A multi-dimensional robot logistics transportation scheduling system and method for a hospital and a storage medium thereof

Abstract

The application discloses a kind of hospital logistics robot multidimensional transport scheduling system, method and its storage medium, it is related to hospital logistics technical field, by constructing the mapping relationship of medical waste type and biological safety grade and the hierarchical risk system of hospital area, biological safety risk is converted into dynamic correction factor, and is fused with traditional path length weight to form comprehensive path weight, so that robot can actively avoid high-risk exposure area when planning path, preferentially select the transport route of the lowest biological safety risk;When transport task is indeed unable to completely avoid high-risk area, the system can automatically trigger forced clearance early warning and link hospital infection control management system, and continuously monitor the sealing state of robot and the personnel density of passing area during task execution, immediately stop alarm when abnormal.

Description

Technical Field

[0001] This invention relates to the field of hospital logistics technology, specifically to a multi-dimensional robotic transportation scheduling system, method, and storage medium for hospital logistics. Background Technology

[0002] With the rapid development of medical technology and the continuous advancement of intelligent hospital construction, logistics robots have been widely used in hospital material transportation and medical waste transfer. Most existing hospital logistics robot scheduling systems focus on finding the shortest path, using traditional path planning algorithms such as Dijkstra's algorithm and A* algorithm to plan the robot's path.

[0003] However, hospitals, as special public places, contain a large amount of materials with biosafety risks, especially medical waste. The pathogens carried by medical waste can be spread through the air and through contact, posing a serious threat to the health of medical staff and patients. Current technologies only consider the fixed risk level of a region, ignoring the differences in biosafety risks of different types of medical waste. This leads to a serious underestimation of the cumulative risk effect when high-risk waste passes through medium- and low-risk areas. When planning routes, simply pursuing the shortest path may cause high-risk waste to pass through low-risk areas, increasing the probability of biosafety accidents. Furthermore, the traditional A* algorithm only uses path length as a cost function and cannot incorporate biosafety risks into the optimization objective of route planning. When the transportation of high-risk materials cannot completely avoid high-risk exposure areas, the existing system cannot automatically trigger mandatory clearing and early warning processes, resulting in a disconnect from the hospital's infection control management system and posing a significant biosafety hazard. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a multi-dimensional robotic transportation scheduling system, method, and storage medium for hospital logistics. By quantifying the biosafety risks of medical waste itself and the exposure risks of the hospital area, a dynamically corrected comprehensive path weight model is constructed. By using an improved A* algorithm for biosafety risk global path planning, multi-dimensional global path planning with biosafety as the core is realized, and a deep linkage mechanism with the hospital infection control system is established, effectively solving the technical problem of insufficient biosafety risk management in existing technologies.

[0005] To solve the aforementioned technical problems, this invention provides the following technical solution: Firstly, a multi-dimensional robotic transportation scheduling system for hospital logistics, comprising: a biosafety risk matrix construction module, used to establish a mapping relationship between medical waste types and biosafety levels, and assign differentiated basic risk weights to waste of different biosafety levels; a hospital area risk classification module, used to classify the biosafety exposure risk of all areas of the hospital, marking high-risk, medium-risk, and low-risk exposure areas, and assigning an area risk coefficient to each area; and a dynamic path weight calculation module, used to calculate the biosafety risk dynamic correction factor based on traditional path length weights. The system calculates the comprehensive path weight; the multi-dimensional global path planning module uses an improved A* algorithm that incorporates biosafety risks for global path planning, using the comprehensive path weight as the path cost function, prioritizing the path with the lowest comprehensive path weight, and automatically avoiding all high-risk exposure areas; the forced early warning and clearing trigger module generates a forced personnel clearing early warning signal when the global path planning cannot completely avoid high-risk exposure areas, and pushes the early warning information to the medical staff terminals and hospital infection control management system in that area; the task execution and status monitoring module schedules the robot to perform transportation tasks and monitors the robot's position, speed, sealing status, and personnel density in the areas it passes through in real time.

[0006] Furthermore, the biosafety risk matrix construction module classifies medical waste into infectious waste, pathological waste, sharps waste, pharmaceutical waste, and chemical waste according to relevant regulations; based on the pathogenicity, transmission routes, and degree of harm of each type of waste, it classifies their biosafety levels into four levels, with level one being the lowest risk and level four being the highest risk; and assigns basic risk weights to waste at biosafety levels one through four. , , , ,satisfy ,and Biosafety Level 1: Includes ordinary expired pharmaceuticals, discarded medical packaging materials, etc., with no or extremely low pathogenicity; Biosafety Level 2: Includes ordinary infectious waste, sharps waste, low-toxicity chemical waste, etc., which may cause ordinary infections, with limited transmission routes; Biosafety Level 3: Includes waste generated by patients with multidrug-resistant bacterial infections, pathological waste, moderately toxic chemical waste, etc., with relatively strong pathogenicity, which may cause serious infections; Biosafety Level 4: Includes infectious waste generated by patients with highly infectious diseases, with extremely strong pathogenicity and wide transmission routes, and a leak could trigger a large-scale public health event; the potential hazard level of Biosafety Level 4 waste is more than 5 times that of Biosafety Level 1 waste (such as ordinary expired pharmaceuticals), which can be mitigated by setting up... The hard constraints can significantly amplify the biosafety risk weight of Level IV waste in the path planning algorithm, forcing the algorithm to prioritize routes that bypass low-risk areas.

[0007] Furthermore, the hospital area risk classification module collects data on the functional attributes, personnel flow frequency, infection risk level, and distance from clean areas of all hospital areas; based on the collected data, the analytic hierarchy process (AHP) is used to calculate the biosafety exposure risk value for each area; according to the risk value, the areas are divided into high-risk exposure areas, medium-risk exposure areas, and low-risk exposure areas, and a regional risk coefficient is assigned to the high-risk exposure areas. The risk coefficient of medium-risk exposure areas is allocated to the area. Low-risk exposure areas are allocated regional risk coefficients. and satisfy ,and The concentration of pathogens in the air and on object surfaces in high-risk exposure areas is 10-100 times higher than in low-risk exposure areas. Furthermore, people in high-risk areas are often susceptible patients or potentially infected individuals carrying pathogens. If medical waste leaks into such areas, the probability and severity of cross-infection will increase exponentially. Simultaneously, relevant regulations explicitly require strict physical isolation between high-risk and clean areas, prohibiting entry by non-essential personnel. By setting up... The coefficient difference ensures that the path planning algorithm automatically marks all high-risk areas as impassable nodes under normal circumstances; only when all paths cannot avoid high-risk areas will a forced clearing warning process be triggered, thereby minimizing the risk of personnel exposure.

[0008] Furthermore, the dynamic path weight calculation module obtains the total length of the path to be planned. Calculate the traditional path length weight ,in This is the length weighting coefficient. Calculate the sum of dynamic correction factors for biosafety risks for all road segments along the planned route. ,in As the basic risk weight for the current transportation of medical waste, For the first The regional risk coefficient of the areas traversed. The total number of regions traversed; calculate the overall path weight. ,in For risk weighting coefficients, ,and It can be dynamically adjusted according to the hospital's infection control requirements.

[0009] Furthermore, the multi-dimensional global path planning module is specifically used to: construct a two-dimensional grid map of the hospital, discretizing all areas of the hospital into grids with side lengths of... The grid consists of square grid nodes, each storing the regional risk coefficient of the area to which it belongs. The improved A* algorithm is used for path search, and its cost function is: ,in, To start from the beginning Passing through the node Reach the finish line The total cost, To start from the beginning To the node The actual comprehensive path weight, For the node To the finish line The heuristic cost estimation method is used; during the path search process, all grid nodes corresponding to high-risk exposure areas are marked as impassable nodes and removed from the open list to ensure that the algorithm prioritizes avoiding high-risk areas during the search process. Then, following the process of the traditional A* algorithm, the algorithm continuously selects from the open list. The node with the smallest value is expanded until the destination T is reached; if there are multiple paths with the same comprehensive path weight, the path with the shortest path length is selected as the optimal path; when the robot encounters temporary obstacles or the risk level of the area changes during operation, the comprehensive path weight is recalculated in real time and the optimal path is updated.

[0010] Furthermore, the actual integrated path weight The calculation formula is: ,in, To start from the beginning To the node The actual comprehensive path weight, For nodes To the node The grid side length, For nodes The regional risk coefficient of the area. As the basic risk weight for the current transportation of medical waste, This is the length weighting coefficient. This is the risk weighting coefficient.

[0011] Furthermore, the heuristic cost estimation The calculation formula is: ,in, For nodes To the finish line Manhattan distance, For the node To the finish line The average regional risk coefficient of all possible routes through which the regions pass. The grid side length As the basic risk weight for the current transportation of medical waste, This is the length weighting coefficient. This is the risk weighting coefficient.

[0012] Furthermore, in the mandatory early warning and clearing trigger module, when the global path planning algorithm traverses all possible paths and finds that none of the paths can completely avoid high-risk exposure areas, a mandatory early warning process is triggered; an early warning message containing the robot number, transport task type, estimated passage time, name of the high-risk area passed through, and clearing requirements is generated; the early warning message is simultaneously pushed to the mobile terminals of all on-duty medical staff in the high-risk area, the regional broadcast system, and the hospital infection control management system; after receiving the clearing completion signal from the hospital infection control management system, a passage permission instruction is sent to the task execution and status monitoring module.

[0013] Furthermore, the task execution and status monitoring module schedules the corresponding model of robot to perform the transportation task according to the optimal path. Among them, waste with level 4 biosafety level must be transported by a special robot with level 3 sealing protection function. The module collects data on the robot's position, speed, battery level, sealed chamber pressure and sealing status in real time. It also collects data on the population density of the areas it passes through through cameras deployed in various areas of the hospital. When an abnormal sealing status of the robot is detected or the population density of the area it passes through exceeds a preset threshold, the robot immediately stops operating and issues an emergency alarm signal.

[0014] Secondly, a multi-dimensional transportation scheduling method for hospital logistics robots based on dynamic correction of biosafety risks includes the following steps: S1, establishing a mapping relationship between medical waste type and biosafety level, and assigning differentiated basic risk weights to waste of different biosafety levels; S2, classifying the biosafety exposure risk of all areas of the hospital, marking high-risk, medium-risk, and low-risk exposure areas, and assigning a regional risk coefficient to each area; S3, receiving medical waste transportation tasks and obtaining information on the type, biosafety level, and origin and destination of the waste to be transported; S4, based on the traditional path length weight, introducing a dynamic correction factor for biosafety risks to calculate the comprehensive path weight; wherein, the biosafety risk... The dynamic correction factor is obtained by multiplying the basic risk weight of medical waste by the regional risk coefficient of the area it passes through; S5, construct a two-dimensional grid map of the hospital, and use the improved A* algorithm that integrates biosafety risks for global path planning. The comprehensive path weight is used as the path cost function, and the path with the lowest comprehensive path weight is selected first, while automatically avoiding all high-risk exposure areas; S6, determine whether high-risk exposure areas can be completely avoided. If not, generate a mandatory personnel clearing warning signal and push the warning information to the medical staff terminal and the hospital infection control management system in the area. After receiving the clearing completion signal, continue to execute the task; S7, schedule the robot to perform the transportation task and monitor the robot's position, speed, sealing status and personnel density in the area it passes through in real time.

[0015] Thirdly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned multi-dimensional transport and scheduling system for hospital logistics robots.

[0016] Compared with existing technologies, this multi-dimensional transportation scheduling system, method, and storage medium for hospital logistics robots have the following beneficial effects: By constructing a mapping relationship between medical waste types and biosafety levels, and a graded risk system for hospital areas, this invention transforms biosafety risks into dynamic correction factors and integrates them with traditional path length weights to form comprehensive path weights. This changes the logic of existing scheduling systems that only optimize for the shortest distance, enabling robots to proactively avoid high-risk exposure areas when planning routes and prioritize transportation routes with the lowest biosafety risks, thus reducing the infection threat of medical waste to medical staff and patients during transportation. When a transportation task cannot completely avoid high-risk areas, the system can automatically trigger a forced clearing warning and link with the hospital's infection control management system. Furthermore, it continuously monitors the robot's sealing status and the density of people in the areas it passes through during task execution, immediately stopping and alarming upon any abnormality. It also supports dynamic path replanning due to changes in regional risk levels or temporary obstacles, improving the intelligence level and practical usability of hospital logistics robot transportation scheduling at the biosafety prevention and control level.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 This is a structural block diagram of a multi-dimensional robotic transportation and scheduling system for hospital logistics. Figure 2 A flowchart of a multi-dimensional global path planning module for a multi-dimensional transport and scheduling system for robots in hospital logistics; Figure 3 This is a flowchart of a multi-dimensional transportation scheduling method for robots in hospital logistics. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] This invention provides a multi-dimensional robotic transportation scheduling system, method, and storage medium for hospital logistics. By quantifying the biosafety risks of medical waste itself and the exposure risks in the hospital area, a dynamically corrected comprehensive path weight model is constructed. By introducing an improved A* algorithm for biosafety risk, global path planning is performed, realizing multi-dimensional global path planning with biosafety as the core. Furthermore, a deep linkage mechanism with the hospital's infection control system is established, effectively solving the technical problem of insufficient biosafety risk management in existing technologies.

[0022] The following is combined Figures 1-3 This invention describes a multi-dimensional transportation scheduling system, method, and storage medium for hospital logistics robots based on dynamic correction of biosafety risks. The multi-dimensional transportation scheduling system for hospital logistics robots based on dynamic correction of biosafety risks provided in this embodiment is deployed on an edge server, such as... Figure 1 As shown, the system includes: a biosafety risk matrix construction module, used to establish a mapping relationship between medical waste types and biosafety levels, and assign differentiated basic risk weights to waste of different biosafety levels; a hospital area risk classification module, used to classify the biosafety exposure risk of all areas of the hospital, marking high-risk, medium-risk, and low-risk exposure areas, and assigning an area risk coefficient to each area; a dynamic path weight calculation module, used to calculate the comprehensive path weight based on the traditional path length weight and introducing a biosafety risk dynamic correction factor; a multi-dimensional global path planning module, used to perform global path planning using an improved A* algorithm that integrates biosafety risks, using the comprehensive path weight as the path cost function, prioritizing the path with the lowest comprehensive path weight, and automatically avoiding all high-risk exposure areas; a mandatory early warning and clearing trigger module, used to generate a mandatory personnel clearing early warning signal when the global path planning cannot completely avoid high-risk exposure areas, and push the early warning information to the medical staff terminals in that area and the hospital infection control management system; and a task execution and status monitoring module, used to schedule robots to perform transportation tasks and monitor the robot's position, speed, sealing status, and personnel density in the areas it passes through in real time.

[0023] The biosafety risk matrix construction module classifies medical waste into five categories based on their hazardous characteristics: infectious waste, pathological waste, sharps waste, pharmaceutical waste, and chemical waste. Based on the pathogenicity, transmission routes, infectious doses, and harmful consequences of each type of waste, its biosafety level is classified into four levels, from Level 1 to Level 4, with Level 1 being the lowest risk and Level 4 the highest risk. The specific mapping relationship is as follows: Level 1 Biosafety: Includes ordinary expired drugs, discarded medical packaging materials, etc., with no or extremely low pathogenicity; Level 2 Biosafety: Includes ordinary infectious waste, sharps waste, low-toxicity chemical waste, etc., which may cause ordinary infections, with limited transmission routes; Level 3 Biosafety: Includes waste generated by patients infected with multidrug-resistant bacteria, pathological waste, moderately toxic chemical waste, etc., with relatively strong pathogenicity, which may cause serious infections; Level 4 Biosafety: Includes infectious waste generated by patients with highly infectious diseases, with extremely strong pathogenicity, wide transmission routes, and a potential large-scale public health event if leaked.

[0024] Assigning basic risk weights to waste of different biosafety levels , , , ,satisfy ,and\ In this embodiment, the specific value is: =1, =3, =10, =50. The potential hazard level of Level 4 biosafety waste is more than 50 times that of Level 1 waste. By setting weight differences, the highest level of biosafety can be forced to be prioritized in the path planning algorithm. Even if the path length increases by more than 50%, as long as the overall risk is lower, it will be selected as the optimal path.

[0025] The hospital area risk classification module collects multi-dimensional data from all areas of the hospital, including: functional attribute data: including the area's medical function, whether it is an infection control area, and whether it is a densely populated area; personnel flow data: through the video surveillance system, the average daily personnel flow, peak personnel flow, and personnel stay time of each area are counted; infection risk data: assessed by the hospital's infection control department based on the occurrence of infection cases and pathogen monitoring results in each area; and spatial distance data: calculating the shortest path length from each area to the center of the hospital's clean zone.

[0026] A regional biosafety exposure risk assessment model was constructed using the analytic hierarchy process (AHP), and a judgment matrix was built, as shown in Table 1. Table 1. Multi-dimensional data collected from all hospital regions using the hospital regional risk grading module employing the Analytic Hierarchy Process (AHP).

[0027]

[0028] By calculating the maximum eigenvalue and eigenvector of the judgment matrix, the weights of each evaluation indicator are obtained: functional attribute weight is 0.5, personnel flow weight is 0.2, infection risk weight is 0.2, and distance from the clean zone weight is 0.1. After standardizing each indicator for each area, the comprehensive risk value R is calculated. ,in, Standardize the scores for functional attributes. Standardized scoring for personnel mobility, Standardized score for infection risk, To standardize the scores based on distance from the clean zone, all scores were normalized to the 0–100 range.

[0029] Based on the comprehensive risk value, the area is divided into three categories: High-risk exposure areas: This includes fever clinics, isolation wards, infectious disease wards, and temporary medical waste storage areas; medium-risk exposure areas: This includes operating rooms, emergency rooms, laboratories, and general ward corridors; low-risk exposure areas: It includes administrative office areas, rest rooms for medical staff, and a hospital canteen.

[0030] Assign regional risk coefficients to areas with different risk levels , , ,satisfy ,and In this embodiment, the specific value is: =10, =2, =0.1, the value is based on the fact that the concentration of pathogens in the air and on the surface of objects in high-risk exposure areas is 10-100 times that in low-risk areas, and the probability of infection for people in high-risk areas is significantly higher. By setting a coefficient difference of 100 times, it can be ensured that the path planning algorithm will regard high-risk areas as impassable areas under normal circumstances, thereby minimizing the risk of human exposure.

[0031] After receiving a delivery task, the dynamic path weight calculation module obtains the total length of the path to be planned. Calculate the traditional path length weight ,in This is the length weighting coefficient. ; Calculate the sum of dynamic correction factors for biosafety risks corresponding to all road segments along the planned route. ,in As the basic risk weight for the current transportation of medical waste, For the first The regional risk coefficient of the areas traversed. Given the total number of regions traversed, we then iterate through all possible candidate paths and calculate the comprehensive path weight for each path.

[0032] The formula for calculating the overall path weight is: ,in, To achieve comprehensive path weighting, The length weighting coefficient (in this embodiment) =1), This is the total path length. Risk weighting coefficient (in this embodiment) =1), which can be dynamically adjusted according to the hospital's infection control requirements. As the basic risk weight for the current transportation of medical waste, Let m be the regional risk coefficient of the j-th transit area, and m be the total number of transit areas.

[0033] In this embodiment, the specific task received is to transfer 5 kg of biosafety level 4 infectious waste generated by the fever clinic to a medical waste temporary storage point, which must be completed within 30 minutes. The starting point coordinates are (100, 200), and the ending point coordinates are (500, 600). For this task, the system generates two candidate paths: Path 1: Total Length meters, passing through a medium-risk area =2 and 1 low-risk areas =0.1, the overall path weight is calculated as follows: Path 2: Total Length Rice, only passing through low-risk areas =0.1, the overall path weight is calculated as follows: Since the two paths have the same comprehensive path weight, the system will further compare the path lengths and select the shorter path 1 as the initial candidate path.

[0034] like Figure 2 As shown, the multi-dimensional global path planning module constructs a two-dimensional grid map of the hospital, discretizing all areas of the hospital into square grid nodes with a side length d = 0.5 meters, generating a total of 1000 × 800 grid nodes. Each grid node stores the following information: node coordinates (x, y), whether it is passable, and the regional risk coefficient of its area. Mark all grid nodes corresponding to high-risk exposure areas as impassable nodes.

[0035] The core cost function of the improved A* algorithm is: ,in, Let S be the total cost to travel from the starting point S through node n to the ending point T. The actual comprehensive path weight from the starting point S to node n. This is a heuristic estimate of the cost from node n to the destination T.

[0036] Actual integrated path weight Calculated using a recursive method: ,in, From the starting point S to the node The actual comprehensive path weight, For nodes The grid edge length to node n (horizontal / vertical movement is d = 0.5 meters, diagonal movement is...) rice), Let n be the regional risk coefficient of the region to which node n belongs.

[0037] Heuristic cost estimation An improved Manhattan distance calculation method is used, incorporating biosafety risk factors: ,in, Let n be the Manhattan distance from node n to the destination T, i.e. ; ; The average regional risk coefficient of all possible paths from node n to destination T (pre-calculated in this embodiment) is the average regional risk coefficient of the regions traversed. =0.5).

[0038] The specific calculation process is as follows: Initialize the open list OpenList and the close list CloseList, and set the starting point... Add to OpenList, set Heuristic cost estimation for the starting point : ; The total cost of calculating the starting point Select from OpenList The node with the smallest value is selected as the current node and moved from the OpenList to the CloseList. The eight adjacent nodes of the current node are traversed. For each adjacent node: if the node is impassable or already in the CloseList, it is skipped; the temporary value of the node is calculated. value: If the node is not in the OpenList, or Smaller than the original value of this node If the value is not found, then update the parent node of the current node to the parent node, and update the parent node of the current node. Recalculate and Add it to OpenList; repeat the calculation until the endpoint T(500, 600) is added to CloseList, at which point the algorithm search ends; start from the endpoint T and backtrack the parent node until the starting point S is reached to obtain the optimal path.

[0039] During the path search process, all high-risk exposure areas are automatically avoided. If the robot encounters temporary obstacles (such as hospital beds or trolleys) or an area is temporarily upgraded to a high-risk area due to a sudden outbreak, the dynamic replanning process is immediately triggered to update the passable markers and regional risk coefficients on the grid map, recalculate the comprehensive weight of all candidate paths, and schedule the robot to switch to the new optimal path.

[0040] The mandatory early warning and clearing trigger module monitors the global path planning results in real time. When it is found that all paths cannot completely avoid the high-risk exposure area, the mandatory early warning and clearing process is automatically triggered. In this embodiment, it is assumed that the medical waste temporary storage point is being renovated and all paths leading to the temporary storage point need to pass through a high-risk exposure area with coordinates ranging from (450, 550) to (500, 600). At this time, after the global path planning algorithm traverses all possible paths, it finds that it cannot completely avoid the high-risk area and triggers the mandatory early warning process.

[0041] The mandatory early warning and evacuation trigger module generates an early warning message, which includes: Robot R007 is performing a Level 4 biosafety medical waste transfer mission and is expected to pass through a high-risk exposure area between 10:30 and 10:32. All personnel in the area are requested to evacuate to a safe area immediately. The system simultaneously pushes this early warning message to the mobile terminals of all on-duty medical staff in the Infectious Diseases Department, the broadcast system on the first floor of the Infectious Diseases Department, and the hospital infection control management system.

[0042] After receiving the early warning information, the hospital infection control management system assigns a dedicated person to be responsible for clearing the area. After the clearing is completed, the system sends a clearing completion signal. Upon receiving the clearing completion signal, the forced early warning and clearing trigger module sends a permission instruction to the task execution and status monitoring module, allowing the robot to pass through the high-risk area.

[0043] The task execution and status monitoring module schedules the corresponding model of robot to perform the delivery task according to the optimal path, and monitors the robot's operating status and environmental status in real time.

[0044] The robot scheduling rules are as follows: waste of biosafety levels 1 to 3 is handled by general-purpose logistics robots with level 2 sealing protection; waste of biosafety level 4 must be handled by dedicated medical waste transport robots with level 3 sealing protection. In this embodiment, the dedicated medical waste transport robot with scheduling number R007 performs the task.

[0045] During the robot's task execution, the system collects the following data in real time: Robot status data: position (obtained through a UWB positioning system with an accuracy of ±10 cm), speed, battery level, sealed chamber pressure, and sealed door locking status; Environmental status data: real-time personnel density data of the areas it passes through is collected by high-definition cameras deployed in various areas of the hospital for personnel detection and counting.

[0046] The system will immediately stop the robot and issue an emergency alarm signal when any of the following abnormal conditions are detected: the pressure in the robot's sealed chamber is lower than the preset threshold (50Pa in this embodiment) or the sealed door is not locked; the population density in the area it passes through exceeds 0.5 people / square meter; the robot deviates from the optimal path by more than 5 meters; or the robot's battery power is lower than 20%.

[0047] The emergency alarm signal is simultaneously sent to the hospital's logistics management center and infection control management center, where it is handled by designated personnel. Once the abnormal situation is resolved, the system will reschedule the robot to continue performing the task or arrange for a backup robot to take over the task.

[0048] Figure 3 This is a flowchart illustrating a multi-dimensional transportation and scheduling method for hospital logistics robots provided by the present invention, as shown below. Figure 3As shown, the method includes the following steps: S1, the biosafety risk matrix construction module establishes a mapping relationship between medical waste type and biosafety level, and assigns differentiated basic risk weights to waste of different biosafety levels; S2, the hospital area risk classification module classifies the biosafety exposure risk of all areas of the hospital, marks high-risk exposure areas, medium-risk exposure areas, and low-risk exposure areas, and assigns an area risk coefficient to each area; S3, the system receives medical waste transportation tasks submitted by medical staff through mobile terminals, and obtains information on the type, biosafety level, weight, and origin and destination of the waste to be transported; S4, the dynamic path weight calculation module, based on the traditional path length weight, introduces a biosafety risk dynamic correction factor to calculate the comprehensive path weight of all candidate paths; S5, multi-dimensional global... The path planning module constructs a two-dimensional grid map of the hospital and uses an improved A* algorithm that incorporates biosafety risks for global path planning. It uses the comprehensive path weight as the path cost function, prioritizing the path with the lowest comprehensive path weight and automatically avoiding all high-risk exposure areas. In step S6, the system determines whether the optimal path can completely avoid high-risk exposure areas. If not, the forced warning and clearing trigger module generates a forced personnel clearing warning signal and pushes the warning information to the medical staff terminals in the area and the hospital's infection control management system. Upon receiving the clearing completion signal, the task continues. In step S7, the task execution and status monitoring module schedules the corresponding model of robot to perform the delivery task according to the optimal path and monitors the robot's position, speed, sealing status, and personnel density in the area it passes through in real time. If any abnormality is detected, operation is immediately stopped and an alarm is triggered.

[0049] The present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the multi-dimensional transportation scheduling method for hospital logistics robots based on dynamic correction of biosafety risks provided in the above embodiments.

[0050] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A multi-dimensional robotic transportation and scheduling system for hospital logistics, characterized in that, The system includes: The biosafety risk matrix construction module is used to establish a mapping relationship between medical waste types and biosafety levels, and to assign differentiated basic risk weights to waste of different biosafety levels. The hospital area risk classification module is used to classify the biosafety exposure risk of all areas of the hospital, mark high-risk exposure areas, medium-risk exposure areas and low-risk exposure areas, and assign an area risk coefficient to each area; The dynamic path weight calculation module is used to calculate the comprehensive path weight by introducing a dynamic correction factor for biosafety risks based on the traditional path length weight. The multi-dimensional global path planning module is used to perform global path planning using an improved A* algorithm that incorporates biosafety risks. It uses the comprehensive path weight as the path cost function, prioritizes the path with the lowest comprehensive path weight, and automatically avoids all high-risk exposure areas. The mandatory early warning and clearing trigger module is used to generate a mandatory personnel clearing early warning signal when the global path planning cannot completely avoid high-risk exposure areas, and push the early warning information to the medical staff terminal and hospital infection control management system in the area; The task execution and status monitoring module is used to schedule robots to perform delivery tasks and monitor the robot's position, speed, sealing status, and personnel density in the areas it passes through in real time.

2. The multi-dimensional robotic transportation and scheduling system for hospital logistics according to claim 1, characterized in that, The biosafety risk matrix construction module classifies medical waste into infectious waste, pathological waste, sharps waste, pharmaceutical waste, and chemical waste according to relevant regulations. Based on the pathogenicity, transmission routes, and severity of each type of waste, its biosafety level is classified into four levels, from Level 1 to Level 4, with Level 1 being the lowest risk and Level 4 the highest risk. Basic risk weights are assigned to waste at biosafety levels 1 through 4. , , , ,satisfy ,and .

3. The multi-dimensional robotic transportation and scheduling system for hospital logistics according to claim 1, characterized in that, The hospital area risk classification module collects data on the functional attributes, personnel flow frequency, infection risk level, and distance from clean areas of all hospital areas; based on the collected data, the analytic hierarchy process is used to calculate the biosafety exposure risk value of each area. The region is divided into high-risk, medium-risk, and low-risk exposure areas based on risk values, and a regional risk coefficient is assigned to the high-risk exposure areas. The risk coefficient of medium-risk exposure areas is allocated to the area. Low-risk exposure areas are allocated regional risk coefficients. and satisfy ,and .

4. A multi-dimensional robotic transportation and scheduling system for hospital logistics according to claim 1, characterized in that, The dynamic path weight calculation module obtains the total length of the path to be planned. Calculate the traditional path length weight ,in This is the length weighting coefficient. ; Calculate the sum of dynamic correction factors for biosafety risks for all road segments along the planned route. ,in As the basic risk weight for the current transportation of medical waste, For the first The regional risk coefficient of the areas traversed. This represents the total number of areas traversed. Calculate the overall path weight ,in For risk weighting coefficients, .

5. A multi-dimensional robotic transportation and scheduling system for hospital logistics according to claim 1, characterized in that, The multi-dimensional global path planning module is specifically used to: construct a two-dimensional grid map of the hospital, discretizing all areas of the hospital into grids with side lengths of [missing information]. The grid consists of square grid nodes, each storing the regional risk coefficient of the area to which it belongs. ; The improved A* algorithm is used for path search, and its cost function is: ,in, To start from the beginning Passing through the node Reach the finish line The total cost, To start from the beginning To the node The actual comprehensive path weight, For the node To the finish line Heuristic cost estimation; During the path search process, all grid nodes corresponding to high-risk exposure areas are marked as impassable nodes and removed from the open list; If there are multiple paths with the same overall path weight, the path with the shortest path length is selected as the optimal path. When the robot encounters temporary obstacles or changes in the risk level of an area during operation, the comprehensive path weight is recalculated in real time and the optimal path is updated.

6. A multi-dimensional robotic transportation and scheduling system for hospital logistics according to claim 5, characterized in that, The actual integrated path weight The calculation formula is: ,in, To start from the beginning To the node The actual comprehensive path weight, For nodes To the node The grid side length, For nodes The regional risk coefficient of the area. As the basic risk weight for the current transportation of medical waste, This is the length weighting coefficient. This is the risk weighting coefficient.

7. A multi-dimensional robotic transportation and scheduling system for hospital logistics according to claim 5, characterized in that, The heuristic cost estimation The calculation formula is: ,in, For nodes To the finish line Manhattan distance, For the node To the finish line The average regional risk coefficient of all possible routes through which the regions pass. The grid side length As the basic risk weight for the current transportation of medical waste, This is the length weighting coefficient. This is the risk weighting coefficient.

8. A multi-dimensional robotic transportation and scheduling system for hospital logistics according to claim 1, characterized in that, In the mandatory early warning and clearing trigger module, when the global path planning algorithm traverses all possible paths and finds that none of the paths can completely avoid high-risk exposure areas, a mandatory early warning process is triggered; an early warning message is generated, including the robot number, the type of transport task, the estimated passage time, the name of the high-risk area passed through, and the clearing requirements; the early warning message is simultaneously pushed to the mobile terminals of all on-duty medical staff in the high-risk area, the regional broadcast system, and the hospital infection control management system; after receiving the clearing completion signal from the hospital infection control management system, a passage permission instruction is sent to the task execution and status monitoring module.

9. A multi-dimensional robot transportation scheduling method for hospital logistics, applicable to the multi-dimensional robot transportation scheduling system for hospital logistics as described in any one of claims 1-8, characterized in that, The method includes: S1. Establish a mapping relationship between medical waste types and biosafety levels, and assign differentiated basic risk weights to wastes of different biosafety levels; S2. Classify all areas of the hospital for biosafety exposure risk, mark high-risk, medium-risk, and low-risk exposure areas, and assign a regional risk coefficient to each area; S3. Receive medical waste transportation tasks and obtain information on the type, biosafety level, and origin and destination of the waste to be transported. S4. Based on the traditional path length weight, a dynamic correction factor for biosafety risk is introduced to calculate the comprehensive path weight. S5. Construct a two-dimensional grid map of the hospital, and use the improved A* algorithm that integrates biosafety risks for global path planning. Use the comprehensive path weight as the path cost function, prioritize the path with the lowest comprehensive path weight, and automatically avoid all high-risk exposure areas. S6. Determine whether high-risk exposure areas can be completely avoided. If not, generate a mandatory personnel clearing warning signal and push the warning information to the medical staff terminal and hospital infection control management system in the area. After receiving the clearing completion signal, continue to execute the task. S7. Schedule robots to perform delivery tasks and monitor the robot's position, speed, sealing status, and personnel density in the areas it passes through in real time.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements a multi-dimensional robotic transportation and scheduling system for hospital logistics as described in any one of claims 1 to 8.

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