A warehouse system for intelligent management of electronic components

Abstract

This invention relates to the field of electronic component warehousing management technology, specifically disclosing an intelligent warehousing system for managing electronic components. It aims to solve the technical problems of inaccurate static countdown lifespan of moisture-sensitive components, unreasonable first-in-first-out (FIFO) single sorting, difficulty in quality traceability, and lack of electrostatic discharge (ESD) risk control. This invention periodically collects temperature and humidity data from each storage area, calculates temperature and humidity influencing factors in segments, synthesizes environmental correction factors, and dynamically updates the remaining lifespan of components. When components are shipped out, the remaining lifespan is prioritized first, followed by the arrival time, and then ESD exposure count is used as a secondary priority. Key operations generate logs containing temperature, humidity, and lifespan data, enabling closed-loop management after shipment. This invention ensures that the remaining lifespan accurately matches the actual moisture risk, prioritizes high-risk components, reduces soldering defects, minimizes unnecessary baking, achieves end-to-end quality traceability, and offers a low-cost, low-computing-power solution suitable for various electronic manufacturing warehousing scenarios.

Description

Technical Field

[0001] This invention relates to the field of electronic component warehousing management technology, and in particular to an intelligent warehousing system for managing electronic components. Background Technology

[0002] In the electronics manufacturing industry, moisture-sensitive devices (MSDs) must be managed in accordance with the JEDEC J-STD-033 standard. Devices of different MSL levels have a fixed workshop life after being taken out of the moisture-proof bag. Traditional warehouse management systems (WMS) generally adopt a static countdown mode, which linearly deducts the remaining life according to the actual time and uses first-in-first-out (FIFO) as the ordering rule for outbound shipment.

[0003] In real-world warehousing scenarios, there are significant differences in temperature and humidity across different shelves and areas. Areas near doors, windows, and vents are prone to high temperature and humidity, while dedicated drying cabinets provide low temperature and low humidity environments. Furthermore, temperature and humidity fluctuate dynamically with the seasons and air conditioning operation. Traditional static countdown timers are based entirely on standard environmental settings, failing to account for actual temperature and humidity variations in storage environments. This results in devices in high-humidity, high-temperature areas absorbing moisture at rates far exceeding standard values. Consequently, the system may display sufficient remaining lifespan, but the devices may actually be excessively damp. This can lead to poor soldering, short circuits, and other quality defects after installation. Conversely, devices in low-humidity, low-temperature areas absorb moisture at rates far below standard values, yet the system still deducts lifespan at a fixed rate. This causes devices to be prematurely locked out even when they are not actually damp, requiring frequent drying, which wastes energy and can damage device performance.

[0004] Furthermore, traditional WMS only uses a single FIFO sorting rule, which does not take into account the dynamic moisture risk of devices. This often results in devices that were put into storage earlier but have sufficient remaining lifespan being released first, while devices that were put into storage later but have tight remaining lifespan are stockpiled. When FIFO and lifespan priority conflict, it relies on subjective human decision-making without objective basis, which can easily lead to delays in the release of devices with high moisture risk, increase the risk of production defects, and further aggravate potential product quality problems.

[0005] Therefore, there is an urgent need for an intelligent warehousing system for managing electronic components to solve the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide a method for intelligently managing the storage of electronic components, comprising the following steps: The system periodically collects real-time temperature and relative humidity values ​​for each storage area and obtains the initial workshop life, remaining life variables, and storage time for each moisture-sensitive device. Based on the real-time temperature and relative humidity values, the environmental correction factor of the current storage area is calculated according to the preset environmental correction factor mapping rule. The fixed period duration is multiplied by the environmental correction factor to obtain the equivalent time increment, and the equivalent time increment is subtracted from the remaining lifetime variable to update the remaining lifetime. When a shipment request containing the target model is received, all unlocked devices with matching models are filtered out and sorted in ascending order of remaining lifespan. For devices with a remaining lifespan difference less than a first preset threshold, they are sorted in ascending order of entry time and the first device after sorting is selected as the device to be shipped. Each time a manual operation or lifetime update triggers a lock, a log record is generated. This log record includes the device identifier, timestamp, operation type, current temperature and humidity values ​​of the storage area, and remaining lifetime values ​​before and after the update. Output the identification information of the device to be shipped out, and remove the device from the inventory.

[0007] Furthermore, the present invention also discloses a warehouse system for intelligent management of electronic components, comprising: The data acquisition module is used to periodically collect real-time temperature and relative humidity values ​​for each storage area, and to obtain the initial workshop life, remaining life variables, and storage time for each moisture-sensitive device. The update module is used to calculate the environmental correction factor of the current storage area according to the real-time temperature value and relative humidity value and a preset environmental correction factor mapping rule, multiply the fixed period duration by the environmental correction factor to obtain the equivalent time increment, and subtract the equivalent time increment from the remaining lifetime variable to update the remaining lifetime. The sorting module is used to filter out all unlocked devices with matching models when a warehouse request containing the target model is received. The devices are sorted in the first level according to the remaining lifespan from smallest to largest. For devices with a remaining lifespan difference less than a first preset threshold, the devices are sorted in the second level according to the warehouse entry time from earliest to latest. The first device after sorting is taken as the device to be shipped. The generation module is used to generate a log record each time a manual operation or lifetime update triggers a lock. The log record includes the device identifier, timestamp, operation type, temperature and humidity values ​​of the current storage area, and remaining lifetime values ​​before and after the update. The output module is used to output the identification information of the device to be shipped out and remove the device from the inventory.

[0008] Furthermore, the output module includes: The sending unit is used to encapsulate the identification information, location coordinates, current remaining lifespan, and recommended path of the device to be shipped into a shipping instruction, send it to the automated handling equipment, and push the task list to the shipping outlet workstation; The receiving unit is used to mark the corresponding device as out of the warehouse and add an out-of-warehouse completion log after receiving the pickup confirmation signal; The update unit is used to generate an emergency use prompt if the remaining lifespan of the device to be shipped is less than a preset emergency threshold, requiring the device to be mounted and a batch tracking number to be added within a specified time. After the shipment is completed, the remaining lifespan of all devices in the remaining inventory is updated again.

[0009] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described intelligent management method for storing electronic components.

[0010] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described intelligent management method for storing electronic components.

[0011] The beneficial effects of this application are as follows: Firstly, this invention enables dynamic and accurate updates to remaining device lifespan, closely aligning with actual moisture risks. By collecting real-time temperature and humidity data from the shelf area and precisely calculating temperature and humidity correction factors, it dynamically converts the equivalent lifespan consumption time, ensuring a high degree of match between the remaining lifespan value and the actual moisture level of the device. Devices in high-humidity and high-temperature areas can avoid the problem of exceeding moisture limits without warning, significantly reducing soldering defects. Devices in low-humidity and low-temperature areas do not require premature baking, greatly reducing the frequency of baking and lowering energy consumption and device performance loss.

[0012] Secondly, this invention optimizes the outbound sorting logic, eliminating manual intervention and balancing risk and turnover. It prioritizes dynamic remaining lifespan as the first priority, inbound time as the second priority, and electrostatic discharge (ESD) exposure count as a secondary priority, automatically coordinating moisture risk with inventory turnover needs. It prioritizes devices with high moisture risk and high ESD exposure risk, avoiding human decision-making bias and ensuring production stability.

[0013] Third, this invention can construct an environmental linkage log system to achieve full lifecycle quality traceability. It fully covers key operations and associates core data such as temperature and humidity, remaining lifespan, and electrostatic discharge count, supporting chain-like playback and visual traceability of the entire device lifecycle, quickly locating the root cause of quality problems, and improving quality control efficiency. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of a method flow proposed in an embodiment of this application.

[0015] Figure 2 This is a schematic diagram of the system structure proposed in an embodiment of the present invention.

[0016] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] like Figure 1 As shown, this application provides a method for intelligently managing the storage of electronic components, including the following steps: S1: Periodically collect the real-time temperature and relative humidity values ​​of each storage area, and obtain the initial workshop life, remaining life variables and storage time of each moisture-sensitive device; S2: Calculate the environmental correction factor of the current storage area based on the real-time temperature and relative humidity values, multiply the fixed period duration by the environmental correction factor to obtain the equivalent time increment, and subtract the equivalent time increment from the remaining lifetime variable to update the remaining lifetime. S3: When a delivery request containing the target model is received, all unlocked devices with matching models are filtered out and sorted in ascending order of remaining lifespan. For devices with a remaining lifespan difference less than the first threshold, they are sorted in ascending order of entry time and the first device after sorting is taken as the device to be delivered. S4: Each time a manual operation or lifetime update triggers a lock, a log record is generated. This log record includes the device identifier, timestamp, operation type, current temperature and humidity values ​​of the storage area, and remaining lifetime values ​​before and after the update. S5: Output the identification information of the device to be shipped out and remove the device from the inventory.

[0019] As described in steps S1-S5 above, in the entire process of electronic component warehousing management, the rate of depletion of the workshop life of moisture-sensitive components changes dynamically in real time with the temperature and humidity of the storage area. The traditional fixed countdown mode cannot match the environmental differences. The outbound sorting relies solely on the first-in-first-out rule, which cannot identify the level of moisture risk. Key operations such as warehousing, transfer, locking, and outbound lack standardized log records, making it difficult to accurately trace quality problems. Incomplete outbound instruction information, reliance on manual updates of inventory status, and lack of emergency control for components with short remaining lifespan can easily lead to delays in picking, inventory data deviations, and delays in the use of high-risk components, directly affecting product welding quality, production progress, and enterprise operating costs.

[0020] In existing technologies, temperature and humidity data are mostly collected sporadically from single points, lacking regional representativeness and with no standardized sampling period. This fails to accurately reflect the actual storage environment of the devices. Remaining lifespan is updated statically by directly deducting a fixed amount of time at a fixed period, without considering the dynamic impact of temperature and humidity on the moisture absorption rate. This results in a large discrepancy between the lifespan value and the actual degree of moisture absorption. Outbound sorting only follows a first-in, first-out (FIFO) principle, without distinguishing between devices' dynamic moisture risks. This can easily lead to high-risk devices being stockpiled while low-risk devices are prioritized for outbound processing. Logs simply record inbound and outbound transactions, lacking crucial information such as environmental data, lifespan changes, and static electricity counts. There is no standardized storage and traceability mechanism. Outbound process instructions are simplistic, inventory is updated manually, and there is no risk warning or data synchronization logic. The entire process is poorly managed, leading to significant quality risks and efficiency issues. This solution proposes a comprehensive technical framework that addresses these shortcomings. It includes regionalized temperature and humidity data collection, accurate dynamic lifespan updates, multi-level priority sorting, end-to-end log traceability, and closed-loop outbound execution. This framework forms a comprehensive management system from environmental perception to execution feedback, systematically overcoming the shortcomings of existing technologies.

[0021] This invention constructs a complete intelligent warehouse management method for electronic components by periodically collecting regional temperature and humidity data, dynamically updating the remaining lifespan of moisture-sensitive devices through temperature and humidity coupling, implementing multi-level priority outbound sorting, recording key operation logs across all scenarios, and executing a closed-loop process of outbound command issuance and automatic inventory removal. It systematically solves the core technical problems of traditional warehousing, such as ignoring the dynamic impact of the environment, inaccurate calculation of remaining lifespan, unreasonable outbound sorting, difficulty in operational traceability, and crude outbound control, through five core links: environmental data collection, dynamic control of device lifespan, intelligent decision-making on outbound priorities, quality traceability, and a closed-loop outbound process. This achieves precise control of quality risks for moisture-sensitive devices, efficient and reasonable inventory turnover, and a significant reduction in production defect rates.

[0022] The core working principle of this invention is a linked closed-loop mechanism integrating environmental perception, dynamic lifespan correction, intelligent priority sorting, full-link log traceability, and outbound closed-loop management. First, temperature and humidity sensors are deployed at the shelf level to periodically collect real-time temperature and humidity data from the storage area. Second, humidity and temperature influence factors are calculated based on temperature and humidity segmentation rules, and an environmental correction factor is synthesized. The actual sampling period is converted into a standard environmental equivalent time increment, dynamically deducting the remaining lifespan of moisture-sensitive devices to accurately match the actual moisture risk. Then, with dynamic remaining lifespan as the first priority, warehousing time as the second priority, and electrostatic exposure count as an auxiliary priority, intelligent sorting of outbound devices is completed. Simultaneously, standardized logs are generated for key operations such as warehousing, transfer, locking, and outbound, achieving chain-like traceability of the entire device lifecycle. Finally, the information of devices to be outbound is encapsulated into instructions for automatic update of inventory status. Emergency alerts are triggered for devices with short remaining lifespans, and the remaining lifespan of inventory devices is recalculated in batches after outbound. This forms a complete technical link from data collection, lifespan control, sorting decisions, quality traceability to execution feedback, upgrading the management of moisture-sensitive devices from a static countdown to a dynamic risk-driven mode.

[0023] This invention dynamically adjusts the remaining lifespan based on environmental conditions and prioritizes it for outbound shipment. It accurately identifies and prioritizes moisture-sensitive components with the highest risk of moisture damage, significantly reducing soldering defects caused by excessive moisture in high-humidity and high-temperature environments. Simultaneously, it reduces unnecessary baking operations in low-humidity environments, achieving both improved product quality and reduced operating costs. Specifically, for components in high-humidity and high-temperature areas, the problem of insufficient system lifespan despite actual excessive moisture damage is avoided, resulting in a significant decrease in soldering defects. For components in low-humidity areas, premature baking is unnecessary, significantly reducing energy consumption and component losses. Outbound shipment prioritization naturally balances moisture risk and inventory turnover, eliminating the need for manual intervention and significantly improving production stability and efficiency.

[0024] In one embodiment, step S1 specifically includes: S11: Install a temperature and humidity sensor at the midpoint of each shelf. Each sensor covers all storage locations within a 1.5-meter vertical range above and below it. The sensors operate at fixed intervals. Collect data once, The values ​​are taken from 10 minutes to 30 minutes, and the collected temperature and relative humidity values ​​are uploaded to the warehouse management system server. This step replaces the dense deployment at the warehouse location level with a shelf-level area deployment, effectively reducing the cost of sensor hardware procurement and the complexity of wiring construction. The 10-30 minute sampling cycle matches the hourly lifespan of humidity-sensitive devices, balancing environmental data acquisition accuracy with system computing power. Continuing with the aforementioned medium-sized warehouse application scenario, five temperature and humidity sensors are deployed for five shelves, with a total hardware cost of approximately 500 yuan. With a sampling cycle of 15 minutes, the sensor collects data every 15 minutes, uploading the real-time temperature and relative humidity values ​​of the shelf area to the server, providing accurate and regionally representative basic environmental data for subsequent calculation of environmental correction factors.

[0025] S12: Pre-set the initial shop life for each moisture-sensitive device based on its MSL level. MSL3 level corresponds to Equal to 168 hours, corresponding to MSL4 level Equal to 72 hours, corresponding to MSL2 level This is equal to 336 hours, and this initial workshop life is written into the initial life field of the device database table; Step S12 standardizes the initial workshop lifespan classification configuration according to JEDEC industry standards, eliminating errors caused by manual parameter settings, ensuring a consistent lifespan calculation benchmark for moisture-sensitive devices of the MSL level, and storing the initial lifespan field in the database for quick parameter retrieval and long-term stable storage. Continuing with the aforementioned MSL3 level chip application example, this type of device is uniformly configured... =168 hours. Write this value into the initial lifespan field of the database. You can then directly call this parameter to initialize the remaining lifespan variable.

[0026] S13: Maintain a dynamic remaining lifetime variable for each device that is in storage and unlocked. The initial value of this variable is equal to the corresponding initial lifetime, and it is updated by decreasing according to the temperature and humidity of the storage area when each fixed period is reached. At the same time, the storage timestamp is maintained to record the precise time when the device is first stored in the warehouse. This step establishes a dynamic remaining lifetime variable for moisture-sensitive devices, replacing the fixed countdown mode of existing technology. It enables the remaining lifetime to dynamically decrease and update with changes in temperature and humidity. Accurate maintenance of the entry timestamp provides a unique time reference for the secondary sorting of outgoing devices, solving the technical problem of inaccurate lifetime updates caused by the inability of fixed countdowns to adapt to environmental differences. Continuing with the aforementioned MSL3 level chip application example, its... The initial value is set to 168 hours, and the inbound timestamp is recorded as 10:00 AM on March 10, 2025. Subsequent timestamps are updated every 15 minutes based on the temperature and humidity of the designated shelf area. Decreasing update.

[0027] S14: For non-moisture-sensitive components, the system assigns a constant to their remaining lifespan variable that is much larger than their actual service life. This is equivalent to 9999 hours, which ensures that this type of device is naturally placed after all moisture-sensitive devices when it is sorted out of the warehouse, and that they are sorted in a first-in, first-out manner only according to their entry time.

[0028] Step S14, through differentiated remaining lifetime parameter configuration, clearly distinguishes the outbound priority of moisture-sensitive components and non-moisture-sensitive components, ensuring that moisture-sensitive components with a high risk of moisture damage are shipped out first. Non-moisture-sensitive components are sorted according to the conventional first-in-first-out (FIFO) rule, avoiding non-sensitive components crowding out the priority resources of sensitive components. Continuing with the aforementioned example of ordinary resistor applications, its... When assigned a value of 9999 hours, the device is automatically sorted after all moisture-sensitive devices during the outbound sorting process. Ordinary resistors of the same model are sorted in a first-in-first-out manner according to their respective entry times.

[0029] As described in steps S11-S14 above, in the actual application scenario of electronic component warehousing management, the moisture absorption rate of moisture-sensitive devices is directly related to the real-time temperature and humidity of the storage area. The temperature and humidity fluctuations within a 1.5-meter vertical range on the same shelf are minimal, eliminating the need for individual sensor deployment at each location. Furthermore, the JEDEC industry standard specifies fixed initial workshop lifespans for moisture-sensitive devices of different MSL levels, while non-moisture-sensitive components do not require workshop lifespan management. If temperature and humidity sensors are densely deployed at the location level, it will significantly increase hardware procurement costs and wiring complexity. Inappropriate sampling cycle settings will lead to excessive database write pressure with high-frequency sampling and delayed or invalid environmental data with low-frequency sampling. The initial workshop lifespan is not standardized according to MSL levels, and relying on manual input can easily result in parameter errors. The lack of parameter maintenance rules that differentiate between moisture-sensitive and non-moisture-sensitive components will directly cause disordered outbound sorting priorities, leading to technical problems such as delayed outbound shipments of high-risk moisture-affected devices, premature baking of low-risk devices, and non-sensitive devices crowding out priority sorting resources.

[0030] In existing technologies, temperature and humidity sensors are generally deployed at the warehouse location level, which results in high hardware costs and limited wiring distances. The sampling period lacks clear specifications, which can easily lead to an imbalance between data accuracy and system computing power. The initial workshop lifespan of moisture-sensitive devices is not standardized according to the MSL level, and the reliance on manual input can easily lead to parameter deviations. The remaining lifespan uses a fixed countdown mode, which cannot adapt to the differences in lifespan consumption caused by dynamic changes in temperature and humidity. Furthermore, no differentiated sorting parameters are set for non-moisture-sensitive components, resulting in a mixed sorting of sensitive and non-sensitive components when leaving the warehouse, and failing to ensure that sensitive components with a high risk of moisture are given priority in leaving the warehouse.

[0031] This invention aims to complete the collection of basic data on the storage environment, the solidification of device lifespan benchmarks, the tracking of dynamic variables, and the differentiation of outbound sorting priorities by deploying shelf-level temperature and humidity sensing areas, standardizing the initial workshop lifespan configuration of moisture-sensitive devices at the MSL level, maintaining dynamic remaining lifespan variables and inbound timestamps for devices, and setting differentiated remaining lifespan parameters for non-moisture-sensitive components. This provides complete and reliable basic data support for updating the dynamic remaining lifespan of moisture-sensitive devices and accurately sorting them out of the warehouse, thus solving the core technical problems of unreasonable environmental data collection, chaotic lifespan benchmarks, and mixed sorting logic for different types of components from the source.

[0032] In one embodiment, step S2 specifically includes: S21: Based on the real-time relative humidity value obtained from the sensor, calculate the humidity influence factor according to the following piecewise linear rule: First, determine whether the relative humidity is less than or equal to 40%. If so, the humidity influence factor is calculated as follows: ,and ≥0.3; If the relative humidity is greater than 40% and less than 65%, then the humidity influence factor is equal to 1.0; If the relative humidity is greater than or equal to 65%, the humidity influence factor is calculated as follows: ,and ≤2.0, wherein, the The humidity influence factor is used to characterize the degree of influence of humidity on the moisture absorption rate of MSD devices. RH represents the real-time relative humidity. Step S21 refines and quantifies the impact of humidity on the moisture absorption rate using a three-segment piecewise linear rule, setting upper and lower limits to avoid extreme factors and improve the accuracy of humidity correction. For example, in a medium-sized warehouse application scenario, if a sensor on a certain shelf collects a real-time relative humidity value of 72%, which is greater than or equal to 65%, the humidity influence factor calculated using the formula is 1.48. This value does not exceed the upper limit of 2.0, effectively reflecting the characteristic of accelerated moisture absorption rate in high humidity environments. If the relative humidity value of another shelf is 35%, the humidity influence factor calculated using the formula is 0.55, effectively reflecting the characteristic of slowed moisture absorption rate in low humidity environments.

[0033] S22: Based on the real-time temperature value obtained from the sensor, calculate the temperature influence factor according to the following rules: Determine whether the current temperature is less than or equal to 30 degrees Celsius. If so, the temperature influence factor is equal to 1.0. If the current temperature is greater than 30 degrees Celsius, the temperature influence factor is equal to 1.0 plus the current temperature minus 30 (in parentheses) and then multiplied by 0.05. The upper limit of the value of this factor is 1.5. Step S22 can quantify the accelerating effect of high temperature on the moisture absorption rate, set an upper limit to avoid over-correction of high temperature, and supplement the temperature influence dimension not covered by humidity correction. Continuing the aforementioned shelf application scenario, the real-time shelf temperature value corresponding to 72% relative humidity is 32 degrees Celsius. This temperature value is greater than 30 degrees Celsius. Substituting it into the formula, the temperature influence factor is equal to 1.1. If the temperature value of another shelf is 28 degrees Celsius, the temperature influence factor is 1.0, which is consistent with the characteristic of no additional acceleration at room temperature.

[0034] S23: Multiply the humidity influence factor by the temperature influence factor to obtain the environmental correction factor for the current storage area; Step S23 integrates the dual effects of humidity and temperature to synthesize a unified environmental correction coefficient, accurately characterizing the combined impact of current regional temperature and humidity on the lifespan of moisture-sensitive devices. In a shelf application scenario, multiplying the humidity influence factor of 1.48 by the temperature influence factor of 1.1 yields a current regional environmental correction factor of 1.628, accurately reflecting that the lifespan consumption rate under high humidity and high temperature conditions on this shelf is 1.628 times that of the standard environment.

[0035] S24: Let the fixed sampling period of the sensor be Δt (unit: hours). For each moisture-sensitive device in an unlocked state, firstly, calculate the equivalent time increment of this period according to the environmental correction factor corresponding to its current location. The equivalent time increment is equal to the sampling period multiplied by the environmental correction factor. Then, perform the remaining lifetime update, subtract the equivalent time increment from the remaining lifetime value before the update, and obtain the updated remaining lifetime value. Step S24 converts the actual sampling duration into the equivalent lifetime consumption time under standard conditions, enabling the remaining lifetime to decrease dynamically and accurately with environmental changes. Continuing the aforementioned application scenario, the sampling cycle duration Δt is 15 minutes, or 0.25 hours. The equivalent time increment is equal to 0.25 hours multiplied by 1.628, resulting in 0.457 hours. This corresponds to a remaining lifetime of 168 hours for the MSL3 level chip on the shelf before the update. Subtracting 0.457 hours, the remaining lifetime after the update is 167.543 hours, completing this cycle lifetime update.

[0036] S25: After each update, determine whether the updated remaining lifetime value is less than or equal to 0 for the first time. If so, set the status field of the device to "locked" and generate a baking suggestion record containing the device identifier, locking time, temperature and humidity at the locking time, and remaining lifetime value, and push it to the warehouse management interface.

[0037] Step S25 accurately triggers the lock based on the initial judgment, avoiding duplicate record generation. Simultaneously, it generates complete baking recommendation information to provide a basis for subsequent device processing. Continuing with the aforementioned MSL3 level chip application scenario, the chip is continuously stored on the high-humidity, high-temperature shelf. Its remaining lifespan decreases every 15 minutes in equivalent time increments. When the remaining lifespan is updated to less than or equal to 0 for the first time, the system sets its status field to locked, generates a baking recommendation record containing the chip identifier, locking time, 72% relative humidity, 32 degrees Celsius temperature, and a remaining lifespan value of 0, and pushes it to the warehouse management interface, indicating that the chip has excessive moisture and requires baking.

[0038] As described in steps S21-S25 above, in the electronic component storage scenario, the moisture absorption rate of moisture-sensitive devices is positively correlated with ambient humidity and temperature. The higher the humidity and temperature, the faster the moisture absorption rate, and the greater the actual consumption of the device's lifespan in the workshop. If only a fixed coefficient is used for correction or the influence of temperature and humidity is ignored, the remaining lifespan update value will deviate too much from the actual degree of moisture absorption. In high humidity and high temperature environments, the actual moisture absorption of the device exceeds the standard, but the remaining lifespan still shows as sufficient. In low humidity and low temperature environments, the remaining lifespan of the device has not been exhausted but it is mistakenly locked. At the same time, the remaining lifespan update is not converted into equivalent duration according to the sampling period, making it impossible to accurately quantify the impact of the environment on the lifespan. The lack of initial judgment for locking triggers can easily lead to duplicate records, directly affecting the accuracy of warehouse management and production stability.

[0039] In existing technologies, the remaining lifespan of humidity-sensitive devices is often updated by directly deducting a fixed amount of time at a fixed period, without fine-grained segmented correction for humidity and temperature. This results in low correction accuracy, with the influence of temperature often being ignored or only a single correction coefficient being set. The lack of an upper limit on the value of this coefficient can easily lead to over-correction. Furthermore, the environmental correction factor is not linked to the sampling period to calculate the equivalent time increment, making it impossible to accurately quantify the actual lifespan consumption under different temperatures and humidity levels. Device lockout triggers do not determine whether the remaining lifespan is less than or equal to 0 for the first time, which can easily lead to repeated generation of baking suggestion records and increase system data redundancy.

[0040] This invention calculates humidity and temperature influencing factors in segments, synthesizes environmental correction factors, and converts them into equivalent time increments. This enables the remaining lifespan of moisture-sensitive devices to be dynamically updated as temperature and humidity decrease. At the same time, it accurately triggers device locking and generates baking suggestion records. This solves the core problems of fixed countdown modes being unable to match environmental differences, inaccurate lifespan updates, and non-standard locking triggers. It provides the remaining lifespan data that truly reflects the risk of moisture exposure for outbound sorting.

[0041] In one embodiment, step S3 specifically includes: S31: When a delivery request is received from the manufacturing execution system, the request contains the model and quantity of the components to be retrieved. The system first filters out all components with matching models and whose status is not "locked" from the inventory database to form a candidate set. Step S31 constructs an effective candidate set through precise screening, eliminating interference from locked devices and ensuring that subsequent sorting is only for available devices, avoiding invalid calculations. Continuing with the aforementioned medium-sized warehouse application scenario, the manufacturing execution system sends an outbound request for 10 MSL3 level chips. The system filters all MSL3 level chips with matching models and unlocked status from the inventory database, forming a candidate set containing 20 chips.

[0042] S32: For each device in the candidate set, read its current remaining lifespan and entry time. Sort all devices in ascending order of their current remaining lifespan to obtain the first-level sorting sequence. Devices with smaller remaining lifespans have a higher actual risk of moisture damage and should be prioritized for removal from the warehouse. Step S32 prioritizes devices with high moisture risk by using dynamic remaining lifespan as the core sorting criterion, directly addressing the core deficiency of traditional sorting methods that ignore the dynamic impact of the environment. Continuing with the aforementioned candidate set scenario, device A has a current remaining lifespan of 2 hours and was put into storage on March 9, 2025, while device B has a current remaining lifespan of 5 hours and was put into storage on March 8, 2025. After sorting by remaining lifespan from smallest to largest, device A is placed before device B, prioritizing the risk of moisture.

[0043] S33: Traverse the first-level sorting sequence and compare the difference in remaining lifespan between any two adjacent devices with the first threshold, which is 1 hour. When the difference in remaining lifespan is less than the first threshold, divide these devices with similar remaining lifespans into the same group. Within the same group, sort them in the second level according to their entry time from earliest to latest, i.e., first-in-first-out. When the difference in remaining lifespan is greater than or equal to the first threshold, keep the original order based on remaining lifespan unchanged. Step S33 balances the priority of moisture risk with the fairness of inventory turnover by setting a first threshold of 1 hour, avoiding frequent sorting adjustments due to small differences in remaining lifespan. Simultaneously, sorting by entry time ensures the first-in, first-out (FIFO) turnover logic for devices with similar lifespans. In the candidate set, device C has a remaining lifespan of 2.2 hours, and device D has a remaining lifespan of 2.5 hours. The difference in their remaining lifespans is 0.3 hours, which is less than the first threshold of 1 hour, so they are grouped together. Device C's entry time is March 9, 2025, and device D's entry time is March 8, 2025. After sorting by entry time within the group, device D is ranked before device C.

[0044] S34: After sorting, take the first device in the sequence as the device to be shipped out this time, and return its remaining life, entry time and location information to the manufacturing execution system, and reduce the inventory count of the device; if the candidate set is empty, issue a material shortage alarm signal to prompt the operator to check whether there are locked devices that need to be baked or need to be purchased. Step S34 clarifies the execution logic of the sorting results, synchronously feeds back key information and updates inventory, triggers a material shortage warning when the candidate set is empty, and ensures a closed loop in the outbound process. Continuing the aforementioned sorting scenario, if the first item in the final sorting sequence is device A, the system returns the device A identifier, 2 hours remaining lifespan, warehousing time of March 9, 2025, and corresponding shelf location information to the manufacturing execution system, and reduces the chip inventory count from 20 to 19. If the candidate set is empty, the system issues a material shortage alarm, prompting the operator to check and lock the device or initiate a purchase.

[0045] S35: An additional electrostatic discharge (ESD) exposure count is maintained for each device, with an initial value of 0. Each time a transfer or outbound scanning operation is performed at a non-ESD workstation, the ESD exposure count is incremented by 1. After the first-level sorting, for device groups with a remaining lifespan difference less than the second threshold (the second threshold is 0.5 hours), the ESD exposure counts of each device in the group are further compared. Devices with larger ESD exposure counts are prioritized for outbound processing. When the ESD exposure count reaches 5, the system will pop up a warning. When it reaches 8, the device will be automatically locked until it can be unlocked after manual verification using an ESD tester.

[0046] Step S35 introduces electrostatic discharge (ESD) exposure counts as an auxiliary sorting criterion. For devices with similar remaining lifespans, devices with higher ESD risk are prioritized. A tiered control threshold is set to mitigate ESD damage risks. Continuing with the scenario where devices C and D are in the same group, if the difference in their remaining lifespans is less than the second threshold of 0.3 hours to 0.5 hours, device C has an ESD exposure count of 3, and device D has an ESD exposure count of 5. Device D, with the higher ESD exposure count, is prioritized for removal from the warehouse. If the ESD exposure count of a device subsequently accumulates to 5, the system will issue a warning. When the count reaches 8, the device will be automatically locked until manual ESD verification, at which point it will be unlocked for use.

[0047] As described in steps S31-S35 above, in the scenario of electronic component outbound management, the dynamic remaining lifespan of moisture-sensitive devices of the same model varies due to differences in temperature and humidity in the storage area, and the moisture risk level is significantly different. When multiple devices have similar remaining lifespans, it is not possible to distinguish priorities based solely on remaining lifespan. It is necessary to combine the warehousing time to maintain the fairness of turnover. In addition, the operation of devices in non-anti-static workstations will accumulate the risk of electrostatic exposure. The more exposures, the higher the probability of electrostatic damage. The existing sorting logic does not include this dimension, which can easily lead to the delayed outbound of high electrostatic risk devices, affecting the product welding quality and production stability.

[0048] In existing technologies, the outbound sorting only adopts a single first-in-first-out rule, without considering the dynamic differences in the remaining lifespan of the devices. This can easily lead to situations where devices that were put into storage earlier but have sufficient remaining lifespan are prioritized for outbound processing, while devices that were put into storage later but have tight remaining lifespans are stockpiled. There is no clear sorting standard for devices with similar remaining lifespans, relying on manual intervention. The decision-making lacks objective basis, and the electrostatic discharge (ESD) exposure count is not maintained or included in the sorting conditions. This makes it impossible to prioritize the processing of devices with high ESD exposure risk, and the potential for ESD damage is difficult to avoid.

[0049] By screening matching and unlocked devices, sorting them by remaining lifespan as the first level, sorting them by entry time when the difference in remaining lifespan is less than the first threshold, and supplementing the sorting by electrostatic exposure count when the difference in remaining lifespan is less than the second threshold, the system accurately determines the devices to be shipped out. This solves the core problems of traditional first-in-first-out sorting ignoring dynamic moisture risk, lacking a unified rule for sorting devices with similar lifespans, and not including electrostatic exposure risk in the sorting criteria. It ensures that devices with the highest moisture risk and more severe electrostatic exposure are shipped out first, while standardizing the logic of material shortage warning and electrostatic risk management.

[0050] In one embodiment, step S4 specifically includes: S41: Define the set of operation types triggered by log recording. This set includes: inbound operation, transfer operation, outbound operation, automatic locking operation due to the remaining lifespan reaching zero, manual forced unlocking operation, and status reset operation after baking is completed. Each time any of the above operations occurs, the system automatically generates a unique log identifier. Step S41 fully covers the critical operations throughout the device's lifecycle, preventing key actions from going unrecorded. Unique log identifiers ensure that each record is independently identifiable and unique. Continuing with the aforementioned MSL3-level chip device A application scenario, device A undergoes three critical operations: warehousing, transfer to a high-humidity shelf, and automatic locking when its remaining lifespan reaches zero. Each operation triggers log generation, each assigned a unique log identifier (001, 002, 003) to ensure that the records for each of the three operations are independently traceable.

[0051] S42: Each log record contains the following fields: device identifier, operation timestamp, operation type, location identifier before operation, temperature and relative humidity values ​​of the location at the time of operation, remaining lifespan before operation, remaining lifespan after operation, and operator identifier. For lock operations caused by automatic lifespan updates, the operation type is marked as "automatic lock", the remaining lifespan after operation is recorded as 0, and the operator identifier is filled with "system". Step S42 standardizes core log fields to fully associate device information, environmental data, lifespan changes, and the operating entity, automatically locking the scenario and assigning unified field values ​​to ensure that log information is standardized, complete, and traceable. Continuing with the aforementioned automatic locking operation scenario for device A, the record fields corresponding to log identifier 003 are: device identifier A001, operation timestamp March 10, 2025, 15:00, operation type automatic locking, storage location before operation (shelf 5-03), temperature value 32 degrees Celsius, relative humidity value 72%, remaining lifespan before operation 0.457 hours, remaining lifespan after operation 0 hours, and operator identification system. The field information completely restores the environment and device status at the time of locking.

[0052] S43: All log records are indexed using device identifiers and operation timestamps and stored in a dedicated log table in a relational database. Logs exceeding 180 days are automatically transferred to an archive table weekly. When a quality engineer enters a device identifier or production batch number, the system replays the complete lifecycle curve of the device in chronological order, including the rate of lifespan decline for each operation, the cumulative count for each non-electrostatic operation, and the environmental snapshot at the time of final shipment. The remaining lifespan is displayed as a curve of change with actual storage time in a visual chart format.

[0053] Step S43 improves log query efficiency through composite indexes, reduces database redundancy through periodic archiving, and enables full lifecycle traceability through chained replay and visualization, accurately locating abnormal nodes. Continuing with the aforementioned application scenario of device A, when querying device identifier A001, the system quickly retrieves logs 001, 002, and 003 through composite indexes. The chained replay shows the complete process of device A from its initial lifespan of 168 hours after entering the warehouse, to its lifespan decreasing by 15 minutes at a time on the high-humidity shelf, and finally reaching zero and locking. The visualized chart clearly shows the decreasing trend of remaining lifespan with storage time. Combined with environmental data of 72% relative humidity and 32 degrees Celsius, the system can quickly locate that high-humidity storage is the core reason for the device's excessive moisture.

[0054] As described in steps S41-S43 above, in the scenario of electronic component warehousing management, the risk of moisture damage and quality problems of moisture-sensitive components are often directly related to the storage environment and operational behavior. Key operations such as warehousing, transfer, locking, and unlocking must all be fully recorded. At the same time, the amount of log data continues to grow over time. Without a standardized storage and indexing mechanism, query efficiency will be low. When tracing quality, it is necessary to completely restore the entire life cycle status of the component from warehousing to outbound. Relying solely on batch information cannot locate the cause of anomalies in a single component, which directly affects the efficiency of production quality control and problem investigation.

[0055] In existing technologies, warehouse logs only record inbound and outbound operations, failing to cover key processes such as warehouse transfer, automatic locking, forced unlocking, and baking reset. Log fields only contain basic operational information, lacking core data such as temperature and humidity changes at the time of operation and remaining lifespan changes. Logs lack dedicated storage tables and indexes, resulting in scattered data, slow querying, and no periodic archiving mechanism. Long-term operation leads to database redundancy, and traceability only supports batch-level queries, making it impossible to chain-replay the entire lifecycle data of a single device, thus making it difficult to accurately locate the root cause of quality problems.

[0056] This invention constructs a complete warehouse operation log management system by defining a set of log-triggered operations, standardizing log recording fields, establishing a composite index and periodic archiving mechanism, and realizing chained playback and visual traceability of the device lifecycle. It solves the core problems of lack of unified log recording for key operations, incomplete log field information, redundant data storage, and difficulty in quality traceability, and provides complete data support for locating device quality problems, tracing operational responsibility, and analyzing lifespan changes.

[0057] In one embodiment, step S5 specifically includes: S51: The identification information, location coordinates, current remaining lifespan value, and recommended handling path of the device to be shipped are encapsulated into a shipping instruction and sent to the control system of the automated guided vehicle or intelligent material tower via wireless network. At the same time, a list of tasks to be picked up is pushed to the workstation at the anti-static shipping port. Step S51 encapsulates all information in the outbound instruction to ensure that the automated guided vehicle (AGV) can accurately retrieve goods based on the location coordinates and recommended path. The workstation simultaneously acquires task information to connect the handover process, avoiding retrieval delays or handover confusion due to missing information. Continuing with the aforementioned medium-sized warehouse application scenario, the device to be outbound is device A, whose identification information is A001, its location coordinates are shelf 5-03, its current remaining lifespan is 2 hours, and the recommended handling path is from the middle of shelf 5 to the anti-static outbound exit. The system encapsulates the above information into an outbound instruction and sends it to the automated guided vehicle (AGV). At the same time, the workstation at the outbound exit displays the list of tasks to be retrieved for device A.

[0058] S52: After the operator or automated handling equipment completes the actual pickup, the system receives a confirmation signal from the barcode scanner or RFID reader, marks the corresponding device as "outbound" in the inventory master table, and adds an outbound completion record to the log table. This record includes the outbound time, target production line number, and operation verification person. Step S52 automatically receives confirmation signals to update the inventory status in real time, avoiding delays and errors caused by manual entry. Simultaneously, it adds outbound log records, forming a complete closed-loop operation from inbound to outbound, providing a basis for subsequent quality traceability. Continuing with the aforementioned application scenario for component A, after the automatically guided cart completes the pickup, the barcode scanner scans the component A identifier and sends a confirmation signal. The system marks the status of component A in the main inventory table as outbound, and simultaneously adds a record to the log table, specifying the outbound time as 15:10 on March 10, 2025, the target production line as SMT production line 1, and the operator verification person as operator 001.

[0059] S53: If the remaining lifespan of a component to be shipped is less than 1 hour at the time of shipment, the system generates an additional "emergency use prompt" and sends it to the production line operator's terminal, requiring the placement operation to be completed within 4 hours. The system also automatically adds the batch tracking number of the component to the production line material change record to facilitate targeted traceability of soldering quality. After shipment is completed, the system re-executes the update of the remaining lifespan of all components in the remaining inventory to ensure that the next shipment sorting is based on the latest data.

[0060] Step S53 sets an emergency alert for components with high moisture risk, forcibly limiting the placement operation time to avoid potential quality problems. Simultaneously, it binds batch tracking numbers to achieve targeted traceability of soldering quality. After shipment, the remaining lifespan is recalculated in batches to ensure the real-time validity of subsequent sorting data and maintain the accuracy of dynamic priority logic. Continuing the aforementioned application scenario, if component A has a remaining lifespan of 2 hours at the time of shipment, no emergency alert is triggered. If another component awaiting shipment has a remaining lifespan of 0.8 hours, the system sends an emergency usage alert to the production line operator terminal, requiring placement to be completed within 4 hours, and adding the corresponding batch tracking number to the material change record. After component A is shipped, the system recalculates the remaining lifespan of the remaining 18 MSL3 level chips in inventory based on temperature and humidity data in 15-minute cycles, ensuring that the next shipment sorting is based on the latest remaining lifespan data.

[0061] As described in steps S51-S53 above, in the scenario of electronic component warehousing and outbound execution, the automatic guided vehicle, intelligent material tower and production line workstation need complete component information to accurately complete the picking and handover. If the inventory status after outbound relies on manual updates, data delays or input errors are likely to occur. Components with less than 1 hour of remaining lifespan are at extremely high risk of moisture after outbound, which will delay the chip mounting operation and lead to poor soldering. If the remaining lifespan of the inventory components is not recalculated after the outbound operation, the next outbound sorting will be based on expired data, and the sorting priority will be disordered, which will directly affect the production progress and product yield.

[0062] In existing technologies, outbound instructions only include the device number and lack key information such as location coordinates, remaining lifespan, and handling route. This results in low efficiency of automated equipment picking, easy errors in manual handover, and reliance on manual barcode scanning for inventory status marking. Data updates are delayed and prone to recording errors. There is no emergency alert mechanism for devices with short remaining lifespans, and high-risk devices are easily delayed in use after being outbound. Furthermore, the outbound operation does not trigger a batch recalculation of remaining lifespans, and subsequent sorting uses old data, rendering the dynamic priority logic ineffective.

[0063] By encapsulating and issuing outbound instructions with full information, automatically updating and marking inventory status, providing emergency usage alerts for components with short remaining lifespans, and recalculating the remaining lifespan of inventory components after outbound shipment, the system achieves closed-loop execution of the component outbound process, precise information interaction, real-time synchronization of inventory data, and targeted control of emergency moisture risks. This solves the core problems of incomplete outbound instruction information, delayed inventory updates, lack of usage warnings for short-lifespan components, and reliance on old data for subsequent outbound sorting, ensuring efficient outbound execution, accurate inventory management, and controllable component quality risks.

[0064] like Figure 2 As shown, the present invention also discloses a warehouse system for intelligent management of electronic components, comprising: The data acquisition module is used to periodically collect real-time temperature and relative humidity values ​​for each storage area, and to obtain the initial workshop life, remaining life variables, and storage time for each moisture-sensitive device. The update module is used to calculate the environmental correction factor of the current storage area according to the real-time temperature value and relative humidity value and a preset environmental correction factor mapping rule, multiply the fixed period duration by the environmental correction factor to obtain the equivalent time increment, and subtract the equivalent time increment from the remaining lifetime variable to update the remaining lifetime. The sorting module is used to filter out all unlocked devices with matching models when a warehouse request containing the target model is received. The devices are sorted in the first level according to the remaining lifespan from smallest to largest. For devices with a remaining lifespan difference less than a first preset threshold, the devices are sorted in the second level according to the warehouse entry time from earliest to latest. The first device after sorting is taken as the device to be shipped. The generation module is used to generate a log record each time a manual operation or lifetime update triggers a lock. The log record includes the device identifier, timestamp, operation type, temperature and humidity values ​​of the current storage area, and remaining lifetime values ​​before and after the update. The output module is used to output the identification information of the device to be shipped out and remove the device from the inventory.

[0065] In one embodiment, the output module includes: The sending unit is used to encapsulate the identification information, location coordinates, current remaining lifespan, and recommended path of the device to be shipped into a shipping instruction, send it to the automated handling equipment, and push the task list to the shipping outlet workstation; The receiving unit is used to mark the corresponding device as out of the warehouse and add an out-of-warehouse completion log after receiving the pickup confirmation signal; The update unit is used to generate an emergency use prompt if the remaining lifespan of the device to be shipped is less than a preset emergency threshold, requiring the device to be mounted and a batch tracking number to be added within a specified time. After the shipment is completed, the remaining lifespan of all devices in the remaining inventory is updated again.

[0066] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described intelligent management method for storing electronic components.

[0067] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described intelligent management method for storing electronic components.

[0068] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0069] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0070] The above description is merely a preferred embodiment of the present invention and does not limit the scope of this application. Any equivalent results or equivalent process transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of protection of this application.

Claims

1. A method for intelligently managing the storage of electronic components, characterized in that, Includes the following steps: The system periodically collects real-time temperature and relative humidity values ​​for each storage area and obtains the initial workshop life, remaining life variables, and storage time for each moisture-sensitive device. Based on the real-time temperature and relative humidity values, the environmental correction factor of the current storage area is calculated according to the preset environmental correction factor mapping rule. The fixed period duration is multiplied by the environmental correction factor to obtain the equivalent time increment, and the equivalent time increment is subtracted from the remaining lifetime variable to update the remaining lifetime. When a shipment request containing the target model is received, all unlocked devices with matching models are filtered out and sorted in ascending order of remaining lifespan. For devices with a remaining lifespan difference less than a first preset threshold, they are sorted in ascending order of entry time and the first device after sorting is selected as the device to be shipped. Each time a manual operation or lifetime update triggers a lock, a log record is generated. This log record includes the device identifier, timestamp, operation type, current temperature and humidity values ​​of the storage area, and remaining lifetime values ​​before and after the update. Output the identification information of the device to be shipped out, and remove the device from the inventory.

2. The warehousing method for intelligent management of electronic components according to claim 1, characterized in that, The steps of periodically collecting real-time temperature and relative humidity values ​​for each storage area, and obtaining the initial workshop life, remaining life variables, and storage time for each moisture-sensitive device, specifically include: A temperature and humidity sensor is installed at a preset height position on each shelf. Each sensor covers all storage locations within its preset vertical range. The sensor collects data once at a preset fixed period and uploads the collected temperature and relative humidity values ​​to the warehouse management system server. For each moisture-sensitive device, a corresponding initial workshop life is pre-set according to its MSL level, and the initial workshop life is written into the initial life field of the device database table; For each device in storage and not locked, maintain a dynamic remaining lifetime variable. The initial value of this variable is equal to the corresponding initial lifetime, and it is updated by decreasing according to the temperature and humidity of the storage area at the end of each fixed period. At the same time, maintain an entry timestamp to record the precise time when the device is first stored in the warehouse. For non-moisture-sensitive components, the system assigns a preset constant to their remaining lifespan variable that is greater than the initial lifespan of all moisture-sensitive components. This ensures that such components are naturally placed after all moisture-sensitive components when being sorted out of the warehouse, and that they are sorted in a first-in-first-out manner only according to their entry time.

3. The warehousing method for intelligent management of electronic components according to claim 1, characterized in that, The steps of calculating the environmental correction factor of the current storage area according to the real-time temperature and relative humidity values ​​and a preset environmental correction factor mapping rule, multiplying the fixed period by the environmental correction factor to obtain the equivalent time increment, and subtracting the equivalent time increment from the remaining lifetime variable to update the remaining lifetime specifically include: Based on the relative humidity value, a humidity influence factor is determined according to a preset humidity influence mapping relationship, wherein the humidity influence mapping relationship makes the humidity influence factor less than a first threshold in a low humidity region and greater than a second threshold in a high humidity region. Based on the temperature value, a temperature influence factor is determined according to a preset temperature influence mapping relationship, which makes the temperature influence factor increase as the temperature rises when the temperature exceeds a preset temperature threshold. By combining the humidity influence factor with the temperature influence factor, the environmental correction factor for the current storage area is obtained. For each moisture-sensitive device that is unlocked, the equivalent time increment for this cycle is calculated based on the environmental correction factor corresponding to its current location, and the remaining lifetime value before the update is subtracted from the equivalent time increment to obtain the updated remaining lifetime value. After each update, check if the updated remaining lifetime value is less than or equal to zero. If so, lock the device and generate a baking recommendation record.

4. The warehousing method for intelligent management of electronic components according to claim 1, characterized in that, The step of receiving an outbound request containing the target model, filtering out all unlocked devices with matching models, sorting them in ascending order of remaining lifespan, and then sorting devices with remaining lifespan differences less than a first preset threshold in ascending order of entry time, and taking the first device after sorting as the device to be outbound, specifically includes: Receive outbound requests containing the target model, and filter out all matching and unlocked devices from the inventory to form a candidate set; The devices in the candidate set are sorted in the first-level order according to their current remaining lifetime, from smallest to largest. Devices whose remaining lifespan difference after the first level of sorting is less than the first preset threshold are grouped into the same group, and within each group, they are sorted in the second level according to their entry time from earliest to latest. Take the first device in the sorting results as the device to be shipped out, and update the inventory quantity; Maintain an electrostatic discharge (ESD) exposure count for each device. Increase the count when operating in a non-ESD-protected workstation. After the first-level sorting, prioritize the removal of devices with larger ESD exposure counts from the warehouse for device groups whose remaining lifespan difference is less than the second preset threshold. Issue a warning or lock the device when the count reaches the preset threshold.

5. The warehousing method for intelligent management of electronic components according to claim 1, characterized in that, The step of generating a log record each time a manual operation or lifespan update triggers a lock specifically includes: Define the types of operations that trigger logging, including at least inbound, transfer, outbound, automatic locking, forced unlocking, and baking reset; Each time an operation is triggered, a log record is generated containing the device identifier, timestamp, operation type, location, temperature and humidity of the location at that time, remaining lifespan before and after the operation, and operator identifier. Log records are indexed and stored by device identifier and timestamp. Logs that exceed the preset period are archived periodically. When a query request for device identifier or batch number is received, the life cycle data of the device is replayed in chronological order, and the remaining life cycle is displayed in a visual form as a curve of change over time.

6. The warehousing method for intelligent management of electronic components according to claim 1, characterized in that, The step of outputting the identification information of the device to be shipped and removing the device from the inventory specifically includes: The identification information, location coordinates, current remaining lifespan, and recommended path of the device to be shipped are encapsulated into a shipping instruction, which is sent to the automated handling equipment and a task list is pushed to the shipping port workstation. Upon receiving the pickup confirmation signal, the corresponding device is marked as shipped and a shipment completion log is added. If the remaining lifespan of the device to be shipped is less than the preset emergency threshold, an emergency use prompt will be generated, requiring the device to be mounted and a batch tracking number to be added within a specified time. After the shipment is completed, the remaining lifespan of all devices in the remaining inventory will be updated again.

7. A warehouse system for intelligent management of electronic components, characterized in that, include: The data acquisition module is used to periodically collect real-time temperature and relative humidity values ​​for each storage area, and to obtain the initial workshop life, remaining life variables, and storage time for each moisture-sensitive device. The update module is used to calculate the environmental correction factor of the current storage area according to the real-time temperature value and relative humidity value and a preset environmental correction factor mapping rule, multiply the fixed period duration by the environmental correction factor to obtain the equivalent time increment, and subtract the equivalent time increment from the remaining lifetime variable to update the remaining lifetime. The sorting module is used to filter out all unlocked devices with matching models when a warehouse request containing the target model is received. The devices are sorted in the first level according to the remaining lifespan from smallest to largest. For devices with a remaining lifespan difference less than a first preset threshold, the devices are sorted in the second level according to the warehouse entry time from earliest to latest. The first device after sorting is taken as the device to be shipped. The generation module is used to generate a log record each time a manual operation or lifetime update triggers a lock. The log record includes the device identifier, timestamp, operation type, temperature and humidity values ​​of the current storage area, and remaining lifetime values ​​before and after the update. The output module is used to output the identification information of the device to be shipped out and remove the device from the inventory.

8. The intelligent management electronic component warehousing system according to claim 7, characterized in that, The output module includes: The sending unit is used to encapsulate the identification information, location coordinates, current remaining lifespan, and recommended path of the device to be shipped into a shipping instruction, send it to the automated handling equipment, and push the task list to the shipping outlet workstation; The receiving unit is used to mark the corresponding device as out of the warehouse and add an out-of-warehouse completion log after receiving the pickup confirmation signal; The update unit is used to generate an emergency use prompt if the remaining lifespan of the device to be shipped is less than a preset emergency threshold, requiring the device to be mounted and a batch tracking number to be added within a specified time. After the shipment is completed, the remaining lifespan of all devices in the remaining inventory is updated again.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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