A logistics scheduling method, system and device in a hospital environment

Abstract

The present application belongs to the technical field of hospital logistics scheduling, and particularly relates to a logistics scheduling method, system and device in a hospital environment, comprising: S1, calculating a comprehensive priority based on an emergency degree factor, a time-sensitive factor, a current waiting time and a maximum tolerance time of a task, and a time occupation factor of a robot; S2, assigning the task with the highest comprehensive priority to the robot with the smallest time occupation factor; S3, monitoring the running state of the robot in real time, triggering path re-planning when detecting that the conflict probability of the front node exceeds a threshold value, searching for multiple possible paths from the current position to the end point, and generating a candidate path set in order of the comprehensive evaluation score; and S4, selecting a path with an estimated completion time less than or equal to a dynamic time threshold value, and issuing an execution. The present application realizes dynamic optimization of task allocation and real-time re-planning of paths, effectively reduces path conflicts, and improves logistics distribution efficiency.

Description

Technical Field

[0001] This invention belongs to the field of hospital logistics scheduling technology, specifically relating to a logistics scheduling method, system, and device in a hospital environment. Background Technology

[0002] Currently, hospital internal logistics and distribution mainly rely on manual transportation or simple automated transmission. With the continuous growth of hospital business volume, especially the extremely high time requirements for emergency laboratory samples, traditional logistics methods can no longer meet the operational needs of modern hospitals.

[0003] Existing hospital logistics scheduling systems typically employ a static priority allocation strategy, uniformly setting all emergency tasks to the highest priority. This one-size-fits-all approach fails to differentiate the urgency levels within emergency tasks. Test samples from patients with myocardial infarction or stroke differ significantly from those from ordinary emergency patients in terms of timeliness requirements, yet they are often treated the same in existing systems.

[0004] Furthermore, existing systems only consider the attributes of the task itself when assigning tasks, failing to make comprehensive judgments based on the robot's real-time status. Each task is pre-planned with a fixed route, and the robot strictly follows this route during execution, unable to perceive real-time traffic conditions. When multiple robots meet at track intersections, the lack of a dynamic avoidance mechanism often leads to prolonged congestion or even deadlocks. When track malfunctions, is temporarily closed, or a new high-priority task needs to compete for resources, existing systems lack the ability to adjust paths in real time, relying solely on manual intervention. Summary of the Invention

[0005] The purpose of this invention is to provide a logistics scheduling method, system, and apparatus in a hospital environment.

[0006] A logistics scheduling method in a hospital environment includes the following steps: S1. Obtain the urgency factor, time sensitivity factor, current waiting time and maximum tolerance time of the delivery tasks to be assigned, and perform a weighted summation to obtain the comprehensive priority of each delivery task to be assigned. Meanwhile, based on the time required for each transport robot to complete the task of delivering the assigned sample, and the time required to move to the waiting area after completing the task of delivering the assigned sample, the time occupancy factor of each transport robot is calculated. S2. Sort all unassigned sample delivery tasks in the sample delivery task queue according to the comprehensive priority value from largest to smallest, and assign the unassigned sample delivery task with the largest comprehensive priority value to the transport robot with the smallest time occupation factor and sufficient power to complete the current unassigned sample delivery task. If multiple transport robots with sufficient power have the same time occupation factor, assign the unassigned sample delivery task to the transport robot with sufficient power closest to the task start point. After assignment, update the sample delivery task queue and repeat the process of assigning unassigned sample delivery tasks in the sample delivery task queue. Real-time monitoring of the transport robot performing the sample delivery task; calculation of the probability of conflict at the next node; if the probability of conflict is greater than the probability threshold, execute S3. S3. Starting from the current position of the transport robot, search for all possible paths between the starting point and the end point of the task. For each node on each possible path, calculate the evaluation score of each node based on the path length, current waiting time, current average speed of the transport robot and the probability of conflict. Sum the evaluation scores of all nodes on the same possible path to obtain a comprehensive evaluation score. The possible path with the lowest comprehensive evaluation score is taken as the new path. S4. If the estimated completion time of the new path is less than or equal to the dynamic time threshold, the new path will be sent to the corresponding transport robot.

[0007] The evaluation score is calculated as follows: f(n) = g(n) + h(n), Where f(n) is the evaluation score, g(n) is the first score, and h(n) is the second score; , Where L is the path length. The current average speed of the transport robot is T, and the current waiting time is T. , in, For the probability of conflict, The average number of conflicts per 5 minutes on this path in history This is the threshold for the maximum number of collisions along this path.

[0008] The specific method for calculating the overall priority is as follows: , in, For overall priority, P is the urgency factor, and S is the timeliness sensitivity factor. , , These are weights based on urgency, time sensitivity, and basic aging. T is the timeout coefficient, where T is the current waiting time. deadline This is the maximum tolerance time for this task type.

[0009] The probability of conflict is calculated as follows: , in, For the probability of conflict, Δt is the test constant, and Δt is the time difference between the current transport robot and the nearest transport robot arriving at the next node.

[0010] The time occupancy factor is calculated as follows: , in, Time occupancy factor The time required for the transport robot to complete the task of delivering the samples to be assigned. The time required for the transport robot to move to the starting point / waiting area for the next task after completing its current task.

[0011] The estimated completion time is calculated as follows: path length divided by the average speed of the transport robot.

[0012] The dynamic time threshold is the sum of the time required to continue executing the task along the original path and the average time for resolving historical conflicts.

[0013] If the estimated completion time of the new path is greater than the dynamic time threshold, the second lowest possible path in the comprehensive evaluation is selected as the new path. If the estimated completion time of the new path is less than or equal to the dynamic time threshold, it is sent to the corresponding transport robot. Otherwise, the possible path with the estimated completion time less than or equal to the dynamic time threshold is selected as the new path. If none of the possible paths meet the condition that the estimated completion time is less than or equal to the dynamic time threshold, then the task will continue to be executed along the original path.

[0014] A logistics scheduling system for a hospital environment, used to implement a logistics scheduling method in a hospital environment, includes: The information acquisition module obtains the urgency factor, time sensitivity factor, current waiting time and maximum tolerance time of the delivery tasks to be assigned, and performs a weighted summation to obtain the comprehensive priority of each delivery task to be assigned. Meanwhile, based on the time required for each transport robot to complete the task of delivering the assigned sample, and the time required to move to the waiting area after completing the task of delivering the assigned sample, the time occupancy factor of each transport robot is calculated. The task allocation module sorts all unassigned sample delivery tasks in the sample delivery task queue according to their comprehensive priority values ​​from largest to smallest. The unassigned sample delivery task with the largest comprehensive priority value is assigned to the transport robot with the smallest time occupation factor and sufficient power to complete the current unassigned sample delivery task. If multiple transport robots with sufficient power have the same time occupation factor, the unassigned sample delivery task is assigned to the transport robot with sufficient power closest to the task start point. After allocation, the sample delivery task queue is updated, and the process of allocating unassigned sample delivery tasks in the sample delivery task queue is repeated. Real-time monitoring of the transport robot performing sample delivery tasks, and calculation of the probability of conflict at the next node; The path replanning module starts from the current position of the transport robot and searches for all possible paths between the starting point and the task endpoint. For each node on each possible path, it calculates the evaluation score of each node based on the path length, current waiting time, current average speed of the transport robot and conflict probability. The evaluation scores of all nodes on the same possible path are summed to obtain a comprehensive evaluation score. The possible path with the lowest comprehensive evaluation score is taken as the new path. If the estimated completion time of the new path is less than or equal to the dynamic time threshold, the instruction sending module will send the new path to the corresponding transport robot.

[0015] A logistics scheduling device for a hospital environment includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement a logistics scheduling method for a hospital environment.

[0016] The purpose of this invention is to provide a logistics scheduling method in a hospital environment, which has the following advantages compared to the prior art: This application proposes a comprehensive priority calculation method that takes into account factors such as the urgency of the task, time sensitivity, current waiting time, and maximum tolerance time. Different priority values ​​can be assigned to patients with myocardial infarction or stroke compared to ordinary emergency patients, ensuring that the most urgent tasks receive the highest priority treatment.

[0017] This application introduces a time occupancy factor, which comprehensively reflects the time required to complete the assigned sample delivery task and the time required to move to the waiting area after completing the task. Prioritizing the allocation of high-priority tasks to the transport robot with the lowest time occupancy factor ensures high efficiency in terms of time and achieves optimal matching of tasks and robot resources.

[0018] This application designs a path replanning mechanism. During the replanning process, the evaluation score is used as the standard, comprehensively considering time factors and the possibility of conflict, to guide the transport robot to actively avoid potential congestion areas. Using the logistics scheduling method of this application, the time spent on anomaly handling is shortened, the efficiency of anomaly handling is improved, and the stability and reliability of logistics scheduling in a hospital environment are fundamentally improved. Detailed Implementation

[0019] Example 1 To further understand the content of this invention, the invention will be described in detail with reference to the embodiments.

[0020] This invention relates to a logistics scheduling method in a hospital environment, comprising the following steps: S1. Obtain the urgency factor, time sensitivity factor, current waiting time and maximum tolerance time of the delivery tasks to be assigned, and perform a weighted summation to obtain the comprehensive priority of each delivery task to be assigned.

[0021] Meanwhile, based on the time required for each transport robot to complete the task of delivering the assigned samples, and the time required for it to move to the waiting area after completing the task of delivering the assigned samples, the time occupancy factor of each transport robot is calculated.

[0022] Specifically, the urgency factor, time sensitivity factor, current waiting time, and maximum tolerance time of the delivery tasks to be assigned are obtained. Based on these factors, a weighted sum is calculated, and the overall priority is determined as follows: , in, For overall priority, P is the urgency factor, and S is the timeliness sensitivity factor. , , These are weights based on urgency, time sensitivity, and basic aging. T is the timeout coefficient, where T is the current waiting time. deadline This is the maximum tolerance time for this task type.

[0023] Furthermore, it is recommended that the urgency factor be set as follows: myocardial infarction, stroke, massive hemorrhage P=1, critical value test P=0.8, routine emergency P=0.5, routine test P=0.3.

[0024] Based on the HL7 standard fields, the time sensitivity factor is determined, as shown in Table 1: Table 1. Time Sensitivity Factor Acquisition Table , , The recommended values ​​are 0.4, 0.45, and 0.15 respectively. The urgency weight serves as a safety net, ensuring that highly urgent tasks, such as cardiac arrest resuscitation, receive high priority even immediately after being assigned. The timeliness sensitivity weight has dynamic control; as a measure of time, its actual contribution rapidly surpasses that of other weights as waiting time increases. The base aging weight becomes the key factor in determining the ranking. It's a starvation insurance that provides stable growth for tasks with low sensitivity but long waiting times, preventing them from being endlessly cut in line.

[0025] As the current waiting time increases, the overall priority increases. In other words, the longer the waiting time, the higher the probability of the sample being allocated, ensuring that patient samples can be sent for testing in a timely manner.

[0026] If this is the maximum tolerable time for this task type, according to medical guidelines and clinical practice, it represents the maximum acceptable delay from prescription to delivery of a sample to the laboratory. Exceeding this time may invalidate the test results or delay the patient's treatment window. Looking at T alone is meaningless; a 10-minute wait is a serious delay for blood gas analysis but negligible for routine tests. Therefore, only by combining the current waiting time with the maximum tolerable time for this task type can a relative waiting time ratio, T / T, be determined. deadline Only then can the timeliness and urgency of a task be accurately measured.

[0027] Simultaneously, calculate the time occupancy factor for each transport robot: , in, Time occupancy factor The time required for the transport robot to complete the task of delivering the samples to be assigned. The time required for the transport robot to move to the starting point / waiting area for the next task after completing its current task.

[0028] Furthermore, if the transport robot has already received its next task, then The time required to move to the starting point of the next task, if the transport robot has no next task. The time required for the transport robot to return to the waiting area.

[0029] This application proposes a comprehensive priority calculation method that takes into account factors such as the urgency of the task, time sensitivity, current waiting time, and maximum tolerance time. Different priority values ​​can be assigned to patients with myocardial infarction or stroke compared to ordinary emergency patients, ensuring that the most urgent tasks receive the highest priority treatment.

[0030] This application introduces a time occupancy factor, which comprehensively reflects the time required to complete the assigned sample delivery task and the time required to move to the waiting area after completing the task. Prioritizing the allocation of high-priority tasks to the transport robot with the lowest time occupancy factor ensures high efficiency in terms of time and achieves optimal matching of tasks and robot resources.

[0031] S2. Sort all unassigned sample delivery tasks in the sample delivery task queue according to the comprehensive priority value from largest to smallest, and assign the unassigned sample delivery task with the largest comprehensive priority value to the transport robot with the smallest time occupation factor and sufficient power to complete the current unassigned sample delivery task. If multiple transport robots with sufficient power have the same time occupation factor, assign the unassigned sample delivery task to the transport robot with sufficient power closest to the task start point. After allocation, update the sample delivery task queue and repeat the process of allocating unassigned sample delivery tasks in the sample delivery task queue.

[0032] The transport robot performing the sample delivery task is monitored in real time, and the probability of conflict at the next node is calculated. If the probability of conflict is greater than the probability threshold, S3 is executed.

[0033] Specifically, the sample delivery task queue is sorted according to comprehensive priority and assigned to the transport robot with the smallest time occupancy factor; when multiple transport robots take the same amount of time, the task is assigned to the robot closest to the task start point. The transport robot accepts the task on the premise of having enough power to complete the task and aims to complete the task as quickly as possible to reduce the patient's waiting time.

[0034] After allocation, update the sample delivery task queue, remove the allocated tasks from the sample delivery task queue, check if there are any new tasks, and if so, put them into the sample delivery task queue according to the comprehensive priority value for sorting. Then repeat the process of allocating sample delivery tasks to be allocated in the sample delivery task queue and assign the tasks to the transport robot.

[0035] Real-time monitoring of the transport robot performing the sample delivery task to calculate the probability of conflict at the next node: , in, For the probability of conflict, Δt is the test constant, and Δt is the time difference between the current transport robot and the nearest transport robot arriving at the next node.

[0036] The preset probability threshold was set to 0.6 in the experiment. When the preset trajectory of transport robot B intersects with that of transport robot A, meaning the time difference between their arrival at the same node is small, the conflict probability P of that node is calculated. n=0.75, which is greater than the preset probability threshold of 0.6, thus meeting the replanning trigger condition, and S3 is executed.

[0037] S3. Starting from the current position of the transport robot, search for all possible paths between the starting point and the task endpoint. For each node on each possible path, calculate the evaluation score of each node based on the path length, current waiting time, current average speed of the transport robot, and conflict probability. Sum the evaluation scores of all nodes on the same possible path to obtain a comprehensive evaluation score. Select the possible path with the lowest comprehensive evaluation score as the new path.

[0038] Specifically, the evaluation score is calculated as follows: f(n) = g(n) + h(n), Where f(n) is the evaluation score, g(n) is the first score, and h(n) is the second score; , Where L is the path length. The current average speed of the transport robot is T, and the current waiting time is T. , in, For the probability of conflict, The average number of conflicts per 5 minutes on this path in history This is the threshold for the maximum number of collisions along this path.

[0039] The first score takes into account the waiting time cost that may result from avoidance, while the second score is significantly increased due to the high probability of conflict, guiding the selection of detour routes with low conflict.

[0040] The second score includes the congestion cost of the area where the adjacent node is located over a historical preset time period, which is one of the innovations of this application: It represents the real-time risk cost, indicating whether congestion will occur in the future. It is the historical congestion cost, which represents whether a place is usually congested. Combining the two is both forward-looking and experience-based, which is the essential difference from traditional route planning.

[0041] The evaluation scores of all nodes on the same possible path are summed to obtain a comprehensive evaluation score. The possible path with the lowest comprehensive evaluation score is then selected as the new path.

[0042] This application designs a path replanning mechanism. During the replanning process, the evaluation score is used as the standard, comprehensively considering time factors and the possibility of conflict, to guide the transport robot to actively avoid potential congestion areas. Using the logistics scheduling method of this application, the time spent on anomaly handling is shortened, the efficiency of anomaly handling is improved, and the stability and reliability of logistics scheduling in a hospital environment are fundamentally improved.

[0043] S4. If the estimated completion time of the new path is less than or equal to the dynamic time threshold, the new path will be sent to the corresponding transport robot.

[0044] The estimated completion time is calculated as follows: path length divided by the average speed of the transport robot. The dynamic time threshold is calculated as follows: if the task continues to be executed along the original path, the time required is summed with the average time of historical conflict resolution applications to obtain the first time. The first time is then added to the extended time threshold to obtain the dynamic time threshold.

[0045] The original path estimated completion time is the estimated time required for the transport robot to travel from its current location to the task endpoint, assuming it continues along the currently executing path. It is calculated based on the remaining path length and the robot's average speed. The historical conflict resolution average is the average waiting time caused by path conflicts in the area where the transport robot is located over a period of time, reflecting the congestion level of the area. The extension time threshold is a preset acceptable extension time, such as 10 seconds or 30 seconds, indicating that the new path is allowed to be slower than the original path by a certain amount of time in exchange for a more stable driving experience.

[0046] Furthermore, if the estimated completion time of the new path is greater than the dynamic time threshold, the second lowest possible path in the comprehensive evaluation is selected as the new path. If the estimated completion time of the new path is less than or equal to the dynamic time threshold, it is sent to the corresponding transport robot. Otherwise, the possible path with an estimated completion time less than or equal to the dynamic time threshold is selected as the new path. If none of the possible paths meet the condition that the estimated completion time is less than or equal to the dynamic time threshold, then the task will continue to be executed along the original path.

[0047] By trying each possible path sequentially, rather than simply choosing the optimal path and abandoning it, we increase the probability of finding a feasible new path and provide a fallback option with a suboptimal path. Even if the optimal path does not meet the time requirement, there are still suboptimal and even suboptimal paths to choose from, ensuring that no path is left unfinished. When all possible paths fail to meet the conditions, we maintain the original path, avoiding a logical vacuum where there is no way to proceed and providing a clear solution.

[0048] The dynamic time threshold is composed of the original path time + historical conflict resolution time + extended time threshold, which takes into account the current road conditions, incorporates historical experience, and reserves flexibility.

[0049] A tiered decision was made between the overall evaluation score and the estimated completion time. First, the routes were ranked, and then filtered using time conditions to ensure that the selected routes were both easy to travel and fast to reach.

[0050] The logic of this application is as follows: if a path is both fast and good, it can be directly adopted as the best choice; if the optimal path is not fast enough, try the second-best path to avoid focusing solely on scores and to take into account timeliness; if all paths are much slower than the original path, then maintain the original path, continue monitoring and wait for the conflict to be resolved, and avoid blindly detouring, which could lead to worse results.

[0051] A logistics scheduling system for a hospital environment, used to implement a logistics scheduling method in a hospital environment, includes: The information acquisition module obtains the urgency factor, time sensitivity factor, current waiting time and maximum tolerance time of the delivery tasks to be assigned, and performs a weighted summation to obtain the comprehensive priority of each delivery task to be assigned. Meanwhile, based on the time required for each transport robot to complete the task of delivering the assigned sample, and the time required to move to the waiting area after completing the task of delivering the assigned sample, the time occupancy factor of each transport robot is calculated. The task allocation module sorts all unassigned sample delivery tasks in the sample delivery task queue according to their comprehensive priority values ​​from largest to smallest. The unassigned sample delivery task with the largest comprehensive priority value is assigned to the transport robot with the smallest time occupation factor and sufficient power to complete the current unassigned sample delivery task. If multiple transport robots with sufficient power have the same time occupation factor, the unassigned sample delivery task is assigned to the transport robot with sufficient power closest to the task start point. After allocation, the sample delivery task queue is updated, and the process of allocating unassigned sample delivery tasks in the sample delivery task queue is repeated. Real-time monitoring of the transport robot performing sample delivery tasks, and calculation of the probability of conflict at the next node; The path replanning module starts from the current position of the transport robot and searches for all possible paths between the starting point and the task endpoint. For each node on each possible path, it calculates the evaluation score of each node based on the path length, current waiting time, current average speed of the transport robot and conflict probability. The evaluation scores of all nodes on the same possible path are summed to obtain a comprehensive evaluation score. The possible path with the lowest comprehensive evaluation score is taken as the new path. If the estimated completion time of the new path is less than or equal to the dynamic time threshold, the instruction sending module will send the new path to the corresponding transport robot.

[0052] A logistics scheduling device for a hospital environment includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement a logistics scheduling method for a hospital environment.

Claims

1. A logistics scheduling method in a hospital environment, characterized in that, Includes the following steps: S1. Obtain the urgency factor, time sensitivity factor, current waiting time and maximum tolerance time of the delivery tasks to be assigned, and perform a weighted summation to obtain the comprehensive priority of each delivery task to be assigned. Meanwhile, based on the time required for each transport robot to complete the task of delivering the assigned sample, and the time required to move to the waiting area after completing the task of delivering the assigned sample, the time occupancy factor of each transport robot is calculated. S2. Sort all unassigned sample delivery tasks in the sample delivery task queue according to the comprehensive priority value from largest to smallest, and assign the unassigned sample delivery task with the largest comprehensive priority value to the transport robot with the smallest time occupation factor and sufficient power to complete the current unassigned sample delivery task. If multiple transport robots with sufficient power have the same time occupation factor, assign the unassigned sample delivery task to the transport robot with sufficient power closest to the task start point. After assignment, update the sample delivery task queue and repeat the process of assigning unassigned sample delivery tasks in the sample delivery task queue. Real-time monitoring of the transport robot performing the sample delivery task; calculation of the probability of conflict at the next node; if the probability of conflict is greater than the probability threshold, execute S3. S3. Starting from the current position of the transport robot, search for all possible paths between the starting point and the end point of the task. For each node on each possible path, calculate the evaluation score of each node based on the path length, current waiting time, current average speed of the transport robot and the probability of conflict. Sum the evaluation scores of all nodes on the same possible path to obtain a comprehensive evaluation score. The possible path with the lowest comprehensive evaluation score is taken as the new path. S4. If the estimated completion time of the new path is less than or equal to the dynamic time threshold, the new path will be sent to the corresponding transport robot.

2. The logistics scheduling method in a hospital environment according to claim 1, characterized in that, The evaluation score is calculated as follows: f(n) = g(n) + h(n), Where f(n) is the evaluation score, g(n) is the first score, and h(n) is the second score; ， Where L is the path length. The current average speed of the transport robot is T, and the current waiting time is T. ， in, Let's say it's the probability of conflict. The average number of conflicts per 5 minutes on this path in history This is the threshold for the maximum number of collisions along this path.

3. The logistics scheduling method in a hospital environment according to claim 1, characterized in that, The specific method for calculating the overall priority is as follows: , in, For overall priority, P is the urgency factor, and S is the timeliness sensitivity factor. , , These are weights based on urgency, time sensitivity, and basic aging. T is the timeout coefficient, where T is the current waiting time. deadline This is the maximum tolerance time for this task type.

4. The logistics scheduling method in a hospital environment according to claim 1, characterized in that, The probability of conflict is calculated as follows: ， in, Let's say it's the probability of conflict. Δt is the test constant, and Δt is the time difference between the current transport robot and the nearest transport robot arriving at the next node.

5. The logistics scheduling method in a hospital environment according to claim 1, characterized in that, The time occupancy factor is calculated as follows: ， in, Time occupancy factor The time required for the transport robot to complete the task of delivering the samples to be assigned. The time required for the transport robot to move to the starting point / waiting area for the next task after completing its current task.

6. The logistics scheduling method in a hospital environment according to claim 1, characterized in that, The estimated completion time is calculated as follows: path length divided by the average speed of the transport robot.

7. The logistics scheduling method in a hospital environment according to claim 1, characterized in that, The dynamic time threshold is the sum of the time required to continue executing the task along the original path and the average time for resolving historical conflicts.

8. The logistics scheduling method in a hospital environment according to claim 1, characterized in that, If the estimated completion time of the new path is greater than the dynamic time threshold, the second lowest possible path in the comprehensive evaluation is selected as the new path. If the estimated completion time of the new path is less than or equal to the dynamic time threshold, it is sent to the corresponding transport robot. Otherwise, the possible path with the estimated completion time less than or equal to the dynamic time threshold is selected as the new path. If none of the possible paths meet the condition that the estimated completion time is less than or equal to the dynamic time threshold, then the task will continue to be executed along the original path.

9. A logistics scheduling system in a hospital environment, used to implement the logistics scheduling method in a hospital environment as described in any one of claims 1-8, characterized in that, include: The information acquisition module obtains the urgency factor, time sensitivity factor, current waiting time and maximum tolerance time of the delivery tasks to be assigned, and performs a weighted summation to obtain the comprehensive priority of each delivery task to be assigned. Meanwhile, based on the time required for each transport robot to complete the task of delivering the assigned sample, and the time required to move to the waiting area after completing the task of delivering the assigned sample, the time occupancy factor of each transport robot is calculated. The task allocation module sorts all unassigned sample delivery tasks in the sample delivery task queue according to their comprehensive priority values ​​from largest to smallest. The unassigned sample delivery task with the largest comprehensive priority value is assigned to the transport robot with the smallest time occupation factor and sufficient power to complete the current unassigned sample delivery task. If multiple transport robots with sufficient power have the same time occupation factor, the unassigned sample delivery task is assigned to the transport robot with sufficient power closest to the task start point. After allocation, the sample delivery task queue is updated, and the process of allocating unassigned sample delivery tasks in the sample delivery task queue is repeated. Real-time monitoring of the transport robot performing sample delivery tasks, and calculation of the probability of conflict at the next node; The path replanning module starts from the current position of the transport robot and searches for all possible paths between the starting point and the task endpoint. For each node on each possible path, it calculates the evaluation score of each node based on the path length, current waiting time, current average speed of the transport robot and conflict probability. The evaluation scores of all nodes on the same possible path are summed to obtain a comprehensive evaluation score. The possible path with the lowest comprehensive evaluation score is taken as the new path. If the estimated completion time of the new path is less than or equal to the dynamic time threshold, the instruction sending module will send the new path to the corresponding transport robot.

10. A logistics scheduling device in a hospital environment, characterized in that, It includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement a logistics scheduling method in a hospital environment as described in any one of claims 1-8.

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