Material allocation system and method based on internet of things technology

Abstract

The present application relates to the technical field of material allocation, and discloses a material allocation system and method based on Internet of Things technology, which comprises: collecting environmental state data to fit and generate an effective recovery time constant and a first peak temperature rise; based on cumulative load, the above parameters are dynamically corrected to reflect the influence of frost on the thermal inertia of the cold storage; under the condition of a preset throughput, the average temperature and variance in the period corresponding to different wave configurations are calculated based on standard core parameters, and are converted into an evaluation function in combination with the Arrhenius principle; the target material configuration parameters that make the evaluation function optimal are selected from the candidate set, the target wave period is determined, and an execution strategy is generated; finally, the storage location identification and picking are executed by the intelligent execution terminal according to the period. The present application can significantly reduce the temperature exposure risk of biological samples by avoiding thermal resonance beats without reducing the total amount of work.

Description

Technical Field

[0001] This invention relates to the field of material allocation technology, and more specifically, to a material allocation system and method based on Internet of Things (IoT) technology. Background Technology

[0002] In the storage, transportation, and experimental distribution of biological resources, common material allocation methods rely heavily on manual picking or wave operations performed at fixed time intervals. In recent years, to improve allocation speed and accuracy, intelligent light strip systems have been introduced into scenarios such as cold chain warehousing, experimental sample management, and ultra-low temperature freezers, enabling rapid material identification and location confirmation through visual guidance. However, existing systems generally prioritize operational efficiency, generating picking cycles based on fixed order throughput, without considering the impact of temperature fluctuations on the stability of biological resources. For live samples, vaccines, cells, and nucleic acid materials, even small temperature fluctuations can affect their preservation quality or experimental validity. The time intervals, door opening frequencies, and batch settings of existing systems largely rely on experience or average load models, failing to reflect the dynamic changes in equipment thermal inertia and recovery time under real-world conditions.

[0003] Ultra-low temperature refrigerators experience significant hot air infiltration after each door opening, causing a short-term rise in internal temperature. The refrigerator then relies on its refrigeration system to gradually restore the set temperature. Actual measurement data shows that the recovery time depends on the duration of the refrigerator door opening, the amount of humid air infiltration, the thickness of the frost layer, and the heat transfer performance of the refrigeration system. When the picking rhythm is improperly set or the door opening frequency exceeds the refrigerator's heat recovery speed, residual heat cannot dissipate completely and accumulates gradually in subsequent cycles, leading to a stable upward shift in the internal temperature. Simultaneously, the infiltration of humid air accelerates frost growth, further reducing heat transfer efficiency and prolonging the recovery time, resulting in a continuously compounding temperature deviation. Because the intelligent light strip system controls the refrigerator door opening rhythm with rhythmic task releases, if the rhythm falls within the critical time range of the refrigerator's heat recovery, temperature fluctuations will be periodically amplified, causing a deviation between the actual temperature distribution and the set temperature.

[0004] The degradation processes of biological samples, drugs, and cell products typically follow temperature-dependent chemical kinetics. Experimental and pharmacopoeia data show that when the temperature deviates from the set range, the degradation rate increases exponentially; even short-term temperature rises can lead to significant stability losses over time. In current dispensing systems, the door opening cycle is set to a fixed parameter, failing to reflect real-time changes in the equipment's thermal state. As a result, even if the duration of each door opening is controlled, if the cycle period is close to the equipment's thermal recovery cycle, it will cause an increase in the average temperature and greater fluctuations, shortening the effective preservation time of biological samples and even triggering batch failure risks. This problem is not caused by operational errors, but rather stems from a systemic temperature accumulation effect caused by the inherent difference between the cycle control strategy and the equipment's thermal response characteristics. Summary of the Invention

[0005] This invention provides a material allocation system and method based on Internet of Things (IoT) technology, which solves the technical problem that, under the condition of fixed picking throughput, the existing material allocation process based on intelligent light strip systems cannot avoid the temperature accumulation deviation caused by the mismatch between the cycle time setting and the thermal recovery characteristics of cold chain equipment, thereby causing a decline in the stability of biological resource storage and quality risks.

[0006] Firstly, a material allocation method based on Internet of Things (IoT) technology is characterized by including:

[0007] Collect environmental status data and generate core parameters based on the environmental status data;

[0008] The associated state parameters are updated based on the preset load parameters, and the core parameters are corrected by the associated state parameters to obtain the standard core parameters;

[0009] Under the preset throughput conditions, the wave cycle is determined based on the material allocation parameters;

[0010] Based on the standard core parameters and wave period, calculate the temperature correlation parameters within the period;

[0011] Select target material configuration parameters from the preset candidate configuration set, determine the target wave period, and form a wave execution strategy;

[0012] The wave execution strategy is sent to the intelligent execution terminal, which then performs location identification and material picking operations according to the target wave cycle.

[0013] Secondly, a material allocation system based on Internet of Things (IoT) technology, applied to any of the aforementioned material allocation methods based on IoT technology, includes:

[0014] The environmental data fitting unit collects environmental state data and generates core parameters based on the fitting of the environmental state data.

[0015] The standard parameter derivation unit updates the associated state parameters based on the preset load parameters, and corrects the core parameters through the associated state parameters to obtain the standard core parameters;

[0016] The cycle conversion unit determines the wave period based on the material configuration parameters under the preset throughput conditions;

[0017] The thermal response prediction unit calculates temperature correlation parameters within the cycle based on standard core parameters and wave period.

[0018] The target configuration decision unit selects target material configuration parameters from a preset candidate configuration set, determines the target wave cycle, and forms a wave execution strategy.

[0019] The work instruction distribution unit sends the wave execution strategy to the intelligent execution terminal, which then performs location identification and material picking operations according to the target wave cycle.

[0020] The beneficial effects of this invention are as follows:

[0021] 1. This invention identifies dangerous beat bands that resonate with the thermal recovery time constant of cold storage by establishing a deep coupling model between the beat frequency and the MKT (Mean Time). While maintaining the total throughput, the invention optimizes the wave configuration to actively avoid these dangerous frequency bands, effectively suppressing temperature baseline drift and variance deterioration caused by thermal pulse superposition and control integral saturation, thus keeping the average kinetic temperature of biological samples at a minimum.

[0022] 2. This invention, by introducing a load accumulation coefficient and a frost layer correction mechanism, can detect evaporator frost formation and heat transfer degradation caused by seepage through door openings in real time. As operating time progresses, the system automatically adjusts the standard effective recovery time constant, thereby dynamically correcting the cycle strategy and preventing a safe cycle from turning into a dangerous cycle due to the slowdown of the cold storage.

[0023] 3. This invention breaks through the limitations of traditional warehouse scheduling that only focuses on efficiency, integrating thermodynamics (seepage), heat transfer (frost formation), and control theory (integral saturation) into the logistics algorithm. Through the mandatory cycle guidance of intelligent execution terminals (such as electronic tag systems), it ensures that the theoretically optimal thermodynamic strategy can be strictly executed in physical operations. Attached Figure Description

[0024] Figure 1 This is a flowchart of the material allocation method based on Internet of Things technology of the present invention;

[0025] Figure 2 This is a block diagram of the material allocation system based on Internet of Things technology of the present invention. Detailed Implementation

[0026] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0027] Example 1: As Figure 1 As shown, the material allocation method based on Internet of Things (IoT) technology includes:

[0028] Collect environmental status data and generate core parameters based on the environmental status data;

[0029] The associated state parameters are updated based on the preset load parameters, and the core parameters are corrected by the associated state parameters to obtain the standard core parameters;

[0030] Under the preset throughput conditions, the wave cycle is determined based on the material allocation parameters;

[0031] Based on the standard core parameters and wave period, calculate the temperature correlation parameters within the period;

[0032] Select target material configuration parameters from the preset candidate configuration set, determine the target wave period, and form a wave execution strategy;

[0033] The wave execution strategy is sent to the intelligent execution terminal, which then performs location identification and material picking operations according to the target wave cycle.

[0034] In one embodiment of the present invention, environmental state data is collected, and core parameters are generated based on the environmental state data, including:

[0035] Determine the closing time and set the length of the fitting time window;

[0036] Starting from the closing time, within the time window corresponding to the length of the fitting time window, a sampling time interval is set, and the temperature time function and the set temperature are obtained according to the sampling time interval. The temperature deviation is calculated based on the obtained temperature time function and the set temperature.

[0037] A linear fit is performed on the logarithm of the temperature deviation and time. The fitting slope and fitting intercept are obtained through the linear fit. The effective recovery time constant is determined based on the fitting slope obtained from the linear fit, and the first peak temperature rise is determined based on the fitting intercept obtained from the linear fit.

[0038] In a preferred embodiment of the present invention, the core parameters are not obtained by simply reading temperature counts, but by employing a kinetic fitting method based on system identification. This is because the temperature drop process of an ultra-low temperature cold storage unit after the door is closed thermodynamically approximately follows the laws of a first-order inertial system. This is to accurately capture the current thermal recovery capability and disturbance resistance of the cold storage unit.

[0039] This embodiment uses the following steps for processing:

[0040] Step 1: The system first precisely locks the door closing moment using signals from the door magnetic sensor or light strip gateway. This moment marks the end of external thermal disturbance input and the start of the system's self-recovery process. Subsequently, the system sets a preset fitting time window length.

[0041] It should be noted that traditional temperature monitoring typically only focuses on whether the temperature has dropped back to the set value, or records the total time required for the temperature to drop. However, simply counting the total drop time is insufficient, as it obscures the magnitude of the initial disturbance with the equipment's own cooling rate. To decouple equipment performance (internal factors) from the intensity of the disturbance (external factors), a specific interval after the door is closed must be analyzed. This interval should avoid the turbulent airflow period immediately after the door is closed and should fall within the linear response range of the refrigeration unit operating at full speed.

[0042] Step 2: Taking the closing moment as zero, within the aforementioned fitting time window, the system continuously collects the current temperature measurement value at fixed sampling time intervals. Then, the temperature deviation at each sampling moment is calculated. This deviation is defined as the difference between the current measured temperature and the system's set temperature. Since the cold storage is in a rewarming state, this difference is theoretically a positive value that decays over time.

[0043] Step 3: Logarithmic transformation and linearization.

[0044] Based on Newton's law of cooling and the thermal principle of lumped parameter method, the temperature deviation decays exponentially over time. Directly fitting the exponential curve involves large computational costs and is prone to getting trapped in local optima. Therefore, this embodiment takes the natural logarithm of the calculated temperature deviation. Through this natural logarithmic mathematical transformation, the originally complex exponential decay curve is transformed into an approximately straight line trajectory in a logarithmic and time coordinate system. This allows the system to use a computationally inexpensive linear regression algorithm to process the data, significantly reducing the computing load on IoT edge devices.

[0045] Step 4: The system performs a linear fit on the natural logarithm of the temperature deviation and time to obtain the slope and intercept of the fitted line.

[0046] These two mathematical parameters are handled in the following way:

[0047] The value obtained by taking the reciprocal of the fitted slope and then taking its opposite (i.e., negative one divided by the fitted slope) is confirmed as the effective recovery time constant.

[0048] It should be noted that the effective recovery time constant characterizes the intrinsic rate at which the cold storage eliminates thermal deviations. A smaller value indicates faster refrigeration recovery; a larger value indicates that the system has become sluggish due to frosting or aging. This serves as the time reference for subsequently determining whether the cycle time has fallen into the danger zone.

[0049] The power value with the natural constant as the base and the fitting intercept as the exponent was calculated, and the result was confirmed as the first peak temperature rise.

[0050] It should be noted that the initial temperature rise represents the equivalent initial thermal shock magnitude caused by this door opening event. Even if the highest temperature is not captured in the actual measurement due to sensor lag, this fitted parameter can still deduce the theoretical instantaneous maximum temperature rise, thus providing an accurate energy input benchmark for subsequent assessment of the total risk after wave superposition.

[0051] In one embodiment of the present invention, associated state parameters are updated based on preset load parameters, and core parameters are corrected using the associated state parameters to obtain standard core parameters, including:

[0052] Calculate the product of the load accumulation coefficient and the preset load parameter, and add the product to the value of the associated state parameter before the update to obtain the value of the associated state parameter after the update.

[0053] Calculate the product of the recovery time correction coefficient and the updated value of the associated state parameter, add the product to one to obtain the first correction factor, calculate the product of the effective recovery time constant and the first correction factor, and determine the product as the standard effective recovery time constant.

[0054] Calculate the product of the temperature rise correction factor and the updated value of the associated state parameter, add the product to one to obtain the second correction factor, calculate the product of the first peak temperature rise and the second correction factor, and determine the standard first peak temperature rise as the product of the first peak temperature rise and the second correction factor.

[0055] In a preferred embodiment of the present invention, the system introduces a dynamic correction mechanism to solve the parameter drift problem caused by the deterioration of operating conditions during continuous operation of the ultra-low temperature cold storage.

[0056] It should be noted that in existing cold chain logistics scheduling technologies, the refrigeration performance of cold storage is generally considered constant. However, with each wave of operations, the physical characteristics of the cold storage undergo a significant unidirectional decline. Specifically, each door opening operation (i.e., the process of generating preset load parameters) not only introduces heat but also introduces external humid air. This humid air condenses into a frost layer on the evaporator surface. As a porous medium, the frost layer not only increases thermal resistance but also increases flow resistance on the air side, leading to a decrease in the overall heat transfer coefficient of the system. If the scheduling algorithm ignores this cumulative effect and continues to use the initial parameters for calculation, the safe cycle time calculated later may actually become a dangerous cycle time. Therefore, this embodiment introduces associated state parameters to digitally characterize this cumulative effect (such as frost layer thickness or system fatigue) and corrects the core parameters in real time accordingly.

[0057] Read the preset load parameters representing the intensity of this door-opening operation (e.g., the estimated wet load based on the doorway geometry and operation duration). Calculate the product of the load accumulation factor and the preset load parameter. This load accumulation factor is an empirical conversion value used to transform a one-time operation load into a persistent incremental impact on the system state. Add the above product to the previously updated associated state parameters to obtain the updated values ​​of the associated state parameters. This simulates the irreversible process of frost layer thickening with each operation. It gives the system a memory function, enabling it to perceive the historical accumulated load from the start of the operation to the present.

[0058] The correction of the effective recovery time constant (first-dimensional correction) addresses the issue that the rate at which cold storage absorbs heat slows down as associated state parameters (such as frost layer) increase. To quantify this phenomenon, the system calculates the product of the recovery time correction coefficient and the updated associated state parameters, and adds this product to one to obtain the first correction factor. Subsequently, the original effective recovery time constant is multiplied by this first correction factor to obtain the standard effective recovery time constant.

[0059] It should be noted that, since the first correction factor is typically greater than one and increases with the increase of the state parameter, this calculation process numerically amplifies the standard effective recovery time constant. This accurately reflects the objective fact that frost formation in cold storage causes slower temperature recovery and increased thermal inertia.

[0060] The correction for the initial peak temperature rise (second-dimensional correction) not only results in a slower recovery, but also, under harsh operating conditions, can lead to a higher instantaneous temperature rise from the same door opening action. Therefore, the system calculates the product of the temperature rise correction coefficient and the updated associated state parameters, and adds this product to one to obtain the second correction factor. Multiplying the original initial peak temperature rise by this second correction factor yields the standard initial peak temperature rise. This reflects that when the evaporator's heat exchange efficiency decreases, its ability to absorb instantaneous thermal shock weakens, causing the peak temperature inside the cold storage to rise higher than under clean conditions.

[0061] In one embodiment of the present invention, under a preset throughput condition, determining the wave period based on material configuration parameters includes:

[0062] Calculate the quotient of the material allocation parameters divided by the preset throughput, and determine the quotient as the wave period.

[0063] In this embodiment, the system performs the crucial step of mapping the quantity parameters of the logistics dimension to the time parameters of the thermodynamic dimension. This establishes a time scheduling benchmark under the constraint of total workload conservation. The specific implementation process and principle are as follows:

[0064] The system reads the preset throughput and the resource configuration parameters of the currently traversed resource.

[0065] The preset throughput represents the total amount of material allocation tasks that must be completed per unit time, and is a boundary constraint of the system; the material allocation parameters represent the quantity of materials covered in a single door opening operation, and are independent variables to be optimized.

[0066] The system calculates the quotient of the material configuration parameters divided by the preset throughput, and identifies this quotient as the wave period.

[0067] It should be noted that in conventional warehouse scheduling, the frequency of operations is often random or set based on human experience, and lacks correlation with the total workload.

[0068] It should be noted that temperature fluctuations within the cold storage are determined by the frequency of thermal pulses, while logistics tasks are determined by the quantity of goods. By accurately converting the material configuration parameters in the quantity domain into wave periods in the time domain, a prerequisite for subsequent thermal resonance analysis is established. This allows the thermodynamic model to identify the frequency characteristics hidden behind different picking strategies (such as high frequency for small quantities versus low frequency for large quantities).

[0069] In one embodiment of the present invention, temperature correlation parameters within a period are calculated based on standard core parameters and wave period, including:

[0070] Calculate the quotient of the standard effective recovery time constant divided by the wave period, and multiply the quotient by the standard first peak temperature rise to obtain the average temperature parameter within the period;

[0071] The first ratio is obtained by dividing the standard effective recovery time constant by the wave period.

[0072] The first exponent is obtained by dividing the negative of the wave period by the standard effective recovery time constant, and the second exponent is obtained by dividing twice the negative of the wave period by the standard effective recovery time constant.

[0073] Calculate the difference between one and the second power of the natural constant, divide by the square of the difference between one and the first power of the natural constant, and obtain the fractional factor.

[0074] The quotient of the standard effective recovery time constant divided by twice the wave period is multiplied by the fractional factor to obtain the first term. The square of the first ratio is then calculated to obtain the second term.

[0075] Calculate the difference between the first term and the second term, and multiply this difference by the square of the standard first peak temperature rise to obtain the temperature variance parameter within the period.

[0076] In this embodiment, the system derives the statistical characteristics of temperature fluctuations inside the cold storage through analytical calculations based on thermodynamic characteristics (standard core parameters) and logistical characteristics (wave cycle). This links the physical environment with biological risk assessment.

[0077] It should be noted that existing cold chain monitoring technologies typically only focus on whether the real-time temperature exceeds limits, or predict temperature trends through long-term simulations. Long-term simulations are too time-consuming and cannot meet the needs of real-time scheduling; while simple threshold judgments cannot quantify the cumulative damage to biological samples caused by long-term, minute fluctuations. Therefore, this embodiment models the temperature response process of the cold storage as a linear time-invariant system under periodic pulse excitation. Based on this model, an analytical expression for the system after it enters thermal steady state is constructed, thus allowing for the instantaneous calculation of the temperature mean and variance at a specific cycle without waiting for actual operation.

[0078] Specifically, the system first calculates the average temperature parameter within the cycle. It then calculates the ratio of the standard effective recovery time constant to the wave period, and multiplies this ratio by the standard peak temperature rise. The average temperature parameter within the cycle characterizes the overall rise in the temperature baseline caused by untimely cold air recovery at the current cycle time. If the wave period is too short (i.e., the frequency is too fast), this average parameter will increase significantly, meaning the cold storage will operate at a new baseline higher than the set temperature for an extended period.

[0079] Specifically, the system calculates the temperature variance parameter within a cycle, which quantifies the severity of temperature fluctuations. Using the natural constant, and with the ratio of the wave period to the standard effective recovery time constant and its multiples as exponents, the system calculates decay terms. These decay terms reflect the proportion of residual heat from previous openings, the period before that, and even further back, remaining at the current moment. Subsequently, the system constructs a complex formula containing fractional factors. This formula is essentially a variation of the summation formula for an infinite geometric series in variance calculation.

[0080] The difference between the first and second terms is calculated, and the result is multiplied by the square of the standard peak temperature rise, thus decoupling the DC component (mean) and AC component (fluctuation) in temperature fluctuations. It should be noted that this is achieved by accurately capturing the residual heat superposition effect. When the wave period is close to the effective recovery time constant (i.e., the beat frequency falls into the resonance region of the system's intrinsic frequency), the denominator term in the formula (one minus the natural constant exponent) approaches zero, causing the calculated variance parameter to exhibit a non-linear, explosive growth. Therefore, without the need for destructive, frequent door-opening experiments in cold storage, dangerous beats that cause drastic temperature oscillations can be accurately identified solely through mathematical analysis.

[0081] In one embodiment of the present invention, selecting target material configuration parameters from a preset candidate configuration set, determining the target wave period, and forming a wave execution strategy includes:

[0082] For each material configuration parameter in the preset candidate configuration set, calculate the corresponding evaluation function value. The process of calculating the evaluation function value includes:

[0083] The ratio of the material configuration parameter to the preset throughput is determined as the temporary wave period, and the time ratio factor is calculated based on the ratio of the standard effective recovery time constant to the temporary wave period.

[0084] The average temperature parameter within the period is obtained by multiplying the standard peak temperature rise by the time ratio factor. The first decay term and the second decay term are calculated by using the natural constant with the negative of the time ratio factor and twice the negative of the time ratio factor as exponents. A fractional factor is constructed based on the first decay term and the second decay term. The product of half of the time ratio factor and the fractional factor is calculated and the square of the time ratio factor is subtracted. The difference is multiplied by the square of the standard peak temperature rise to obtain the temperature variance parameter within the period.

[0085] Calculate the product of the first weight parameter in the evaluation weight parameters and the mean temperature parameter within the period, and calculate the product of the second weight parameter in the evaluation weight parameters and the square root of the temperature variance parameter within the period. The sum of the two is determined as the evaluation function value corresponding to the material allocation parameter.

[0086] The material configuration parameter corresponding to the minimum evaluation function value in the preset candidate configuration set is identified as the target material configuration parameter, and the ratio of the target material configuration parameter to the preset throughput is identified as the target wave period.

[0087] Based on the initial value of the wave start time, the execution time of each wave is generated in increments of integer multiples of the target wave period, and the wave execution strategy is constituted by the execution time.

[0088] In this embodiment, the system executes the core decision-making logic, which, under the premise of satisfying the total throughput constraint, traverses all possible job configuration schemes to find the unique solution that minimizes the potential damage to biological samples. This step transforms the complex nonlinear optimization problem into a computable algebraic evaluation process.

[0089] It should be noted that the system first iterates through a preset candidate configuration set, which contains all possible single-pick quantity schemes allowed in actual logistics operations. For each candidate scheme, the system enforces the equal throughput principle and calculates the corresponding temporary wave period. Next, the system calculates the ratio of the standard effective recovery time constant to the temporary wave period, defined as the time ratio factor.

[0090] It's important to note that simply focusing on the frequency of door openings is insufficient for assessing risk. The real risk lies in the relative relationship between the operating cycle and the cold storage's own thermal recovery capacity. The aforementioned time ratio factor is a dimensionless number that precisely characterizes the degree of coupling between the frequency of external disturbances and the internal recovery rate. When this factor approaches a specific value range, it signifies a resonance between the external operating cycle and internal thermal inertia, which is the root cause of temperature runaway.

[0091] To quantify the risk of each candidate solution, the system calculates parameters in two dimensions:

[0092] The average temperature parameter within the cycle is obtained by multiplying the standard peak temperature rise by the time ratio factor. The average temperature parameter within the cycle characterizes the overall upward shift of the temperature baseline due to insufficient replenishment of cooling capacity.

[0093] It should be noted that the system utilizes the exponential decay characteristic of the natural constant to construct a complex formula containing fractional factors. This formula is the analytical solution of the superposition of infinite thermal pulse sequences. By calculating the difference between the first term (containing fractional factors) and the second term (the square of the time ratio factor), the system successfully separates the energy component purely caused by fluctuations.

[0094] The system calculates the product of the first weight parameter and the mean parameter, and the product of the second weight parameter and the square root of the variance parameter, and adds the two together to obtain the evaluation function value.

[0095] It's important to note that the evaluation function is not a simple weighted summation, but rather based on the convexity principle of biochemical reaction kinetics (Arrhenius equation) and Jensen's inequality. The degradation rate of biological samples depends not only on the average temperature but is also highly sensitive to the variance of temperature fluctuations. The above calculation process is actually an efficient linearized approximation of the mean kinetic temperature (MKT) model. The mean term reflects the direct risk from an increase in baseline temperature, while the variance term (arithmetic square root) reflects the risk of accelerated degradation due to drastic temperature fluctuations. By finding the minimum value of this evaluation function, we are actually looking for the operating point with the lowest equivalent degradation rate of the biological sample, rather than simply finding the lowest temperature point.

[0096] The system compares the evaluation function values ​​of all candidate solutions, selects the material configuration parameters corresponding to the minimum value as the target parameters, and locks in the target wave cycle accordingly. Finally, using the initial value of the wave start time as the anchor point, the system generates a strict time series table according to integer multiples of the target wave cycle. This transforms the abstract mathematical optimization results into an executable time schedule. It ensures that subsequent physical operations can accurately fall within the theoretically calculated safe cycle time, thereby maximizing the utilization of the cold storage's heat recovery gap without reducing logistics efficiency, and achieving global optimization of biological sample protection and logistics turnover efficiency.

[0097] In one embodiment of the present invention, the wave execution strategy is distributed to the intelligent execution terminal, which then performs location identification and material picking operations according to the target wave cycle, including:

[0098] Receive wave execution strategy, which includes target wave period, target material configuration parameters, initial value of wave start time, and the set of storage locations and picking quantity for each storage location corresponding to each wave.

[0099] For any non-negative integer representing the wave sequence number, calculate the product of the non-negative integer and the target wave period, add the product to the initial value at the start time of the wave, and determine the execution time of that wave.

[0100] When the system time reaches the execution time, the identifier status of each location in the location set corresponding to this wave is activated, and the material picking operation is performed, wherein the sum of the picking quantities of all locations in the location set is equal to the target material configuration parameters.

[0101] In this embodiment, an intelligent execution terminal (such as an electronic tag picking system) is used as a physical metronome to force the intervention of manual operation processes.

[0102] The intelligent execution terminal first receives the wave execution strategy from the host computer. This strategy includes not only simple logistics information (such as the collection of storage locations and the picking quantity), but also time parameters with thermodynamic constraints (target wave period and initial value of wave start time).

[0103] It should be noted that the system uses the target material configuration parameters as the overall control index, ensuring that the sum of the picking quantities allocated to each specific storage location is strictly equal to these configuration parameters. This constraint guarantees that the total wet load introduced by each door opening operation is consistent with the value estimated in the theoretical model, preventing the thermal shock model from failing due to deviations in the workload.

[0104] The terminal's internal processor performs time calculation logic for consecutive non-negative integers representing wave sequence numbers: it calculates the product of the wave sequence number and the target wave period, and adds the result to the initial value at the start time, thereby generating a series of wave execution times accurate to the second.

[0105] It should be noted that in traditional cold chain operations, the operating time is usually determined by the operator's subjective will. This random door opening interval can lead to unpredictable fluctuations in cold storage temperature, and may even unintentionally fall into dangerous time zones. This embodiment, by pre-generating a strict time grid at the terminal side, deprives operators of the arbitrary right to decide when to open doors, transforming the adaptation of humans to equipment into a workflow adapting to thermodynamic laws.

[0106] The intelligent execution terminal will only activate the corresponding warehouse location status (e.g., light up the indicator light on the electronic tag) when the system clock reaches the calculated wave execution time.

[0107] Example 2: A material allocation system based on Internet of Things (IoT) technology, applied to any of the material allocation methods based on IoT technology described above, includes:

[0108] The environmental data fitting unit collects environmental state data and generates core parameters based on the fitting of the environmental state data.

[0109] The standard parameter derivation unit updates the associated state parameters based on the preset load parameters, and corrects the core parameters through the associated state parameters to obtain the standard core parameters;

[0110] The cycle conversion unit determines the wave period based on the material configuration parameters under the preset throughput conditions;

[0111] The thermal response prediction unit calculates temperature correlation parameters within the cycle based on standard core parameters and wave period.

[0112] The target configuration decision unit selects target material configuration parameters from a preset candidate configuration set, determines the target wave cycle, and forms a wave execution strategy.

[0113] The work instruction distribution unit sends the wave execution strategy to the intelligent execution terminal, which then performs location identification and material picking operations according to the target wave cycle.

[0114] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A material allocation method based on Internet of Things (IoT) technology, characterized in that, include: The system collects environmental condition data and uses this data to generate core parameters, including: determining the door closing time and setting the fitting time window length; the system first accurately locks the door closing time using signals from the door magnetic sensor or light strip gateway; this moment marks the end of external thermal disturbance input and the beginning of the system's self-recovery process; this interval should avoid the airflow turbulence period immediately after door closing and be within the linear response segment of the refrigeration unit operating at full speed; starting from the door closing time, within the time window corresponding to the fitting time window length, a sampling time interval is set, and the temperature-time function and set temperature are obtained according to the sampling time interval; the temperature deviation is calculated based on the obtained temperature-time function and set temperature; the logarithm of the temperature deviation is linearly fitted to time, and the fitting slope and fitting intercept are obtained through linear fitting; the effective recovery time constant is determined based on the fitting slope obtained from linear fitting, and the first peak temperature rise is determined based on the fitting intercept obtained from linear fitting; the effective recovery time constant characterizes the intrinsic rate of the cold storage to eliminate thermal deviation; the first peak temperature rise characterizes the equivalent initial thermal shock amplitude caused by this door opening event; The associated state parameters are updated based on preset load parameters, and the core parameters are corrected using the associated state parameters to obtain standard core parameters, including: Calculate the product of the load accumulation coefficient and the preset load parameter, add the product to the value of the associated state parameter before the update, and obtain the value of the associated state parameter after the update; read the preset load parameter representing the intensity of this door opening operation; the load accumulation coefficient here is an empirical conversion value used to convert a one-time operation load into a persistent incremental impact on the system state; thus simulating the irreversible process of frost layer continuously thickening with the number of operations; Calculate the product of the recovery time correction coefficient and the updated value of the associated state parameter, add the product to one to obtain the first correction factor, calculate the product of the effective recovery time constant and the first correction factor, and determine the product as the standard effective recovery time constant. Calculate the product of the temperature rise correction factor and the updated value of the associated state parameter, add the product to one to obtain the second correction factor, calculate the product of the first peak temperature rise and the second correction factor, and determine the standard first peak temperature rise as the product of the first peak temperature rise. Under the preset throughput condition, the wave period is determined based on the material allocation parameters, including: calculating the quotient of the material allocation parameters divided by the preset throughput, and determining the quotient as the wave period; the preset throughput represents the total amount of material allocation tasks that must be completed per unit time, which is a boundary constraint condition of the system; the material allocation parameters represent the quantity of materials covered by a single opening operation, which is an independent variable to be optimized. Based on the standard core parameters and wave period, the temperature correlation parameters within the period are calculated, including: calculating the quotient of the standard effective recovery time constant divided by the wave period, multiplying the quotient by the standard first peak temperature rise to obtain the mean temperature parameter within the period; calculating the quotient of the standard effective recovery time constant divided by the wave period to obtain the first ratio; calculating the negative of the wave period divided by the standard effective recovery time constant to obtain the first exponent; calculating twice the negative of the wave period divided by the standard effective recovery time constant to obtain the second exponent; calculating the difference between one and the second exponent raised to the power of the natural constant, dividing by the square of the difference between one and the first exponent raised to the power of the natural constant to obtain the fractional factor; calculating the quotient of the standard effective recovery time constant divided by twice the wave period, multiplying by the fractional factor to obtain the first term; calculating the square of the first ratio to obtain the second term; calculating the difference between the first term and the second term, multiplying the difference by the square of the standard first peak temperature rise to obtain the temperature variance parameter within the period. Select target material configuration parameters from a preset candidate configuration set, determine the target wave period, and form a wave execution strategy, including: For each material configuration parameter in the preset candidate configuration set, calculate the corresponding evaluation function value. The process of calculating the evaluation function value includes: The ratio of the material configuration parameter to the preset throughput is determined as the temporary wave period, and the time ratio factor is calculated based on the ratio of the standard effective recovery time constant to the temporary wave period. The average temperature parameter within the period is obtained by multiplying the standard peak temperature rise by the time ratio factor. The first decay term and the second decay term are calculated by using the natural constant with the negative of the time ratio factor and twice the negative of the time ratio factor as exponents. A fractional factor is constructed based on the first decay term and the second decay term. The product of half of the time ratio factor and the fractional factor is calculated and the square of the time ratio factor is subtracted. The difference is multiplied by the square of the standard peak temperature rise to obtain the temperature variance parameter within the period. The evaluation function is calculated by multiplying the first weight parameter in the evaluation weight parameters with the mean temperature parameter within the period, and by multiplying the second weight parameter in the evaluation weight parameters with the square root of the temperature variance parameter within the period. The sum of these two values ​​is determined as the evaluation function value corresponding to the material configuration parameter. The mean temperature parameter within the period represents the overall increase in the temperature baseline caused by the untimely recovery of cooling capacity under the current cycle. The temperature variance parameter within the period is used to quantify the severity of temperature fluctuations. The construction of the evaluation function is not a simple weighted summation, but is based on the convexity principle of biochemical reaction kinetics and Jensen's inequality. The mean term reflects the direct risk brought about by the increase in baseline temperature, and the variance term reflects the risk of accelerated degradation caused by severe temperature fluctuations. The material configuration parameter corresponding to the minimum evaluation function value in the preset candidate configuration set is identified as the target material configuration parameter, and the ratio of the target material configuration parameter to the preset throughput is identified as the target wave period. Based on the initial value of the wave start time, the execution time of each wave is generated in increments of integer multiples of the target wave period, and the wave execution strategy is composed of the execution times. The wave execution strategy is sent to the intelligent execution terminal, which then performs location identification and material picking operations according to the target wave cycle.

2. The material allocation method based on Internet of Things technology according to claim 1, characterized in that, The wave execution strategy is distributed to the intelligent execution terminal, which then performs location identification and material picking operations according to the target wave cycle, including: Receive wave execution strategy, which includes target wave period, target material configuration parameters, initial value of wave start time, and the set of storage locations and picking quantity for each storage location corresponding to each wave. For any non-negative integer representing the wave sequence number, calculate the product of the non-negative integer and the target wave period, add the product to the initial value at the start time of the wave, and determine the execution time of that wave. When the system time reaches the execution time, the identifier status of each location in the location set corresponding to this wave is activated, and the material picking operation is performed, wherein the sum of the picking quantities of all locations in the location set is equal to the target material configuration parameters.

3. A material allocation system based on Internet of Things (IoT) technology, applied in the material allocation method based on IoT technology as described in claim 1 or 2, characterized in that, include: The environmental data fitting unit collects environmental state data and generates core parameters based on the fitting of the environmental state data. The standard parameter derivation unit updates the associated state parameters based on the preset load parameters, and corrects the core parameters through the associated state parameters to obtain the standard core parameters; The cycle conversion unit determines the wave period based on the material configuration parameters under the preset throughput conditions; The thermal response prediction unit calculates temperature correlation parameters within the cycle based on standard core parameters and wave period. The target configuration decision unit selects target material configuration parameters from a preset candidate configuration set, determines the target wave cycle, and forms a wave execution strategy. The work instruction distribution unit sends the wave execution strategy to the intelligent execution terminal, which then performs location identification and material picking operations according to the target wave cycle.

4. A material allocation system based on Internet of Things (IoT) technology, applied in the material allocation method based on IoT technology as described in any one of claims 1-3, characterized in that, include: The environmental data fitting unit collects environmental state data and generates core parameters based on this data, including: determining the door closing time and setting the fitting time window length; the system first accurately locks the door closing time using signals from the door magnetic sensor or light strip gateway; this moment marks the end of external thermal disturbance input and the beginning of the system's self-recovery process; this interval should avoid the airflow turbulence period immediately after door closing and be within the linear response segment of the refrigeration unit's full-speed operation; starting from the door closing time, within the time window corresponding to the fitting time window length, a sampling time interval is set, and the temperature-time function and set temperature are obtained according to the sampling time interval; the temperature deviation is calculated based on the obtained temperature-time function and set temperature; the logarithm of the temperature deviation is linearly fitted to time, and the fitting slope and fitting intercept are obtained through linear fitting; the effective recovery time constant is determined based on the fitting slope obtained from linear fitting, and the first peak temperature rise is determined based on the fitting intercept obtained from linear fitting; the effective recovery time constant characterizes the intrinsic rate of the cold storage to eliminate thermal deviation; the first peak temperature rise characterizes the equivalent initial thermal shock amplitude caused by this door opening event; The standard parameter derivation unit updates the associated state parameters based on preset load parameters, and corrects the core parameters through the associated state parameters to obtain the standard core parameters, including: Calculate the product of the load accumulation coefficient and the preset load parameter, add the product to the value of the associated state parameter before the update, and obtain the value of the associated state parameter after the update; read the preset load parameter representing the intensity of this door opening operation; the load accumulation coefficient here is an empirical conversion value used to convert a one-time operation load into a persistent incremental impact on the system state; thus simulating the irreversible process of frost layer continuously thickening with the number of operations; Calculate the product of the recovery time correction coefficient and the updated value of the associated state parameter, add the product to one to obtain the first correction factor, calculate the product of the effective recovery time constant and the first correction factor, and determine the product as the standard effective recovery time constant. Calculate the product of the temperature rise correction factor and the updated value of the associated state parameter, add the product to one to obtain the second correction factor, calculate the product of the first peak temperature rise and the second correction factor, and determine the standard first peak temperature rise as the product of the first peak temperature rise. The cycle conversion unit determines the wave period based on the material allocation parameters under the preset throughput condition. This includes: calculating the quotient of the material allocation parameters divided by the preset throughput, and determining the quotient as the wave period. The preset throughput represents the total amount of material allocation tasks that must be completed per unit time, which is a boundary constraint of the system. The material allocation parameters represent the quantity of materials covered by a single door opening operation, which is an independent variable to be optimized. The thermal response prediction unit, based on standard core parameters and wave period, calculates temperature-related parameters within the cycle, including: calculating the quotient of the standard effective recovery time constant divided by the wave period, multiplying this quotient by the standard first peak temperature rise to obtain the mean temperature parameter within the cycle; calculating the quotient of the standard effective recovery time constant divided by the wave period to obtain the first ratio; calculating the negative of the wave period divided by the standard effective recovery time constant to obtain the first exponent; calculating twice the negative of the wave period divided by the standard effective recovery time constant to obtain the second exponent; calculating the difference between a power of one minus the second exponent of the natural constant, dividing by the square of the difference between a power of one minus the first exponent of the natural constant to obtain the fractional factor; calculating the quotient of the standard effective recovery time constant divided by twice the wave period, multiplying by the fractional factor to obtain the first term; calculating the square of the first ratio to obtain the second term; calculating the difference between the first term and the second term, multiplying this difference by the square of the standard first peak temperature rise to obtain the temperature variance parameter within the cycle. The target configuration decision unit selects target material configuration parameters from a preset candidate configuration set, determines the target wave period, and forms a wave execution strategy, including: For each material configuration parameter in the preset candidate configuration set, calculate the corresponding evaluation function value. The process of calculating the evaluation function value includes: The ratio of the material configuration parameter to the preset throughput is determined as the temporary wave period, and the time ratio factor is calculated based on the ratio of the standard effective recovery time constant to the temporary wave period. The average temperature parameter within the period is obtained by multiplying the standard peak temperature rise by the time ratio factor. The first decay term and the second decay term are calculated by using the natural constant with the negative of the time ratio factor and twice the negative of the time ratio factor as exponents. A fractional factor is constructed based on the first decay term and the second decay term. The product of half of the time ratio factor and the fractional factor is calculated and the square of the time ratio factor is subtracted. The difference is multiplied by the square of the standard peak temperature rise to obtain the temperature variance parameter within the period. The evaluation function is calculated by multiplying the first weight parameter in the evaluation weight parameters with the mean temperature parameter within the period, and by multiplying the second weight parameter in the evaluation weight parameters with the square root of the temperature variance parameter within the period. The sum of these two values ​​is determined as the evaluation function value corresponding to the material configuration parameter. The mean temperature parameter within the period represents the overall increase in the temperature baseline caused by the untimely recovery of cooling capacity under the current cycle. The temperature variance parameter within the period is used to quantify the severity of temperature fluctuations. The construction of the evaluation function is not a simple weighted summation, but is based on the convexity principle of biochemical reaction kinetics and Jensen's inequality. The mean term reflects the direct risk brought about by the increase in baseline temperature, and the variance term reflects the risk of accelerated degradation caused by severe temperature fluctuations. The material configuration parameter corresponding to the minimum evaluation function value in the preset candidate configuration set is identified as the target material configuration parameter, and the ratio of the target material configuration parameter to the preset throughput is identified as the target wave period. Based on the initial value of the wave start time, the execution time of each wave is generated in increments of integer multiples of the target wave period, and the wave execution strategy is composed of the execution times. The work instruction distribution unit sends the wave execution strategy to the intelligent execution terminal, which then performs location identification and material picking operations according to the target wave cycle.

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