A method, device, medium, and procedure for dynamic control of blood glucose meter test strips.

Abstract

A method, device, medium, and program product for dynamic management of blood glucose meter test strips, relating to the medical device field, is disclosed. The method quantifies the multidimensional environmental stress of materials using the Arrhenius equation and transforms instantaneous environmental impacts into cumulative effective aging time through integral calculations. Furthermore, by splitting products in the same physical batch into several logical sub-batches based on the dispersion of the remaining accuracy index and matching them based on tolerance thresholds, refined and dynamic inventory management is achieved. This process not only ensures that requests for high-precision materials are met with high-quality supplies but also effectively identifies and utilizes inventory resources at different aging stages. This improves the overall utilization rate of inventory while ensuring the reliability of clinical measurement accuracy, and reduces medical risks and material waste caused by improper matching.

Description

Technical Field

[0001] This application relates to the field of medical devices, and more particularly to a method, device, medium, and procedure for dynamic control of blood glucose meter test strips. Background Technology

[0002] In the field of diabetes management, blood glucose test strips are a key point-of-care testing (POCT) medical consumable. The enzyme biological system built into these test strips is sensitive to environmental temperature and humidity. During storage, transportation, and distribution, adverse environmental conditions can accelerate the degradation of their chemical components, leading to a non-linear decline in measurement accuracy over time.

[0003] In this technology, the calibration information for each batch of test strips is pre-integrated into specific electrode contacts or printed conductive carbon layer patterns on each strip at the factory. When a user inserts a new test strip into the blood glucose meter, the meter's internal reading device automatically identifies and retrieves the batch calibration data carried by the strip, and uses this data to set the calculation parameters for the current measurement. The entire calibration process is completed instantly and automatically without any manual intervention from the user, effectively avoiding measurement inaccuracies caused by human error in calibration and simplifying the operation process.

[0004] However, this time-based compensation scheme relies on the premise that the test strips are always stored in the standard-specified environment. In actual industrial production and commercial distribution, products need to go through complex, multi-level supply chains, from warehousing and logistics to different levels of medical terminals, and the ambient temperature of the test strips is difficult to maintain constant. Related technologies struggle to detect and quantify the non-linear accelerated degradation of test strips caused by abnormal temperature exposure (such as prolonged high-temperature storage) during distribution. This can lead to an uncorrectable discrepancy between the theoretical activity predicted by the blood glucose meter based on the standard model and the actual activity of the test strip after damage, resulting in errors in the final blood glucose measurement results. Summary of the Invention

[0005] This application provides a method, device, medium, and program product for dynamic management of blood glucose meter test strips, which can improve the measurement accuracy and reliability of blood glucose meter test strips in complex supply chain scenarios.

[0006] Firstly, this application provides a dynamic management method for blood glucose meter test strips, applied to management equipment. The method includes: calculating, in real time, the aging acceleration factor relative to a standard storage environment at the current moment using the Arrhenius equation, based on the absolute temperature, relative humidity, and factory testing data of the current environment of a target batch of products, where the target batch of products is the smallest inventory unit distributed under different storage environments; integrating the aging acceleration factor to obtain the accumulated effective aging time of the target batch of products; obtaining the current remaining accuracy index of the same batch of products in the current inventory, including the target batch of products, based on the effective aging time of products produced in the same batch as the target batch of products and a preset standard performance decay function; iterating through the remaining accuracy index to determine several sub-batch products based on the dispersion of the remaining accuracy index; when a material allocation request is received, determining the matching relationship between the remaining accuracy index of each sub-batch in the current inventory and the tolerance threshold corresponding to the material allocation request; and if the remaining accuracy index of the target sub-batch is higher than the tolerance threshold, using the target sub-batch as a candidate for the material allocation request.

[0007] By adopting the above technical solution, the multidimensional environmental stress of materials is quantified using the Arrhenius dynamic equation, and instantaneous environmental impacts are transformed into cumulative effective aging time through integral calculations. Furthermore, by splitting products in the same physical batch into several logical sub-batches based on the dispersion of the remaining accuracy index, and matching them based on tolerance thresholds, refined and dynamic inventory management is achieved. This process not only ensures that high-precision allocation requests are met by high-quality materials, but also effectively identifies and utilizes inventory resources at different aging stages. Therefore, while ensuring the reliability of clinical measurement accuracy, it improves the effective utilization rate of the entire network's inventory and reduces medical risks and material waste caused by improper matching.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the Arrhenius equation of motion is: The factory test data includes activation energy parameters, packaging material humidity sensitivity index, and pre-exponential factor parameters. Let t be the aging acceleration factor at the current time. This is the relative humidity value collected at the current moment. This is the absolute temperature value collected at the current moment. The preset standard storage relative humidity, The preset standard storage absolute temperature, The molar gas constant, The reaction rate constant under standard storage conditions. The activation energy parameter, The moisture sensitivity index of the packaging material. This is the parameter of the pre-exponential factor.

[0009] By adopting the above technical solution, activation energy parameters, packaging material humidity sensitivity index, and pre-exponential factor, which characterize product properties, are introduced into the calculation of the aging acceleration factor. This enables the kinetic model to reflect the nonlinear corrosive effect of high humidity environment on material packaging and internal biochemical components. The impact of micro-environmental fluctuations on macro-physicochemical performance is quantified, thereby improving the confidence level of the remaining precision index calculation and providing data support for subsequent high-precision grading and matching.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of traversing the remaining precision index and determining multiple products in the same batch into several sub-batch products based on the dispersion of the remaining precision index specifically includes: traversing the remaining precision index of the products in the same batch within a preset time window to construct a set of remaining precision index distributions; performing multi-peak density clustering analysis on the set of remaining precision index distributions to identify at least two high-density distribution intervals, and determining the remaining precision index corresponding to the peak point of each high-density distribution interval as the cluster center; after determining the logical segmentation boundary based on the cluster center, dividing multiple products in the same batch into several sub-batch products based on the remaining precision index of the products in the same batch and the logical segmentation boundary.

[0011] By employing the aforementioned technical solution and utilizing multi-peak density clustering analysis to process the residual precision index distribution set, it is possible to capture the performance differentiation phenomenon within the same production batch caused by differences in storage environment (such as the temperature difference between the sun-facing and shaded sides). By identifying high-density distribution intervals and determining logical segmentation boundaries, products from the same batch that are physically mixed but whose actual performance has diverged are divided into several sub-batches with converging characteristics. This ensures a high degree of quality consistency within each logical sub-batch, thereby improving the accuracy and predictability of subsequent material allocation and matching.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the step of including the target sub-batch as a candidate for the material allocation request when it is determined that the remaining accuracy index of the target sub-batch is higher than the tolerance threshold specifically includes: obtaining meteorological forecast data for the geographical region from which the material allocation request originates within a future preset transportation and storage period based on the geographical region; determining the expected aging increment of the target sub-batch at the end of the preset transportation and storage period using the Arrhenius dynamics equation and the meteorological forecast data; calculating the terminal prediction accuracy index of the target sub-batch after subtracting the expected aging increment from the current remaining accuracy index of the target sub-batch; and including the target sub-batch as a candidate when it is determined that the terminal prediction accuracy index is lower than the tolerance threshold corresponding to the material allocation request.

[0013] By adopting the above technical solution, when matching and screening materials, not only is the current static remaining accuracy considered, but also the environmental stress impact that materials may face during future transportation and storage cycles is introduced. By combining meteorological forecast data to calculate the expected aging increment, and using the terminal forecast accuracy index derived from this as the admission criterion, the hidden risk of "qualified upon departure but invalid upon arrival" is effectively avoided. This ensures that materials still meet tolerance requirements when finally delivered and used, improving the reliability of delivery quality at the end of the supply chain. It is particularly suitable for complex logistics scenarios involving long distances and cross-climate regions.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, before determining the matching relationship between the remaining precision index of each sub-batch in the current inventory and the tolerance threshold corresponding to the material transfer request when a material transfer request is received, the method further includes: if it is determined that there are sub-batch products with the remaining precision index within a preset critical warning interval, determining a list of storage nodes where the current absolute temperature value and relative humidity value are both lower than a preset threshold, the lower limit of the critical warning interval being higher than the tolerance threshold; for a target storage node in the list of storage nodes, calculating the transfer aging cost incurred in transferring the sub-batch products to the target storage node and the reduction in storage aging rate after the transfer compared to the original storage location, the transfer aging cost being an aging increment calculated based on environmental prediction data on the transfer path; if it is determined that the aging gain corresponding to the storage aging rate within the preset remaining storage period is greater than the transfer aging cost, generating a transfer instruction, the transfer instruction transferring the sub-batch products to the target storage node.

[0015] By adopting the above technical solution, risky inventory in the critical warning range can be proactively identified before materials become obsolete. A cost-benefit analysis model based on the game between transshipment costs and storage aging gains is introduced. Physical relocation is only triggered when the lifespan extension benefit from transferring to a low-temperature, low-humidity environment significantly outweighs the wear and tear costs during transportation. This effectively balances asset preservation and operating costs.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the step of integrating the aging acceleration factor to obtain the cumulative effective aging time of the target batch of products specifically includes: collecting the aging acceleration factor according to a preset discrete sampling period, and using the product of the aging acceleration factor and the discrete sampling period as a single aging increment; superimposing the single aging increment into the global aging time variable, and updating the current cumulative effective aging time of the target batch of products.

[0017] By employing the above technical solution, the continuous time axis is divided into several micro-elements using discrete sampling periods, and the instantaneous aging acceleration factor is converted into a single aging increment within each micro-element and accumulated. This avoids the accumulation of errors caused by averaging estimation.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of determining the absolute temperature value, relative humidity value, and factory test data of the current environment of the target batch of products, the method further includes: determining a corresponding standard environmental reference curve according to a preset storage scenario type in which the target batch of products is located; if a numerical deviation corresponding to the target batch of products is received, then superimposing the numerical deviation onto the reference value of the standard environmental reference curve at the current moment to obtain reconstructed real-time environmental data, wherein the numerical deviation is the numerical deviation between the real-time environmental data of the target batch of products and the standard environmental reference curve at the corresponding moment, and the numerical deviation is greater than or equal to a preset transmission trigger threshold, and the real-time environmental data includes the absolute temperature value and the relative humidity value; if the numerical deviation is not received, then using the reference value of the standard environmental reference curve at the current moment as the reconstructed real-time environmental data.

[0019] By adopting the above technical solution, deviation data is reported only when significant environmental anomalies occur (exceeding the transmission trigger threshold), while under normal circumstances, a standard environmental baseline curve is used for default filling. This mechanism ensures that the cloud can obtain high-fidelity environmental data while reducing the communication frequency and energy consumption of IoT devices. It not only extends the battery life of terminal devices and reduces network congestion, but also achieves low-cost continuous monitoring of remote environmental conditions through cloud data reconstruction, effectively resolving the contradiction between data integrity and transmission costs in large-scale wide-area deployments.

[0020] In conjunction with some embodiments of the first aspect, in some embodiments, the control device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the control device to perform the method as claimed in any one of claims 1-7.

[0021] In a second aspect, this application provides a computer program product containing instructions, characterized in that, when the computer program product is run on a control device, the control device performs the method as described in the first aspect and any possible implementation thereof.

[0022] Thirdly, this application provides a computer-readable storage medium including instructions that, when executed on a control device, cause the control device to perform the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. By adopting a technology that calculates the cumulative aging acceleration factor based on real-time environmental perception and dynamically splits physical batches into logical sub-batches corresponding to the remaining precision index for tolerance matching, the technology effectively solves the technical problems in related technologies, such as the unknowable actual effectiveness of inventory materials due to ignoring micro-environmental differences, and the mixed delivery of high and low precision products due to excessively coarse batch management granularity. This achieves transparent hierarchical management of the precision of materials across the entire network without changing the physical inventory attributes, and improves the measurement accuracy matching rate of material allocation and the safe utilization rate of inventory resources in complex supply chain scenarios.

[0025] 2. By adopting a technique based on multi-peak density clustering analysis to identify the distribution characteristics of the residual precision index and constructing logical segmentation boundaries to divide the same batch of products into several sub-batches with consistent internal performance, the technique achieves precise isolation and refined definition of material performance differences within a batch, thereby improving the quality uniformity and predictability of a sub-batch as an independent management unit.

[0026] 3. By adopting a game analysis model based on the relationship between aging gain and transfer aging cost, and using a technical means to make proactive warehouse transfer decisions for materials in the critical warning range, the technology achieves the technical effect of proactively extending the life of high-risk inventory and protecting assets while ensuring that logistics costs and physical losses are controllable, thus achieving a balance between supply chain operating costs and material quality and safety. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a method for dynamic control of blood glucose meter test strips in an embodiment of this application;

[0028] Figure 2 This is another flowchart illustrating a method for dynamic control of blood glucose meter test strips in an embodiment of this application;

[0029] Figure 3 This is a schematic diagram of an exemplary hardware structure of the control device in the embodiments of this application. Detailed Implementation

[0030] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0031] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0032] Please see Figure 1 This is a flowchart illustrating a method for dynamic control of blood glucose meter test strips in an embodiment of this application.

[0033] S101. Based on the absolute temperature, relative humidity, and factory test data of the target batch of products in their current environment, calculate the aging acceleration factor relative to the standard storage environment in real time using the Arrhenius dynamics equation.

[0034] The target batch of products refers to physical entities with a specific unique identifier (UID) controlled by this system. It typically refers to biochemical consumables (such as enzyme preparations and immunochromatographic test strips) or precision chemicals sensitive to microscopic environmental stress, whose physicochemical properties of key components decay nonlinearly with time and environmental changes. Factory testing data refers to the set of parameters characterizing the unique kinetic characteristics of the batch of materials, fitted through accelerated damp heat aging experiments conducted on samples from that specific production batch during the manufacturing stage. This set serves as the initial boundary conditions for the discretized damage rate model. Activation energy parameters ( The temperature coefficient (TCC) is used to represent the minimum energy threshold required for key chemical components in a target batch of materials to cause performance degradation during thermal aging. Its value reflects the sensitivity of the degradation rate of that batch of materials to temperature changes; the smaller the value, the larger the temperature coefficient. Packaging material humidity sensitivity index (…). ) refers to the dimensionless empirical coefficient used to correct the standard thermal aging model, whose value comprehensively characterizes the water vapor transmission rate of the outer packaging material of the target batch of goods. The affinity of internal chemical reagents for water molecules is used to describe the nonlinear amplification effect of ambient relative humidity on the aging rate. Aging Accelerator Factor (AEC) This refers to the ratio of the actual environmental stress of the target batch of products at the current sampling time to the pre-set ideal standard storage environment stress, used to quantify the instantaneous performance degradation rate.

[0035] This step typically begins when the material terminal device (such as a smart tag or recorder) is activated and initialized, and continues throughout the product's entire logistics and distributed storage cycle. In practice, the material terminal operates independently in a physical environment with varying temperature and humidity, relying on its own power supply and computing unit. During operation, the material terminal retrieves a factory test data package bound to its UID from a cloud database or production line write device via a narrowband IoT communication module or near-field communication interface. This data package includes at least the aforementioned activation energy parameters, packaging material humidity sensitivity index, and standard benchmark values ​​for various storage environments (…). Subsequently, the material terminal enters a periodic monitoring state, controlling the built-in high-precision temperature and humidity sensor to sample the surrounding microenvironment at preset time intervals to obtain the current absolute temperature value. (Units converted to Kelvin (K)) and relative humidity value (Unit: percentage). After acquiring the above real-time data, the control device calls the built-in modified Arrhenius dynamics algorithm for calculation. This modified Arrhenius dynamics algorithm is as follows:

[0036]

[0037] The factory test data includes activation energy parameters, packaging material humidity sensitivity index, and pre-exponential factor parameters. Let t be the aging acceleration factor at the current time. This is the relative humidity value collected at the current moment. The preset standard storage relative humidity, The moisture sensitivity index of packaging materials. The preset standard storage absolute temperature, This is the absolute temperature value collected at the current moment. The reaction rate constant under standard storage conditions. The activation energy parameter, The molar gas constant, This refers to the pre-exponential factor parameter.

[0038] The equipment calculates the humidity stress component, which is the ratio of real-time relative humidity to standard relative humidity. Power-law operations are used to simulate the nonlinear process of moisture permeating the packaging film and undergoing hydration or deliquescence reactions with internal reagents; simultaneously, temperature stress components are calculated using activation energy parameters and gas constants. The reciprocal difference between the standard temperature and the real-time temperature is used to perform an exponential calculation to simulate the exponential acceleration effect of thermal energy on the rate of chemical bond breaking or protein denaturation. Finally, the microcontroller multiplies the humidity stress component with the temperature stress component to obtain the dimensionless scalar—the aging acceleration factor—at the current moment. This value reflects how many times faster the product is aging at that instant compared to a standard laboratory environment.

[0039] Understandably, the determination of this activation energy parameter and the packaging material humidity sensitivity index typically requires a controlled accelerated aging experiment conducted on samples taken from a specific production batch during the quality inspection stage before the product leaves the factory. Specifically, for a particular production batch, technicians first randomly select a number of samples (e.g., 30-50) from the finished product and divide them into a baseline group, a high-temperature group, and a high-humidity group, placing them in environmental test chambers with constant temperature and humidity. The baseline group is stored in a standard environment (e.g., 25°C, 40%RH); the high-temperature group maintains constant humidity but raises the temperature to a preset stress level (e.g., 50°C, 40%RH); and the high-humidity group maintains constant temperature but raises the humidity to a preset stress level (e.g., 25°C, 80%RH). Subsequently, technicians perform physicochemical performance tests (such as enzyme activity assays or chemical reaction rate tests) on each group of samples at fixed time intervals, recording the failure time required for each group's performance to decay to the failure threshold or calculating its reaction rate constant (k). For degradation reactions following first-order kinetics, the reaction rate constant can be calculated using the following formula:

[0040] By performing linear regression on the experimental data, the slope of the resulting straight line represents the apparent reaction rate under those conditions. Based on this, by comparing the reaction rate difference between the high-temperature group and the baseline group, the logarithmic form of the Arrhenius equation (the classical Arrhenius equation can be expressed as:) is used. Taking the natural logarithm of both sides of the above equation, we get Where A is the pre-exponential factor, R is the molar gas constant (8.314 J / (mol·K)), and T is the absolute temperature (Kelvin K). The activation energy parameter characterizing the temperature sensitivity of this batch of products can then be calculated. If the decay rate of the high-temperature group is significantly higher than that of the baseline group, the calculated value will be smaller, indicating that the thermal stability of this batch of products is poor. By comparing the difference in reaction rates between the high-humidity group and the baseline group, the packaging material humidity sensitivity index, which characterizes the humidity sensitivity of this batch of products, can be calculated. The humidity correction term used in the embodiments of this application... This study is based on a modified Peck hydrothermal stress model. Theoretically, the aging of enzymes and chemical media in blood glucose meter test strips mainly stems from hydrolysis and pH shifts in the microenvironment caused by hygroscopic absorption. According to chemical kinetics, the reaction rate is proportional to the power of the reactant concentration. Within the unsaturated adsorption range, the equilibrium water content inside the test strip and the relative humidity (RH) follow Freundlich adsorption isotherms or Henry's law. Therefore, the relative humidity, through the permeation of the packaging material, nonlinearly determines the effective reactive water concentration inside the test strip.

[0041] Specifically, the value can be determined by the following relationship: the ratio of the reaction rate of the high humidity group to the reaction rate of the reference group is equal to the ratio of the relative humidity of the high humidity group to the relative humidity of the reference group. The power of 1. If the decay rate of the high humidity group increases dramatically and non-linearly relative to the baseline group, then the calculated... A larger value (e.g., greater than 1) indicates that the packaging of this batch of products has weak moisture-proof performance or that the internal components are extremely prone to moisture absorption and degradation.

[0042] The results obtained through the above experimental fitting and The numerical value, combined with the pre-fingerprint factor A corrected based on the dual stress group (simultaneous high temperature and high humidity), constitutes the unique physicochemical fingerprint of this production batch, which is then entered into the cloud database for subsequent distributed computing.

[0043] In some embodiments, the acquisition and parameter configuration of the factory inspection data in the above steps can be achieved in various ways. Optionally, it can be achieved through dynamic cloud distribution: when the material terminal is powered on and connected to the network for the first time, it sends its own UID to the server for authentication; the server retrieves the quality inspection report data of the batch to which the UID belongs from the Manufacturing Execution System (MES), including data fitted through experiments. and The server packages these parameters into configuration instructions and sends them to the material terminal. The terminal writes them to a specific address in its local non-volatile storage area, serving as a constant reference for all subsequent calculations. Optionally, this can be achieved through near-field writing: during the product packaging process, an industrial-grade RFID / NFC reader / writer is used to write the batch's... and The parameters are directly written to the user data area of ​​the smart tag; during subsequent operation, the MCU of the material terminal directly reads these parameters from the local RFID chip's storage area for calculation, without relying on a wide area network connection. It is understandable that this can also be achieved using a combination of preset default values ​​and subsequent corrections; that is, the terminal presets general parameters, and if updated parameters are detected after connecting to the cloud, they are overwritten. This is not limited here. Furthermore, in the calculation logic, to address potential sensor reading drift, the system can add a data cleaning step before inputting into the formula, such as using Kalman filtering or moving average algorithms to remove instantaneous noise, ensuring the accuracy of the input to the Arrhenius equation. and It can accurately represent the state of the environment.

[0044] In some embodiments, the control device determines a corresponding standard environmental baseline curve based on the preset storage scenario type of the target batch of products (e.g., a specific temperature-controlled cold storage room or a specific cold chain transportation route). This curve is a set of predefined time-temperature and humidity data pairs that change over time (e.g., the cold storage room's set temperature curve is a constant 4°C). After collecting the measured data locally, the measured data is first compared with the baseline value of the standard environmental baseline curve stored locally on the device at the current moment, and the numerical deviation (Delta) is calculated. The terminal determines whether the numerical deviation is greater than a preset transmission trigger threshold (e.g., temperature deviation > 0.5°C). If the terminal reports the numerical deviation (indicating a significant environmental anomaly), the control device adds the deviation to the baseline value of the standard environmental baseline curve at the current moment to obtain the reconstructed real-time environmental data, and uses this reconstructed data to perform calculations using the Arrhenius equation. If the aforementioned numerical deviation is not received (indicating that the terminal remains silent and the environment is normal), the control device will directly assume that the current environment meets expectations and use the benchmark value of the standard environment baseline curve at the current moment as the real-time environment data, and substitute it into the equation for calculation.

[0045] S102. Integrate the aging acceleration factor to obtain the cumulative effective aging time of the target batch of products.

[0046] This step is executed immediately after the control device calculates the current instantaneous aging acceleration factor and is continuously executed in a loop within the control device's local computing unit. It smoothly converts a series of instantaneous, irregularly fluctuating values ​​into a monotonically increasing cumulative value that intuitively reflects the product's lifecycle wear and tear. The control device names the register variable "Cumulative Effective Aging Duration" based on its built-in non-volatile memory (such as Flash or EEPROM) and clears it to zero during system initialization. At the end of each sampling period, the control device performs a recursive update operation.

[0047] Specifically, the control device reads the accumulated effective aging time stored from the previous moment. Next, the control device multiplies the aging acceleration factor for the current moment, which was just calculated, with the sampling period duration to calculate the incremental aging time within that sampling period. This product term has a clear physical meaning: it represents how much of the product's lifespan was accelerated due to environmental stress within the recently passed time window. The control device adds this incremental value to the register using an addition operation, i.e., executing the formula: And will update Write back to memory to overwrite the old value. To improve integration accuracy and reduce the rectangular approximation error caused by excessively large sampling intervals, the control equipment prioritizes the trapezoidal integration algorithm when performing the above calculations. That is, it utilizes the acceleration factor cached from the previous sampling time. With the current moment Take the average, then multiply by This makes the discrete calculation results closer to the real continuous environmental change curve.

[0048] S103. Based on the effective aging time of products produced in the same batch as the target batch in the current inventory, and the preset standard performance decay function, obtain the current remaining accuracy index of the same batch of products.

[0049] Among them, products in the same batch refer to a set of material entities that are manufactured and packaged on the same production line within the same time period in the physical production process, using the same batch of raw materials, and having the same production batch number. Although they are physically from the same source, due to the discreteness of the logistics distribution process, the individuals in this set are often stored in different warehouse nodes or transport vehicles, and each has its own environmental history. There are differences; the standard efficiency decay function ( A residual precision index (RPI) is a mathematical model that describes the theoretical decline of key physicochemical indicators (such as enzyme activity, reagent sensitivity, or active ingredient concentration) of a target batch of products over time under ideal standard storage conditions. This function is typically determined by the product's biochemical reaction order, reflecting a deterministic mapping between "time" and "quality." The residual precision index is a normalized dimensionless value (usually 0-1 or 0-100%) used to quantify the degree to which a product retains its current performance relative to its initial performance after a certain effective aging period.

[0050] This step is typically triggered periodically by the control equipment or in real-time upon receiving a logistics allocation request for a specific batch. Specifically, the control equipment uses the production batch number as an index key to retrieve and extract the latest effective aging time data generated by all currently available control equipment belonging to the target batch, constructing an effective aging time data vector. Next, a pre-defined standard performance decay function is invoked. This function is usually constructed based on long-term laboratory stability data, and its decay model can be expressed as:

[0051]

[0052] in, Let be the efficiency value at time t. Initial factory activity. The standard attenuation constant, For input variables.

[0053] Subsequently, the control equipment uses each specific value in the aforementioned effective aging time data vector as a variable in turn. Substituting the above decay function The mapping calculation is performed to obtain a set of corresponding residual precision values.

[0054] In some embodiments, the control device further calculates the accuracy dispersion and distribution characteristics of the batch of products. Specifically, the control device can logically split physically identical batches of products into several virtual sub-batches based on a preset accuracy tolerance band. For example, the control device can execute the following hierarchical logic: mark individuals with a remaining accuracy index higher than a first threshold (e.g., 95%) as high-sperm batches and associate them with high-sensitivity application scenario tags; mark individuals with a remaining accuracy index between the first and second thresholds (e.g., 85%-95%) as standard sub-batches and associate them with regular application scenario tags; mark individuals with a remaining accuracy index lower than the second threshold (e.g., 85%) as downgraded sub-batches or perform a freeze operation.

[0055] Understandably, the establishment of the standard performance decay function often relies on the accelerated aging test before shipment mentioned in the preceding steps. While determining the activation energy parameter and the packaging material humidity sensitivity index, technicians will measure the natural decay curve under standard conditions, use regression analysis to fit the optimal function expression, and then embed it into the algorithm library of the control system. Furthermore, considering the different sensitivities to accuracy in different application scenarios, the function can also include weighting coefficients specific to those scenarios. For example, for test strips used for qualitative detection, the decay function can be step-like; while for reagents used for quantitative detection, the decay function is a smooth, continuous curve.

[0056] S104. Traverse the remaining precision index and determine several sub-batch products based on the dispersion of the remaining precision index among multiple products in the same batch.

[0057] The control system first iterates through and sorts the set of remaining precision indices generated in step S103 to construct an ordered numerical sequence arranged from high to low. Subsequently, the control system performs distribution density analysis or clustering algorithms (such as K-Means clustering or density-based DBSCAN clustering) to identify natural breaks or sparse regions in the ordered sequence. These breakpoints typically correspond to different historical logistics patterns (e.g., some products have been stored in a temperature-controlled warehouse, with their RAI values ​​densely distributed in the 98%-99% range; other products have undergone a long-distance general cargo transport, with their RAI values ​​densely distributed in the 90%-92% range; there is a clear numerical "gap" between the two sets of data). The specific control system uses a dynamic differential threshold method to determine the boundaries of sub-batches. The difference (i.e., gradient) between two adjacent values ​​in the ordered sequence is calculated sequentially. When the difference at a certain position exceeds a preset clustering threshold (e.g., a precision jump greater than 2.0%), that position is determined as the sub-batchlet split point. In this way, the system divides the original single physical batch into N logical sub-batches. Furthermore, after completing the sub-batchlet division, an extended batch number is assigned to each newly generated sub-batchlet (e.g., adding -A, -B, -C, etc. identifiers to the suffix of the original batch number), and the mapping relationship in the inventory database is updated.

[0058] Understandably, in subsequent outbound or transfer operations, matching is based on the average remaining precision index (RAI) of each sub-batch. For example, for research-grade orders with extremely high sensitivity requirements, the system automatically locks and assigns the sub-batch with the highest average RAI; while for teaching demonstrations or general initial screening orders, the system assigns sub-batches with lower RAI but still within the acceptable range. This dynamic clustering mechanism based on traversing the degree of dispersion maximizes the utilization value of materials within the same batch, avoids the situation where an entire batch of materials is misjudged or downgraded due to the aging of a few individuals, and achieves refined inventory value stratification.

[0059] S105. When a material allocation request is received, determine the matching relationship between the remaining precision index of each sub-batch in the current inventory and the tolerance threshold corresponding to the material allocation request.

[0060] A material allocation request refers to an order instruction initiated by a downstream user (such as a hospital laboratory or research lab) or a downstream node in the supply chain (such as a regional distribution center), containing a clear quantity requirement and specific quality standards. In addition to the usual material category and quantity information, this instruction explicitly or implicitly includes a tolerance threshold, which limits the minimum performance standard or accuracy range of the materials allowed to be received in this order. Matching relationship refers to whether the average remaining accuracy index of each logical sub-batch in the inventory falls within the tolerance allowable range specified in the allocation request, specifically including perfect match, downgraded match (below optimal but above the minimum), or mismatch (below the minimum).

[0061] Specifically, when the control system receives a material allocation request, it first parses the tolerance threshold corresponding to the request. This parsing process can be implemented in two ways: one is direct reading, where the request itself explicitly carries specific precision parameters; the other is associative mapping, where the system automatically matches a preset industry standard based on the business attributes of the request initiator. For example, if the initiator is a "Grade A tertiary hospital central laboratory," the system automatically retrieves the corresponding stringent tolerance threshold; if the initiator is a "community health service station," the system matches a standard tolerance threshold. Subsequently, the control system traverses all sub-batch information for this category of materials in the current inventory database (i.e., each virtual sub-batch generated in step S104). The system compares the average remaining precision index of each sub-batch with the tolerance threshold obtained from the above parsing. If the remaining precision index of a sub-batch is significantly higher than the tolerance threshold (e.g., more than 5%), it is generally not recommended as a priority to avoid wasting high-quality resources on low-end requirements. If the remaining precision index of a sub-batch is slightly higher than and closest to the tolerance threshold (e.g., 0%-2%), the system assigns it the highest allocation priority. If the remaining precision index of a sub-batch is lower than the tolerance threshold, the system marks it as "unavailable" and strictly prohibits its allocation to that order. Based on the above judgments, the system generates a matching candidate list, which only contains sub-batch IDs that meet the specific order quality requirements.

[0062] S106. If the remaining precision index of the target sub-batch is determined to be higher than the tolerance threshold, the target sub-batch shall be considered as a candidate for material allocation request.

[0063] In step S105, the control system has traversed all sub-batches in the current inventory and generated a matching candidate list. Each sub-batch in this list satisfies the basic condition of "Remaining Precision Index (RAI) ≥ Tolerance Threshold". Throughout the traversal process, the control device synchronously records the total number of sub-batches that meet the condition and their cumulative available quantity. When the traversal is complete, if the candidate list is empty, an inventory shortage warning mechanism is triggered, and a replenishment request signal is sent to the upstream production scheduling system; if the candidate list is not empty,

[0064] The control equipment calculates the difference between the RAI value and the lower limit of the tolerance threshold for each candidate sub-batch. This difference is called the accuracy redundancy. The smaller the accuracy redundancy, the closer the accuracy status of the sub-batch is to the admission boundary. Then, the control equipment queries the entry timestamp of each sub-batch and calculates its inventory dwell time. The accuracy redundancy and inventory dwell time are weighted and summed to obtain a comprehensive score. The weight coefficient is dynamically adjusted according to the current overall inventory tightness. For example, when the inventory is sufficient, the weight of inventory dwell time is increased to prioritize the consumption of old inventory. When the inventory is tight, the weight of accuracy redundancy is increased to prioritize the retention of high-precision inventory. Finally, the control equipment sorts the candidate list from low to high according to the comprehensive score, and the sub-batch with the lowest score is given priority to be allocated to the current material allocation request. For example, suppose there is a sub-batch A with a RAI value of 0.99 and stored for 30 days, and a sub-batch B with a RAI value of 0.95 and stored for 10 days. The lower limit of the RAI corresponding to the tolerance threshold is 0.93. Then the accuracy redundancy of sub-batch A is 0.99-0.93=0.06, and the accuracy redundancy of sub-batch B is 0.95-0.93=0.02. If the weight configuration is accuracy redundancy of 0.3 and inventory dwell time of 0.7 under the current state of sufficient inventory, then the comprehensive score of sub-batch A is 0.06×0.3+30×0.7=21.018, and the comprehensive score of sub-batch B is 0.02×0.3+10×0.7=7.006. Therefore, the control equipment prioritizes sub-batch B to the current request, thereby accelerating the turnover of inventory with high aging risk while meeting the accuracy requirements. This method effectively solves the optimal allocation decision problem in scenarios with multiple options.

[0065] In this embodiment, by introducing a real-time calculation of the aging acceleration factor based on batch physicochemical fingerprints and environmental stress integrals, the monitoring of the true availability status of the distributed inventory across the entire network is achieved without increasing the cost of sensor hardware, providing reliable non-intrusive data support for subsequent accuracy grading matching and optimal logistics routing.

[0066] In the above embodiments, the control equipment can achieve transparent monitoring of the true performance status of distributed inventory. In practical applications, when the above method is implemented, if it is detected that some high-value inventory is experiencing a rapid decline in RAI (Recovery Index), relying solely on passively waiting for orders to consume the inventory often leads to these materials falling below critical tolerances and becoming scrapped before orders arrive, resulting in huge asset waste. This technical problem can be solved by a dynamic inventory transfer risk avoidance mechanism based on the game between aging gain and transfer costs, thereby improving the asset preservation rate and average effective lifespan of the entire network's inventory.

[0067] Please see Figure 2 This is another flowchart illustrating a method for dynamic control of blood glucose meter test strips in an embodiment of this application.

[0068] S201. Based on the absolute temperature, relative humidity, and factory test data of the target batch of products in their current environment, calculate the aging acceleration factor relative to the standard storage environment in real time using the Arrhenius dynamics equation.

[0069] S202. Integrate the aging acceleration factor to obtain the cumulative effective aging time of the target batch of products.

[0070] S203. Based on the effective aging time of products produced in the same batch as the target batch in the current inventory, and the preset standard performance decay function, obtain the current remaining accuracy index of the same batch of products.

[0071] S204. Traverse the remaining precision index and determine several sub-batch products based on the dispersion of the remaining precision index among multiple products in the same batch.

[0072] Steps S201~S204 and Figure 1 The steps S101 to S104 in the illustrated embodiment are similar, and can be referred to the descriptions in steps S101 to S104, which will not be repeated here.

[0073] S205. If it is determined that there are sub-batch products with remaining accuracy index within the preset critical warning range, determine the list of storage nodes where the current ambient absolute temperature and relative humidity values ​​are both lower than the preset threshold.

[0074] This step is executed after the RAI value of the entire network inventory has been updated, but before processing specific shipping orders, as a periodic background health scan task. Specifically, the control equipment first performs a full scan of all currently in-stock sub-batches, identifying sub-batches whose RAI values ​​fall within a preset critical warning range (e.g., 0.95-0.98, assuming clinical requirements >0.98). The control equipment then requests access to meteorological service data interfaces and indoor sensor data from each warehouse, filtering out all available warehouse nodes with an absolute temperature below 15°C and relative humidity below 35%, constructing a candidate storage node list. The purpose of this step is to proactively find a better storage environment that can "freeze" the degradation state before physical degradation occurs, thereby providing a feasible set of target addresses for subsequent inventory relocation decisions.

[0075] In some embodiments, determining the list of storage nodes can be achieved in several ways: Optionally, a real-time environmental snapshot filtering method can be used, where the control device sends a broadcast query command to all warehouse nodes in the network, and each node immediately returns the current temperature and humidity sensor readings. The control device directly compares the readings with the thresholds and filters out node IDs that meet the conditions to add to the list. Optionally, a future trend prediction filtering method can be used, where the control device does not rely on instantaneous data, but instead calls the weather forecast model for the area where each node is located for the next 72 hours, combined with the historical temperature and humidity performance curves of the warehouse, to predict the future micro-environment trend of each node, and only includes nodes whose temperature and humidity are expected to remain below the safe threshold for the next 72 hours in the list. It is understood that a hybrid filtering method based on geographical location and transportation capacity constraints can also be used, that is, while filtering nodes that meet the temperature and humidity standards, nodes that are more than 500 kilometers away from the current sub-healthy inventory location or that currently have no available transportation vehicles are excluded to narrow the calculation range; this is not limited here.

[0076] S206. For the target storage node in the storage node list, calculate the transfer aging cost of transferring the sub-batch products to the target storage node and the reduction in storage aging rate compared to the original storage location after the transfer.

[0077] Among them, the transit aging cost refers to the expected performance degradation increment caused by environmental stress (such as exposure to sunlight and high temperature, vibration) in the cargo compartment of the transport vehicle during the transportation of a sub-batch of products from the current source warehouse to the target warehouse. It is usually quantified by the decrease in RAI value or the equivalent aging hours. The storage aging rate refers to the rate at which the RAI value of a product decays per unit time under a specific storage environment (source warehouse or target warehouse), which is determined by the temperature and humidity of the environment and the batch physicochemical fingerprint of the product. The aging gain refers to the total RAI value degradation that can be avoided by placing the product in the target warehouse compared to leaving it in the source warehouse within the preset remaining storage period.

[0078] The control equipment, based on the transit route and estimated transit time planned by the navigation map, combined with meteorological data along the route, estimates the average environmental stress during transit and substitutes it into the Arrhenius equation to calculate the transit aging cost. Secondly, the control equipment calculates the total attenuation of the product if it continues to be stored in the current warehouse at the current aging rate for the expected remaining sales period (e.g., 30 days), and subtracts the total attenuation of the product if it were stored in the target warehouse for the same period; this difference is the aging gain.

[0079] In some embodiments, the calculation of transport aging costs can be achieved in several ways: Optionally, a path integral-based dynamic estimation method can be used, where the control equipment divides the transport route into several segments, acquires the predicted meteorological data for each segment, calculates the aging increment segment by segment and accumulates them, while simultaneously overlaying a preset "vehicle heat accumulation coefficient" (considering the stuffy effect of the carriage); Optionally, a worst-case prediction method can be used, where the highest historical temperature and humidity that may occur on the transport route are directly taken as constant environmental parameters, and the aging amount for the entire transport time is calculated as a conservative upper limit of the cost. It is understood that the difference in storage aging rates can also be calculated using a lookup method based on historical decay curves, where the difference is calculated by directly querying the standard decay rate table for the batch of products under the source and target environments, which is not limited here.

[0080] S207. If it is determined that the aging gain corresponding to the storage aging rate within the preset remaining storage period is greater than the transfer aging cost, a transfer instruction is generated.

[0081] Among them, the transfer instruction refers to the control data packet generated by the control equipment to drive the warehousing and logistics system to perform physical handling operations. The instruction includes the source warehouse ID, the target warehouse ID, the UID list of the materials to be transferred, and the priority marker; the preset remaining storage period refers to the length of time that the materials are expected to remain in the warehouse before being purchased or consumed by the end user, as estimated by the sales turnover rate prediction model of this batch of products.

[0082] Specifically, the control equipment compares the aging gain calculated in S206 with the aging cost during transit. If the aging gain is greater than the aging cost during transit (and the difference exceeds a certain threshold to ensure significant benefits), the control equipment generates a transfer instruction and sends it to the source warehouse and logistics carrier through the WMS (Warehouse Management System) interface, triggering the specific picking and loading process. After the instruction is generated, the system will lock the status of that batch of materials as "in transit" and suspend receiving ordinary external transfer requests to prevent data conflicts.

[0083] In some embodiments, the generation of transfer instructions can be implemented in several ways: Optionally, a batch aggregation trigger mode can be used, that is, a transfer instruction is officially generated and sent only when the total amount of similar materials to be transferred in a source warehouse reaches the minimum loading rate of a transport vehicle (e.g., 30%), in order to balance logistics costs; Optionally, an emergency direct delivery mode can be used, that is, for high-value test strips whose RAI value is about to fall below the clinical-grade tolerance threshold, regardless of the quantity, a highest-priority transfer instruction is immediately generated and matched with express-grade logistics resources. It is understood that multi-hop transit instruction generation can also be used, that is, if a single ideal target warehouse cannot be found, a multi-stage transfer instruction is generated to reach the final destination via a transit warehouse, which is not limited here.

[0084] S208. When a material allocation request is received, determine the matching relationship between the remaining precision index of each sub-batch in the current inventory and the tolerance threshold corresponding to the material allocation request.

[0085] S209. If the remaining precision index of the target sub-batch is determined to be higher than the tolerance threshold, the target sub-batch shall be considered as a candidate for material allocation request.

[0086] In this embodiment, by comparing the damage caused by long-distance transport with the lifespan bonus gained from slowing down degradation through environmental improvements when materials are detected to be in a critical early warning range of accelerated aging, this effectively solves the technical problems of passive depreciation of high-value inventory due to poor local storage environments and increased logistics costs and material damage caused by blind transfers and relocations. It achieves a balance between asset preservation and logistics costs.

[0087] The exemplary control device 300 provided in the embodiments of this application is described below. Figure 3 This is an exemplary hardware structure diagram of the control device 300 provided in this application embodiment.

[0088] In some embodiments, the control device 300 is a computer device or includes a computer device. The computer device includes a processor, memory, and a network interface connected via a system bus. 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, 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 medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0089] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0090] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0091] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0092] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0093] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for dynamic control of blood glucose meter test strips, characterized in that, The method, applied to control equipment, includes: Based on the absolute temperature, relative humidity and factory test data of the current environment of the target batch of products, the aging acceleration factor relative to the standard storage environment is calculated in real time using the Arrhenius dynamic equation. The target batch of products is the smallest inventory unit distributed under different storage environments. The cumulative effective aging time of the target batch of products is obtained by integrating the aging acceleration factor. Based on the effective aging time of products from the same batch as the target batch of products in the current inventory, and a preset standard performance decay function, the current remaining accuracy index of the same batch of products is obtained, and the same batch of products includes the target batch of products. By iterating through the remaining precision indices, multiple products in the same batch are determined into several sub-batch products based on the dispersion of the remaining precision indices. When a material allocation request is received, the matching relationship between the remaining precision index of each sub-batch in the current inventory and the tolerance threshold corresponding to the material allocation request is determined. If the remaining precision index of the target sub-batch is determined to be higher than the tolerance threshold, the target sub-batch will be considered as a candidate for the material allocation request.

2. The method according to claim 1, characterized in that, The Arrhenius dynamic equation is: The factory test data includes activation energy parameters, packaging material humidity sensitivity index, and pre-exponential factor parameters. Let t be the aging acceleration factor at the current time. This is the relative humidity value collected at the current moment. The preset standard storage relative humidity, The humidity sensitivity index of the packaging material. The preset standard storage absolute temperature, This is the absolute temperature value collected at the current moment. The reaction rate constant under standard storage conditions. The activation energy parameter, The molar gas constant, is the pre-exponential factor parameter.

3. The method according to claim 1, characterized in that, The step of traversing the remaining precision indices and determining the dispersion of multiple products in the same batch into several sub-batch products based on the remaining precision indices specifically includes: Traverse the remaining precision index of the same batch of products within a preset time window to construct a set of remaining precision index distributions; Multi-peak density clustering analysis is performed on the set of residual precision index distributions to identify at least two high-density distribution intervals, and the residual precision index corresponding to the peak point of each high-density distribution interval is determined as the cluster center. After determining the logical segmentation boundary based on the cluster center, the multiple products in the same batch are divided into several sub-batch products based on the remaining precision index of the products in the same batch and the logical segmentation boundary.

4. The method according to claim 1, characterized in that, The step of considering the target sub-batch as a candidate for the material allocation request when the remaining precision index of the target sub-batch is determined to be higher than the tolerance threshold specifically includes: Based on the geographical region from which the material allocation request originates, obtain meteorological forecast data for the geographical region within a future preset transportation and storage period; Using the Arrhenius dynamics equation and the meteorological forecast data, the expected aging increment of the target sub-batch at the end of the preset transportation and storage cycle is determined; Calculate the terminal prediction accuracy index after subtracting the expected aging increment from the current remaining accuracy index of the target sub-batch; If the terminal prediction accuracy index is determined to be lower than the tolerance threshold corresponding to the material allocation request, the target sub-batch will be included in the list of options.

5. The method according to claim 1, characterized in that, Before the step of determining the matching relationship between the remaining precision index of each sub-batch in the current inventory and the tolerance threshold corresponding to the material allocation request when a material allocation request is received, the method further includes: If it is determined that there are sub-batch products whose remaining accuracy index is within the preset critical warning range, a list of storage nodes whose current environmental absolute temperature and relative humidity values ​​are both lower than the preset threshold is determined, and the lower limit of the critical warning range is higher than the tolerance threshold. For the target storage node in the storage node list, calculate the transfer aging cost of transferring the sub-batch products to the target storage node and the storage aging rate reduced after the transfer compared to the original storage location. The transfer aging cost is the aging increment calculated based on environmental prediction data on the transfer path. If it is determined that the aging gain corresponding to the storage aging rate is greater than the transfer aging cost within the preset remaining storage period, a transfer instruction is generated, which transfers the sub-batch of products to the target storage node.

6. The method according to claim 1, characterized in that, The step of integrating the aging acceleration factor to obtain the cumulative effective aging time of the target batch of products specifically includes: The aging acceleration factor is collected according to a preset discrete sampling period, and the product of the aging acceleration factor and the discrete sampling period is used as the single aging increment. The single aging increment is added to the global aging time variable to update the current accumulated effective aging time of the target batch of products.

7. The method according to claim 6, characterized in that, Prior to the step of determining the absolute temperature, relative humidity, and factory inspection data of the current environment of the target batch of products, the method further includes: Based on the preset storage scenario type of the target batch of products, determine the corresponding standard environment baseline curve; If a numerical deviation corresponding to the target batch of products is received, the numerical deviation is superimposed on the baseline value of the standard environmental reference curve at the current moment to obtain the reconstructed real-time environmental data. The numerical deviation is the numerical deviation between the real-time environmental data of the target batch of products and the standard environmental reference curve at the corresponding moment, and the numerical deviation is greater than or equal to a preset transmission trigger threshold. The real-time environmental data includes the absolute temperature value and the relative humidity value. If the numerical deviation is not received, the baseline value of the standard environmental baseline curve at the current moment will be used as the reconstructed real-time environmental data.

8. A control device, characterized in that, The control device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the control device to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on the control device, the control device performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the control device, the control device performs the method as described in any one of claims 1-7.

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