Sample in-out method and system

Abstract

This invention provides a sample entry / exit method and system. Upon receiving sample entry / exit instructions, it automatically determines the task sequence and generates a task scheduling strategy based on the current number of idle robots. This achieves a fully automated process from task reception to execution, significantly reducing manual intervention and improving work efficiency. It also ensures the rationality and timeliness of task allocation, demonstrating a high level of intelligence. By comprehensively considering the task sequence and the number of idle robots, task allocation can be dynamically adjusted to ensure optimal resource allocation, thereby improving overall entry / exit efficiency. Furthermore, based on warehouse layout, shelf location, sample type and specifications, and task allocation results, precise movement paths can be planned for each target robot. This not only shortens robot travel time and reduces energy consumption but also avoids collisions and path conflicts between robots, ensuring the safety and smoothness of entry / exit operations.

Description

Technical Field

[0001] This invention relates to the field of sample management technology, and in particular to a method and system for sample entry and exit from storage. Background Technology

[0002] In current dairy production processes, it is common practice to take a certain amount of finished product samples and store them in a warehouse. After a period of time, the samples are retrieved for testing to simulate actual storage conditions and assess their stability and quality changes during storage. This ensures the accuracy and applicability of the test results. This helps producers better control and optimize product quality, while also providing consumers with safer and more reliable products.

[0003] However, the entire process of sample entry and exit from the warehouse currently relies heavily on manual operation. From sample extraction to recording entry and exit information, sample handling and placement, monitoring ambient temperature, and finally sample retrieval for testing, every step requires manual intervention, resulting in low overall operational efficiency and making it difficult to meet the needs of large-scale production. Furthermore, it is difficult for personnel to place samples in higher locations within the warehouse, leading to underutilization of some storage areas and reducing the overall utilization rate of the warehouse. Summary of the Invention

[0004] This invention provides a method and system for sample entry and exit from storage, which solves the problems of low operational efficiency and wasted storage space in related technologies.

[0005] This invention provides a method for sample entry and exit from a warehouse, comprising: Upon receiving a sample entry / exit instruction, the task sequence is determined based on the instruction, and the current number of idle robots is obtained. Based on the task sequence and the number of idle robots, a task scheduling strategy is generated; Based on the task scheduling strategy, at least one target robot is identified from the currently idle mobile robots, and inbound / outbound tasks are assigned to each target robot to obtain the task allocation result. Based on the warehouse layout, shelf location, sample type and specifications, and the task allocation results, the movement path corresponding to each target robot is determined, and the movement path is sent to each target robot so that each target robot can perform the inbound and outbound tasks according to the received movement path.

[0006] According to a sample entry / exit method provided by the present invention, the step of generating a task scheduling strategy based on the task sequence and the number of idle robots includes: A task scheduling model is constructed based on the task sequence, the number of idle robots, and the maximum load of the mobile robot. A multi-objective optimization algorithm is applied to solve the task scheduling model to obtain the task scheduling strategy, which includes the number of target robots, the number of samples for each target robot, and the sampling order.

[0007] According to a sample entry / exit method provided by the present invention, the task scheduling model includes an objective function and constraints. The task scheduling strategy is obtained by solving the task scheduling model using a multi-objective optimization algorithm, including: Based on the task sequence, the number of idle robots, the maximum load of the mobile robot, and the constraints, an initial task scheduling strategy set is generated. Based on the estimated path and the objective function, the fitness function value of each scheduling strategy in the initial task scheduling strategy set is calculated, and the scheduling strategies in the initial task scheduling strategy set are filtered according to the fitness function value to obtain the first strategy set. The scheduling policies in the initial scheduling policy set are cross-mutated to generate a second policy set, and the first policy set and the second policy set are merged to obtain a merged policy set; Based on the estimated path and the objective function, the fitness function value of each scheduling strategy in the merging strategy set is calculated, and the scheduling strategies in the merging strategy set are filtered according to the fitness function value to obtain a new task scheduling strategy set. The task scheduling strategy is iteratively optimized based on the new set of task scheduling strategies until the iteration process ends, thus obtaining the task scheduling strategy.

[0008] According to a sample entry / exit method provided by the present invention, the step of determining the estimated path includes: Based on the coordinates of any mobile robot, the shelf coordinates, and the coordinates of the inbound / outbound workstation, find the target path from the path set; If the target path is not found, an estimated path for any mobile robot is generated based on the coordinates of the mobile robot, the coordinates of the shelf, and the coordinates of the inbound / outbound workbench, and the estimated path is stored in the path set. If the target path is found, the target path is used as the estimated path.

[0009] According to a sample entry and exit method provided by the present invention, determining the movement path corresponding to each target robot based on warehouse layout, shelf location, sample type and specifications, and the task allocation result includes: Based on the warehouse layout, shelf location, sample type and specifications, and the task allocation results, determine the global static path corresponding to any target robot; By taking the turning points on the global static path as sub-target points, and applying the dynamic window method to plan the path, the movement path corresponding to any target robot is obtained.

[0010] A sample entry and exit method according to the present invention further includes: When performing inbound / outbound operations, the samples to be inbound / outbound are identified, the sample information of the samples to be inbound / outbound is obtained, and the sample information and inbound / outbound records of the samples to be inbound / outbound are saved.

[0011] A sample entry and exit method according to the present invention further includes: Monitor the status of samples that have been stored and save the monitoring data; An alarm will be issued if any abnormality is detected in the condition of any sample.

[0012] A sample entry and exit method according to the present invention further includes: Receive user query requests; In response to the user query operation, the system obtains the sample information, entry and exit records, and monitoring data of the target sample, and displays the sample information, entry and exit records, and monitoring data of the target sample.

[0013] The present invention also provides a sample entry and exit system, comprising: User interface module, used to receive sample entry and exit instructions; The task scheduling module is used to determine the task sequence based on the sample entry and exit instructions, and to obtain the current number of idle robots. Based on the task sequence and the number of idle robots, it generates a task scheduling strategy. It is also used to determine at least one target robot from the currently idle mobile robots based on the task scheduling strategy, and to assign entry and exit tasks to each target robot to obtain the task allocation result. The path planning module is used to determine the movement path corresponding to each target robot based on the warehouse layout, shelf location, sample type and specifications, and the task allocation result, and send the movement path to each target robot so that each target robot can perform the inbound and outbound tasks according to the received movement path.

[0014] A sample entry and exit system according to the present invention further includes: The sample identification module is used to identify samples to be entered or exited from the warehouse based on radio frequency identification technology or barcode technology, and obtain sample information of the samples to be entered or exited from the warehouse. The status monitoring module is used to monitor the status of samples that have been put into storage; The alarm notification module is used to issue an alarm when an abnormal state of the sample is detected. The database module is used to store sample information, entry and exit records, and monitoring data. The user interface module is also used to receive user queries and display sample information, entry and exit records, and monitoring data of the target sample.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the sample entry and exit method as described above.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the sample entry and exit method as described above.

[0017] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the sample entry and exit method as described above.

[0018] The sample entry / exit method and system provided by this invention automatically determines the task sequence after receiving sample entry / exit instructions and generates a task scheduling strategy based on the current number of idle robots. This achieves a fully automated process from task reception to task execution, significantly reducing manual intervention and improving work efficiency. It also ensures the rationality and timeliness of task allocation, demonstrating a high level of intelligence. Furthermore, by comprehensively considering the task sequence and the number of idle robots, the system can dynamically adjust task allocation to ensure optimal resource configuration, thereby improving overall entry / exit efficiency. In addition, based on warehouse layout, shelf location, sample type and specifications, and task allocation results, the system can accurately plan the movement path for each target robot. This personalized path planning not only shortens robot travel time and reduces energy consumption but also avoids collisions and path conflicts between robots, thus ensuring the safety and smoothness of entry / exit operations. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the sample entry and exit method provided by the present invention; Figure 2 This is one of the structural schematic diagrams of the sample entry and exit system provided by the present invention; Figure 3This is the second schematic diagram of the sample entry and exit system provided by the present invention; Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] Figure 1 This is a flowchart illustrating the sample entry and exit method provided by the present invention, as shown below. Figure 1 As shown, the method includes: Step 110: Upon receiving a sample entry / exit instruction, determine the task sequence based on the sample entry / exit instruction and obtain the current number of idle robots.

[0023] Specifically, the method provided in this embodiment of the invention can be applied to a sample entry / exit system. This system has a user interface through which it can receive sample entry / exit instructions input by the user. Here, the sample entry / exit instruction is an indication used to tell the system which samples need to be entered or exited. This instruction may contain detailed information about the samples, such as sample name, quantity, storage location (or target location), priority, etc.

[0024] Upon receiving a sample inbound / outbound instruction, the system determines a series of tasks to be executed based on information in the instruction, such as sample priority, quantity, and location, as well as possible business rules (e.g., first-in, first-out, priority for urgent tasks). These tasks are arranged in a logical order, forming a task sequence. Here, a task sequence refers to a series of tasks arranged in a certain logical order, where each task can represent a specific inbound / outbound operation, such as moving a sample from shelf A to the outbound workstation, or moving a sample from the inbound workstation to a designated location.

[0025] Simultaneously, the system will also detect the number of currently available idle robots for subsequent task scheduling and allocation. Specifically, the system can maintain a robot status table or database, which records the current status of each mobile robot (such as idle, busy, faulty, etc.). When it needs to obtain the number of idle robots, the system only needs to query this status table or database to count the number of robots currently in an idle state. It should be understood that the number of idle robots is crucial for generating task scheduling strategies, as it determines how many robots can be assigned tasks simultaneously.

[0026] Step 120: Generate a task scheduling strategy based on the task sequence and the number of idle robots.

[0027] Specifically, based on the task sequence and the number of idle robots, the system can generate a task scheduling strategy by invoking a task scheduling algorithm. Specifically, to improve sample entry and exit efficiency, mobile robots can typically transport multiple samples at a time to save time. In multi-sample transport mode, adjusting the sampling tasks and order of each mobile robot during task scheduling can effectively improve entry and exit efficiency. Therefore, to optimize the scheduling of entry and exit task sequences, a task scheduling model is established with three objective functions: minimizing the required number of mobile robots, minimizing the maximum running time among all mobile robots, and minimizing mobile robot conflicts. This model is then solved using a multi-objective optimization algorithm to obtain the task scheduling strategy. Furthermore, when generating the task scheduling strategy, it is also necessary to consider avoiding conflicts between mobile robots, using this as a constraint condition for the solution. For example, two mobile robots cannot simultaneously carry the same sample, the number of samples collected by each mobile robot in one trip cannot exceed its maximum load capacity, and the total number of samples transported by all mobile robots equals the number of tasks in the task sequence.

[0028] Here, task scheduling strategy refers to a series of rules and algorithms established by the system to optimize task execution efficiency, reduce resource conflicts, and ensure timely task completion. It can include the actual number of mobile robots required, the number of samples taken by each mobile robot, and the sampling order. A reasonable task scheduling strategy can ensure that sample entry and exit tasks are completed efficiently and accurately.

[0029] Step 130: Based on the task scheduling strategy, at least one target robot is identified from the currently idle mobile robots, and inbound / outbound tasks are assigned to each target robot to obtain the task allocation result.

[0030] Specifically, based on the actual number of mobile robots required in the task scheduling strategy, the target robot can be determined from the currently idle mobile robots. Here, the target robot refers to the idle mobile robot selected to perform a specific inbound / outbound task according to the task scheduling strategy. Subsequently, the system will assign the inbound / outbound tasks in the task sequence to the determined target robot according to the sampling quantity and sampling order of each mobile robot in the task scheduling strategy, thereby obtaining the task allocation result. Here, inbound / outbound tasks refer to specific tasks related to sample inbound / outbound operations. Among them, outbound tasks refer to moving samples from shelves to outbound workstations, and inbound tasks refer to moving samples from inbound workstations to designated shelves.

[0031] Step 140: Based on the warehouse layout, shelf location, sample type and specifications, and the task allocation result, determine the movement path corresponding to each target robot, and send the movement path to each target robot so that each target robot performs the inbound and outbound tasks according to the received movement path.

[0032] Specifically, after determining the task allocation results, the movement path for each target robot can be generated based on the task allocation results, warehouse layout, shelf locations, and sample types and specifications. Here, warehouse layout refers to the internal space planning and layout design of the warehouse, including the arrangement of shelves, aisle width, and the division of storage areas (such as inbound, storage, and outbound areas). Shelf location refers to the specific location of each shelf in the warehouse layout. Sample types refer to the different types of samples that need to be stored and managed, such as room-temperature dairy products and chilled dairy products; specifications refer to detailed information such as the dimensions, weight, and packaging method of each sample. It should be understood that sample types and specifications are crucial for determining storage and handling methods; based on the sample types and specifications, appropriate shelf areas can be selected for storage.

[0033] Specifically, for each target robot, the system can determine its specific inbound / outbound task based on the task allocation results. Based on this task, it can then determine the samples the robot needs to transport. From this, the system can further determine the shelf locations for these samples based on their specifications and types. Subsequently, based on the warehouse layout and shelf locations, a path planning algorithm (such as...) can be used. The system uses algorithms such as Dijkstra's algorithm to calculate the optimal movement path for the target robot. Furthermore, when planning the path, the system also needs to consider avoiding conflicts and collisions between robots, as well as obstacles within the warehouse (such as other equipment). Therefore, the system can dynamically adjust the path planning based on real-time data and abnormal situations to ensure the smooth operation of inbound and outbound tasks.

[0034] Here, the movement path of each target robot refers to one or more feasible paths from the starting point (such as the robot's current position or a designated starting point) to the ending point (such as the shelf location, outbound area, etc.). These paths are determined comprehensively based on factors such as warehouse layout, shelf location, sample type and specifications, and task scheduling results, aiming to ensure that the robot can complete inbound and outbound tasks efficiently and accurately.

[0035] After planning the movement path for each target robot, the system sends it to the corresponding robot. Upon receiving the path, the target robot first analyzes it, identifying the start and end points, as well as key points along the path (such as turning points and obstacles). The target robot then uses its sensors (such as LiDAR and cameras) to perceive its surroundings in real time and adjusts its movement accordingly to ensure it follows the planned path. When the robot reaches the designated shelf location, it performs corresponding operations according to the task requirements, such as picking up or placing samples and scanning sample labels (such as barcodes, QR codes, and RFID tags). During task execution, the target robot updates its status information (such as location, battery level, and task progress) in real time and feeds this information back to the system, allowing the system to monitor the robot's working status and make necessary adjustments. After completing all assigned inbound and outbound tasks, the target robot returns to its designated location (such as the charging area or standby area) to await the next task assignment.

[0036] The method provided in this invention automatically determines the task sequence after receiving sample entry / exit instructions and generates a task scheduling strategy based on the current number of idle robots. This achieves a fully automated process from task reception to task execution, significantly reducing manual intervention and improving work efficiency. It also ensures the rationality and timeliness of task allocation, demonstrating a high level of intelligence. Furthermore, by comprehensively considering the task sequence and the number of idle robots, the system can dynamically adjust task allocation to ensure optimal resource configuration, thereby improving overall entry / exit efficiency. In addition, based on warehouse layout, shelf location, sample type and specifications, and task allocation results, the system can accurately plan the movement path for each target robot. This personalized path planning not only shortens robot travel time and reduces energy consumption but also avoids collisions and path conflicts between robots, thus ensuring the safety and smoothness of entry / exit operations.

[0037] Based on the above embodiments, step 120 specifically includes: Step 121: Based on the task sequence, the number of idle robots, and the maximum load of the mobile robot, construct a task scheduling model, which includes an objective function and constraints.

[0038] Specifically, the maximum payload of a mobile robot refers to the maximum weight that the robot can safely and stably transport or carry. A task scheduling model can be constructed based on the task sequence, the number of currently idle robots, and the maximum payload of the mobile robots. Here, the task scheduling model refers to a mathematical model used to optimize resource allocation and task execution. In the sample inbound / outbound scenario, the task scheduling model aims to determine the optimal task allocation based on factors such as the task sequence, the number of idle robots, and the maximum payload of the robots. The task scheduling model typically includes two parts: an objective function and constraints, which guide the solution process and ensure the feasibility of the solution.

[0039] The task scheduling model can be constructed through the following steps: First, a series of variables can be defined to represent elements such as tasks, robots, time, and samples; second, an objective function is established and constraints are set. The objective function is the goal of model optimization, and the constraints are used to ensure the feasibility of the solution; finally, the variables, objective function, and constraints are integrated into a complete mathematical model to obtain the task scheduling model.

[0040] Understandably, the objective function is the goal of model optimization, typically expressed as a mathematical expression that needs to be minimized or maximized. In task scheduling models, the objective function can include multiple aspects, such as minimizing the number of mobile robots, minimizing the maximum running time of mobile robots, and minimizing mobile robot conflicts. These objectives can be combined and weighted according to actual needs.

[0041] Constraints are restrictions on the feasibility of a solution. In the task scheduling model, constraints may include: ① In a batch of inbound / outbound tasks, each sample can only be transported by the mobile robot once, i.e., multiple mobile robots cannot transport the same sample; ② A mobile robot can transport multiple samples at a time, and after taking one sample, there is only one sample to take next, i.e., after taking a certain sample, the next destination of the mobile robot is definite and unique; ③ The number of samples taken by each mobile robot in one trip cannot exceed its maximum load capacity; ④ In a batch of inbound / outbound tasks, the total number of samples transported by all mobile robots is equal to the total number of tasks in the task sequence.

[0042] Step 122: Apply a multi-objective optimization algorithm to solve the task scheduling model to obtain the task scheduling strategy. The task scheduling strategy includes the number of target robots, the number of samples for each target robot, and the sampling order.

[0043] Specifically, after constructing the task scheduling model, a multi-objective optimization algorithm can be used to solve the task scheduling model, thereby obtaining the task scheduling strategy. Here, objective optimization algorithm refers to a class of algorithms used to solve optimization problems involving multiple objective functions. These algorithms typically need to make trade-offs and compromises among multiple objectives to find a solution that satisfies all objective requirements.

[0044] Specifically, based on the specific requirements of the task scheduling model and the characteristics of the algorithm, a suitable multi-objective optimization algorithm can be selected, such as genetic algorithm, particle swarm optimization, ant colony optimization, etc., and the algorithm can be run to solve the problem. During the solution process, the algorithm will search and iterate according to the objective function and constraints to find a solution that meets all requirements.

[0045] Understandably, by applying a multi-objective optimization algorithm to solve the task scheduling model, we can obtain information such as the number of target robots, the sampling quantity for each target robot, and the sampling order. This information forms the task scheduling strategy. Here, the number of target robots refers to the determined number of robots actually used to perform inbound and outbound tasks. The sampling quantity for each target robot refers to the number of samples that each selected robot needs to handle, and the sampling order refers to the order in which each robot handles the samples when performing inbound and outbound tasks.

[0046] Based on any of the above embodiments, step 122 specifically includes: Step 1221: Generate an initial task scheduling strategy set based on the task sequence, the number of idle robots, the maximum load of the mobile robot, and the constraints. Specifically, the initial task scheduling set refers to a set of possible task scheduling schemes randomly generated during the solution of the task scheduling problem, based on a given task sequence, the number of idle robots, the maximum load of the mobile robots, and constraints. These schemes represent combinations of different numbers of robots, the number of samples per robot, and the sampling order.

[0047] To generate the initial task scheduling set, the given task sequence must first be parsed to clarify the specific requirements and constraints of each task. Then, based on the number of idle robots, the maximum load of the mobile robots, and the constraints, 30 possible scheduling strategies are randomly generated. These strategies should satisfy constraints such as each task being assigned to only one robot and each robot's load not exceeding its maximum load capacity. Finally, combining all the generated scheduling strategies forms the initial task scheduling set (i.e., the initial population).

[0048] Step 1222: Based on the estimated path and the objective function, calculate the fitness function value of each scheduling strategy in the initial task scheduling strategy set, and filter each scheduling strategy in the initial task scheduling strategy set according to the fitness function value to obtain the first strategy set; It should be noted that when applying multi-objective optimization algorithms, the solution to the task scheduling problem relies on the time and path calculated by path planning, while the solution to the path planning problem relies on the scheduling scheme calculated by task scheduling. Therefore, without basic robot sampling time and potential conflicts between robots, it is difficult to schedule tasks, and without robot task scheduling results, it is difficult to find the target for path planning. Therefore, to ensure the accuracy of the task scheduling algorithm when path planning results are unavailable, this embodiment of the invention proposes a path pre-estimation strategy. This strategy uses a relatively simple heuristic algorithm to estimate feasible paths and establishes a common solution space to store feasible path solutions (i.e., estimated paths).

[0049] Specifically, the objective function can include minimizing the number of mobile robots, minimizing the maximum running time of mobile robots, and minimizing mobile robot conflicts. Minimizing the number of mobile robots can be expressed by the following formula: in, This indicates the current number of idle robots. Indicates the number is mobile robots Indicate whether to select the first option A mobile robot transports samples; 0 indicates that it is selected, and 1 indicates that it is not selected. This represents minimizing the number of mobile robots, which is the first objective in the fitness function.

[0050] The shortest maximum running time for a mobile robot can be expressed by the following formula: in, Indicates the number is mobile robots Indicates the number is Estimated sample transport time for the mobile robot, This represents the estimated time required for the robot with the longest sample transport time among all idle mobile robots to transport samples. In other words, it represents the estimated maximum running time of all mobile robots when processing a batch of inbound and outbound tasks. This represents minimizing the estimated maximum running time of the mobile robot, which is the second objective in the fitness function.

[0051] The minimum number of collisions for a mobile robot can be expressed by the following formula: in, Indicates the number is mobile robots Indicates the number is mobile robots Indicates the number is The mobile robot and the number Estimation conflicts between mobile robots. Minimize the conflicts that occur between moving individuals; this is the third objective in the fitness function.

[0052] The fitness function is based on multiple objectives and is established through weight values. Multiply by the target value, and then add the above target values ​​together to obtain the fitness function value. The formula for the fitness function is as follows: In the above formula, the weight values The total value is 1, that is , .

[0053] Understandably, to calculate the estimated running time of each mobile robot and the estimated number of conflicts between them, a path estimation strategy can be used to estimate the running path of each mobile robot. Then, based on the running speed of the mobile robots, the maximum running time of all mobile robots can be obtained, thus calculating the estimated running time of each robot. Similarly, by extracting the running paths of the two robots and recording the number of times the two robots are located at the same coordinate point at the same time as a conflict between the mobile robots, the calculation can be performed. Furthermore, based on the number of tasks in the task sequence, the number of idle robots, and the constraints, the following can be determined: Therefore, based on the above formula for calculating the fitness function, the fitness function value of each scheduling policy in the initial task scheduling policy set can be calculated.

[0054] Here, the fitness function value of each scheduling strategy is used as an indicator to measure the quality of that strategy. In task scheduling problems, by calculating the fitness function value of each scheduling strategy, we can compare the merits of different strategies, thereby guiding the subsequent selection and optimization process.

[0055] After calculating the fitness function value for each scheduling strategy, the strategies can be sorted accordingly, and a subset of strategies with higher fitness can be selected and retained to form the first strategy set. Here, the first strategy set refers to the set of scheduling strategies with higher fitness selected from the initial task scheduling strategy set. These strategies perform better on the objective function compared to other strategies in the initial task scheduling strategy set.

[0056] Step 1223: Perform cross-mutation on each scheduling policy in the initial scheduling policy set to generate a second policy set, and merge the first policy set and the second policy set to obtain a merged policy set; Specifically, crossover and mutation of the scheduling strategies in the initial task scheduling strategy set is an operation in genetic algorithms. It aims to increase population diversity by introducing new gene combinations, potentially leading to better solutions. Specifically, two scheduling strategies can be randomly selected from the initial task scheduling strategy set as parents. Some genes of the two parents (such as the number of robots sampled, the sampling order, etc.) are exchanged to generate two new offspring. These new offspring are then subjected to random mutation, such as changing the number of robots sampled or adjusting the sampling order, to increase population diversity.

[0057] The second strategy set refers to the new scheduling strategy set generated through crossover and mutation operations. These strategies differ from those in the initial task scheduling strategy set in terms of their genetic combinations, and therefore may exhibit better performance on the objective function. After generating the second strategy set, it is merged with the first strategy set to obtain the merged strategy set.

[0058] Step 1224: Based on the estimated path and the objective function, calculate the fitness function value of each scheduling strategy in the merging strategy set, and filter each scheduling strategy in the merging strategy set according to the fitness function value to obtain a new task scheduling strategy set. Step 1225: Iteratively optimize based on the new task scheduling strategy set until the iteration process ends, and obtain the task scheduling strategy.

[0059] Specifically, after obtaining the set of merging strategies, the fitness function value of each scheduling strategy in the set can be recalculated based on the estimated path and objective function. The specific calculation process can be referred to the calculation process in step 1222 above, and will not be repeated here.

[0060] Subsequently, the scheduling strategies are sorted according to their fitness function values, and strategies with relatively high fitness are selected from the merged strategy set to form a new set of task scheduling strategies (i.e., the next generation population). Steps 1222-1224 are repeated until the preset number of iterations is reached or a solution that meets the requirements is found. The final task scheduling strategy is the optimal solution.

[0061] Based on any of the above embodiments, the step of determining the predicted path includes: Based on the coordinates of any mobile robot, the shelf coordinates, and the coordinates of the inbound / outbound workstation, find the target path from the path set; If the target path is not found, an estimated path for any mobile robot is generated based on the coordinates of the mobile robot, the coordinates of the shelf, and the coordinates of the inbound / outbound workbench, and the estimated path is stored in the path set. If the target path is found, the target path is used as the estimated path.

[0062] Specifically, when there are inbound / outbound tasks to be executed, a task scheduling model can be established first based on the task sequence, the current number of idle robots, and the maximum load of the mobile robots. Then, a multi-objective optimization algorithm is used to solve the task scheduling model. During the solution process, the multi-objective optimization algorithm uses a path prediction strategy to calculate the fitness function value of each scheduling strategy by predicting the path.

[0063] For each mobile robot, to obtain its estimated path, we can first query the path set to see if the required target path exists, based on the robot's coordinates, shelf coordinates, and inbound / outbound workstation coordinates. Here, the path set is the established common solution space, which is a data structure storing multiple feasible paths. The target path refers to the optimal or shortest path from the robot's current position to the target position (such as a shelf or workstation).

[0064] When the target path is not found in the path set, a heuristic algorithm (such as...) can be used. Algorithms (such as Dijkstra's algorithm) generate a predicted path for the mobile robot without considering conflicts. The predicted path can then be fed back to a multi-objective optimization algorithm for application. Furthermore, the calculated predicted path can be stored in a path set for later reuse.

[0065] If the target path to be found exists in the path set, the path can be directly returned as the estimated path to the multi-objective algorithm so that the algorithm can use the estimated path for calculation.

[0066] Based on any of the above embodiments, in step 140, determining the movement path corresponding to each target robot based on warehouse layout, shelf location, sample type and specifications, and the task allocation result includes: Step 141: Based on the warehouse layout, shelf location, sample type and specifications, and the task allocation results, determine the global static path corresponding to any target robot; Step 142: Using the turning points on the global static path as sub-target points, the dynamic window method is applied to plan the path to obtain the movement path corresponding to any target robot.

[0067] It should be noted that in the real-world working environment of mobile robots, there are always uncertainties. To achieve more efficient operation, path planning for mobile robots requires finding the shortest path while avoiding dynamic obstacles in the environment. Static path planning algorithms cannot avoid unknown obstacles in the working environment, while dynamic path planning algorithms, although capable of obstacle avoidance, are prone to getting trapped in local optima. Therefore, to meet these two requirements, this invention combines static and dynamic path planning. Static path planning yields the globally optimal path, while dynamic path planning addresses the real-time obstacle avoidance problem.

[0068] Specifically, when performing static path planning, it's first necessary to understand the warehouse layout, including the arrangement of shelves, aisle widths, and the locations of entrances and exits. This information forms the basis for path planning. Based on the warehouse layout, an environmental representation such as a grid map or feature map can be constructed. Then, based on the task allocation results, the samples to be transported by the target robot can be determined. According to the sample specifications and types, as well as the shelf locations, the starting point (e.g., the receiving workbench) and ending point (e.g., a specified shelf location) of the target robot can be determined. Subsequently, a global path planning algorithm (such as...) is used in the constructed map. The algorithm searches for the shortest path from the starting point to the destination. Finally, after obtaining the initial global path, it can be further optimized based on the repository layout and task requirements to obtain the final global static path. It should be understood that the initial global path can also be found from the path set, thereby reducing the time required for path planning.

[0069] After planning the global static path for the target robot, the turning points on the static path can be extracted and used as sub-target points in the dynamic window method. After reaching a sub-target point, the target robot switches to the next turning point and continues driving, repeating this process until the final target point is reached. It should be understood that since the sub-target points are determined by the turning points on the global static path, and the target robot travels according to the path direction planned by the global static path, the robot's direction of travel is already the optimal path in the static environment. Therefore, the final planned movement path is optimal, and the real-time obstacle avoidance problem is also solved.

[0070] Specifically, applying the dynamic window method to plan the path for the target robot can be achieved through the following steps: All turning points on the global static path are recorded as sub-target points for dynamic path planning. Based on the coordinates of these sub-target points, the dynamic window method is used for path planning, and the robot moves towards the sub-target points. During this process, the target robot uses sensors to perceive the surrounding environment. When dynamic obstacles appear, their location information is added to the map, and the robot avoids them. After successfully avoiding dynamic obstacles, the target robot continues along the global static path. Upon reaching the current sub-target point, it is determined whether it is the destination. If not, it continues towards the next sub-target point; if it is the destination, the global dynamic path planning task ends.

[0071] Based on any of the above embodiments, the method further includes: When performing inbound / outbound operations, the samples to be inbound / outbound are identified, the sample information of the samples to be inbound / outbound is obtained, and the sample information and inbound / outbound records of the samples to be inbound / outbound are saved.

[0072] Specifically, the sample entry / exit system may include a sample identification module. During entry / exit operations, this module identifies the samples to be entered or exited, enabling automatic sample identification and tracking. The sample identification module can use Radio Frequency Identification (RFID) or barcode technology to identify the samples. Once a sample is identified, relevant sample information (such as sample number, type, batch number, etc.) is captured by the system, and entry / exit records (including entry / exit time, quantity, etc.) are generated simultaneously. This information is then stored in a database for subsequent querying and management.

[0073] Based on any of the above embodiments, the method further includes: Monitor the status of samples that have been stored and save the monitoring data; An alarm will be issued if any abnormality is detected in the condition of any sample.

[0074] Specifically, the sample entry and exit system can also include a status monitoring module and an alarm notification module. For samples already in storage, the status monitoring module can monitor environmental status data such as temperature and humidity in real time to ensure compliance with storage conditions. Here, the status monitoring module can be implemented by integrating sensors. By installing sensors at key locations in the warehouse storage area, the status of the samples can be monitored in real time.

[0075] The status monitoring module processes and analyzes the collected data to assess whether the sample's condition is within the normal range. Based on preset alarm conditions (such as temperature exceeding a set threshold or humidity being too low), the module determines whether an alarm needs to be triggered. Once an alarm condition is triggered, the alarm notification module immediately sends an alarm message, which can be sent via various methods, such as SMS, email, or telephone notification.

[0076] Based on any of the above embodiments, the method further includes: Receive user query requests; In response to the user query operation, the system obtains the sample information, entry and exit records, and monitoring data of the target sample, and displays the sample information, entry and exit records, and monitoring data of the target sample.

[0077] Specifically, the system also provides a user-friendly interface for easy operation and querying. When a user needs to query information about a sample, they can enter a query request through the system's user interface (such as a webpage, application interface, etc.). This query request can be for information about a specific sample (i.e., the target sample).

[0078] After receiving a user's query request, the system will begin processing it, retrieving sample information (such as sample name, type, batch, etc.), inbound and outbound records (such as inbound and outbound time, quantity, etc.), and monitoring data (such as monitoring results of environmental parameters such as temperature and humidity) from the database. Finally, the system will organize the retrieved information and display it to the user through the user interface, which can be in the form of tables, charts, text, etc.

[0079] The sample entry and exit system provided by the present invention is described below. The sample entry and exit system described below can be referred to in correspondence with the sample entry and exit method described above.

[0080] Based on any of the above embodiments Figure 2 This is one of the structural schematic diagrams of the sample entry and exit system provided by the present invention, such as... Figure 2 As shown, the system includes: User interface module 210 is used to receive sample entry and exit instructions; The task scheduling module 220 is used to determine the task sequence based on the sample entry and exit instructions, and to obtain the current number of idle robots. Based on the task sequence and the number of idle robots, it generates a task scheduling strategy. It is also used to determine at least one target robot from the currently idle mobile robots based on the task scheduling strategy, and to assign entry and exit tasks to each target robot to obtain the task allocation result. The path planning module 230 is used to determine the movement path corresponding to each target robot based on the warehouse layout, shelf location, sample type and specifications, and the task allocation result, and send the movement path to each target robot so that each target robot can perform the inbound and outbound tasks according to the received movement path.

[0081] The system provided in this invention automatically determines the task sequence after receiving sample entry / exit instructions and generates a task scheduling strategy based on the current number of idle robots. This achieves a fully automated process from task reception to task execution, significantly reducing manual intervention and improving work efficiency. It also ensures the rationality and timeliness of task allocation, demonstrating a high level of intelligence. Furthermore, by comprehensively considering the task sequence and the number of idle robots, the system can dynamically adjust task allocation to ensure optimal resource configuration, thereby improving overall entry / exit efficiency. In addition, based on warehouse layout, shelf location, sample type and specifications, and task allocation results, it can accurately plan the movement path for each target robot. This personalized path planning not only shortens robot travel time and reduces energy consumption but also avoids collisions and path conflicts between robots, thus ensuring the safety and smoothness of entry / exit operations.

[0082] Based on any of the above embodiments Figure 3 This is the second schematic diagram of the sample entry and exit system provided by the present invention, as shown below. Figure 3 As shown, the system also includes: The sample identification module 240 is used to identify samples to be entered or exited from the warehouse based on radio frequency identification technology or barcode technology, and obtain sample information of the samples to be entered or exited from the warehouse. The status monitoring module 250 is used to monitor the status of samples that have been put into storage; The alarm notification module 260 is used to issue an alarm when an abnormal state of the sample is detected. Database module 270 is used to store sample information, entry and exit records and monitoring data of samples; The user interface module 210 is also used to receive user query operations and display sample information, entry and exit records and monitoring data of the target sample.

[0083] Based on any of the above embodiments, the task scheduling module 220 includes: The model building unit is used to build a task scheduling model based on the task sequence, the number of idle robots, and the maximum load of the mobile robot. The task scheduling model includes an objective function and constraints. The strategy determination unit is used to apply a multi-objective optimization algorithm to solve the task scheduling model and obtain the task scheduling strategy, which includes the number of target robots, the number of samples for each target robot, and the sampling order.

[0084] Based on any of the above embodiments, the strategy determination unit is specifically used for: Based on the task sequence, the number of idle robots, the maximum load of the mobile robot, and the constraints, an initial task scheduling strategy set is generated. Based on the estimated path and the objective function, the fitness function value of each scheduling strategy in the initial task scheduling strategy set is calculated, and the scheduling strategies in the initial task scheduling strategy set are filtered according to the fitness function value to obtain the first strategy set. The scheduling policies in the initial scheduling policy set are cross-mutated to generate a second policy set, and the first policy set and the second policy set are merged to obtain a merged policy set; Based on the estimated path and the objective function, the fitness function value of each scheduling strategy in the merging strategy set is calculated, and the scheduling strategies in the merging strategy set are filtered according to the fitness function value to obtain a new task scheduling strategy set. The task scheduling strategy is iteratively optimized based on the new set of task scheduling strategies until the iteration process ends, thus obtaining the task scheduling strategy.

[0085] Based on any of the above embodiments, the task scheduling module 220 further includes a path estimation unit, which is used for: Based on the coordinates of any mobile robot, the shelf coordinates, and the coordinates of the inbound / outbound workstation, find the target path from the path set; If the target path is not found, an estimated path for any mobile robot is generated based on the coordinates of the mobile robot, the coordinates of the shelf, and the coordinates of the inbound / outbound workbench, and the estimated path is stored in the path set. If the target path is found, the target path is used as the estimated path.

[0086] Based on any of the above embodiments, the path planning module 230 is specifically used for: Based on the warehouse layout, shelf location, sample type and specifications, and the task allocation results, determine the global static path corresponding to any target robot; By taking the turning points on the global static path as sub-target points, and applying the dynamic window method to plan the path, the movement path corresponding to any target robot is obtained.

[0087] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a sample entry / exit method. This method includes: upon receiving a sample entry / exit instruction, determining a task sequence based on the instruction and obtaining the current number of idle robots; generating a task scheduling strategy based on the task sequence and the number of idle robots; determining at least one target robot from the currently idle mobile robots based on the task scheduling strategy, and assigning entry / exit tasks to each target robot to obtain a task allocation result; determining the movement path corresponding to each target robot based on the warehouse layout, shelf location, sample type and specifications, and the task allocation result, and sending the movement path to each target robot so that each target robot executes the entry / exit task according to the received movement path.

[0088] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to related technologies, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0089] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the sample entry / exit method provided by the above methods. The method includes: upon receiving a sample entry / exit instruction, determining a task sequence based on the sample entry / exit instruction and obtaining the current number of idle robots; generating a task scheduling strategy based on the task sequence and the number of idle robots; determining at least one target robot from the currently idle mobile robots based on the task scheduling strategy, and assigning entry / exit tasks to each target robot to obtain a task allocation result; determining the movement path corresponding to each target robot based on the warehouse layout, shelf location, sample type and specifications, and the task allocation result, and sending the movement path to each target robot so that each target robot performs the entry / exit task according to the received movement path.

[0090] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the sample entry / exit method provided by the above methods. The method includes: upon receiving a sample entry / exit instruction, determining a task sequence based on the sample entry / exit instruction and obtaining the current number of idle robots; generating a task scheduling strategy based on the task sequence and the number of idle robots; determining at least one target robot from the currently idle mobile robots based on the task scheduling strategy, and assigning entry / exit tasks to each target robot to obtain a task allocation result; determining the movement path corresponding to each target robot based on the warehouse layout, shelf location, sample type and specifications, and the task allocation result, and sending the movement path to each target robot so that each target robot performs the entry / exit task according to the received movement path.

[0091] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0092] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for sample entry and exit from a warehouse, characterized in that, include: Upon receiving a sample entry / exit instruction, the task sequence is determined based on the instruction, and the current number of idle robots is obtained. Based on the task sequence and the number of idle robots, a task scheduling strategy is generated; Based on the task scheduling strategy, at least one target robot is identified from the currently idle mobile robots, and inbound / outbound tasks are assigned to each target robot to obtain the task allocation result. Based on the warehouse layout, shelf location, sample type and specifications, and the task allocation results, the movement path corresponding to each target robot is determined, and the movement path is sent to each target robot so that each target robot can perform the inbound and outbound tasks according to the received movement path.

2. The sample entry and exit method according to claim 1, characterized in that, The step of generating a task scheduling strategy based on the task sequence and the number of idle robots includes: A task scheduling model is constructed based on the task sequence, the number of idle robots, and the maximum load of the mobile robot. A multi-objective optimization algorithm is applied to solve the task scheduling model to obtain the task scheduling strategy, which includes the number of target robots, the number of samples for each target robot, and the sampling order.

3. The sample entry and exit method according to claim 2, characterized in that, The task scheduling model includes an objective function and constraints. A multi-objective optimization algorithm is applied to solve the task scheduling model to obtain the task scheduling strategy, including: Based on the task sequence, the number of idle robots, the maximum load of the mobile robot, and the constraints, an initial task scheduling strategy set is generated. Based on the estimated path and the objective function, the fitness function value of each scheduling strategy in the initial task scheduling strategy set is calculated, and the scheduling strategies in the initial task scheduling strategy set are filtered according to the fitness function value to obtain the first strategy set. The scheduling policies in the initial scheduling policy set are cross-mutated to generate a second policy set, and the first policy set and the second policy set are merged to obtain a merged policy set; Based on the estimated path and the objective function, the fitness function value of each scheduling strategy in the merging strategy set is calculated, and the scheduling strategies in the merging strategy set are filtered according to the fitness function value to obtain a new task scheduling strategy set. The task scheduling strategy is iteratively optimized based on the new set of task scheduling strategies until the iteration process ends, thus obtaining the task scheduling strategy.

4. The sample entry and exit method according to claim 3, characterized in that, The steps for determining the estimated path include: Based on the coordinates of any mobile robot, the shelf coordinates, and the coordinates of the inbound / outbound workstation, find the target path from the path set; If the target path is not found, an estimated path for any mobile robot is generated based on the coordinates of the mobile robot, the coordinates of the shelf, and the coordinates of the inbound / outbound workbench, and the estimated path is stored in the path set. If the target path is found, the target path is used as the estimated path.

5. The sample entry and exit method according to claim 1, characterized in that, The process of determining the movement path for each target robot based on warehouse layout, shelf location, sample type and specifications, and task allocation results includes: Based on the warehouse layout, shelf location, sample type and specifications, and the task allocation results, determine the global static path corresponding to any target robot; By taking the turning points on the global static path as sub-target points, and applying the dynamic window method to plan the path, the movement path corresponding to any target robot is obtained.

6. The sample entry and exit method according to any one of claims 1 to 5, characterized in that, Also includes: When performing inbound / outbound operations, the samples to be inbound / outbound are identified, the sample information of the samples to be inbound / outbound is obtained, and the sample information and inbound / outbound records of the samples to be inbound / outbound are saved.

7. The sample entry and exit method according to any one of claims 1 to 5, characterized in that, Also includes: Monitor the status of samples that have been stored and save the monitoring data; An alarm will be issued if any abnormality is detected in the condition of any sample.

8. The sample entry and exit method according to any one of claims 1 to 5, characterized in that, Also includes: Receive user query requests; In response to the user query operation, the system obtains the sample information, entry and exit records, and monitoring data of the target sample, and displays the sample information, entry and exit records, and monitoring data of the target sample.

9. A sample entry and exit system, characterized in that, include: User interface module, used to receive sample entry and exit instructions; The task scheduling module is used to determine the task sequence based on the sample entry and exit instructions, and to obtain the current number of idle robots. Based on the task sequence and the number of idle robots, it generates a task scheduling strategy. It is also used to determine at least one target robot from the currently idle mobile robots based on the task scheduling strategy, and to assign entry and exit tasks to each target robot to obtain the task allocation result. The path planning module is used to determine the movement path corresponding to each target robot based on the warehouse layout, shelf location, sample type and specifications, and the task allocation result, and send the movement path to each target robot so that each target robot can perform the inbound and outbound tasks according to the received movement path.

10. The sample entry and exit system according to claim 9, characterized in that, Also includes: The sample identification module is used to identify samples to be entered or exited from the warehouse based on radio frequency identification technology or barcode technology, and obtain sample information of the samples to be entered or exited from the warehouse. The status monitoring module is used to monitor the status of samples that have been put into storage; The alarm notification module is used to issue an alarm when an abnormal state of the sample is detected. The database module is used to store sample information, entry and exit records, and monitoring data. The user interface module is also used to receive user queries and display sample information, entry and exit records, and monitoring data of the target sample.

Images