Sample storage management method based on automatic transfer system

Abstract

The invention relates to the field of sample storage, and discloses a sample storage management method based on an automatic transfer system, and the method comprises the following steps: defining the motion state of a transfer trolley according to sample characteristics and storage requirements, constructing a path planning model through an optimal control theory, and optimizing a sample transfer path; introducing a stochastic optimization model, modeling uncertainty factors in a storage environment, and considering environment change factors in transfer path planning so as to adjust transfer path and storage area configuration in real time; and optimizing the distribution of the storage space by using linear programming. Through collaborative optimization of path planning and storage space management, dynamic adjustment of a storage environment, optimization of storage space distribution and introduction of a stochastic optimization model, the adaptive capacity of the system is improved, and the problems of mismatching of paths and storage, poor environment change adaptability, low storage efficiency and the like in the prior art are solved; and the transfer efficiency, the storage space utilization rate and the system robustness are obviously improved.

Description

Technical Field

[0001] The present invention relates to the field of sample storage, and particularly to a sample storage management method based on an automatic transfer system. Background Art

[0002] Most of the existing automatic transfer systems focus on the separate optimization of path planning or storage space management; generally, the path planning module only considers the shortest transfer time or the minimum energy consumption of the sample, and the storage space management is separately optimized according to the storage requirements of the sample; such a separate processing results in the lack of overall optimization of the system, the utilization efficiency of the storage space is often not high, and the storage plan cannot be dynamically adjusted according to the actual situation of the transfer path; in fact, the interdependence between the transfer path and the storage space is very strong. If the spatial and environmental conditions of the storage area cannot be considered when planning the path, it may lead to efficiency losses during the transfer process.

[0003] Currently, the path planning and storage management in many systems are processed independently and cannot achieve dynamic adjustment; for example, the path planning system often selects the transfer route based on the preset shortest path or the most time-saving path, ignoring the possible capacity problems, environmental problems, etc. in the storage area; this approach of ignoring the storage requirements and environmental conditions often results in the inability of the path planning to match the actual storage conditions, thus leading to the risk of waste of storage space or the exposure of samples to unsuitable environments.

[0004] More importantly, the existing technologies often cannot handle the impacts brought by uncertain factors, such as temperature and humidity fluctuations, equipment failures, etc.; these unpredictable environmental changes pose challenges to path planning and storage space management, but most systems do not have an effective mechanism to cope with such changes; the existing path planning and storage management technologies mostly adopt fixed strategies and do not have the adaptive ability, resulting in problems such as low efficiency and poor sample safety of the system under environmental changes; therefore, the present invention proposes a sample storage management method based on an automatic transfer system to solve the deficiencies of the existing technologies. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technologies, the present invention provides a sample storage management method based on an automatic transfer system, which solves the problem of the lack of collaborative optimization between path planning and storage space management; by combining path planning and storage space management and introducing dynamic programming and stochastic optimization models, the present invention can adjust the path and storage configuration in real time during the transfer process to ensure the efficient transfer and storage of samples under the best environmental conditions, significantly improving the overall efficiency and adaptability of the system, and showing stronger robustness especially when dealing with environmental changes and uncertain factors.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A sample storage management method based on an automatic transfer system, comprising the following steps: Define the motion states of the transfer carts according to the sample characteristics and storage requirements, and construct a path planning model through the optimal control theory to optimize the sample transfer paths; Introduce a stochastic optimization model to model the uncertain factors in the storage environment, and consider the environmental change factors in the transfer path planning to adjust the transfer paths and storage area configurations in real time; Use linear programming to optimize the allocation of storage space, ensure the efficient utilization of storage space, meet the sample storage requirements and comply with the storage environment condition limitations; Adjust the storage environment conditions according to the real-time sensor data through dynamic programming to ensure that the samples are stored under the best environmental conditions; Co-optimize the path planning and storage space management to ensure the efficient collaborative execution of the sample transfer and storage tasks in the system.

[0007] Preferably, the path planning model of the optimal control theory is described by the following steps: Analyze the motion states of the transfer carts through the optimal control theory, and control the speed and acceleration of the carts to optimize the paths; The objective function includes the speed and acceleration of the transfer carts, and the relative importance of the speed and acceleration is balanced by a weighting coefficient; wherein, the objective function is: where: J is the objective function of path planning, representing the comprehensive cost of time and energy in the optimization process; c1 and c2 are weighting coefficients, respectively controlling the contribution degrees of the speed and acceleration to the total cost, and are usually adjusted through experiments or actual requirements; ||v(t)|| and ||a(t)|| are the magnitudes of the speed and acceleration of the transfer cart at time t, respectively, representing the energy consumption and motion states; The solution of the optimal control equation uses the Pontryagin maximum principle to ensure that the transfer path is achieved under the premise of minimizing time and energy consumption.

[0008] Preferably, the stochastic optimization model is solved in the following manner: Model the random perturbations in the storage environment, and use stochastic optimization methods to adjust the transfer paths; Set the perturbation in the environment as a random variable ε(t), and adjust the transfer path through the following optimization objective: Where: u(s) is the control strategy, representing the path adjustment strategy in the random state s; C(u(s)) is the cost function of the transfer path, representing the total cost of path optimization, including factors such as transfer time, energy consumption, and environmental adaptability; is the expectation operator, representing the expected optimization of the path under environmental uncertainty; s is the state of random perturbation, representing the uncertain factors in the storage environment, such as temperature and humidity, path blockage, equipment failure, etc.; The control strategy optimizes the transfer process in real time by considering environmental uncertainty to ensure the effective management of samples in a random environment.

[0009] Preferably, the storage space optimization is carried out by the linear programming method, and the storage space optimization problem is completed through the following steps: In the storage space optimization, a linear programming model is established to allocate the storage space required for samples; The constraint conditions include the total capacity of the storage space and the storage requirements of each sample. The optimization goal is to maximize the utilization rate of the storage space and ensure that the sample storage requirements and environmental conditions are met.

[0010] Preferably, the regulation of the storage environmental conditions is completed in the following way: Real-time sensor data is used to monitor the temperature and humidity environmental parameters of the storage area, and the storage conditions are regulated in real time according to the data; The dynamic programming method is used to calculate the optimal adjustment strategy of the storage environmental conditions to ensure that the samples are stored under the best environmental conditions.

[0011] Preferably, the collaborative optimization of path planning and storage space management is completed through the following steps: Integrate the path planning results with the storage space optimization results to ensure that the transfer path not only meets the optimal path conditions but also can be efficiently executed within the actual storage area; The global scheduling algorithm is used to optimize the coordination of transfer tasks and storage management in real time to ensure efficient sample storage and transfer.

[0012] Preferably, the real-time sensor data acquisition includes temperature and humidity, gas concentration sensors to monitor the changes in the storage area environment in real time, and the temperature and humidity environmental conditions of the storage area are adjusted through a PID controller to ensure that the sample storage conditions are always optimal.

[0013] Preferably, the calculation processes of path planning, storage space optimization, and environmental regulation are implemented by a computer system. The computer system includes at least one processor and a memory connected thereto. The memory is used to store calculation models, path planning algorithms, storage space optimization algorithms, and environmental regulation algorithms.

[0014] Preferably, each step of the path planning, storage space management, and environmental regulation adopts a modular design. The method is implemented through multiple software modules, and data exchange and collaborative work are carried out between the modules through interfaces.

[0015] The present invention also provides a sample storage management system based on an automatic transfer system, including: A path planning module, configured to calculate an optimal transfer path according to sample storage requirements and environmental conditions. The path planning module optimizes the transfer path through the optimal control theory; A storage space optimization module, configured to optimize the allocation of storage space according to sample storage requirements and storage environmental conditions through a linear programming algorithm to ensure that the storage conditions of the samples are met; An environmental regulation module, configured to monitor the environmental data of the storage area in real time and regulate the storage environment, and adjust the temperature and humidity conditions through a dynamic programming algorithm to ensure that the samples are stored in the best environment; A coordination and scheduling module, configured to coordinate and optimize the results of path planning and storage space management to ensure the efficient collaborative execution of sample transfer and storage tasks.

[0016] The present invention provides a sample storage management method based on an automatic transfer system. It has the following beneficial effects: 1. The present invention adopts a collaborative optimization technical solution for path planning and storage space management, achieving the technical effect of the efficient collaborative execution of sample transfer and storage tasks; compared with the technical solution in the prior art where path planning and storage management are executed independently, the present invention effectively solves the problem of mismatch between the path and storage space, improves the transfer efficiency, and avoids space waste.

[0017] 2. The present invention combines dynamic programming with real-time sensor data to automatically adjust the storage environmental conditions to ensure that the samples are always stored in the best environment; compared with the system in the prior art that cannot adapt to environmental changes in real time, the present invention can cope with fluctuations in environmental conditions such as temperature, humidity, and gas concentration, and avoids damage to the samples due to environmental problems.

[0018] 3. The present invention optimizes the allocation of storage space by introducing a linear programming algorithm to ensure the maximized utilization of storage space; compared with the situation of insufficient space configuration and resource waste in the prior art, the present invention ensures that each sample can obtain a reasonable storage space through precise calculation and dynamic adjustment, and improves the storage efficiency.

[0019] 4. The present invention adopts a stochastic optimization model to handle the uncertain factors in the storage environment, and adjusts the path and storage configuration in real time, thereby enhancing the adaptability and robustness of the system. Compared with the existing systems that cannot effectively cope with environmental changes and equipment failures, the present invention can quickly respond to the dynamic changes of storage conditions and path planning, reducing the potential system operation risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is the flowchart of the method of the present invention; Figure 2 is the system architecture diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] Please refer to Figure 1 , the embodiments of the present invention provide a sample storage management method based on an automatic transfer system, including the following steps: S1. Define the motion state of the transfer cart according to the sample characteristics and storage requirements, and construct a path planning model through the optimal control theory to optimize the sample transfer path; In the automatic transfer system, the planning of the transfer path is a key link to ensure that the sample can move from one position to another. The present invention provides a path planning method based on the optimal control theory. By comprehensively considering the sample characteristics, storage requirements and the motion state of the transfer cart, a control model for optimizing the path is constructed. This process can ensure that the sample consumes as little time and energy as possible during the transfer process and maximizes the transfer efficiency.

[0023] Generally, the motion state of the transfer cart is affected by its motion parameters such as speed and acceleration, and the changes of these parameters directly determine the path selection and execution process. To achieve the optimal planning of the path, the present invention sets the motion state of the transfer cart through the optimal control theory and designs a path planning model, so that the transfer cart can select the best transfer route while meeting the sample storage requirements.

[0024] In this embodiment, the motion state of the transfer cart is described by the position x(t), velocity v(t), and acceleration a(t); the path planning model optimizes the transfer path by controlling these state variables, enabling the sample to move from the starting position to the target position in the shortest time while considering the energy consumption during the transfer; the motion state of the transfer cart is described by the following equations: where: x(t) is the position of the transfer cart at time t (unit: meter); v(t) is the velocity of the transfer cart (unit: meter per second); a(t) is the acceleration of the transfer cart (unit: meter per second 2 )

[0025] Specifically, to optimize the path, the optimal control theory takes the velocity and acceleration of the cart as control variables and designs an optimization objective function to minimize the total energy consumption and transfer time during the transfer; this optimization objective function is defined as: where: J is the objective function of path planning, representing the comprehensive cost of time and energy in the optimization process; c1 and c2 are weighting coefficients that respectively control the contribution degrees of velocity and acceleration to the total cost, and are usually adjusted through experiments or actual requirements; ||v(t)|| and ||a(t)|| are the norms of the velocity and acceleration of the transfer cart at time t, representing energy consumption and motion state.

[0026] As an option, for certain specific scenarios, the optimization objective may not only include time and energy, but may also introduce other constraints, such as path safety, environmental conditions, etc.; in this case, the optimization objective will be adjusted according to different application requirements; for example, if it is necessary to consider the temperature control of the path passed by the transfer cart in a specific area, the objective function of path planning may add a cost item related to temperature control.

[0027] Furthermore, to solve the optimal control problem, this optimization problem can be handled by the Pontryagin maximum principle; the Hamiltonian function is constructed using the Lagrange multiplier method: H = c1·||v(t)|| 2 + c2·||a(t)|| 2 + λ1v(t) + λ2a(t); Where: H: Hamiltonian function, which is used to describe the combination of the objective function and constraint conditions of the optimal control problem; c1: weighting coefficient, which is used to control the contribution degree of the velocity v(t) to the objective function; c2: weighting coefficient, which is used to control the contribution degree of the acceleration a(t) to the objective function; ||v(t)||: the norm of the velocity of the transfer cart at time t (unit: m / s), representing the motion state of the transfer cart; ||a(t)||: the norm of the acceleration of the transfer cart at time t (unit: m / s 2 ), representing the acceleration state of the transfer cart; λ1: Lagrange multiplier, representing the influence of the path planning constraint condition on the velocity v(t), which helps to consider the velocity constraint in the optimization process; λ2: Lagrange multiplier, representing the influence of the path planning constraint condition on the acceleration a(t), which helps to consider the acceleration constraint in the optimization process.

[0028] In a possible implementation, in order to more accurately control the path, the system can adjust the optimization strategy according to real-time feedback (such as the current velocity, position, and acceleration of the cart); this means that the path planning is not only based on the initially set optimal conditions, but also dynamically adjusted according to the actual situation during the transfer process to ensure that the optimal path always adapts to the current environment and requirements.

[0029] Dynamically adjusting the path is a key part of the path planning model. Especially in an automated transfer system, the external environment (such as temperature, humidity, obstacles, etc.) may change at any time; the optimal control model can dynamically recalculate the path according to real-time feedback data and adjust the driving route of the transfer cart; for example, if a certain section of the path becomes unsuitable for the transfer cart to pass due to a malfunction of the temperature control system, the system will automatically select a new path to ensure the safety and transfer efficiency of the sample.

[0030] Dynamic path update: The present invention supports dynamic path update; through the environmental data collected by the real-time monitoring system (such as the input of temperature and humidity sensors), the path planning can respond immediately to environmental changes; assuming that the path of the transfer cart encounters obstacles or uncontrollable environmental factors (for example, the temperature and humidity exceed the set values), the system will ensure the continuation of the transfer process by recalculating the optimal path.

[0031] Environmental adaptability: This path planning model not only optimizes the transfer path itself, but also takes into account environmental factors in path selection; for samples that require special storage conditions, the path planning will consider factors such as temperature and humidity control equipment and the capacity of the storage area in real time to avoid exposing the samples to inappropriate environments during the transfer process.

[0032] S2. Introduce a stochastic optimization model to model the uncertain factors in the storage environment and consider environmental change factors in the transfer path planning to adjust the transfer path and storage area configuration in real time; In an automatic transfer system, the planning of transfer paths not only needs to consider the basic requirements of samples and the constraints of the storage environment, but also the uncertain factors in the environment; these uncertain factors include, but are not limited to, temperature and humidity fluctuations, equipment failures, traffic jams, etc., which may have a significant impact on path selection and storage area configuration during the transfer process; therefore, in order to ensure the efficiency and flexibility of the transfer system, the present invention introduces a stochastic optimization model to model these uncertain factors and adjusts the path and storage area configuration in real time during the transfer path planning to cope with environmental changes.

[0033] Generally, the impact of uncertain factors in the environment on transfer path planning is dynamic and variable; for example, some paths may become unsuitable for use due to equipment failures or non-compliance with temperature and humidity requirements, while storage areas may be unable to continue storing certain samples due to temperature fluctuations; therefore, the present invention considers the changing factors of the environment when planning paths by introducing a stochastic optimization model and adjusts the transfer path and storage area configuration in real time.

[0034] In this embodiment, first, we establish a stochastic perturbation model for the uncertain factors in the storage environment; assume that the perturbations in the storage environment are represented in the form of a random variable ε(t), and these perturbations include, but are not limited to, temperature and humidity fluctuations, sensor errors, equipment failures, etc.; in order to handle these uncertainties, the present invention uses a stochastic optimization method to adjust the transfer path and storage space to achieve the optimization of path planning and storage management.

[0035] Specifically, the core of the stochastic optimization model is to consider the impact of stochastic perturbations on path planning; we define an objective function that comprehensively considers the cost of the transfer path, sample safety, and the impact brought by environmental changes; the objective function is expressed as: where: u(s) is the control strategy, representing the path adjustment strategy in the stochastic state s; C(u(s)) is the cost function of the transfer path, representing the total cost of path optimization, including factors such as transfer time, energy consumption, and environmental adaptability; is the expectation operator, representing the expected optimization of the path under environmental uncertainty; s is the state of stochastic perturbation, representing the uncertain factors in the storage environment, such as temperature and humidity, path blockage, equipment failure, etc.

[0036] As an option, in this optimized model, additional constraint conditions can be added according to the priority of the transportation task, the sample type, and the changes in the storage environment; for example, if a certain sample requires specific temperature and humidity conditions, when optimizing the path, not only the shortest path needs to be considered, but also it is necessary to ensure that the areas passed by the path meet these conditions; these constraint conditions can be realized by adding additional limiting terms to the objective function to ensure that the optimization process is carried out under environmental constraints.

[0037] In a possible implementation, the solution of this model uses the Monte Carlo simulation method or other stochastic optimization algorithms to evaluate the performance of different paths under various environments; by calculating a large number of random samples, the system can update the path planning in real time and select the path plan with the lowest expected cost.

[0038] Furthermore, stochastic optimization is not only applied to path planning, but also plays an important role in optimizing the storage space; the storage area may change due to factors such as temperature and humidity fluctuations and equipment damage, resulting in the original storage configuration becoming no longer applicable; for this reason, the present invention further introduces a stochastic optimization method for real-time adjustment of the storage area configuration to ensure that samples are always stored in a compliant environment; the storage space optimization model is expressed as: Where: x i is the storage requirement of the i-th sample, usually including requirements for temperature, humidity, and space; N is the total number of samples; represents the expected calculation of the total storage requirements of all samples in a stochastic environment.

[0039] In some embodiments, when optimizing the storage space, the system not only considers the storage requirements of the samples, but also considers the dynamic changes in the environmental conditions; for example, when the temperature of a certain storage area exceeds the set range, the system will automatically adjust the temperature and humidity of that area, or transfer the samples to other compliant storage areas to avoid storage inadaptability caused by environmental changes.

[0040] In another possible implementation, the real-time adjustment of path planning and storage space optimization can be further improved by introducing a multi-objective optimization method; in multi-objective optimization, the system not only optimizes the transportation path and the use of storage space, but also comprehensively considers multiple objectives, such as the urgency of sample transportation, the response speed to environmental changes, the availability of equipment, etc., and finally selects the path and storage plan with the optimal comprehensive benefits.

[0041] S3. Use linear programming to optimize the allocation of storage space to ensure the efficient use of storage space, meet the sample storage requirements and comply with the storage environment condition restrictions; In an automatic transfer system, the optimal configuration of storage space is crucial for improving storage efficiency, ensuring sample safety, and environmental adaptability. Especially when multiple samples have different storage requirements and environmental conditions, how to reasonably allocate limited storage space has become a key issue. The present invention optimizes the allocation of storage space by introducing linear programming technology and utilizing its powerful constraint handling ability to ensure the efficient use of storage space and meet the storage requirements and environmental conditions of each sample.

[0042] Generally, when storing samples, multiple factors will be affected, such as the volume of the samples, the environmental conditions required for storage (such as temperature, humidity, etc.), and the limitations of the storage area (such as the total capacity of the storage space). Therefore, the goal of optimizing the storage space allocation is not only to simply maximize space utilization but also to consider the specific requirements of each sample and the environmental adaptability of the storage area. In this case, linear programming technology provides an effective solution for such optimization problems.

[0043] In this embodiment, the capacity limit of the storage area and the storage requirements of each sample are first defined. The total capacity C of the storage area is a known quantity, and the storage requirement of each sample i is x i , including its volume and environmental requirements (such as temperature, humidity). In this model, the optimization goal is to maximize the utilization rate of the storage space while meeting the storage requirements and environmental constraint conditions of each sample. The objective function is: where: N is the total number of samples; x i is the storage requirement of the i-th sample (unit: cubic meters), including the volume of the sample and its requirements for the environment (temperature, humidity, etc.).

[0044] As an option, to ensure that the storage requirements and environmental conditions of the samples are fully met, the allocation of storage space needs to consider multiple constraints. These constraints include: Total capacity limit of the storage space: The sum of the storage requirements of all samples cannot exceed the total capacity C of the storage area. Storage requirement of each sample: The storage requirement x i of each sample i must be greater than or equal to the minimum storage requirement x min of this sample and not exceed its maximum storage requirement x max ; Limitations of storage environmental conditions, such as the range of temperature θ i and humidity φ i ; Where: θ i is the storage temperature of the i-th sample; φ i is the storage humidity of the i-th sample; θ min , θ max are the minimum and maximum limits of temperature; φ min , φ max are the minimum and maximum limits of humidity.

[0045] Specifically, within the framework of the above objective function and constraints, the linear programming model maximizes the utilization rate of the storage space by adjusting the storage location and environmental conditions of each sample, while ensuring that the storage requirements and environmental conditions of each sample are met; the system calculates the optimal storage space allocation plan in real time according to the type, storage requirements, and environmental conditions of the samples, so as to maximize the efficiency of sample storage under the condition of meeting the capacity limit.

[0046] In a possible implementation, through the real-time data monitoring of the system, the environmental conditions (such as temperature and humidity) of the storage area can be dynamically adjusted; when there are temperature and humidity fluctuations in the storage area, or when the temperature control equipment in some areas fails, the system can re-evaluate the usage of the storage space and adjust the configuration of the storage area or the allocation of samples in a timely manner; for example, some samples that do not meet the temperature and humidity requirements can be transferred to other more suitable storage areas to ensure the stability of the storage conditions and the safety of the samples.

[0047] Furthermore, in order to improve the real-time performance and accuracy of storage space optimization, the linear programming model of the present invention supports dynamic updates; the system can calculate and adjust the storage space allocation plan in real time according to factors such as new sample storage requirements, environmental condition changes, and equipment status; whenever the storage conditions or sample requirements change, the system will recalculate the optimal storage configuration according to the latest data to ensure the maximization of storage efficiency.

[0048] In some embodiments, when facing large-scale sample storage requirements, the linear programming model may become very complex; to improve the solution efficiency, the present invention can adopt optimization methods such as heuristic algorithms and genetic algorithms to solve the linear programming problem; these methods can quickly find a solution close to the optimal one, especially when the number of samples is large or the configuration of the storage area is very complex, which can effectively improve the calculation efficiency.

[0049] In another possible implementation, the optimization model can also cooperate with other automation systems (such as temperature and humidity sensors, equipment monitoring systems, etc.) to further improve the intelligent management of the storage space; for example, when the sensor detects that the temperature in a certain area is too high, the system can automatically move the relevant samples to a cooler area to avoid affecting the samples due to inappropriate storage environment.

[0050] S4. Adjust the storage environment conditions according to real-time sensor data through dynamic programming to ensure that the samples are stored under the optimal environmental conditions; In an automatic transfer system, ensuring that samples are stored in a suitable environment is crucial for the quality and stability of the samples; different sample types may require different storage conditions, such as temperature, humidity, gas concentration, etc.; and the changes in the storage environment are usually unpredictable, such as fluctuations in external temperature, equipment failures, sensor errors, etc.; these factors may cause the storage conditions to not meet the requirements, thereby affecting the quality of the samples; therefore, the present invention introduces a dynamic programming method to precisely adjust the environmental conditions of the storage area according to real-time sensor data, so as to ensure that the samples are always stored under the optimal environmental conditions.

[0051] Generally, devices such as temperature and humidity sensors and gas concentration sensors are equipped in the storage area of the transfer system, and these sensors provide real-time storage environment data; the system needs to continuously dynamically adjust parameters such as temperature, humidity, and gas concentration according to the sensor data to ensure that each sample is in the most suitable storage conditions during transfer and storage; the dynamic programming method can efficiently handle multiple environmental changes and achieve optimal environmental control under changing storage conditions.

[0052] In this embodiment, first, the system monitors the environmental data of the storage area through real-time sensors; these sensors can obtain information such as the temperature T(t) and humidity H(t) of the storage area and upload them to the system for processing in real time; based on these data, the system evaluates the adaptability of the current storage environment in real time and calculates the optimal environmental adjustment strategy through a dynamic programming algorithm to ensure that the storage requirements of all samples are met.

[0053] Specifically, the core of the dynamic programming method is to optimize the storage environment condition adjustment strategy at each time step; set the state of the storage area as x(t), and this state includes temperature, humidity, and other environmental factors; and the adjustment strategy u(t) represents the adjustment operation to be performed on the storage environment at time t (such as turning on or off air conditioners, humidifiers, etc.); the goal is to make the state x(t) of the storage environment finally converge to the preset optimal storage condition x target , that is, the ideal environment required for sample storage; for this purpose, the objective function of the system can be expressed as: where: J is the optimization objective function, representing the total environmental adjustment cost from time 0 to T; x(t) is the state of the storage area at time t (such as the values of environmental variables such as temperature and humidity); x targetis the target storage condition (ideal temperature, humidity, etc.), that is, the environmental requirements for the optimal storage of samples; u(t) is the operation taken for environmental adjustment at time t (such as the control signal for temperature and humidity adjustment); c1 and c2 are weighting coefficients used to balance the deviation of environmental adjustment and the consumption of control signals.

[0054] As an option, through dynamic programming, the system adjusts environmental conditions such as temperature, humidity, and gas concentration according to real-time data; the core of dynamic programming lies in backtracking to solve the optimal adjustment strategy; specifically, through backward reasoning from time T to time 0, the system gradually calculates the optimal control strategy u(t) based on the environmental state and adjustment strategy at each time step, so that the samples are always stored in the best environment.

[0055] In one possible implementation, to more accurately control the path, the system can introduce multiple environmental control devices, such as air conditioners, humidifiers, dehumidifiers, etc., and adjust the working states of these devices according to the data fed back by sensors; the system collects sensor data in real time, inputs parameters such as temperature and humidity into the dynamic programming model, and dynamically adjusts the environmental conditions to ensure that the samples are in the required storage conditions under any environmental changes.

[0056] Furthermore, the system can not only adjust environmental conditions such as temperature and humidity according to real-time sensor data, but also make personalized adjustments to the environment according to the type and requirements of the samples; for example, for biological samples that require ultra-low temperature storage, the system will preferentially store them in a low-temperature environment and adjust the temperature control equipment in real time to ensure that they are in a suitable temperature range.

[0057] In some embodiments, when dealing with more complex storage environments, the system may need to adjust multiple environmental parameters simultaneously, such as temperature, humidity, oxygen concentration, etc.; at this time, the dynamic programming method can handle multi-dimensional storage environmental conditions, and select the most appropriate adjustment strategy through an optimization algorithm, so that the changes of all environmental parameters are within the minimum range to achieve the best storage effect.

[0058] In another possible implementation, to improve the adaptive ability of the system, the system can combine other intelligent algorithms, such as machine learning algorithms, prediction models, etc., to predict in advance the possible environmental fluctuations in the storage area and make pre-adjustments; for example, by analyzing historical data and trends, the system can predict temperature and humidity changes in advance and start the control equipment to ensure that the samples have adapted to the new storage conditions before the environmental changes.

[0059] S5. Co-optimize path planning and storage space management to ensure the efficient coordinated execution of sample transfer and storage tasks in the system; In an automatic transfer system, path planning and storage space management are two key tasks that are interrelated and jointly affect the overall performance of the system; path planning aims to select the optimal path for samples from one location to another, while storage space management focuses on how samples are effectively configured and managed within the storage area; in order to ensure that the system can optimize the use of storage space while efficiently transferring samples, the present invention proposes a collaborative optimization method for path planning and storage space management to ensure the efficient collaborative execution of transfer and storage tasks in the system.

[0060] Generally, path planning and storage space management are tasks optimized independently. Path planning usually focuses on the goals of shortest time and lowest energy consumption, while storage space management emphasizes maximizing the utilization rate of storage space; however, these two are often mutually influential. The path selection may be restricted due to the vacancy of storage space or changes in environmental conditions. Conversely, the allocation of storage space may also affect the choice of transfer route; therefore, how to collaboratively optimize these two tasks is an important technical goal of the present invention.

[0061] In this embodiment, first, by integrating the objective functions of path planning and storage space management, a multi-objective optimization model is constructed; the goal of path planning is usually to minimize time and energy consumption, while the goal of storage space management is to improve storage efficiency and maximize the utilization rate of space; in order to achieve the balance of these two goals, the present invention combines the objective functions of path planning and storage space management to form the following unified optimization function: J total =α·J path +β·J storage ; Where: J total is the comprehensive objective function, representing the overall optimization goal of path planning and storage space management; J path is the optimization goal of path planning, usually the sum of the time and energy consumption of the transfer path; J storage is the optimization goal of storage space management, representing the utilization efficiency of storage space; α and β are weight coefficients used to balance the relative importance of path planning and storage space management.

[0062] As an option, in the collaborative optimization model, not only the path selection is optimized, but also the actual situation of the storage space and the sample storage conditions should be considered; specifically, the configuration of the storage space should meet the environmental requirements of the samples (such as temperature, humidity, etc.), and at the same time, the capacity and load conditions of the storage area should be considered; for example, if the storage area passed by a certain path cannot provide the required environmental conditions, the system should automatically adjust the path selection and transfer the sample to a suitable storage area; therefore, the interaction between path planning and storage space management must be fully considered in the optimization process.

[0063] Specifically, the collaborative optimization method of the present invention links path planning and storage space management through real-time data collection and analysis; the storage space management adjusts the path planning in real time according to the current storage status by monitoring factors such as the space utilization and environmental conditions of the storage area; for example, if the temperature in a certain storage area exceeds the preset range, the system will immediately adjust the path to avoid the sample passing through this area, so as to ensure that the sample is stored in a suitable environment.

[0064] In a possible implementation, the collaborative optimization process can be achieved through a global scheduling algorithm; this scheduling algorithm can evaluate the path selection of the transfer cart, the status of the storage area, and the storage requirements of the sample in real time, so as to dynamically adjust the path and storage space configuration; for example, when each transfer task arrives, the system can calculate the optimal path by combining the current environment and capacity of the storage area, and adjust the storage plan, so that the transfer and storage tasks are always highly coordinated.

[0065] Furthermore, the collaborative optimization is not limited to the optimization of path planning and storage space; in some complex applications, other optimization objectives can also be added, such as the priority of transfer tasks, the sensitivity requirements of samples, etc.; the system selects the most suitable path planning and storage plan for the current environment by comprehensively considering various optimization objectives to ensure the efficient execution of sample transfer and storage tasks.

[0066] In some embodiments, in order to enhance the adaptive ability of the system, the system can also combine machine learning algorithms to make optimization decisions based on historical data and real-time feedback; this method can learn the effects of different path selections and storage space configurations, so as to achieve more precise collaborative optimization; for example, the system can analyze historical data to identify the most commonly used path and storage area configuration patterns, and make predictions and optimizations based on this.

[0067] In another possible implementation, in order to improve the flexibility of the system and its ability to handle complex situations, the system can integrate multiple optimization algorithms (such as heuristic algorithms, genetic algorithms, etc.) to quickly solve the problems of path planning and storage space optimization; these algorithms can handle large-scale sample storage problems and quickly calculate an approximately optimal path planning and storage space configuration plan.

[0068] Please refer to Figure 2 , a sample storage management system based on an automatic transfer system, including: Path planning module. The path planning module is a core component of the automatic transfer system, mainly used to calculate the optimal transfer path according to the sample storage requirements, storage environmental conditions and the needs of transfer tasks. This module optimizes the transfer path through the optimal control theory to ensure that the transfer trolley can transport samples from one storage area to another in the shortest time, safely and efficiently. Path planning not only considers the shortest distance and time of the path, but also makes dynamic adjustments according to storage conditions, environmental requirements of samples (such as temperature and humidity control) and the load situation of storage areas. Storage space optimization module. The storage space optimization module is responsible for optimizing the allocation of storage space according to the storage requirements of samples and the environmental conditions of storage areas through the linear programming algorithm, ensuring that samples are reasonably allocated during storage and meet the environmental conditions. This module optimizes the spatial configuration according to the quantity, volume, storage requirements of samples and the capacity of storage areas. During the optimization process, the system needs to balance storage efficiency and environmental adaptability to ensure that the storage requirements of each sample can be met and the utilization rate of storage space is maximized. Environmental regulation module. The environmental regulation module is used to monitor the environmental data of the storage area in real time and regulate conditions such as temperature and humidity of the storage environment to ensure that samples are stored in the best environment. This module relies on real-time sensor data and adjusts the environmental conditions of the storage area through the dynamic programming algorithm to keep parameters such as temperature and humidity always within the range required by samples, avoiding damage to samples caused by environmental factors. Coordination and scheduling module. The coordination and scheduling module is a key component to ensure the efficient collaborative work of the system. This module coordinates and optimizes the results of the three modules of path planning, storage space management and environmental regulation to ensure that the transfer and storage tasks of samples can be efficiently and collaboratively executed throughout the system. The coordination and scheduling module will obtain the data of each module in real time and optimize the collaborative work between modules according to the current task priority, the usage of storage space, the feasibility of transfer paths and the requirements of environmental conditions.

[0069] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A sample storage management method based on an automatic transport system, characterized in that: The following steps are involved: According to the sample characteristics and storage requirements, the motion state of the transfer vehicle is defined, and the path planning model is constructed through optimal control theory to optimize the sample transfer path; Introduce a stochastic optimization model to model the uncertainty factors in the storage environment and consider environmental changes in the transfer path planning to adjust the transfer path and storage area configuration in real time; Use linear programming to optimize the allocation of storage space to ensure efficient use of storage space, meet sample storage requirements and comply with storage environment conditions; Dynamic programming adjusts storage environment conditions based on real-time sensor data to ensure samples are stored in optimal environmental conditions; Path planning and storage space management are collaboratively optimized to ensure that sample transportation and storage tasks are efficiently and collaboratively executed in the system.

2. A sample storage management method based on an automatic transport system according to claim 1, characterized in that: The path planning model of the optimal control theory is described by the following steps: Analyze the motion state of the transfer vehicle through optimal control theory, and control the speed and acceleration of the vehicle to optimize the path; The objective function includes the speed and acceleration of the transfer vehicle, and the relative importance of speed and acceleration is balanced by a weighted coefficient; the objective function is: Where: J is the objective function of path planning, which represents the comprehensive cost of time and energy in the optimization process; c1 and c2 are weighted coefficients, which respectively control the contribution of speed and acceleration to the total cost, and are usually adjusted through experiments or actual needs; ∥v(t)∥ and ∥a(t)∥ are the modulus of the speed and acceleration of the transfer vehicle at time t, respectively, representing energy consumption and motion state; The solution of the optimal control equation utilizes the Pontryagin maximum principle to ensure that the transport path is achieved while minimizing time and energy consumption.

3. A sample storage management method based on an automatic transport system according to claim 1, characterized in that: The stochastic optimization model is solved in the following way: Model random disturbances in the storage environment and use stochastic optimization methods to adjust the transfer path; The disturbance in the environment is set as a random variable ε(t), and the transport path is adjusted by the following optimization objective: Where: u(s) is the control strategy, which represents the path adjustment strategy under the random state s; C(u(s)) is the cost function of the transfer path, which represents the total cost of path optimization, including factors such as transfer time, energy consumption, and environmental adaptability; is the expectation operator, which indicates the expected optimization of the path under environmental uncertainty; s is the state of random disturbance, which indicates the uncertainty factors in the storage environment, such as temperature and humidity, path obstruction, equipment failure, etc.; The control strategy optimizes the transport process in real time by taking environmental uncertainties into account, ensuring that samples are effectively managed in a random environment.

4. A sample storage management method based on an automatic transport system according to claim 1, characterized in that: The storage space optimization is performed by a linear programming method, and the storage space optimization problem is solved by the following steps: In storage space optimization, a linear programming model is established to allocate the storage space required for samples; The constraints include the total storage space capacity and the storage requirements of each sample. The optimization goal is to maximize the utilization of storage space and ensure that the sample storage requirements and environmental conditions are met.

5. The sample storage management method based on the automatic transport system according to claim 1, characterized in that: The storage environment condition regulation is accomplished by: Real-time sensor data is used to monitor the temperature and humidity environmental parameters of the storage area, and to adjust storage conditions in real time based on the data; Dynamic programming methods are used to calculate the optimal adjustment strategy for storage environment conditions to ensure that samples are stored under the best environmental conditions.

6. A sample storage management method based on an automatic transport system according to claim 1, characterized in that: The coordinated optimization of path planning and storage space management is achieved through the following steps: Integrate the path planning results with the storage space optimization results to ensure that the transfer path not only meets the optimal path conditions but can also be efficiently executed in the actual storage area; The global scheduling algorithm is used to optimize the coordination of transport tasks and storage management in real time to ensure efficient sample storage and transport.

7. The sample storage management method based on the automatic transport system according to claim 1, characterized in that: The real-time sensor data acquisition includes temperature, humidity, and gas concentration sensors to monitor changes in the storage area environment in real time, and adjust the temperature and humidity environmental conditions of the storage area through a PID controller to ensure that the sample storage conditions are always optimal.

8. The sample storage management method based on the automatic transport system according to claim 1, characterized in that: The path planning, storage space optimization, and environment control calculation processes are implemented through a computer system, which includes at least one processor and a memory connected thereto, and the memory is used to store calculation models, path planning algorithms, storage space optimization algorithms, and environment control algorithms.

9. The sample storage management method based on the automatic transport system according to claim 1, characterized in that: Each step of the path planning, storage space management and environment control adopts a modular design, the method is implemented through multiple software modules, and the modules exchange data and work together through interfaces.

10. A sample storage management system based on an automatic transport system, applied to a sample storage management method based on an automatic transport system as claimed in any one of claims 1 to 9, characterized in that: include: The path planning module is used to calculate the optimal transfer path according to the sample storage requirements and environmental conditions. The path planning module optimizes the transfer path through optimal control theory; The storage space optimization module is used to optimize the allocation of storage space through a linear programming algorithm according to the sample storage requirements and storage environment conditions to ensure that the storage conditions of the samples are met; The environmental control module is used to monitor the environmental data of the storage area in real time and control the storage environment. It adjusts the temperature and humidity conditions through a dynamic programming algorithm to ensure that the samples are stored in the best environment; The coordination and scheduling module is used to coordinate and optimize the path planning and storage space management results to ensure the efficient and coordinated execution of sample transportation and storage tasks.

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