the preset heat load is 500W. The midpoint of the preset temperature range for station 2 is (18+22) / 2=20℃, and the preset heat load is 450W. The temperature control baseline value for station 1 is 22.5×(1+500 / 1000)=33.75℃ (rounded to 34℃), and the temperature control baseline value for station 2 is 20×(1+450 / 1000)=29℃.

[0025] Optionally, the above data can be stored in array form, such as [[1,34], [2,29]]. The analysis process considers the influence of heat conduction between workstations. Assuming that the heat conduction coefficient of workstations 1 and 2 is 0.1, the baseline value for workstation 1 is adjusted to 34-0.1×(34-29)=33.5℃. Finally, through iterative algorithm optimization, the temperature of each workstation is ensured to be within ±0.5℃ error, and a control baseline value table is generated and stored in the system memory for real-time retrieval. Supplementing business relevance, if a workstation produces electronic components, excessively high temperatures may cause component aging. It is necessary to combine the component temperature resistance data (such as a maximum of 80℃) for constraint, and adjust the baseline value to not exceed the upper limit of 75℃.

[0026] In one embodiment of this disclosure, step S130 involves acquiring the temperature value and heat load of the current workstation. Specifically, the system first collects the temperature data of the current workstation using a high-precision infrared temperature sensor array. Assuming the initial temperature distribution of a certain workstation is between 25.5℃ and 28.3℃, and the baseline value of the mapping table is 26.0℃, the sensors collect data in real time at 0.1-second intervals, generating a time-series dataset, for example, [25.8, 26.2, 27.1, 26.8]℃.

[0027] In one embodiment of this disclosure, in step S140, it is determined whether the temperature deviation between the current workstation's temperature value and its corresponding temperature control reference value exceeds a deviation threshold. In step S150, if it is determined that the deviation threshold is exceeded, a heat balance request is sent to any workstation. The heat balance request includes the current workstation's temperature deviation value and the heat load to be transferred. Specifically, the temperature sensor at workstation A detects a current temperature of 28.5°C, while its temperature control reference value is 27°C, resulting in a deviation of 1.5°C, exceeding the deviation threshold of 1.0°C. The system automatically triggers an abnormal alarm mechanism and sends heat balance requests to any workstations B and C through a distributed control architecture. The heat balance request includes the current temperature deviation value of workstation A and the amount of heat load to be transferred.

[0028] In one embodiment of this disclosure, step S170, which involves redistributing the heat load of the current workstation and the heat load of any workstation based on the heat balance request, the temperature value of any workstation, and the heat load of any workstation, further includes the following steps: Figure 2 As shown, the specific content is as follows: Step S210: Determine the load rate and remaining heat capacity of any workstation based on the heat load of any workstation; Step S220: Determine the percentage of remaining heat capacity of any workstation based on the remaining heat capacity of any workstation; Step S230: Determine whether the heat balance request of the current workstation can be accommodated based on the remaining heat capacity ratio of any workstation. Step S240: If it is determined that the heat balance request of the current workstation can be accommodated, then the heat load of the current workstation and the heat load of any workstation are redistributed.

[0029] Specifically, the heat balance request includes the current temperature deviation of station A and the required heat load to be transferred, assumed to be 5.0 kJ. Next, the current load status of stations B and C is obtained. Station B returns a current temperature of 25.5℃, a load rate of 60%, and a remaining heat handling capacity of 3.0 kJ; station C returns a temperature of 26.0℃, a load rate of 40%, and a remaining heat handling capacity of 4.5 kJ. The remaining capacity percentages of stations B and C are calculated: B is 3.0 / (3.0+4.5)=40%, and C is 4.5 / (3.0+4.5)=60%. It can be seen that the heat balance request of station A can be accommodated, that is, the excess 5 kJ of heat from station A can be accommodated. Based on the heat load distribution ratio, station B needs to receive 5.0 × 40% = 2.0 kJ, and station C needs to receive 5.0 × 60% = 3.0 kJ. The system adjusts the heat output valve of station A through control commands to reduce the heat by 5.0 kJ, and at the same time sends commands to the heat input modules of stations B and C to increase the heat input by 2.0 and 3.0 kJ respectively, to ensure overall heat balance. All data in the process is recorded in real time to the log system for subsequent analysis and optimization.

[0030] In one embodiment of this disclosure, step S170, in addition to reallocating the heat load of the current workstation and the heat load of any workstation according to the heat balance request, the temperature value of any workstation, and the heat load of any workstation, also includes the following steps: Figure 3 As shown, the specific content is as follows: Step S310: Obtain the current load of each workstation; Step S320: Determine whether the current load of each workstation exceeds the preset load threshold; Step S330: If it is determined that the load exceeds the preset load threshold, then mark it as an overloaded workstation; Step S340: Calculate the difference between each overload station and the preset load threshold, and use the difference as the overshoot of each overload station; Step S350: If it is determined that the preset load threshold has not been exceeded, then mark it as a low load station; Step S360: Calculate the difference between each low-load station and the preset load threshold, and use the difference as the spare capacity of each low-load station. Step S370: Calculate the spare capacity of all low-load stations to determine the total spare capacity of low-load stations, and calculate the overshoot of all overload stations to determine the total overshoot of overload stations. Step S380: Multiply the total overshoot of the overloaded station by the margin of each low-loaded station, and then compare the result of the multiplication with the total margin of the low-loaded station. Use the ratio as the acceptance amount of each low-loaded station.

[0031] Specifically, suppose a production line has 5 workstations with a preset load threshold of 50 units (workstations exceeding this threshold are considered overloaded, while those below are considered underloaded). The current loads of each workstation are as follows: Workstation 1: 60 units; Workstation 2: 70 units; Workstation 3: 30 units; Workstation 4: 40 units; Workstation 5: 50 units. Comparing the load of each workstation with the preset load threshold, it can be seen that workstations 1 (60>50) and 2 (70>50) are marked as overloaded workstations and need to output heat load. Their overshoot is calculated to be 10 units (60-50) and 20 units (70-50), respectively, for a total overshoot of 30 units. Workstations 3 (30<50) and 4 (40<50) are marked as overloaded workstations and can input heat load. Their spare capacity is calculated to be 20 units (50-30) and 10 units (50-40), respectively, for a total spare capacity of 30 units. The acceptance amount of the low-load station is the result of multiplying the overshoot of each overloaded station by the spare capacity of each low-load station, and then dividing the result by the total spare capacity of the low-load stations. The acceptance amount of station 3 is (20×30) / 30=20 units, and the acceptance amount of station 4 is (10×30) / 30=10 units. Overloaded station 1 transfers 10 units, and overloaded station 2 transfers 20 units, for a total of 30 units. Low-load station 3 receives 20 units (the load becomes 50 units), and station 4 receives 10 units (the load becomes 50 units). The load of all stations reaches the threshold of 50 units, achieving dynamic balance.

[0032] The above method achieves precise allocation of heat load across multiple workstations by acquiring the load of each workstation, distinguishing between overloaded and underloaded workstations, calculating overshoot and spare capacity, and then determining the load capacity of underloaded workstations proportionally. Quantitative calculations avoid subjective bias, ensuring load balancing accuracy. Proportional allocation based on spare capacity improves resource utilization, adapting to complex multi-workstation scenarios. This ensures stable load at each workstation, reduces temperature fluctuations, improves product quality consistency, and provides strong support for the efficient and stable operation of the production line.

[0033] In one embodiment of this disclosure, after redistributing the heat load of the current workstation and the heat load of any workstation according to the heat balance request, the temperature value of any workstation, and the heat load of any workstation in step S170, the following steps are further included: Figure 4 As shown, the specific content is as follows: Step S410: Obtain the load rate of any assigned workstation; Step S420: Determine whether the load rate of any workstation exceeds the safety threshold; Step S430: If it is determined that the safety threshold has not been exceeded, then control the opening of the heat valves of the current workstation and any workstation.

[0034] Specifically, based on the above embodiment, the system verified the load rate of each workstation after allocation. The load rate of workstation B rose to 75%, and that of workstation C rose to 65%, both of which did not exceed the safety threshold of 80%, thus confirming that the acceptance conditions were met. Finally, the system adjusted the heat output valve of workstation A through control commands, reducing the heat by 5.0 kJ, while simultaneously sending commands to the heat input modules of workstations B and C to increase the heat input by 2.0 and 3.0 kJ respectively, ensuring overall heat balance. All data during the process was recorded in real time to the log system for subsequent analysis and optimization.

[0035] In one embodiment of this disclosure, after step S170, the following steps are further included: Figure 5 As shown, the specific content is as follows: Step S510: Obtain the adjusted temperature values ​​for each workstation; Step S520: Determine the time for the adjusted temperature value of each workstation to return to the corresponding temperature control reference value based on the adjusted temperature value of each workstation and the temperature control reference value of each workstation. Step S530: During the time it takes for the temperature value of each workstation to return to the temperature control reference value, determine whether the difference between the temperature value of each workstation and the temperature control reference value exceeds the allowable error range. Step S540: If the error exceeds the allowable error range, proceed to abnormal state detection; Step S550: Obtain historical operation data of abnormal workstations; Step S560: Determine solutions for abnormal states based on historical operational data.

[0036] Specifically, the system determines whether an anomaly has occurred at a workstation by determining the time it takes for the temperature value at each workstation to return to its corresponding temperature control baseline. If an anomaly occurs, the fault diagnosis module is triggered to analyze the cause of the anomaly and initiate an anomaly solution. This effectively addresses temperature fluctuations and equipment malfunctions during production, improving the stability of the production process.

[0037] In one embodiment of this disclosure, in step S520, the calculation formula for determining the time it takes for the temperature value of each workstation to return to the corresponding temperature control reference value is as follows: Where t is the time it takes for the temperature value at each workstation to return to the corresponding temperature control reference value. To adjust the temperature value of the workstation, T is the temperature control reference value, V is the heat capacity of the workstation, and P is the cooling power.

[0038] Specifically, the current temperature at a certain workstation is 28℃, the temperature control baseline is 25℃, and the deviation is 3℃. The system automatically generates heating or cooling commands, using a PID control algorithm with a proportional coefficient P set to 2, an integral coefficient I set to 0.5, and a derivative coefficient D set to 0.1. The calculated cooling power adjustment value is 6.2 kW, and the command is sent to the workstation's cooling equipment. Subsequently, to meet the requirements of independent temperature control, the system finely adjusts the heating or cooling parameters of each workstation. For example, the initial cooling power is 5 kW, dynamically adjusted to 6.2 kW based on the deviation. Simultaneously, considering the workstation's heat capacity (assumed to be 200 kJ / ℃), the temperature drop rate is calculated: 6.2 / 200 = 0.031, resulting in a temperature drop of 0.031℃ per minute. Based on this rate, the system predicts that it will take approximately 97 minutes (3 / 0.031) for the temperature to drop from 28℃ to 25℃. This time window is integrated with the production scheduling system to ensure that control is completed during off-peak hours, avoiding impact on production efficiency. Logically, this forms a fully automated management chain from heat distribution to temperature return.

[0039] In one embodiment of this disclosure, in step S530, during the time it takes for the temperature value of each workstation to return to the temperature control reference value, it is determined whether the difference between the temperature value of each workstation and the temperature control reference value exceeds the allowable error range. In step S540, if it is determined that the difference exceeds the allowable error range, an abnormal state detection is initiated.

[0040] Specifically, in a production workshop, if the system monitors a workstation and the temperature fails to return to the temperature control baseline value of 25.0℃ within a set time window, for example, for 30 consecutive minutes, and the current temperature remains at 28.5℃, exceeding the allowable error range of ±1.0℃, the system will automatically trigger an anomaly detection mechanism. First, the system will write the abnormal status of the workstation to the database through the log recording module. The recorded content includes the workstation number W001, the anomaly timestamp, the current temperature value of 28.5℃, and the baseline value of 25.0℃. At the same time, an anomaly code E001 will be generated for subsequent tracking.

[0041] In one embodiment of this disclosure, step S560, which involves determining an abnormal state solution based on historical operating data, further includes the following steps: Figure 6 As shown, the specific content is as follows: Step S610: Determine the fault type based on historical operating data. Fault types include workstation cooling equipment faults and temperature sensor faults. Step S620: Determine the source of the fault and the fault classification label based on the fault type; Step S630: Determine the priority processing order based on the source of the fault; Step S640: Determine the solution for the abnormal state based on the fault classification label and priority processing order.

[0042] Specifically, the system extracts key indicators from historical operational data over the past 30 days, such as equipment operating time, failure frequency, and downtime. Assuming that workstation A has an average downtime of 2.5 hours / day and a failure frequency of 3 times / day, significantly higher than the averages of 1.2 hours / day and 1.5 times / day for other workstations, after data preprocessing, time series analysis algorithms are used to identify outliers. The calculation shows that workstation A's outlier deviates from the standard deviation by 2.3 times, classifying it as a high-risk workstation. Next, a pre-defined fault diagnosis model, such as a classification model based on a random forest algorithm, is used. Historical data from workstation A is input into the model, and combined with feature importance analysis, it is determined that the fault is mainly related to motor overload, contributing 65%, and a fault classification label of "motor overload fault" is generated. Addressing system stability challenges, the system calculates the stability index of workstation A to be 0.42 using a stability assessment algorithm, which is lower than the normal threshold of 0.75. Combined with the fault label, the problem is determined to be system instability caused by motor overload. Finally, based on the priority ranking algorithm, the impact weight of the failure at workstation A was set to 0.8 (out of 1.0). Combining downtime and production impact, its priority score was calculated to be 85 points, higher than the average of 60 points for other workstations, automatically ranking it as a priority. The entire process forms a closed-loop logic through data analysis, model prediction, and algorithm ranking, ensuring seamless integration from data to decision-making. Simultaneously, it is linked to the production scheduling system, automatically generating adjustment suggestions, such as reducing the load on workstation A to 80% to reduce the risk of motor overload and ensure overall production stability.

[0043] The above method constructs a systematic fault handling framework through progressive steps of "determining the fault type → clarifying the fault source and classification → setting priorities → providing solutions". It can quickly identify fault types by analyzing historical operating data, thereby providing convenience for maintenance personnel and resolving workstations with problems at a faster speed.

[0044] In one embodiment of this disclosure, step S610, determining the fault type as a temperature sensor fault, includes the following steps: Figure 7 As shown, the specific content is as follows: Step S710: Obtain the temperature sensor calibration data of the abnormal workstation based on the historical operating data of the abnormal workstation; Step S720: Determine the temperature sensor error based on the temperature sensor calibration data of the abnormal workstation; Step S730: Determine whether the temperature sensor error exceeds the preset error range; Step S740: If the error exceeds the preset error range, the fault type is determined to be a temperature sensor fault.

[0045] Specifically, based on the above embodiments, when an abnormal state is determined, the system initiates fault diagnosis. By comparing with the hardware fault feature library, it analyzes whether the temperature sensor has drift error. Assuming that the feature library defines the preset error range of the temperature sensor as ±0.5℃, while the error range of the temperature sensor in the most recent 3 days in historical data is 1~1.5℃, it is inferred that there may be an aging problem of the temperature sensor.

[0046] In one embodiment of this disclosure, step S610, determining the fault type as a station cooling equipment fault, includes the following steps: Figure 8 As shown, the specific content is as follows: Step S810: Obtain the operating power of the cooling equipment at the abnormal workstation based on the historical operating data of the workstation; Step S820: Determine whether the operating power of the workstation cooling equipment is less than the rated power of the cooling equipment; Step S830: If it is determined that the power is less than the rated power of the cooling equipment, then the fault type is determined to be a fault of the workstation cooling equipment.

[0047] Specifically, historical operational data revealed that the operating power of the workstation cooling equipment was only 60%, below the normal range of 80% of its rated power, indicating a potential cooling equipment malfunction. The system then compiled the analysis results into a diagnostic report, recording the potential problem as "temperature sensor error and insufficient cooling equipment power," and automatically generated a maintenance work order number M001, pushing it to the equipment management system to trigger subsequent automated inspection processes. This ensured timely problem resolution, forming a complete logical chain from anomaly detection to fault inference.

[0048] In one exemplary embodiment of this disclosure, the method further includes continuously monitoring the temperature data of abnormal workstations and arbitrary workstations based on the operating status of the temporary solution, and periodically updating the temperature control benchmark value to address quality fluctuation issues, thereby continuously determining whether the overall production line's heat load balance has reached a stable state. Specifically, temperature change data of abnormal workstations and arbitrary workstations are collected in real time through a sensor network and stored in a central database. The collected raw data is preprocessed to obtain a pre-cleaned temperature dataset. Based on the pre-cleaned temperature dataset, a time series analysis method is used to identify the temperature change trend and fluctuation pattern of abnormal workstations and determine the temporal distribution characteristics of abnormal points. By combining the temporal distribution characteristics of abnormal points with the temperature change data of arbitrary workstations, a temperature distribution mapping table is constructed to analyze the distribution of heat load on the overall production line and determine whether there is a local overload phenomenon. If a local overload phenomenon exists, a preset threshold is called for comparison in the high-load area of ​​the distribution mapping table to obtain the workstation identifiers that exceed the thresholds and determine the key areas that need adjustment. Based on the workstation identifiers of the key areas, the linkage control system adjusts the operating parameters of the relevant workstations and updates the temperature distribution mapping table in real time to obtain the adjusted heat load distribution data. By analyzing the adjusted heat load distribution data, the overall production line's balance is determined. If the balance does not meet the preset standard, the aforementioned adjustment process is repeated to obtain new distribution data. Based on the cyclically adjusted distribution data, the heat load changes of the entire production line are continuously monitored, and regression analysis is used to predict the balance trend over a future period to determine the final stable state.

[0049] This disclosure also provides an intelligent production control system for electronic components. The system may include a first acquisition module, a determination module, a second acquisition module, a judgment module, a sending module, a third acquisition module, and an allocation module. The first acquisition module is used to acquire the preset temperature range and preset heat load of each workstation in the electronic component production process. The determination module is used to determine the temperature control reference value of each workstation based on the preset temperature range and preset heat load. The second acquisition module is used to acquire the temperature value and heat load of the current workstation. The judgment module is used to determine whether the temperature deviation between the temperature value of the current workstation and its corresponding temperature control reference value exceeds a deviation threshold. The sending module is used to send a heat balance request to any workstation if the deviation threshold is exceeded. The heat balance request includes the temperature deviation value of the current workstation and the heat load to be transferred. The third acquisition module is used to acquire the temperature value and heat load of any workstation. The allocation module is used to redistribute the heat load of the current workstation and the heat load of any workstation based on the heat balance request, the temperature value of any workstation, and the heat load of any workstation.

[0050] According to the intelligent production control system for electronic components provided in this disclosure, the system can obtain the preset temperature range and preset heat load of each workstation in the production of electronic components. Based on the preset temperature range and preset heat load, it determines the temperature control reference value for each workstation; obtains the temperature value and heat load of the current workstation; determines whether the temperature deviation between the current workstation's temperature value and its corresponding temperature control reference value exceeds a deviation threshold; if it determines that the deviation threshold is exceeded, it sends a heat balance request to any workstation, the heat balance request including the current workstation's temperature deviation value and the heat load to be transferred; obtains the temperature value and heat load of any workstation; and redistributes the heat load of the current workstation and the heat load of any workstation based on the heat balance request, the temperature value of any workstation, and the heat load of any workstation. In this way, it can meet different temperature and heat load requirements. When the temperature of a certain workstation exceeds the threshold, it can quickly adjust the heat load of each workstation by sending a heat load balancing request to any workstation. This achieves dynamic adjustment of the heat load distribution of each workstation and precise control of the temperature of multiple workstations. This effectively copes with temperature fluctuations in the production process of electronic components, improves the stability of the production process and the quality of products, and provides an innovative solution for temperature management in parallel production environments.

[0051] This disclosure also provides an intelligent production control system for electronic components. The system may further include a fourth acquisition module, a second determination module, a second judgment module, a detection module, a fifth acquisition module, and a third determination module. Specifically, the fourth acquisition module acquires the adjusted temperature values ​​of each workstation; the second determination module determines the time required for the adjusted temperature values ​​of each workstation to return to their corresponding temperature control reference values ​​based on the adjusted temperature values ​​of each workstation and the temperature control reference values ​​of each workstation; the second judgment module determines whether the difference between the temperature values ​​of each workstation and the temperature control reference values ​​exceeds an allowable error range within the time period during which the temperature values ​​of each workstation return to the temperature control reference values; the detection module initiates an abnormal state detection if the difference exceeds the allowable error range; the fifth acquisition module acquires historical operating data of the abnormal workstations; and the third determination module determines an abnormal state solution based on the historical operating data.

[0052] This disclosure also provides an intelligent production control system for electronic components. This system may further include a sixth acquisition module, a third judgment module, a first marking module, a first calculation module, a second marking module, a second calculation module, a statistics module, and a fourth determination module. The sixth acquisition module is used to acquire the current load of each workstation; the third judgment module is used to determine whether the current load of each workstation exceeds a preset load threshold; the first marking module is used to mark a workstation as overloaded if the load exceeds the preset load threshold; the first calculation module is used to calculate the difference between each overloaded workstation and the preset load threshold, and use the difference as the overshoot of each overloaded workstation; the second marking module is used to mark a workstation as underloaded if the load does not exceed the preset load threshold; the second calculation module is used to calculate... The system calculates the difference between each low-load station and the preset load threshold, and uses the difference as the margin of each low-load station; a statistics module is used to calculate the margin of all low-load stations to determine the total margin of the low-load stations, and calculates the overshoot of all overload stations to determine the total overshoot of the overload stations; a fourth determination module is used to multiply the total overshoot of the overload stations by the margin of each low-load station, and compare the multiplication result with the total margin of the low-load stations, and use the ratio result as the acceptance amount of each low-load station.

[0053] It should be noted that the embodiments of the intelligent production control system for electronic components provided in this application can be used to execute the processing flow of the embodiments of the intelligent production control method for electronic components in the above embodiments. Its functions will not be repeated here, but can be referred to the detailed description of the above method embodiments.

[0054] This disclosure also provides an electronic device including one or more processors and memory resources represented by a memory for storing instructions executable by the processor, such as application programs. The application programs stored in the memory may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor is configured to execute instructions to perform the aforementioned method for intelligent production control of electronic components.

[0055] The electronic device may also include a power supply component configured to perform power management of the electronic device, a wired or wireless network interface configured to connect the electronic device to a network, and an input / output (I / O) interface. The electronic device can be operated based on operating devices stored in memory, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0056] In one embodiment, a computer device, which may be a server, is also provided. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device stores data. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for intelligent production control of electronic components.

[0057] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for intelligent production control of electronic components. The display unit of the computer device is used to form a visually visible image and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0058] This disclosure also provides a non-transitory computer-readable storage medium. When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is enabled to execute a method for intelligent production control of electronic components. The method includes: acquiring a preset temperature range and a preset heat load for each workstation in the production of electronic components; determining a temperature control reference value for each workstation based on the preset temperature range and the preset heat load; acquiring the temperature value and heat load of the current workstation; determining whether the temperature deviation between the temperature value of the current workstation and its corresponding temperature control reference value exceeds a deviation threshold; if the deviation threshold is exceeded, sending a heat balance request to any workstation, the heat balance request including the temperature deviation value of the current workstation and the heat load to be transferred; acquiring the temperature value and heat load of any workstation; and redistributing the heat load of the current workstation and the heat load of any workstation based on the heat balance request, the temperature value of any workstation, and the heat load of any workstation. In this way, it can meet different temperature and heat load requirements. When the temperature of a certain workstation exceeds the threshold, a heat load balancing request is sent to any workstation to quickly adjust the heat load of each workstation. This achieves dynamic adjustment of the heat load distribution of each workstation, enabling precise control of the temperature of multiple workstations. This effectively addresses temperature fluctuations in the production process of electronic components, improves the stability of the production process and the quality of the products, and provides an innovative solution for temperature management in parallel production environments.

[0059] This disclosure can take the form of a computer program product implemented on one or more storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing program code. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0060] It should be noted that although the steps of the intelligent production control method for electronic components in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that these steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps, such as omitting certain steps, combining multiple steps into one step, and / or decomposing one step into multiple steps, should all be considered part of this disclosure.

[0061] It should be understood that this disclosure is not limited to the detailed structure and arrangement of the components presented in this specification. This disclosure is capable of other embodiments and can be implemented and performed in various ways. The foregoing variations and modifications fall within the scope of this disclosure. It should be understood that this disclosure, as disclosed and defined in this specification, extends to all alternative combinations of two or more individual features mentioned or apparent in the text and / or drawings. All these different combinations constitute multiple alternative aspects of this disclosure. The embodiments described in this specification illustrate the best known mode for implementing this disclosure and will enable those skilled in the art to utilize this disclosure.

Claims

1. A method for intelligent production control of electronic components, characterized in that, include: Obtain the preset temperature range and preset heat load of each station in the production of electronic components; The temperature control reference value for each workstation is determined based on the preset temperature range and the preset heat load. Obtain the temperature and heat load of the current workstation; Determine whether the temperature deviation between the current workstation's temperature value and its corresponding temperature control reference value exceeds a deviation threshold. If the deviation threshold is exceeded, a heat balance request is sent to any workstation. The heat balance request includes the temperature deviation value of the current workstation and the heat load to be transferred. Obtain the temperature value and heat load of any workstation; The heat load of the current workstation and the heat load of the arbitrary workstation are redistributed based on the heat balance request, the temperature value of the arbitrary workstation, and the heat load of the arbitrary workstation.

2. The intelligent production control method for electronic components according to claim 1, characterized in that, Also includes: Obtain the adjusted temperature values ​​for each workstation; The time it takes for the temperature value of each workstation to return to the corresponding temperature control reference value after adjustment is determined based on the adjusted temperature value of each workstation and the temperature control reference value of each workstation. Within the time it takes for the temperature value at each workstation to return to the temperature control reference value, determine whether the difference between the temperature value at each workstation and the temperature control reference value exceeds the allowable error range. If the error exceeds the allowable range, then proceed to abnormal state detection; Obtain historical operational data for abnormal workstations; Determine solutions for abnormal states based on the historical operational data.

3. The intelligent production control method for electronic components according to claim 1, characterized in that, The step of reallocating the heat load of the current workstation and the heat load of the arbitrary workstation based on the heat balance request, the temperature value of the arbitrary workstation, and the heat load of the arbitrary workstation includes: The load rate and remaining heat capacity of any workstation are determined based on the heat load of that workstation. The percentage of remaining heat capacity of any workstation is determined based on the remaining heat capacity of that workstation. The heat balance request of the current workstation can be accommodated based on the remaining heat capacity percentage of any workstation. If it is determined that the heat balance request of the current workstation can be accommodated, then the heat load of the current workstation and the heat load of any workstation are redistributed.

4. The intelligent production control method for electronic components according to claim 1, characterized in that, The step of reallocating the heat load of the current workstation and the heat load of the arbitrary workstation based on the heat balance request, the temperature value of the arbitrary workstation, and the heat load of the arbitrary workstation includes: Get the current load of each workstation; Determine whether the current load of each workstation exceeds a preset load threshold; If the load exceeds the preset load threshold, it is marked as an overloaded workstation. Calculate the difference between each overloaded station and the preset load threshold, and use the difference as the overshoot of each overloaded station; If it is determined that the load does not exceed the preset load threshold, it is marked as a low-load workstation; Calculate the difference between each of the low-load workstations and the preset load threshold, and use the difference as the spare capacity of each low-load workstation. The total spare capacity of the low-load stations is determined by calculating the spare capacity of all the low-load stations, and the total overshoot of the overload stations is determined by calculating the overshoot of all the overload stations. Multiply the total overshoot of the overloaded workstations by the spare capacity of each of the underloaded workstations, and then compare the result of the multiplication with the total spare capacity of the underloaded workstations. Use the ratio as the acceptance amount of each of the underloaded workstations.

5. The intelligent production control method for electronic components according to claim 2, characterized in that, Based on the historical operational data, solutions for abnormal states are determined, including: The fault type is determined based on the historical operating data, and the fault type includes workstation cooling equipment fault and temperature sensor fault. Determine the source of the fault and the fault classification label based on the fault type; Determine the priority processing order based on the source of the fault; The abnormal state solution is determined based on the fault classification label and the priority processing order.

6. The intelligent production control method for electronic components according to claim 5, characterized in that, The fault type is determined to be a temperature sensor fault, including: Obtain temperature sensor calibration data for abnormal workstations based on historical operational data of the abnormal workstations; The temperature sensor error is determined based on the temperature sensor calibration data of the abnormal workstation. Determine whether the error of the temperature sensor exceeds a preset error range; If the error exceeds the preset error range, the fault type is determined to be a temperature sensor fault.

7. The intelligent production control method for electronic components according to claim 5, characterized in that, The fault type is determined to be a workstation cooling equipment fault, including: The operating power of the cooling equipment at the abnormal workstation is obtained based on the historical operating data of the workstation. Determine whether the operating power of the workstation cooling equipment is less than the rated power of the cooling equipment; If the power is determined to be less than the rated power of the cooling equipment, then the fault type is determined to be a workstation cooling equipment fault.

8. An intelligent production control system for electronic components, characterized in that, include: The first acquisition module is used to acquire the preset temperature range and preset heat load of each station in the production of electronic components. The first determining module is used to determine the temperature control reference value of each workstation based on the preset temperature range and the preset heat load. The second acquisition module is used to acquire the temperature value and heat load of the current workstation. The first judgment module is used to determine whether the temperature deviation between the current workstation temperature value and its corresponding temperature control reference value exceeds the deviation threshold. The sending module is used to send a heat balance request to any workstation if it is determined that the deviation threshold is exceeded. The heat balance request includes the temperature deviation value of the current workstation and the heat load to be transferred. The third acquisition module is used to acquire the temperature value and heat load of the arbitrary workstation; The allocation module is used to redistribute the heat load of the current workstation and the heat load of the arbitrary workstation according to the heat balance request, the temperature value of the arbitrary workstation, and the heat load of the arbitrary workstation.

9. The intelligent production control system for electronic components according to claim 8, characterized in that, Also includes: The fourth acquisition module is used to acquire the adjusted temperature values ​​of each workstation; The second determining module is used to determine the time when the temperature value of each workstation returns to the corresponding temperature control reference value after adjustment, based on the adjusted temperature value of each workstation and the temperature control reference value of each workstation. The second judgment module is used to determine whether the difference between the temperature value of each workstation and the temperature control reference value exceeds the allowable error range within the time period during which the temperature value of each workstation returns to the temperature control reference value. The detection module is used to enter an abnormal state detection if the error exceeds the allowable error range. The fifth acquisition module is used to acquire historical operating data of abnormal workstations; The third determining module is used to determine solutions for abnormal states based on the historical operating data.

10. The intelligent production control system for electronic components according to claim 8, characterized in that, Also includes: The sixth acquisition module is used to acquire the current load of each workstation; The third judgment module is used to determine whether the current load of each workstation exceeds the preset load threshold. The first marking module is used to mark a workstation as overloaded if it is determined that the load exceeds the preset load threshold. The first calculation module is used to calculate the difference between each of the overloaded workstations and the preset load threshold, and to use the difference as the overshoot of each overloaded workstation. The second marking module is used to mark a workstation as low load if it is determined that the load has not exceeded the preset load threshold. The second calculation module is used to calculate the difference between each of the low-load workstations and the preset load threshold, and to use the difference as the spare capacity of each low-load workstation. The statistics module is used to calculate the spare capacity of all the low-load workstations to determine the total spare capacity of the low-load workstations, and to calculate the overshoot of all the overload workstations to determine the total overshoot of the overload workstations. The fourth determining module is used to multiply the total overshoot of the overloaded station by the spare capacity of each of the underloaded stations, and to compare the multiplication result with the total spare capacity of the underloaded stations, and to use the ratio result as the acceptance amount of each of the underloaded stations.

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