A sample storage device automated storage control method and system

Abstract

The application discloses a kind of sample storage equipment automation storage control method and system, it is related to storage equipment control technical field, method includes: obtaining sample access request, residual task quantity and mechanical arm heat load value;Task density analysis is carried out to sample access request, and current task mode is identified;If current task mode is high-density task mode, then according to residual task quantity, queue length influence coefficient, mechanical arm heat load value and heat load influence coefficient, buffer interval time is calculated;According to buffer interval time, sample access request is batched processing;In batch processing, high-density operation mode is executed;If current task mode is low-density task mode, then execute routine operation mode.The application can combine residual task quantity and mechanical arm heat load value to calculate buffer interval time, and sample access request is batched processing, to realize storage control, improve accuracy and reliability.

Description

Technical Field

[0001] This invention relates to the field of storage device control technology, and in particular to an automated storage control method and system for sample storage devices. Background Technology

[0002] In related technologies, fully automated cryogenic sample storage devices (such as automated liquid nitrogen storage facilities) employ a fixed, passive thermal management strategy. Under high-density tasks, continuous operation of the robotic arm can lead to insufficient precooling, causing the robotic arm to become a mobile heat source. This results in samples along the path repeatedly experiencing "temperature rise-cooling" cycles, suffering cumulative thermal stress. Because the control logic of the sample storage device is based on thermal states, it is difficult to detect local sample temperature rises simply by monitoring the overall ambient temperature, leading to low control accuracy and reliability.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this invention is to propose an automated storage control method and system for sample storage devices. By identifying task density in real time, the system dynamically calculates buffer intervals in high-density mode and guides the robotic arm to the heat buffer for active heat dissipation. Simultaneously, it performs intelligent batching based on sample location and thermal sensitivity, and corrects control parameters in real time based on multi-sensor data. This fundamentally blocks the path of heat accumulation, achieving a leap from passive response to active prevention, and significantly improving sample safety, system reliability, and equipment lifespan.

[0005] On one hand, embodiments of the present invention provide an automated storage control method for a sample storage device, comprising the following steps: Obtain sample access requests, remaining task count, and robotic arm thermal load value; Perform task density analysis on the sample access requests to identify the current task mode; If the current task mode is a high-density task mode, then the buffer interval time is calculated based on the remaining number of tasks, the queue length influence coefficient, the robotic arm heat load value, and the heat load influence coefficient. The sample access requests are processed in batches according to the buffer interval time. In batch processing, a high-density operation mode is executed, which is used to control the robotic arm to move to the heat buffer during the buffer interval to release the heat accumulated by the robotic arm. If the current task mode is a low-density task mode, then the regular operation mode is executed. The regular operation mode is used to control the robotic arm to perform continuous access operations.

[0006] On the other hand, embodiments of the present invention provide an automated storage control system for a sample storage device, comprising: The information acquisition module is used to acquire sample access requests, the number of remaining tasks, and the thermal load value of the robotic arm; The task pattern recognition module is used to perform task density analysis on the sample access request and identify the current task pattern. The buffer interval time calculation module is used to calculate the buffer interval time based on the number of remaining tasks, the queue length influence coefficient, the robotic arm heat load value, and the heat load influence coefficient if the current task mode is a high-density task mode. The batch processing module is used to process the sample access requests in batches according to the buffer interval time. A high-density operation mode execution module is used to execute a high-density operation mode in batch processing. The high-density operation mode is used to control the robotic arm to move to the heat buffer zone during the buffer interval time to release the heat accumulated by the robotic arm. The regular operation mode execution module is used to execute the regular operation mode if the current task mode is a low-density task mode. The regular operation mode is used to control the robotic arm to perform continuous storage and retrieval operations.

[0007] The embodiments of this application include at least the following beneficial effects: First, the embodiments of this application acquire sample access requests, the number of remaining tasks, and the thermal load value of the robotic arm. Then, they perform task density analysis on the sample access requests to identify the current task mode. If the current task mode is a high-density task mode, the buffer interval time is calculated based on the number of remaining tasks, the queue length influence coefficient, the robotic arm thermal load value, and the thermal load influence coefficient. The sample access requests are then processed in batches to execute the high-density operation mode. If the current task mode is a low-density task mode, the regular operation mode is executed. By identifying the task density in real time, the buffer interval is dynamically calculated in the high-density mode, and the robotic arm is guided to the heat buffer for active heat dissipation. At the same time, intelligent batching is performed by combining the sample position and thermal sensitivity, and the control parameters are corrected in real time based on multi-sensor data. This fundamentally blocks the heat accumulation path, realizing a leap from passive response to active prevention, and significantly improving sample safety, system reliability, and equipment lifespan.

[0008] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0010] Figure 1 This is a flowchart of an automated storage control method for a sample storage device according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of an automated storage control system for a sample storage device according to an embodiment of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0012] In related technologies, fully automated cryogenic sample storage devices (such as automated liquid nitrogen storage facilities) have a storage chamber at temperatures below -190 degrees Celsius to store a large number of precious samples. Existing control systems monitor temperature, liquid level, and other conditions to control the movement of the robotic arm. In standard operating procedures, upon receiving an extraction command, the existing system, to minimize thermal interference, first moves the robotic arm gripper to an independent pre-cooling chamber for cooling before performing the extraction task, thus minimizing heat when the gripper enters the main chamber. However, in actual scientific research, sample demand is often uneven. For example, when a large project is launched, technicians may submit dozens or even hundreds of extraction requests in a short period. Faced with such high-density tasks, existing systems strictly follow the procedure to continuously execute a "pre-cooling-extraction-return" cycle. However, the task intervals are too short, and the pre-cooling chamber's cooling unit cannot completely dissipate residual heat in time, causing the pre-cooling effect to decrease with each iteration. This results in the robotic arm gripper entering the main chamber at a temperature higher than the standard value in subsequent tasks. This slight temperature difference accumulates during continuous rapid operations, and each time the gripper, not fully cooled, enters the storage chamber, it causes a localized temperature rise around its path. The cooling system struggles to instantly eliminate the transient thermal effects of the robotic arm's movements. As a result, unextracted samples within the storage chamber (especially those along the high-frequency path of the robotic arm) repeatedly undergo minute heating-cooling cycles. These unextracted samples experience cumulative thermal stress, which may lead to decreased activity or structural damage in sensitive cell lines or biomolecules, affecting sample quality and resulting in low accuracy and reliability of storage control.

[0013] Specifically, in high-end biomedical research institutions, sample storage equipment is a core infrastructure for maintaining research continuity and sample integrity. These devices typically rely on a high-precision automated control system to maintain the stability of the internal storage environment, such as precise temperature and humidity control. Initially, the temperature control systems of these devices were designed according to standard sample storage requirements and rigorously calibrated in a controlled laboratory environment. Their internal set of regulating parameters, such as the start-stop logic of the refrigeration compressor, the opening degree of the liquid nitrogen tank control valve, and the speed of the internal circulating fan, are optimized for a single type of sample load with uniformly distributed heat capacity. This optimization ensures that the device maintains a high degree of temperature uniformity and stability under normal operating conditions, thereby guaranteeing the long-term storage quality of samples.

[0014] However, in actual scientific research, the demand for samples is not always evenly distributed. For example, when a large research project is launched, or an experiment requiring a large number of samples for parallel comparison is conducted, technicians may submit dozens or even hundreds of sample retrieval requests in a very short period of time. This sudden, high-density task request poses a severe challenge to the existing control logic. Faced with this "task storm," the central control unit strictly follows the established procedure, continuously executing the "pre-cooling-grabbing-returning" cycle. Due to the extremely short time intervals between task requests, the cooling unit in the pre-cooling chamber cannot completely dissipate the residual heat after each cycle, resulting in a gradual decrease in pre-cooling effectiveness. This means that from the second or third task onwards, the temperature of the robotic arm's gripper when entering the main storage compartment is actually slightly higher than the standard set value.

[0015] These minute temperature differences may be insignificant in a single operation, but their impact accumulates with repeated, rapid operations. Each time a robotic arm, whose temperature is not fully within the acceptable range, enters the storage compartment, it acts as a tiny heat source, bringing a trace of heat to the area along its path. While the cooling system can maintain the average temperature of the entire storage compartment within the acceptable range, it cannot instantly eliminate this localized, momentary temperature rise caused by the robotic arm's movement.

[0016] As a result, samples that were not extracted but happened to be located near the high-frequency travel path of the robotic arm were subjected to extremely subtle "heating-cooling" cycles repeatedly in a short period of time. Although the main temperature sensor reading remained normal, these samples were actually experiencing cumulative thermal stress. For many cell lines or biomolecule samples that are extremely sensitive to temperature changes, such repeated, even negligible, temperature fluctuations can lead to decreased activity or damage to structural integrity. This potential deterioration in sample quality is completely undetectable by existing systems. The system logs only show that all operations were performed within acceptable temperature conditions, thus masking the problem of damage to the unextracted samples.

[0017] The embodiments of this application will be explained in detail below with reference to the accompanying drawings: Figure 1 This is an optional flowchart of an automated storage control method for a sample storage device provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S106.

[0018] Step S101: Obtain the sample access request, the number of remaining tasks, and the thermal load value of the robotic arm; Step S102: Perform task density analysis on sample access requests to identify the current task mode; Step S103: If the current task mode is a high-density task mode, calculate the buffer interval time based on the remaining number of tasks, the queue length influence coefficient, the robotic arm heat load value, and the heat load influence coefficient. Step S104: Process sample access requests in batches according to the buffer interval time; Step S105: In batch processing, execute the high-density operation mode. The high-density operation mode is used to control the robotic arm to move to the heat buffer zone during the buffer interval time to release the heat accumulated by the robotic arm. Step S106: If the current task mode is low-density task mode, then execute the normal operation mode. The normal operation mode is used to control the robotic arm to perform continuous storage and retrieval operations.

[0019] Steps S101 to S106 as shown in the embodiments of this application can calculate the buffer interval time by combining the remaining number of tasks and the thermal load value of the robotic arm, and process the sample access requests in batches to achieve storage control, thereby improving accuracy and reliability.

[0020] In some embodiments, steps S101-S106 may first acquire the sample access request, the remaining number of tasks, and the robotic arm thermal load value. For example, the sample access request may be sent from an external operating terminal to the central control unit via a network interface, or generated by the scheduling system within the device according to a preset program. It is understood that a sample access request refers to an operation instruction issued by a user or system to store or retrieve a sample; these requests typically include a unique identifier for the sample and the desired operation type. The remaining number of tasks can be obtained by querying the length of the task queue in real time. The robotic arm thermal load value can be estimated in real time by installing temperature sensors on key parts of the robotic arm (such as motors, joints, and grippers) and combining this with parameters such as the robotic arm's running time, movement speed, and load, using a preset thermal model. For example, an infrared thermal imager can be used to non-contactly measure the surface temperature of the robotic arm, or current and voltage sensors integrated into the motor driver can be used to calculate the motor's power consumption and thus estimate its heat generation. The preset thermal model can be a thermal path model based on the lumped parameter method. This model treats each key component of the robotic arm (such as the motor, reducer, and gripper body of each joint) as a node with a specific heat capacity and thermal resistance to the environment. Its core idea is to establish a dynamic balance between heat generation and heat loss. This can be achieved by having the robotic arm perform a series of typical motion tasks in a laboratory environment (e.g., high-speed motion without load, low-speed motion with load, continuous stillness, etc.). Operating parameters such as temperature of key parts of the robotic arm, motor current, motor voltage, motor speed, motion speed, and robotic arm load are measured, along with ambient temperature and the flow rate of the cooling medium (such as liquid nitrogen vapor). The thermal model is then fitted using optimization algorithms (such as least squares method, Kalman filtering, genetic algorithms, etc.). Motor current, motor voltage, motor speed, motion speed, robotic arm load, and ambient temperature can be used as model inputs, and the thermal model outputs the robotic arm's thermal load value.

[0021] Then, task density analysis is performed on the sample access requests to identify the current task mode. This can be done by monitoring the number of sample access requests received per unit time. For example, a time window (e.g., 1 minute) can be set to count the number of requests received within that time window. If the number exceeds a preset density threshold, it is identified as a high-density task mode; if it is below the density threshold, it is identified as a low-density task mode. In practical applications, a sliding time window averaging method can be used, which involves continuously calculating the average request rate over the past N seconds and comparing it with the density threshold. For example, when the number of requests per minute exceeds the density threshold three times consecutively, the system can determine that it is currently in a high-density task mode. The density threshold can be calibrated.

[0022] If the current task mode is a high-density task mode, the buffer interval time is calculated based on the remaining number of tasks, the queue length influence coefficient, the robotic arm's thermal load value, and the thermal load influence coefficient. This aims to balance operational efficiency and the robotic arm's heat dissipation requirements. For example, the formula for calculating the buffer interval time is: In the formula, This is the buffer interval time. The number of remaining tasks indicates the greater the workload the system is currently facing. This necessitates longer buffer intervals to distribute tasks and prevent the robotic arm from becoming overloaded. The queue length influence coefficient. This represents the thermal load value of the robotic arm. A higher thermal load value indicates that the robotic arm requires more heat dissipation and a longer buffer interval. This is the heat load influence coefficient. and Both are monotonically increasing functions, indicating that the more tasks and the higher the thermal load, the longer the required buffer time. The queue length influence coefficient represents the relationship between the increase in the number of remaining tasks and the increase in the buffer interval time. The thermal load influence coefficient represents the relationship between the increase in the robotic arm's thermal load value and the increase in the buffer interval time. Both the queue length influence coefficient and the thermal load influence coefficient can be calibrated. For example, when the number of remaining tasks is large, the queue length influence coefficient can be set to a higher value to ensure sufficient buffer time to handle upcoming tasks. It is understandable that task density and thermal load are correlated. In high-density task modes, the robotic arm's thermal characteristics are affected by factors such as the amount of work. Continuous operation of the robotic arm leads to rapid heat accumulation, requiring a longer buffer interval for heat dissipation. The buffer interval time needs to comprehensively consider the number of tasks currently pending (queue length) and the robotic arm's real-time thermal load to minimize the impact on overall operational efficiency while ensuring effective heat dissipation.

[0023] Based on the buffer interval, sample access requests are then processed in batches. This aims to break down the continuous, high-density task flow into several smaller batches, with a calculated buffer interval inserted between batches. For example, if the calculated buffer interval is 10 seconds and there are 20 sample access requests, the system can divide these 20 requests into 4 batches of 5 requests each, and wait 10 seconds after each batch is processed before starting the next batch. Batch processing can be performed using various strategies. For example, it can be simply grouped according to the arrival order of the requests, or intelligently grouped based on factors such as the spatial location and thermal sensitivity of the samples, to optimize overall operation efficiency and heat dissipation.

[0024] In batch processing, a high-density operation mode is implemented. This mode controls the robotic arm to move to a heat buffer zone during buffer intervals to release accumulated heat. After a batch of sample retrieval tasks is completed, the robotic arm does not immediately remain stationary before moving to the next batch; instead, it actively moves to a pre-defined "heat buffer zone." This heat buffer zone can be a specific area inside the storage device, possibly equipped with additional cooling devices, or its ambient temperature can be lower than the average temperature of the main storage compartment, thus more effectively aiding in heat dissipation for the robotic arm. For example, the robotic arm can move to a pre-cooling chamber or a specially designed cavity with higher heat dissipation efficiency. During this time, the robotic arm can remain stationary or perform low-power maintenance actions to further promote heat dissipation.

[0025] If the current task mode is low-density task mode, then the regular operation mode is executed. The regular operation mode controls the robotic arm to perform continuous access operations. In low-density task mode, because the time intervals between task requests are sufficiently long, the robotic arm has ample time to dissipate heat naturally, thus eliminating the need for additional buffer intervals and heat buffers. The robotic arm will perform sample access operations according to a traditional, continuous workflow to maximize operational efficiency. For example, after completing the access of one sample, the robotic arm will directly proceed to the location of the next sample without any additional waiting or movement to the heat dissipation area.

[0026] This embodiment provides comprehensive real-time data for subsequent decision-making by acquiring sample access requests, the number of remaining tasks, and the robotic arm's thermal load value. By analyzing the task density of sample access requests and identifying the current task mode, the system can intelligently determine the current operating environment and adopt different control strategies. When a high-density task mode is identified, the system no longer blindly operates continuously but calculates a buffer interval based on the number of remaining tasks, the queue length influence coefficient, the robotic arm's thermal load value, and the thermal load influence coefficient. This embodiment introduces a dynamic buffering mechanism, enabling the system to intelligently adjust the operating rhythm according to the actual thermal load and task pressure. Based on the buffer interval time, sample access requests are processed in batches, decomposing high-density tasks into manageable batches and inserting heat dissipation opportunities between batches. During batch processing, a high-density operation mode is executed. This high-density operation mode controls the robotic arm to move to the heat buffer zone within the buffer interval time to release the heat accumulated by the robotic arm, ensuring that its own heat is effectively released before entering the main storage bin, thereby fundamentally avoiding thermal shock to surrounding samples. When in low-density task mode, the system executes regular operation mode, which controls the robotic arm to perform continuous access operations, ensuring operational efficiency under non-high-pressure conditions.

[0027] Through the above technical solutions, this embodiment effectively solves the potential damage to samples caused by the accumulation of thermal load on the robotic arm in existing technologies by intelligently recognizing task modes, introducing dynamic buffer intervals in high-density modes, and actively moving the robotic arm to the heat buffer for heat dissipation. This method not only improves the level of automation control of sample storage equipment but also significantly enhances the storage security of samples, ensuring their long-term viability and integrity, and providing a solid guarantee for the smooth progress of scientific research.

[0028] In some embodiments, step S104, processing sample access requests in batches according to the buffer interval time, may include, but is not limited to, the following steps: Select the first target sample and the second target sample from the sample access request; Extract the first sample location information and the first thermal sensitivity index corresponding to the first target sample from the sample database; Extract the second sample location information and the second thermal sensitivity index corresponding to the second target sample from the sample database; Calculate the spatial distance based on the location information of the first sample and the location information of the second sample; Calculate the thermal correlation degree based on spatial distance, the first thermal sensitivity index, and the second thermal sensitivity index; If the thermal correlation degree is greater than the preset thermal correlation threshold, the first target sample and the second target sample are combined to obtain thermal correlation grouping. Sample access requests are processed in batches based on the buffer interval and multiple thermally correlated groups.

[0029] In some embodiments, a first target sample and a second target sample may be selected from the sample access requests. The first target sample and the second target sample refer to two samples to be operated on selected from the queue of pending sample access requests. These samples may be samples that need to be stored or retrieved.

[0030] Then, the first sample location information and the first thermal sensitivity index corresponding to the first target sample are extracted from the sample database. Similarly, the second sample location information and the second thermal sensitivity index corresponding to the second target sample are extracted from the sample database. The sample database stores detailed information for each sample, including its specific location in the storage device and its sensitivity to temperature changes, i.e., the thermal sensitivity index. The first and second sample location information indicate the physical coordinates or logical addresses of the first and second target samples in the storage device, respectively. The first and second thermal sensitivity indices quantify the sensitivity of these two samples to heat; for example, a higher value indicates that the sample is more susceptible to temperature changes.

[0031] Then, based on the location information of the first and second samples, the spatial distance is calculated. Spatial distance refers to the physical distance between the first and second target samples in the storage device, which can be obtained using Euclidean distance, Manhattan distance, or other spatial distance calculation methods. The thermal correlation is then calculated based on the spatial distance, the first thermal sensitivity index, and the second thermal sensitivity index. The thermal correlation can be calculated by comprehensively considering the spatial distance between samples and their respective thermal sensitivity indices. This is used to assess the potential mutual thermal influence between two samples during continuous processing, aiming to quantify the thermal coupling effect caused by physical proximity and high thermal sensitivity between samples. For example, the formula for calculating the thermal correlation can be: In the formula, For thermal correlation, For spatial distance, The first thermal sensitivity index, The second thermal sensitivity index, This is a scaling factor used to adjust the overall magnitude of thermal correlation to conform to a preset thermal correlation threshold in practical applications. This scaling factor can be calibrated. and All values ​​are exponentially weighted and can be set by operations personnel. It's understandable that samples physically closer together have a greater risk of heat transfer or localized thermal impact from robotic arm operations. Using the reciprocal of spatial distance as a calculation factor ensures that the smaller the distance, the larger the factor becomes, thus more intuitively reflecting the positive impact of physical proximity on thermal correlation. Furthermore, if both samples are highly sensitive to temperature changes, the risk of thermal interaction or external heat sources (such as robotic arms) is higher. Multiplying this by a weighted sum of the thermal sensitivity indices of the two samples comprehensively reflects their shared vulnerability; that is, when both are sensitive, the product increases significantly, indicating a higher potential thermal risk.

[0032] If the thermal correlation degree is greater than a preset thermal correlation threshold, the first target sample and the second target sample are combined to obtain a thermally correlated group. The preset thermal correlation threshold is a pre-set value used to determine whether the thermal correlation degree between two samples reaches a critical value that requires special processing. The preset thermal correlation threshold can be calibrated. When the calculated thermal correlation degree exceeds this threshold, it indicates that the two samples have a strong thermal correlation and should not be operated on continuously or closely. Therefore, a thermally correlated group refers to a sample set formed by combining the first target sample and the second target sample with a thermal correlation degree greater than the preset thermal correlation threshold. Samples within these groups are considered to be thermally correlated and need to be processed collaboratively or at intervals to avoid local heat accumulation. Finally, based on the buffer interval time and multiple thermally correlated groups, sample access requests are processed in batches.

[0033] To illustrate this technical solution more clearly, a specific example is used below. Suppose the sample storage device receives a batch of sample access requests, including sample A, sample B, sample C, and sample D. The system first retrieves the location information and thermal sensitivity index of these samples from the sample database. For example, samples A and B are spatially very close and both have high thermal sensitivity indices; calculations show that their thermal correlation far exceeds a preset threshold. While samples C and D also have high thermal sensitivity, they are spatially distant and have a low thermal correlation. In this case, the system will group samples A and B into a thermally correlated group. In subsequent batch processing, the system will prioritize processing samples with lower thermal correlation based on buffer intervals, or insert additional buffer time or allocate samples within thermally correlated groups to different batches when processing them. This ensures the robotic arm has sufficient time to release heat and avoids excessive thermal impact on samples A and B due to continuous operation. For example, sample C can be processed first, followed by a robotic arm heat release, then sample D can be processed, followed by another heat release, and finally samples A and B in the thermally correlated group can be processed. Alternatively, samples A and B can be divided into different batches, with other low thermally correlated tasks or heat release operations inserted in between.

[0034] Through the above technical solution, this embodiment enables refined thermal management of the sample storage and retrieval process. By considering the spatial location and thermal sensitivity of the samples and calculating thermal correlation, potential thermal risk sample pairs can be effectively identified and grouped for processing. This allows for a more rational arrangement of the robotic arm's work path and sequence during batch processing, avoiding frequent, high-intensity operations in localized areas, thereby significantly reducing the accumulation of thermal load on the robotic arm and extending its service life. Simultaneously, by optimizing the sample batching strategy, the risk of sample damage due to localized temperature increases during operation is reduced, improving the safety and reliability of sample storage.

[0035] In some embodiments, step S103, calculating the buffer interval time based on the remaining number of tasks, the queue length influence coefficient, the robotic arm heat load value, and the heat load influence coefficient, may include, but is not limited to, the following steps: Step S201: Apply a micro-thermal pulse to the robotic arm gripper; Step S202: After applying the micro-heat pulse, monitor the temperature change data of the robotic arm gripper within the target time sequence; Step S203: Generate the current temperature change curve based on the first temperature change data; Step S204: Correct the heat load influence coefficient based on the robot arm's heat load value and the current temperature change curve; Step S205: Calculate the buffer interval time based on the remaining number of tasks, the queue length influence coefficient, the robotic arm heat load value, and the corrected heat load influence coefficient.

[0036] In some embodiments, a micro-thermal pulse can be applied to the robotic arm gripper. A preset, extremely small heat input can be applied to the robotic arm gripper. The purpose is to stimulate a localized thermal response without significantly affecting the normal operation of the robotic arm, so that its heat dissipation characteristics can be monitored subsequently. This micro-thermal pulse can be achieved through a miniature heating element integrated inside the gripper or through brief frictional contact.

[0037] After applying a micro-thermal pulse, monitor the initial temperature change data of the robotic arm gripper within a target timeframe. For example, the target timeframe can be set to 2 seconds. Temperature sensors installed at key locations on the robotic arm gripper can be used to collect and record real-time temperature changes over time within 2 seconds of receiving the micro-thermal pulse. This initial temperature change data reflects the gripper's current heat absorption and dissipation capabilities.

[0038] Then, based on the initial temperature change data, a current temperature change curve is generated. The monitored initial temperature change data can be processed and fitted to form a curve describing the decrease in gripper temperature from its peak. This curve can intuitively show the heat dissipation rate and thermal inertia of the robotic arm gripper under the current working state.

[0039] Then, based on the robotic arm's heat load value and the current temperature change curve, the heat load influence coefficient is corrected. The heat load influence coefficient used to calculate the buffer interval can be dynamically adjusted by combining the robotic arm's current overall heat load state with the real-time heat dissipation characteristics obtained through micro-heat pulse testing. For example, if the current temperature change curve shows a decrease in heat dissipation capacity, the heat load influence coefficient will be increased accordingly to allow for a longer buffer time.

[0040] Finally, the buffer interval time is calculated based on the remaining number of tasks, the queue length influence coefficient, the robotic arm's thermal load value, and the corrected thermal load influence coefficient. The real-time corrected thermal load influence coefficient can be substituted into the original buffer interval time calculation model to obtain a buffer interval time that better reflects the current actual thermal state of the robotic arm.

[0041] To illustrate this technical solution more clearly, a specific example is used below. Assume the sample storage device is performing a series of high-density access tasks. The system first acquires the sample access requests, the remaining number of tasks, and the robotic arm's thermal load value. When a high-density task mode is identified, the system initiates a thermal response test on the robotic arm gripper to accurately calculate the buffer interval. Specifically, a micro-heating pulse with a power of 1 watt, lasting 50 milliseconds, is applied to the gripper via a micro-heating pad integrated inside the gripper. Subsequently, a high-precision thermocouple sensor mounted on the gripper surface begins monitoring the gripper's first temperature change data at a frequency of 100Hz for 2 seconds. The collected temperature data is transmitted to the control unit, which uses this data to generate a current temperature change curve. For example, if the curve shows a slower rate of temperature change than expected, it indicates a decrease in the gripper's heat dissipation efficiency. At this point, the control unit, considering the current robotic arm thermal load value, corrects the preset thermal load influence coefficient, for example, adjusting it from 0.8 to 0.95. Ultimately, the system will use this revised thermal load impact coefficient, along with the remaining task quantity, queue length impact coefficient, and robotic arm thermal load value, to recalculate a more accurate buffer interval time. For example, the calculated buffer interval time may be adjusted from the original 10 seconds to 12 seconds, thus providing the robotic arm with more time to dissipate heat and ensuring its stable operation under high-intensity work.

[0042] Through the above technical solution, this embodiment overcomes the problem of fixed or untimely updates to the thermal load influence coefficient, significantly improving the accuracy and adaptability of buffer interval calculation. By monitoring the thermal response of the robotic arm gripper in real time and dynamically correcting the thermal load influence coefficient, it ensures that the robotic arm receives an appropriate heat dissipation buffer time when performing high-density tasks. This avoids both performance degradation and shortened lifespan due to insufficient heat dissipation, and reduced operational efficiency due to excessively long buffer times. This refined thermal management strategy not only extends the service life of the robotic arm and reduces maintenance costs, but also optimizes the overall operating efficiency and stability of the sample storage device.

[0043] In some embodiments, in step S204, the thermal load influence coefficient is corrected based on the robotic arm's thermal load value and the current temperature change curve, which may include, but is not limited to, the following steps: Step S301: Adjust the intensity of the heat pulse according to the heat load value of the robotic arm; Step S302: Collect the second temperature change data of the inert cryogenic material within the target time series under different heat pulse intensities; Step S303: Generate a reference temperature change curve based on the second temperature change data; Step S304: Compare the current temperature change curve with the reference temperature change curve and calculate the material efficiency coefficient; Step S305: Adjust the correction range of the heat load influence coefficient according to the material efficiency coefficient; Step S306: Adjust the correction frequency of the heat load influence coefficient according to the preset heat load range; Step S307: Correct the heat load influence coefficient according to the correction range and correction frequency of the heat load influence coefficient.

[0044] In some embodiments, the intensity of the heat pulse can be adjusted based on the thermal load value of the robotic arm. The intensity of the micro-heat pulse applied to the robotic arm gripper or its vicinity can be dynamically set. The purpose is to simulate heat accumulation under different thermal load conditions to more accurately evaluate the heat dissipation performance of the robotic arm and the heat absorption capacity of the inert cryogenic material. Secondary temperature change data of the inert cryogenic material within a target time sequence is collected under different heat pulse intensities. For example, the target time sequence can be set to 2 seconds. After applying micro-heat pulses of different intensities, the temperature response of the inert cryogenic material can be continuously monitored using a high-precision temperature sensor, recording its temperature change trajectory over 2 seconds. The purpose is to obtain the actual thermal absorption characteristics of the inert cryogenic material under controlled heat input.

[0045] Then, based on the second temperature change data, a reference temperature change curve is generated. The collected second temperature change data can be processed and fitted to form a curve characterizing the heat absorption and temperature change of the inert cryogenic material under ideal or standard conditions. For example, a polynomial fitting or exponential decay model can be used to construct this curve, the purpose of which is to provide a benchmark for subsequent comparison with the current temperature change curve of the robotic arm in actual operation. The current temperature change curve is then compared with the reference temperature change curve to calculate the material efficiency coefficient. The difference between the heat absorption efficiency of the inert cryogenic material in the current state and the ideal state can be quantified by comparing the shape, slope, or temperature difference at a specific time point of the two curves, serving as the material efficiency coefficient. For example, the area difference or mean square error between the two curves can be calculated to assess the degree of performance degradation of the inert cryogenic material.

[0046] Next, adjust the correction range of the heat load influence coefficient based on the material efficiency coefficient. The material efficiency coefficient represents the heat absorption capacity of inert cryogenic materials. For example, a low material efficiency coefficient indicates a decreased heat absorption capacity of the inert cryogenic material. In this case, the correction range of the heat load influence coefficient should be increased to more conservatively calculate the buffer interval time and ensure sufficient heat dissipation. The purpose is to make the correction process more flexible and accurate. Alternatively, the correction range of the heat load influence coefficient can be determined by consulting a mapping table between the material efficiency coefficient and the correction range of the heat load influence coefficient. This mapping table can be set according to actual needs and is not specifically limited.

[0047] Simultaneously, the correction frequency of the heat load influence coefficient is adjusted according to the preset heat load level range. This preset heat load level range can include high, medium, and low heat load levels. The correction frequency can be dynamically adjusted based on the current heat load level of the robotic arm (e.g., light, medium, heavy load). For example, in the high heat load level range, the robotic arm accumulates heat quickly, so the correction frequency should be increased to respond rapidly to changes in heat load; while in the low heat load level range, the correction frequency can be appropriately decreased. The aim is to optimize system resource consumption while ensuring the correction effect. Alternatively, the correction frequency can be determined by consulting a mapping table between the preset heat load level range and the correction frequency of the heat load influence coefficient. This mapping table can be set according to actual needs and is not specifically limited. Finally, the heat load influence coefficient is corrected based on the correction magnitude and the correction frequency.

[0048] To illustrate this technical solution more clearly, a specific example is used below. Assume a robotic arm in a sample storage device is performing a series of high-density access tasks, with its heat load continuously increasing. To accurately correct the heat load influence coefficient, the system first dynamically adjusts the intensity of the micro-heat pulses applied to the robotic arm's gripper based on the current heat load value. For example, when the heat load is high, a pulse of intensity X is applied; when the heat load is low, a pulse of intensity Y is applied. Subsequently, the system collects temperature change data of the inert cryogenic material under these different pulse intensities for 2 seconds and generates multiple reference temperature change curves accordingly. During the actual operation of the robotic arm, the system continuously monitors the first temperature change data of the gripper within the target time sequence and generates the current temperature change curve. By comparing this current temperature change curve with the pre-generated reference temperature change curves, the system calculates the current material efficiency coefficient. For example, if the rate of decrease in the current temperature change curve is significantly slower than that of the reference temperature change curve, it indicates that the efficiency of the inert cryogenic material has decreased, and the material efficiency coefficient will decrease accordingly. Based on this material efficiency coefficient, the system adjusts the correction range of the heat load influence coefficient. For example, when efficiency decreases, the correction range for the heat load influence coefficient can be increased to calculate the buffer time more conservatively. Alternatively, the corresponding correction range for the heat load influence coefficient can be found in a pre-defined correction range mapping table based on the material efficiency coefficient. The correction range mapping table can be set according to actual needs and is not specifically limited.

[0049] Simultaneously, the system dynamically adjusts the correction frequency of the heat load influence coefficient based on the current instantaneous heat load level of the robotic arm (e.g., determining whether it falls within a preset high, medium, or low range). In the high heat load range, the correction frequency increases to ensure rapid response to heat accumulation; in the low heat load range, the correction frequency decreases. Ultimately, by combining the adjusted correction magnitude and frequency of the heat load influence coefficient, the system corrects the heat load influence coefficient, resulting in a more precise buffer interval time. This guides the robotic arm to enter the heat buffer zone for cooling at the appropriate time, ensuring a balance between operational efficiency and equipment lifespan.

[0050] Through the above technical solution, this embodiment can achieve dynamic and adaptive correction of the thermal load influence coefficient, significantly improving the accuracy of buffer interval time calculation. This embodiment can more precisely consider the real-time thermal load state of the robotic arm and the actual heat absorption efficiency of the inert cryogenic material, effectively avoiding the problem of excessively long or short buffer times due to improper correction. This not only optimizes the operating efficiency of the robotic arm and reduces unnecessary downtime, but also ensures that the heat generated by the robotic arm under long-term high-intensity operation is fully released, thereby effectively extending the service life of the robotic arm and its key components, and improving the overall reliability and stability of the sample storage device.

[0051] In some embodiments, step S305, adjusting the correction range of the heat load influence coefficient based on the material efficiency coefficient, may include, but is not limited to, the following steps: Obtain information on the cumulative service time and historical heat load intensity of inert cryogenic materials; Based on cumulative usage time information and historical heat load intensity data, predict the expected heat absorption efficiency of inert cryogenic materials; The material efficiency coefficient is compared with the expected heat absorption efficiency, and the correction range of the heat load influence coefficient is adjusted.

[0052] In some embodiments, since the actual heat absorption efficiency of inert cryogenic materials gradually decreases with the cumulative use time and changes in historical heat load intensity, if adjustments are made only based on the current material efficiency coefficient, it may not accurately reflect the long-term performance degradation of the material, resulting in insufficient precision in adjusting the heat load influence coefficient correction range and affecting the accuracy of the heat load influence coefficient correction.

[0053] To this end, we can first obtain information on the cumulative usage time and historical heat load intensity of the inert cryogenic material. The cumulative usage time refers to the total working time the material has experienced since it was put into use, which can be recorded through the system's internal timer or operation log. Historical heat load intensity data refers to the magnitude and duration of the heat load the material has endured in different time periods, which can be collected and stored through robotic arm operation data, ambient temperature sensor data, and heat load sensor data. These data collectively reflect the degree of wear and tear on the inert cryogenic material in the actual working environment.

[0054] Then, based on the cumulative usage time and historical heat load intensity data, the expected heat absorption efficiency of the inert cryogenic material is predicted. The heat absorption capacity that the material should possess in the current state can be evaluated using a pre-defined decay model, based on the material's cumulative usage time and historical heat load intensity data. For example, the decay model can be a polynomial regression model, which takes the cumulative usage time and historical heat load intensity as input and outputs an expected heat absorption efficiency value. Historical operating data can be collected from a large number of inert cryogenic material samples, including the cumulative usage time of each material sample and the historical heat load intensity records of each material sample at different time points, and the real-time heat absorption efficiency of the material can be measured as a label. The polynomial regression model is fitted using the least squares method or gradient descent method as the decay model.

[0055] Next, the material efficiency coefficient is compared with the expected heat absorption efficiency, and the correction range of the heat load influence coefficient is adjusted. The material efficiency coefficient obtained through real-time monitoring (reflecting current actual performance) can be compared with the expected heat absorption efficiency obtained through a predictive model (reflecting theoretical performance under long-term degradation trends). If the actual efficiency is lower than the expected efficiency, a larger correction may be needed; conversely, if the actual efficiency is higher than the expected efficiency, the correction range can be appropriately reduced. The aim is to ensure that the correction range of the heat load influence coefficient more accurately reflects the true heat absorption performance of inert cryogenic materials and takes into account their long-term degradation trend, thereby improving the accuracy and adaptability of the heat load influence coefficient correction.

[0056] To illustrate this technical solution more clearly, a specific example is used below. Assume an inert cryogenic material has accumulated 1000 hours of use and has endured an average heat load intensity of 80% over the past 500 hours. The system first retrieves this accumulated usage time information and historical heat load intensity data from the database. Then, using a pre-trained prediction algorithm based on a material aging model, such as a multinomial regression model, and inputting this data, it predicts the current expected heat absorption efficiency of the inert cryogenic material to be 0.85. Simultaneously, through real-time monitoring and calculation, the current material efficiency coefficient is obtained as 0.80. Comparing 0.80 (actual efficiency) with 0.85 (expected efficiency), it is found that the actual efficiency is slightly lower than expected. Based on this difference, the system adjusts the correction range of the heat load influence coefficient. For example, if the preset adjustment rule is "for every 0.01 lower than the expected efficiency, the correction range of the heat load influence coefficient increases by 1%", then in this case, the correction range of the heat load influence coefficient will be appropriately increased to more actively correct the heat load influence coefficient, thereby more accurately reflecting the decline in the material's actual heat absorption capacity. This dynamic adjustment ensures that even if the material properties degrade over a long period, the system can compensate in a timely and accurate manner, maintaining the optimal state of thermal load management for the robotic arm.

[0057] Through the above technical solution, this embodiment overcomes the limitations of relying solely on instantaneous material efficiency coefficients for adjustment, significantly improving the accuracy and adaptability of the correction range for the thermal load influence coefficient. By fully considering the long-term impact of the cumulative usage time of inert cryogenic materials and historical thermal load intensity on material properties, the correction of the thermal load influence coefficient is more precise, thereby optimizing the calculation of the buffer interval time. This not only helps to more effectively manage the thermal load of the robotic arm and prevent overheating risks, but also avoids reduced operational efficiency due to overly conservative corrections, thus improving the overall performance and reliability of the automated storage control method for sample storage equipment.

[0058] In some embodiments, step S306, adjusting the correction frequency of the heat load influence coefficient according to a preset heat load range, may include, but is not limited to, the following steps: Step S401: Monitor the motor current, movement speed, acceleration, and temperature change rate of the robotic arm; Step S402: Calculate the instantaneous heat load level based on the motor current, speed, acceleration, and rate of temperature change; Step S403: If the instantaneous heat load level is within the preset heat load level range, then monitor the working time of the robotic arm within the preset heat load level range. Step S404: Adjust the correction frequency of the heat load influence coefficient according to the operation duration.

[0059] In some embodiments, relying solely on a preset heat load range may not adequately reflect the actual heat load dynamics of the robotic arm under different operational intensities, thus affecting the accuracy and adaptability of the heat load influence coefficient correction frequency. If this problem is not addressed, the correction of the heat load influence coefficient may be untimely or inaccurate, thereby affecting the calculation accuracy of the buffer interval time, reducing the overall operating efficiency of the sample storage device and the reliability of the robotic arm.

[0060] To this end, the motor current, movement speed, acceleration, and temperature change rate of the robotic arm can be monitored first. Key operating parameters of the robotic arm during storage and retrieval operations can be acquired in real time using various sensors integrated on the robotic arm. Among these, motor current reflects the load and power consumption of the robotic arm's motor and is an important indicator of heat generation; movement speed and acceleration characterize the dynamic performance and movement intensity of the robotic arm; high-speed, high-acceleration movements are usually accompanied by higher friction and motor heating; and the temperature change rate reflects the instantaneous heat accumulation of the robotic arm body or key components. The comprehensive monitoring of these parameters aims to provide comprehensive and accurate input data for subsequent calculations of instantaneous heat load magnitudes.

[0061] Then, based on the motor current, movement speed, acceleration, and temperature change rate, the instantaneous heat load magnitude is calculated. These real-time monitored operating parameters can be transformed into a comprehensive heat load index. For example, the instantaneous heat load magnitude can be comprehensively assessed based on the Joule heat and mechanical friction heat generated during the operation of the robotic arm, combined with the robotic arm's own temperature change index. The motor Joule heat can be obtained by multiplying the square of the motor current by the equivalent resistance of the motor's internal windings; the mechanical friction heat can be obtained by multiplying the friction force by the movement speed and acceleration respectively, and then summing them by weight; the temperature change index can be obtained by multiplying the effective heat capacity of the robotic arm by the temperature change rate. Subsequently, the motor Joule heat, mechanical friction heat, and temperature change index are added together to obtain an instantaneous heat load magnitude that can quantify the current heat load intensity of the robotic arm. The purpose is to provide a quantitative basis for determining whether the robotic arm is in a specific heat load magnitude range. It is understandable that Joule's law states that when current flows through a conductor, the heat generated in the conductor is proportional to the square of the current, the resistance of the conductor, and the time of current flow. Furthermore, tribology studies the interactions between contact surfaces of objects, including friction, wear, and lubrication. When the joints, bearings, and transmission components of a robotic arm move, friction is generated. Work is done to overcome this friction, and this mechanical energy is converted into heat. The magnitude of friction is generally related to the normal force between the contact surfaces and the coefficient of friction. When the robotic arm moves, relative motion occurs between its components (such as joints, gears, and bearings), accompanied by friction. Mechanical frictional heat can be expressed as the product of the frictional force and the relative velocity. The higher the speed of the robotic arm, the more work is done to overcome friction per unit time, and the greater the frictional heat generated. During acceleration or deceleration, the robotic arm generates inertial forces. These inertial forces increase the normal force on the joints and bearings, potentially increasing friction and leading to more frictional heat generated in a short period.

[0062] If the instantaneous heat load level falls within a preset heat load level range, the system monitors the duration of the robotic arm's operation within that range. The preset heat load level range can be defined based on the robotic arm's design parameters, material properties, and long-term operational data; for example, it can be divided into multiple ranges such as low load, medium load, and high load. When the calculated instantaneous heat load level falls into a specific range, the system will begin or continue recording the robotic arm's continuous operation time within that range. The purpose is to assess the robotic arm's ability to withstand sustained thermal stress under a specific heat load intensity, as prolonged exposure to high heat loads has a greater impact on the robotic arm's performance and lifespan.

[0063] Next, adjust the correction frequency of the heat load influence coefficient based on the operation duration. If the robotic arm operates continuously for a long time within a certain high heat load range, the heat load influence coefficient needs to be corrected more frequently to ensure that the buffer interval calculation can respond promptly to the actual thermal state of the robotic arm and prevent excessive heat accumulation. Conversely, if the operation duration is short or within a low heat load range, the correction frequency can be appropriately reduced to decrease system overhead. The aim is to enable the correction frequency of the heat load influence coefficient to dynamically adapt to the actual operating conditions of the robotic arm, improving the accuracy and real-time performance of the correction. Alternatively, the correction frequency of the heat load influence coefficient can be determined by consulting a mapping table between operation duration and the correction frequency of the heat load influence coefficient. This mapping table can be set according to actual needs and is not specifically limited.

[0064] To illustrate this technical solution more clearly, a specific example is used below. Suppose a robotic arm in a sample storage device needs to continuously perform a series of high-density sample retrieval tasks. During these tasks, the robotic arm's motor current, movement speed, and acceleration will increase significantly, and the temperature change rate of its key components will also accelerate. The system collects this data in real time through sensors and calculates the instantaneous heat load level based on a preset physical model. For example, if the calculated instantaneous heat load level remains within a "high load level range," the system will start timing to monitor the robotic arm's operating time within this high load level range. If the robotic arm is detected to have been operating continuously within this high load level range for a considerable period, such as exceeding a preset operating time threshold, the system will determine that the robotic arm is experiencing significant continuous thermal stress, and the preset operating time threshold can be calibrated. In this case, to more timely and accurately reflect the actual thermal state of the robotic arm, the system will correspondingly increase the correction frequency of the heat load influence coefficient. This means that the heat load impact coefficient will be updated more frequently, allowing the buffer interval calculation to respond more quickly to the robot arm's heat accumulation and promptly arrange for the robot arm to enter the heat buffer zone for cooling, effectively preventing excessive heat accumulation and ensuring the stable operation of the robot arm. Conversely, if the robot arm is in a low load range for an extended period, the correction frequency can be appropriately reduced to optimize system resources.

[0065] Through the above technical solution, this embodiment enables dynamic and adaptive adjustment of the correction frequency for the thermal load influence coefficient of the robotic arm. By monitoring the robotic arm's operating parameters in real time and calculating the instantaneous thermal load magnitude, this embodiment can more accurately capture the actual thermal state of the robotic arm. Furthermore, considering the duration of operation, the adjustment of the thermal load influence coefficient correction frequency is made more refined and intelligent, effectively avoiding deviations in buffer interval calculations caused by untimely or inaccurate correction of the thermal load influence coefficient. Therefore, it significantly improves the accuracy and efficiency of thermal management in complex and dynamic operating environments, further ensuring the long-term stable operation of the robotic arm and the safety of sample storage.

[0066] In some embodiments, step S404, adjusting the correction frequency of the heat load influence coefficient according to the operation duration, may include, but is not limited to, the following steps: Step S501: Obtain the structural integrity information and current service life stage information of the inert cryogenic material. The structural integrity information includes local structural damage information, microcrack information and surface contamination information. The current service life stage information is used to indicate the specific stage of the inert cryogenic material in its entire life cycle from its introduction to its expected scrapping. Step S502: Based on the current service life stage information, select the target weight adjustment coefficient from the preset weight adjustment rules; Step S503: Determine the abnormal attenuation influence coefficient based on the structural integrity information; Step S504: Adjust the operation duration weight according to the abnormal attenuation influence coefficient and the target weight adjustment coefficient; Step S505: Adjust the correction frequency of the heat load influence coefficient according to the operation duration and operation duration weight.

[0067] In some embodiments, structural integrity information and current service life stage information of the inert cryogenic material can be obtained first. Structural integrity information refers to key data reflecting the material's physical state and performance, including local structural damage information, microcrack information, and surface contamination information. This information can be obtained through optical and ultrasonic sensing modules. Local structural damage information refers to macroscopic or microscopic physical defects appearing on or inside the material, such as scratches, dents, or material spalling. Microcrack information refers to tiny cracks existing inside or on the surface of the material. These cracks may occur during long-term use or under stress and affect the material's overall strength and thermal conductivity. Surface contamination information refers to foreign matter or chemicals adhering to the material's surface. These contaminants may alter the material's surface properties and affect its heat exchange efficiency. This information is used collectively to assess the health status of the inert cryogenic material and potential changes in its heat absorption capacity. Current service life stage information refers to the specific stage of the inert cryogenic material's entire life cycle, from initial use to expected disposal, such as early, middle, or late stage. Different service life stages typically correspond to different performance degradation modes and rates. Current service life stage information can be obtained from an equipment usage database. The device uses a database to record data on the usage of the sample storage device.

[0068] Then, based on the current service life stage information, a target weight adjustment coefficient is selected from the preset weight adjustment rules. The preset weight adjustment rules are a set of adjustment parameters pre-set according to the typical performance degradation patterns of materials at different service life stages. They can exist in the form of functional relationships or mapping tables, and are used to guide how to adjust the operating time weight according to the material's service life stage. The adjustment factor corresponding to the current service life stage information can be selected from the preset weight adjustment rules as the target weight adjustment coefficient. For example, assuming the expected total service life is 5000 hours, if the current service life stage information is 0-1000 hours, indicating stable material performance close to its optimal state, the corresponding target weight adjustment coefficient in the preset weight adjustment rules is 1. If the current service life stage information is 1001-3000 hours, indicating that the material performance begins to show slight degradation, the corresponding target weight adjustment coefficient in the preset weight adjustment rules is 0.95. If the current service life stage information is 3001-5000 hours, indicating that the material performance degradation is more significant, the corresponding target weight adjustment coefficient in the preset weight adjustment rules is 0.85. If the current service life stage information is greater than 5000 hours, it indicates that the material performance may decline significantly, posing a high risk. The corresponding target weight adjustment coefficient in the preset weight adjustment rule is 0.7.

[0069] Next, based on the structural integrity information, the abnormal attenuation influence coefficient is determined. The abnormal attenuation influence coefficient is a correction factor determined based on the structural integrity information of the inert cryogenic material. It aims to quantify the additional attenuation of material properties caused by abnormal conditions such as structural damage, microcracks, or surface contamination. For example, when severe local structural damage or numerous microcracks are detected, this coefficient will increase accordingly to reflect a significant decrease in the material's thermal absorption efficiency. Alternatively, the abnormal attenuation influence coefficient can be determined by consulting a mapping table between structural integrity information and the abnormal attenuation influence coefficient. This mapping table can be set according to actual needs and is not specifically limited.

[0070] Finally, the operating time weight is adjusted based on the abnormal attenuation influence coefficient and the target weight adjustment coefficient. The operating time weight is a weighting factor used to adjust the degree of influence of the original operating time on the correction frequency of the heat load influence coefficient; it is calculated comprehensively by combining the target weight adjustment coefficient and the abnormal attenuation influence coefficient. By adjusting the operating time weight, the contribution of operating time in calculating the correction frequency of the heat load influence coefficient can be more precisely controlled, making it more consistent with the actual performance state of inert cryogenic materials. The correction frequency of the heat load influence coefficient is then adjusted based on the operating time and its weight.

[0071] To illustrate this technical solution more clearly, a specific example is used below. Assume the inert cryogenic material in the sample storage device has been used for a considerable period, and its current service life stage information indicates it is in the "late-mid stage." The system will select an initial target weight adjustment coefficient for the "late-mid stage," for example, 0.8, based on preset weight adjustment rules. Simultaneously, sensor detection reveals minor local structural damage and a few microcracks in the inert cryogenic material, along with slight surface contamination. Based on this structural integrity information, the system calculates an abnormal attenuation impact coefficient, for example, 1.2. Subsequently, the target weight adjustment coefficient of 0.8 and the abnormal attenuation impact coefficient of 1.2 are multiplied to obtain an adjusted operating time weight, for example, 0.96. Finally, the system will adjust the heat load impact coefficient correction frequency based on the currently monitored operating time and this operating time weight of 0.96. For example, if the original operating time is 100 hours, without considering the material condition, 100 hours might be used directly to adjust the frequency; however, now, the weighted operating time (e.g., 100 hours) is used to adjust the frequency. The adjustment frequency of the heat load influence coefficient is adjusted by 0.96 (96 hours), so that the adjustment result more accurately reflects the decrease in heat absorption efficiency caused by material aging and damage, thereby more accurately correcting the heat load influence coefficient.

[0072] Through the above technical solution, this embodiment overcomes the limitations of relying solely on the duration of operation to adjust the correction frequency of the thermal load influence coefficient. By comprehensively considering the structural integrity information and current service life stage information of the inert cryogenic material, the actual performance degradation of the material can be assessed more precisely and accurately. This makes the correction of the thermal load influence coefficient more accurate, thereby improving the accuracy of the buffer interval time calculation and ensuring that the timing and duration of heat release by the robotic arm within the thermal buffer zone are more reasonable. Therefore, it not only effectively extends the service life of the inert cryogenic material and reduces maintenance costs, but also further improves the overall thermal management efficiency and operational stability of the sample storage device, avoiding the risk of equipment performance degradation or sample damage due to heat accumulation.

[0073] In some embodiments, obtaining the structural integrity information of the inert cryogenic material in step S501 may include, but is not limited to, the following steps: Step S601: In a deep cryogenic environment, optical information is collected from the inert cryogenic material through an optical sensing module to obtain local structural damage information and surface contamination information. Step S602: Adjust the ultrasonic emission frequency and pulse width based on the local structural damage information and surface contamination information; Step S603: Collect microcrack information through the ultrasonic sensing module based on the ultrasonic emission frequency and pulse width.

[0074] In some embodiments, comprehensively and accurately acquiring structural integrity information of inert cryogenic materials in deep cryogenic environments faces numerous challenges. For example, traditional optical inspection may be limited by optical distortion or frost formation under low-temperature conditions, and a single inspection method is insufficient to comprehensively capture various types of defects such as local structural damage, microcracks, and surface contamination. If these problems are not addressed, the structural integrity information of inert cryogenic materials may be inaccurate, affecting the calculation accuracy of the abnormal attenuation influence coefficient and the weighting of operation time. This, in turn, leads to suboptimal calculation of the buffer interval time, ultimately impacting the efficiency and security of automated storage control in sample storage devices.

[0075] To address this, optical information can be acquired from inert cryogenic materials in a deep cryogenic environment using an optical sensing module to obtain information on local structural damage and surface contamination. This can be done using an optical sensing module configured to operate stably at extremely low temperatures, imaging or performing spectral analysis on the surface of the inert cryogenic material in a non-contact manner to obtain information on its local structural damage and surface contamination. The advantages of optical information acquisition lie in its intuitiveness and rapid identification of surface defects.

[0076] Then, based on information about local structural damage and surface contamination, the ultrasonic emission frequency and pulse width are adjusted. For example, if optical inspection reveals deep surface cracks or contamination in specific areas, the ultrasonic frequency and pulse width can be optimized to improve penetration and resolution of microcracks within these areas. This adjustment aims to make ultrasonic inspection more targeted, thereby improving the accuracy and efficiency of detecting internal microcracks.

[0077] Then, based on the ultrasonic emission frequency and pulse width, microcrack information is acquired through an ultrasonic sensing module. The ultrasonic sensing module can emit high-frequency sound waves and receive their reflected or transmitted signals within the material. Because internal defects such as microcracks scatter or attenuate the propagation of sound waves, by analyzing the received ultrasonic signals, tiny cracks inside the material can be accurately identified and located; these cracks are typically undetectable by optical inspection.

[0078] To illustrate this technical solution more clearly, a specific example is used below. Suppose that after long-term operation, the inert cryogenic material within the heat buffer of the robotic arm in the sample storage device, used to absorb heat, requires a structural integrity assessment. First, in a cryogenic environment, an optical sensing module integrated with a high-resolution camera is deployed to scan the surface of the inert cryogenic material. Through image processing and analysis, the system identifies a slight frost layer covering the material surface (surface contamination information) and a small, visible scratch (local structural damage information). Based on these optical detection results, the control system intelligently adjusts the operating parameters of the ultrasonic sensing module. Specifically, due to the presence of frost, the ultrasonic emission frequency may be appropriately reduced to enhance penetration; simultaneously, to more accurately detect the internal structure beneath the scratch, the pulse width may be adjusted to improve axial resolution. Subsequently, the ultrasonic sensing module scans the inert cryogenic material with the adjusted frequency and pulse width. By analyzing the ultrasonic echo signal, the system successfully detects several microcracks extending into the material beneath the optical scratch, microcracks that cannot be directly detected by optical detection. Finally, by combining information on surface frost and scratches detected by optical sensors with information on internal microcracks detected by ultrasonic sensors, the system obtained comprehensive and accurate structural integrity information for the inert cryogenic material. This information was then used to accurately calculate the abnormal attenuation impact coefficient and adjust the weight of the operation duration, thereby more accurately correcting the thermal load impact coefficient, ensuring a more reasonable calculation of the buffer interval time, and thus optimizing the robot arm's operation scheduling and extending the service life of the inert cryogenic material.

[0079] Through the above technical solution, this embodiment can achieve comprehensive and high-precision acquisition of structural integrity information of inert cryogenic materials. This embodiment combines the intuitive identification capability of optical sensing for surface defects with the penetrating detection capability of ultrasonic sensing for internal microcracks, and intelligently adjusts the ultrasonic detection parameters based on the optical detection results, significantly improving the targeting and accuracy of the detection. Therefore, the acquired information on local structural damage, surface contamination, and microcracks is more realistic and reliable, providing a solid data foundation for subsequent calculation of the abnormal attenuation influence coefficient and adjustment of the operation time weight. This not only improves the accuracy of coefficient correction but also optimizes the calculation of the buffer interval time, ultimately enabling the automated storage control method of the sample storage device to operate more accurately and efficiently, effectively extending the service life of inert cryogenic materials and ensuring the safety of sample storage.

[0080] In some embodiments, step S601 involves acquiring optical information about the inert cryogenic material using an optical sensing module to obtain local structural damage information and surface contamination information. This may include, but is not limited to, the following steps: After setting multiple micro heating elements on the surface of the optical sensing module, the operating status of each micro heating element is monitored; Select one heating element from multiple miniature heating elements as the target heating element; If the target heating element is in a state of performance failure, increase the heating power and heating time of the adjacent heating elements to defrost the optical sensing module. The optical sensing module after defrosting is used to collect optical information of inert low-temperature materials, thereby obtaining information on local structural damage and surface contamination.

[0081] In some embodiments, the surface of the optical sensing module is prone to frost, which can severely affect the accuracy and reliability of optical information acquisition, leading to deviations in the assessment of material structural integrity. If this problem is not addressed, it may result in misjudgments of the structural integrity of inert cryogenic materials, thereby affecting the long-term stable operation of sample storage devices and the safety of samples.

[0082] To address this, multiple micro-heating elements can be first installed on the surface of the optical sensing module, and the operating status of each micro-heating element can be monitored. Micro-electric heating elements can be integrated into the housing or near the lens of the optical sensing module to prevent or remove frost from its surface through localized heating. These micro-heating elements can be implemented using thin-film heating technology or micro-resistance wire heating technology to ensure rapid response and provide sufficient localized heat in deep cryogenic environments. Simultaneously, the operation of each micro-heating element can be monitored in real time using built-in temperature, current, or voltage sensors. For example, this monitoring can detect whether it is heating normally, or whether there are faults such as short circuits or open circuits. The purpose is to promptly identify and address any abnormalities in the heating elements, ensuring the reliability of the defrosting function.

[0083] Then, one heating element is selected from multiple miniature heating elements as the target heating element. This selection can be based on a preset polling strategy, fault diagnosis results, or the frost condition of a specific area, with the aim of inspecting or addressing each heating element individually.

[0084] If the target heating element is in a state of performance failure, such as exhibiting abnormal temperature, low current, or no response, the heating power and heating time of adjacent heating elements are increased to defrost the optical sensing module. This means that when a heating element fails, the normally functioning heating elements around it will work together to enhance heating to compensate for the functional loss of the failed heating element and ensure that the defrosting effect is not affected.

[0085] The optical sensing module, after defrosting, then collects optical information from the inert low-temperature material to obtain information on local structural damage and surface contamination. This ensures that the light-transmitting surface of the optical sensing module is clear and frost-free, thereby obtaining accurate information on local structural damage and surface contamination.

[0086] To illustrate this technical solution more clearly, a specific example is used below. Assume a sample storage device operates in a cryogenic environment of -150°C. An optical sensing module mounted on its robotic arm needs to periodically acquire images of the surface of the inert cryogenic material in the storage container to detect minute scratches or contaminants. To ensure image clarity, four micro-heating elements are evenly distributed along the lens edge of the optical sensing module. During a routine inspection, the system detects that one of the micro-heating elements has stopped working due to an internal circuit failure, and its operational status is identified as a performance failure. At this point, the control system immediately activates a preset fault handling logic, increasing the heating power of the two adjacent heating elements from the usual 5W to 7W and extending the heating time by 10 seconds to create a locally enhanced heating area, rapidly melting and evaporating any frost that may have formed on the surface of the optical sensing module. After defrosting, the optical sensing module acquires images again. This time, the images are clear and hazy, accurately identifying minute damage to the surface of the inert cryogenic material, thus providing a reliable basis for subsequent material maintenance and sample storage strategy adjustments.

[0087] Through the above technical solution, this embodiment significantly improves the accuracy and reliability of optical information acquisition from inert cryogenic materials in deep cryogenic environments. This embodiment, through an active defrosting mechanism, ensures the normal operation of the optical sensing module under extreme low-temperature conditions, avoiding data distortion or omissions caused by frost, thereby enabling more accurate acquisition of information on local structural damage and surface contamination. Furthermore, this adaptive heating element management strategy enhances the system's robustness and maintenance efficiency, reduces the need for manual intervention, extends the lifespan of the optical sensing module, and ultimately ensures the overall performance of the automated storage control method for sample storage equipment and the safety of the samples.

[0088] The beneficial effects of implementing the embodiments of the present invention include: First, the embodiments of this application obtain the sample access request, the number of remaining tasks, and the thermal load value of the robotic arm. Then, the task density analysis of the sample access request is performed to identify the current task mode. If the current task mode is a high-density task mode, the buffer interval time is calculated based on the number of remaining tasks, the queue length influence coefficient, the robotic arm thermal load value, and the thermal load influence coefficient. The sample access requests are then processed in batches to execute the high-density operation mode. If the current task mode is a low-density task mode, the regular operation mode is executed. By identifying the task density in real time, the buffer interval is dynamically calculated in the high-density mode, and the robotic arm is guided to the heat buffer for active heat dissipation. At the same time, intelligent batching is performed by combining the sample position and thermal sensitivity, and the control parameters are corrected in real time based on multi-sensor data. This blocks the heat accumulation path at the source, realizing a leap from passive response to active prevention, and significantly improving sample safety, system reliability, and equipment life.

[0089] like Figure 2 As shown, this embodiment of the invention also provides an automated storage control system for a sample storage device, comprising: The information acquisition module 701 is used to acquire sample access requests, the number of remaining tasks, and the thermal load value of the robotic arm. The task pattern recognition module 702 is used to perform task density analysis on sample access requests and identify the current task pattern. The buffer interval time calculation module 703 is used to calculate the buffer interval time based on the number of remaining tasks, the queue length influence coefficient, the robotic arm heat load value and the heat load influence coefficient if the current task mode is a high-density task mode. The batch processing module 704 is used to process sample access requests in batches according to the buffer interval time. The high-density operation mode execution module 705 is used to execute the high-density operation mode in batch processing. The high-density operation mode is used to control the robotic arm to move to the heat buffer during the buffer interval to release the heat accumulated by the robotic arm. The regular operation mode execution module 706 is used to execute the regular operation mode if the current task mode is a low-density task mode. The regular operation mode is used to control the robotic arm to perform continuous storage and retrieval operations.

[0090] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0091] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. An automated storage control method for a sample storage device, characterized in that, Includes the following steps: Obtain sample access requests, remaining task count, and robotic arm thermal load value; Perform task density analysis on the sample access requests to identify the current task mode; If the current task mode is a high-density task mode, then the buffer interval time is calculated based on the remaining number of tasks, the queue length influence coefficient, the robotic arm heat load value, and the heat load influence coefficient. The sample access requests are processed in batches according to the buffer interval time. In batch processing, a high-density operation mode is executed, which is used to control the robotic arm to move to the heat buffer during the buffer interval to release the heat accumulated by the robotic arm. If the current task mode is a low-density task mode, then the regular operation mode is executed. The regular operation mode is used to control the robotic arm to perform continuous access operations.

2. The method according to claim 1, characterized in that, The step of processing the sample access requests in batches according to the buffer interval includes: Select a first target sample and a second target sample from the sample access request; Extract the first sample location information and the first thermal sensitivity index corresponding to the first target sample from the sample database; Extract the second sample location information and the second thermal sensitivity index corresponding to the second target sample from the sample database; Calculate the spatial distance based on the location information of the first sample and the location information of the second sample; The thermal correlation is calculated based on the spatial distance, the first thermal sensitivity index, and the second thermal sensitivity index. If the thermal correlation degree is greater than the preset thermal correlation threshold, the first target sample and the second target sample are combined to obtain thermal correlation grouping; The sample access requests are processed in batches according to the buffer interval and the multiple thermally correlated groups.

3. The method according to claim 1, characterized in that, The step of calculating the buffer interval time based on the remaining task quantity, queue length influence coefficient, robotic arm heat load value, and heat load influence coefficient includes: Apply micro-thermal pulses to the gripper of the robotic arm; After applying a micro-thermal pulse, monitor the first temperature change data of the robotic arm gripper within the target time sequence; Based on the first temperature change data, generate the current temperature change curve; The thermal load influence coefficient is corrected based on the robotic arm's thermal load value and the current temperature change curve. The buffer interval time is calculated based on the remaining number of tasks, the queue length influence coefficient, the robotic arm heat load value, and the corrected heat load influence coefficient.

4. The method according to claim 3, characterized in that, The step of correcting the heat load influence coefficient based on the robotic arm's heat load value and the current temperature change curve includes: Adjust the intensity of the heat pulse based on the thermal load value of the robotic arm; Collect second temperature change data of inert cryogenic materials within the target time series under different thermal pulse intensities; Based on the second temperature change data, a reference temperature change curve is generated; The current temperature change curve is compared with the reference temperature change curve to calculate the material efficiency coefficient; Adjust the correction range of the heat load influence coefficient based on the material efficiency coefficient; Adjust the correction frequency of the heat load influence coefficient according to the preset heat load range; The heat load influence coefficient is corrected based on the correction range and the correction frequency of the heat load influence coefficient.

5. The method according to claim 4, characterized in that, The step of adjusting the correction range of the heat load influence coefficient based on the material efficiency coefficient includes: Obtain information on the cumulative service time and historical heat load intensity of inert cryogenic materials; Based on the accumulated usage time information and the historical heat load intensity data, predict the expected heat absorption efficiency of the inert cryogenic material; The material efficiency coefficient is compared with the expected heat absorption efficiency, and the correction range of the heat load influence coefficient is adjusted accordingly.

6. The method according to claim 4, characterized in that, The step of adjusting the correction frequency of the heat load influence coefficient according to the preset heat load range includes: Monitor the motor current, movement speed, acceleration, and temperature change rate of the robotic arm; The instantaneous heat load magnitude is calculated based on the motor current, the speed of motion, the acceleration, and the rate of temperature change. If the instantaneous heat load level is within the preset heat load level range, then monitor the working time of the robotic arm within the preset heat load level range; The correction frequency of the heat load influence coefficient is adjusted according to the operation duration.

7. The method according to claim 6, characterized in that, The step of adjusting the correction frequency of the heat load influence coefficient according to the operation duration includes: The structural integrity information and current service life stage information of the inert cryogenic material are obtained. The structural integrity information includes local structural damage information, microcrack information and surface contamination information. The current service life stage information is used to indicate the specific stage of the inert cryogenic material in its entire life cycle from its introduction to its expected disposal. Based on the current service life stage information, a target weight adjustment coefficient is selected from the preset weight adjustment rules; Based on the structural integrity information, determine the abnormal attenuation impact coefficient; Adjust the operation duration weight according to the abnormal attenuation impact coefficient and the target weight adjustment coefficient; The correction frequency of the heat load influence coefficient is adjusted according to the operation duration and the operation duration weight.

8. The method according to claim 7, characterized in that, The process of obtaining the structural integrity information of the inert cryogenic material includes: In a deep cryogenic environment, optical information is collected from the inert cryogenic material through an optical sensing module to obtain information on local structural damage and surface contamination. Based on the local structural damage information and the surface contamination information, adjust the ultrasonic emission frequency and pulse width; The microcrack information is acquired by the ultrasonic sensing module based on the ultrasonic emission frequency and the pulse width.

9. The method according to claim 8, characterized in that, The step of acquiring optical information from the inert cryogenic material using an optical sensing module to obtain local structural damage information and surface contamination information includes: After setting multiple micro heating elements on the surface of the optical sensing module, the operating status of each micro heating element is monitored; Select one heating element from the plurality of micro heating elements as the target heating element; If the target heating element is in a state of performance failure, the heating power and heating time of the adjacent heating elements are increased to defrost the optical sensing module. The optical sensing module after defrosting collects optical information from the inert low-temperature material to obtain information on local structural damage and surface contamination.

10. An automated storage control system for a sample storage device, characterized in that, include: The information acquisition module is used to acquire sample access requests, the number of remaining tasks, and the thermal load value of the robotic arm; The task pattern recognition module is used to perform task density analysis on the sample access request and identify the current task pattern. The buffer interval time calculation module is used to calculate the buffer interval time based on the number of remaining tasks, the queue length influence coefficient, the robotic arm heat load value, and the heat load influence coefficient if the current task mode is a high-density task mode. The batch processing module is used to process the sample access requests in batches according to the buffer interval time. A high-density operation mode execution module is used to execute a high-density operation mode in batch processing. The high-density operation mode is used to control the robotic arm to move to the heat buffer zone during the buffer interval time to release the heat accumulated by the robotic arm. The regular operation mode execution module is used to execute the regular operation mode if the current task mode is a low-density task mode. The regular operation mode is used to control the robotic arm to perform continuous storage and retrieval operations.

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