An operating room nursing task intelligent allocation method and system

Abstract

The application relates to the technical field of intelligent task allocation, and discloses a kind of operating room nursing task intelligent allocation method and system.The method comprises establishing three-dimensional space coordinate system and dividing hierarchical functional area in operating room;Real-time capture nursing staff position and posture information, generate individual activity atlas;Extract current surgical procedure stage characteristics, match task demand template to analyze nursing task set and its spatial execution domain;Calculate the coupling degree matrix of task space execution domain and individual activity atlas, filter candidate task subset;Simulate the three-dimensional path movement of nursing staff in the task execution process, analyze the space interference degree to correct the task subset, form the preliminary assignment plan;Combined with equipment running state and instrument use data, carry out resource accessibility check, output final task allocation list.The application realizes accurate task allocation based on dynamic behavior semantics and space collaborative simulation, improves operating room nursing efficiency and safety.

Description

Technical Field

[0001] This invention relates to the field of intelligent task allocation technology, specifically to an intelligent allocation method and system for operating room nursing tasks. Background Technology

[0002] Traditional operating room nursing task allocation relies primarily on the head nurse's experience and judgment or on electronic scheduling systems based on fixed rules. Existing solutions typically assign tasks based on the static attributes of nursing staff and simple two-dimensional spatial information. These systems generally lack the ability to dynamically perceive and understand the real-time activity status of personnel and the complex three-dimensional spatial logic of the operating room.

[0003] Existing solutions have technical flaws. Experience-based scheduling is highly subjective, inefficient, and struggles to cope with unexpected changes; automated systems, due to their rigid logic, cannot accurately match the dynamic needs of surgical procedures, easily leading to task allocation delays or resource misallocation. Current technology cannot analyze the real-time work intentions of nursing staff from continuous behavioral data, nor can it proactively assess potential physical space conflicts during multi-person collaborative work. The system can only obtain a planar snapshot of "where the personnel are," but cannot understand the behavioral semantics of "what the personnel are doing and what they are preparing to do"; at the same time, the task allocation process is static, failing to consider the spatial movement and interaction caused by task execution, resulting in a disconnect between the plan and the actual situation.

[0004] Two core issues need to be addressed: how to accurately identify and model the dynamic behavioral intentions of nursing staff to go beyond static role matching; and how to predict and avoid three-dimensional spatial interference caused by staff movement and operations during the task planning phase to ensure the smoothness and safety of the execution process. This requires the allocation method to have deep behavioral semantic analysis and dynamic spatial relationship simulation capabilities. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent allocation method and system for operating room nursing tasks to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an intelligent allocation method for operating room nursing tasks, the method comprising:

[0007] A three-dimensional spatial coordinate system is established in the operating room, dividing the operating room into multiple functional areas with a hierarchical structure;

[0008] Real-time capture of the position and posture information of nursing staff in the three-dimensional spatial coordinate system;

[0009] Based on the location information and posture information, and combined with the hierarchical structure of the multiple functional areas, a personal activity map is generated for each nursing staff member.

[0010] Extract the stage features of the current surgical procedure, and match the preset task requirement template according to the stage features;

[0011] Based on the task requirement template, the set of nursing tasks to be executed and their corresponding spatial execution domains are parsed out;

[0012] The coupling degree is calculated between the spatial execution domain of the nursing task set and the personal activity map to generate a coupling degree matrix;

[0013] Based on the coupling degree matrix, a subset of candidate tasks is selected for each nurse from the set of nursing tasks;

[0014] For the subset of candidate tasks, the path movement of nursing staff in a three-dimensional spatial coordinate system during task execution is simulated, and the degree of spatial interference between tasks is calculated.

[0015] The candidate task subset is modified based on the degree of spatial interference to form a preliminary nursing task assignment plan;

[0016] Acquire operating status data of equipment and real-time usage data of surgical instruments in the operating room, perform resource accessibility verification on the nursing task assignment plan, and output the final task allocation list.

[0017] Preferably, dividing the operating room into multiple functional areas with a hierarchical structure includes:

[0018] Identify the core location of the operating table within the operating room space and define the core sterile zone with the operating table as the center.

[0019] Around the core sterile area, based on the fixed layout of surgical equipment, instrument tables, and medicine cabinets, an instrument preparation area, a medicine supply area, and a waste disposal area are divided.

[0020] Outside the equipment preparation area, drug supply area and waste disposal area, define personnel access and temporary supply channels;

[0021] Different spatial access priorities are assigned to the core sterile area, instrument preparation area, drug supply area, waste disposal area, and personnel passage and temporary supply passage, forming the hierarchical structure.

[0022] Preferably, the step of generating a personal activity map for each caregiver based on the location information and the posture information, combined with the hierarchical structure of the multiple functional areas, includes:

[0023] Continuously record the location information of nursing staff over a period of time to form the original movement trajectory;

[0024] By analyzing the body orientation and hand gestures in the posture information, the potential operational intentions of the nursing staff can be identified;

[0025] The original movement trajectory is associated with the potential operational intent, and different behavioral segments in the trajectory corresponding to observation, preparation, operation, and movement are marked.

[0026] The labeled behavioral fragments are mapped to the multiple functional areas, and the dwell time and behavioral patterns of nursing staff are statistically analyzed under different access priority levels in each functional area.

[0027] Based on the aforementioned stay duration and behavioral patterns, a personal activity map containing spatiotemporal behavioral characteristics is constructed, indexed by the nursing staff's identity.

[0028] Preferably, the step of parsing the set of nursing tasks to be executed and their corresponding spatial execution domains based on the task requirement template includes:

[0029] Read the standard task items, task execution order, and task prerequisites defined in the task requirement template;

[0030] The standard task items are bound to specific items and equipment in the operating room to determine the target object to be operated on for each task;

[0031] Based on the actual storage or installation location of the target object in the three-dimensional spatial coordinate system, a task execution domain is defined for each task, and the task execution domain is a three-dimensional spatial range.

[0032] Considering the associated operations during task execution, the spatial ranges of multiple related target objects are merged to form an extended task execution domain;

[0033] All task items and their corresponding task execution domains or extended task execution domains are aggregated to form the set of nursing tasks to be executed.

[0034] Preferably, the step of calculating the coupling degree between the spatial execution domain of the nursing task set and the personal activity graph to generate a coupling degree matrix includes:

[0035] For each task in the set of nursing tasks, extract the three-dimensional spatial coordinate range of its task execution domain;

[0036] For the aforementioned personal activity map, extract the historical activity heat map of nursing staff at different levels in each functional area;

[0037] The spatial overlap between the three-dimensional spatial coordinate range of the task execution domain and the historical activity heatmap is calculated and used as a spatial coupling factor.

[0038] Analyze the frequency of occurrence of behavioral patterns similar to the current task in the personal activity graph, and use it as an experience coupling factor;

[0039] By combining the spatial coupling factor and the empirical coupling factor, a specific coupling degree value for nursing staff and tasks is obtained through weighted calculation.

[0040] By iterating through all nursing staff and all tasks, all calculated coupling values ​​are arranged into a matrix to generate the coupling matrix.

[0041] Preferably, the step of simulating the path movement of nursing staff in a three-dimensional spatial coordinate system during task execution for the candidate task subset, and calculating the degree of spatial interference between tasks, includes:

[0042] Assign a task execution order from each nurse's subset of candidate tasks;

[0043] Based on the three-dimensional spatial coordinate system, plan the shortest spatial movement path for nursing staff to move sequentially to each task execution domain from their current position.

[0044] The shortest spatial movement paths of all nursing staff are superimposed and projected in a three-dimensional spatial coordinate system;

[0045] Detect whether there are intersections or adjacent points on the spatial coordinates of the movement paths of different nursing staff in the same time slice;

[0046] If there are intersections or adjacent points, then the estimated personnel density and space margin at the intersections or adjacent points of the paths are further calculated under the same time slice.

[0047] Based on the estimated personnel density and space margin, the level of mutual interference between different nursing staff task execution paths is quantitatively assessed as the degree of spatial interference.

[0048] Preferably, the step of modifying the candidate task subset according to the degree of spatial interference to form a preliminary nursing task assignment plan includes:

[0049] Set a spatial interference threshold, and identify path conflict points where the degree of spatial interference exceeds the spatial interference threshold, as well as the tasks and nursing personnel involved;

[0050] Adjust the subset of candidate tasks for nursing staff that involve path conflicts by changing the task execution order or replacing them with other candidate tasks that have suboptimal coupling but no path conflicts.

[0051] After the adjustment, the path planning and spatial interference were recalculated to form a new task allocation scheme;

[0052] The adjustment and recalculation process is repeated iteratively until the spatial interference of all path conflict points is lower than the spatial interference threshold, or the preset maximum number of iterations is reached.

[0053] The final task allocation scheme that meets the conditions is solidified into the preliminary nursing task assignment plan, which clarifies the specific task sequence that each nurse needs to perform within a specific time window.

[0054] Preferably, the step of acquiring operating room equipment operation status data and real-time surgical instrument usage data, and performing resource accessibility verification on the nursing task assignment plan, includes:

[0055] The current operating mode, estimated end time and fault status of key surgical equipment are read through the Internet of Things interface as equipment operating status data.

[0056] By using radio frequency identification or computer vision technology, the real-time location of surgical instruments, whether they have been sterilized and prepared, and their current usage status can be obtained as real-time usage data of surgical instruments;

[0057] Check the equipment or instruments required for each task against the task sequence in the nursing task assignment plan.

[0058] If the equipment required for the task is faulty or occupied, or the required equipment is not in an available or standby position, then the task is determined to be unreachable at the current moment.

[0059] Record all tasks for which resources are unreachable, and mark the reasons for their unreachability and the expected time when they will be available.

[0060] Preferably, the final task assignment list output includes:

[0061] Based on the results of the resource accessibility check, the nursing task assignment plan is fine-tuned in the time dimension, postponing tasks that are not accessible due to unavailable resources until the resources they require become available.

[0062] For tasks that must be performed immediately due to resource issues, alternative caregivers who can perform the task and whose current resources are available are searched in the coupling matrix and reassigned.

[0063] The task assignment plan, after time adjustments and personnel replacements, is integrated and formatted according to the timeline;

[0064] Generate a structured task assignment list that includes nursing staff identification, task content, execution location, start time, and end time;

[0065] The structured task assignment list is sent to the corresponding display terminal in the operating room or the mobile terminal of the nursing staff.

[0066] Preferably, the present invention also includes an intelligent operating room nursing task allocation system, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, it implements the steps of the intelligent operating room nursing task allocation method described above.

[0067] Compared with the prior art, the beneficial effects of the present invention are:

[0068] Based on real-time acquired 3D position and posture information, combined with the hierarchical structure of functional areas within the operating room, the system dynamically constructs an individual activity map. This map, by analyzing the behavioral semantics implied by posture and associating it with spatial hierarchy, can infer the immediate working status and readiness level of nursing staff. This method transforms traditional static matching based on fixed roles and 2D coordinates into continuous modeling of dynamic behavioral intentions. The correlation between task allocation and the actual activity status of personnel is enhanced, improving the alignment between task assignment and the dynamic needs of the surgical process. This reduces inappropriate assignments due to misjudgment of personnel status, decreases waiting and coordination time before task initiation, and enhances the scheduling system's responsiveness to complex changes in surgical situations.

[0069] After generating candidate tasks, the system calculates the degree of spatial interference between different tasks by simulating the expected movement paths of nursing staff performing tasks in three-dimensional space. This process provides a forward-looking quantitative assessment of potential movement conflicts and operational domain competition under task combinations. Based on the assessment results, task subsets are modified and optimized, realizing a shift from static task list allocation to dynamic collaborative path planning. This reduces process interruptions and staff waiting caused by physical path intersections and overlapping operational spaces during actual execution. This improves the overall efficiency and process smoothness when multiple tasks are executed in parallel, and reduces operational delays and safety risks caused by spatial resource competition. Attached Figure Description

[0070] Figure 1 This is a schematic diagram illustrating the working principle of the intelligent allocation method for operating room nursing tasks described in this invention.

[0071] Figure 2 A flowchart for dividing functional areas into hierarchical structures;

[0072] Figure 3 A flowchart for analyzing the set of nursing tasks and the spatial execution domain;

[0073] Figure 4 A diagram showing the relationship between the effective area of ​​each functional area in the operating room and the spatial interference threshold;

[0074] Figure 5 This is a task coupling matrix diagram for nursing staff. Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] Please see Figure 1 This invention provides an intelligent method for allocating nursing tasks in the operating room. The method includes: establishing a three-dimensional spatial coordinate system within the operating room and dividing the operating room into multiple functional areas with a hierarchical structure; capturing the position and posture information of nursing staff in the three-dimensional spatial coordinate system in real time; generating a personal activity map for each nursing staff member based on the position and posture information and the hierarchical structure of the multiple functional areas; extracting the stage features of the current surgical procedure and matching them with a preset task requirement template; parsing the set of nursing tasks to be executed and their corresponding spatial execution domains based on the task requirement templates; calculating the coupling degree between the spatial execution domains of the nursing task set and the personal activity map to generate a coupling degree matrix; selecting a subset of candidate tasks for each nursing staff member from the set of nursing tasks based on the coupling degree matrix; simulating the path movement of nursing staff in the three-dimensional spatial coordinate system during task execution for the candidate task subsets and calculating the degree of spatial interference between tasks; revising the candidate task subsets according to the degree of spatial interference to form a preliminary nursing task assignment plan; obtaining equipment operation status data and real-time usage data of surgical instruments in the operating room to verify the resource accessibility of the nursing task assignment plan; and finally outputting a task allocation list.

[0077] In one embodiment of the present invention, see [reference] Figure 2The operating table is identified as the core location within the operating room space, and a core sterile zone is defined centered on the operating table. Around this core sterile zone, based on the fixed layout of surgical equipment, instrument tables, and medicine cabinets, an instrument preparation area, a medicine supply area, and a waste disposal area are defined. Personnel access and temporary supply channels are defined around these areas, assigning different spatial access priorities to each to create a hierarchical structure. The location information of nursing staff over a period of time is continuously recorded to form raw movement trajectories. Body orientation and operational gestures in the posture information are analyzed to identify the potential operational intentions of the nursing staff. The raw movement trajectories are correlated with potential operational intentions, and different behavioral segments corresponding to observation, preparation, operation, and movement are marked. These marked behavioral segments are mapped to multiple functional areas, and the dwell time and behavioral patterns of nursing staff in each functional area at different access priority levels are statistically analyzed. Based on the dwell time and behavioral patterns, a personal activity map containing spatiotemporal behavioral characteristics is constructed, indexed by the nursing staff's identity.

[0078] In practical implementation, the core location of the operating table within the operating room space is identified, and a core sterile zone is defined with the operating table as the center. For example, in a three-dimensional spatial coordinate system of an operating room with a length of 10 meters, a width of 8 meters, and a height of 3 meters, the center point of the operating table is set to (5,4,1). The core sterile zone is defined as a spherical area with a radius of 1.5 meters centered on this center point, covering the operating table and the adjacent operating space. Around the core sterile zone, based on the fixed layout of surgical equipment, instrument tables, and medicine cabinets, an instrument preparation area, a medicine supply area, and a waste disposal area are defined. The instrument preparation area is located on the northeast side of the operating table, and its spatial range is defined by a cube with coordinates (6,6,0.8) to (8,7,1.5). The medicine supply area is located on the northwest side, and its spatial range is defined by a cube with coordinates (2,6,0.8) to (4,7,1.5). The waste disposal area is located on the southeast side, and its spatial range is defined by a cube with coordinates (6,1,0.8) to (8,2,1.5). Surrounding the instrument preparation area, drug supply area, and waste disposal area, personnel access and temporary supply channels are defined. These channels are defined by paths connecting the operating room entrance to each functional area, with a uniform width of 1.2 meters and a height covering the space from the ground to 2 meters. Different spatial access priorities are assigned to the core sterile area, instrument preparation area, drug supply area, waste disposal area, and personnel access and temporary supply channels, forming a hierarchical structure. For example, the priority value for the core sterile area is 5, for the instrument preparation area it is 4, for the drug supply area it is 3, for the waste disposal area it is 2, and for the personnel access and temporary supply channels it is 1. Higher priority values ​​indicate stricter access restrictions in task allocation.

[0079] In some embodiments, the location information of the nursing staff is continuously recorded over a period of time to form an original movement trajectory. For example, an ultra-wideband positioning system deployed in the operating room captures the three-dimensional coordinates (x, y, z) of the tag worn by the nursing staff at a sampling frequency of 10 times per second, continuously recording a complete surgical procedure for approximately 120 minutes, generating a trajectory sequence containing timestamps and coordinate points. Body orientation and operational gestures in the posture information are analyzed to identify the nursing staff's potential operational intentions. For example, body orientation angle data is collected using inertial measurement unit sensors worn by the nursing staff. When the angle between the torso orientation angle and the center point of the operating table is less than 30 degrees, hand gesture recognition is used. Hand gestures are captured by a camera and processed by a pre-trained gesture classification model. When a "grabbing" gesture is identified, it is marked as a preparatory operation intention; when a "passing" gesture is identified, it is marked as an operational intention. The original movement trajectory is associated with the potential operational intent, and different behavioral segments corresponding to observation, preparation, operation, and movement are marked in the trajectory. For example, in the time interval 10:05:00 to 10:05:30, if the nurse's coordinates change by less than 0.2 meters in the core sterile area and the body orientation is stable, and the gesture classification output is "observation", then this segment is marked as the observation behavior segment; in the time interval 10:06:00 to 10:06:45, if the nurse's coordinates change linearly from the core sterile area to the instrument preparation area and the speed is greater than 0.5 meters / second, then this segment is marked as the movement behavior segment.

[0080] This approach maps labeled behavioral segments to multiple functional areas, allowing for the statistical analysis of nurses' dwell time and behavioral patterns at different access priority levels within each area. For instance, nurse N001's total dwell time in the core sterile area (priority 5) is 1800 seconds, with observation behaviors accounting for 60% and operational behaviors accounting for 40%. In the instrument preparation area (priority 4), the total dwell time is 1200 seconds, with preparation behaviors accounting for 70% and movement behaviors accounting for 30%. Data comparison reveals differences in behavioral pattern distribution among nurses. For example, nurse N002's operational behaviors account for 50% of the total time in the drug supply area (priority 3), while nurse N003's operational behaviors account for 30% in the same area, reflecting individual task preferences. Based on dwell time and behavioral patterns, a personal activity map containing spatiotemporal behavioral characteristics is constructed with nursing staff identification as the index. The personal activity map is stored in matrix form, with rows corresponding to functional areas and priority combinations, columns corresponding to behavioral pattern types, and cell values ​​representing normalized dwell time or frequency.

[0081] Optionally, when analyzing behavioral patterns, a formula can be introduced to calculate the weight of the behavioral patterns to quantify behavioral tendencies. The formula is as follows:

[0082]

[0083] in: This represents the weight of behavior pattern b, which is taken from the set {Observe, Prepare, Operate, Move}. This represents the duration of the k-th behavior pattern b. Indicates the total observation time. This indicates the number of times behavior pattern b occurs.

[0084] In practical implementation, the spatial access priority of the hierarchical structure affects the accuracy of behavior segment mapping. For example, behavior segments in high-priority areas, such as the core sterile area, are segmented with finer temporal granularity, labeled in seconds, while behavior segments in low-priority areas, such as personnel passage and temporary supply channels, are segmented with coarser temporal granularity, labeled in 5-second units. In some embodiments, preprocessing of the original movement trajectory includes filtering and smoothing, such as applying a low-pass filter to remove positioning noise and ensure the continuity of trajectory coordinates. Body orientation analysis in posture information involves angle calculation, such as calculating the dot product of the nurse's torso direction vector and the reference vector with the center of the operating table as a reference point to determine orientation consistency. Optionally, latent operational intent recognition fuses multi-sensor data, such as acceleration and angular velocity provided by an inertial measurement unit and hand contours provided by a camera, and outputs intent classification through decision-level fusion.

[0085] In one embodiment of the present invention, see [reference] Figure 3 The system reads the standard task items, execution order, and preconditions defined in the task requirement template, and binds these standard task items to specific items and equipment in the operating room to determine the target objects for each task. Based on the actual storage or installation location of the target objects in the three-dimensional coordinate system, a task execution domain is defined for each task; this domain is a three-dimensional spatial range. Considering the associated operations during task execution, the spatial ranges of multiple related target objects are merged to form an extended task execution domain. All task items and their corresponding task execution domains or extended task execution domains are then aggregated to form a set of nursing tasks to be executed.

[0086] In practice, the standard task items, task execution order, and task prerequisites defined in the task requirement template are read. The task requirement template is associated with specific surgical types and stages. For example, for the "start of surgery" stage of "cholecystectomy," the task requirement template includes the standard task item sequence "establishing intravenous access," "anesthesia monitoring," "surgical area disinfection," and "draping with sterile sheets." The prerequisite for the task "establishing intravenous access" is "the patient has entered the operating room and completed basic information verification." The standard task items are bound to specific items and equipment in the operating room to determine the target objects for each task. For example, the target objects bound to the task item "establishing intravenous access" include "IV stand" and "venous puncture kit," the target objects bound to the task item "surgical area disinfection" include "sterile solution container" and "sterile forceps," and the target objects bound to the task item "draping with sterile sheets" include "sterile sheet pack" and "instrument table."

[0087] In some embodiments, a task execution domain is defined for each task based on the actual storage or installation location of the target object in a three-dimensional spatial coordinate system. The task execution domain is a three-dimensional spatial range. For example, the position of the target object "IV stand" is defined by the coordinates of the bottom support point (1.2, 3.5, 0) and the coordinates of the top hook point (1.2, 3.5, 2.2), and its task execution domain is a cylindrical space with a radius of 0.5 meters centered on the line connecting these two points. The target object "disinfectant container" is stored in a fixed cabinet in the drug supply area, and its task execution domain is a cube corresponding to the cabinet space, with coordinates ranging from (2.1, 6.3, 0.9) to (2.4, 6.6, 1.2). Data comparison shows that the task execution domains defined for different target objects differ in spatial size and shape. For example, the task execution domain of "intravenous puncture kit," as a small item, is a cube with a side length of 0.3 meters, while the task execution domain of "instrument table," as a large piece of equipment, is a cube with a side length of 1.5 meters.

[0088] It's understandable that, considering the associated operations during task execution, the spatial ranges of multiple related target objects are merged to form an extended task execution domain. For example, the task "establishing intravenous access" not only requires manipulating the "IV stand" and "IV puncture kit," but nurses typically need to retrieve a "saline bag" from a specific compartment in the "medication supply area." Therefore, the extended task execution domain for this task is the merged cylindrical space containing the "IV stand," the cubic space containing the "IV puncture kit," and the cubic space containing the "saline bag." The merged extended task execution domain is a minimal outer envelope cubic space that encompasses these three spatial ranges. The calculation of the extended task execution domain is achieved by determining the minimum and maximum coordinate values ​​of the vertices of all associated target object spaces.

[0089] Optionally, a formula for spatial fusion is introduced in the extended task execution domain computation, the formula being:

[0090]

[0091] in: Indicates an extended task execution domain. This represents the original task execution domain defined by the i-th associated target object. Indicates the total number of target objects associated with this task, symbol Union operation representing spatial range.

[0092] In practical implementation, the order of task execution affects the dynamic delineation of the extended task execution domain. For example, the task "anesthesia monitoring" immediately follows "establishing intravenous access," and its target objects, "anesthesia machine" and "monitor," are located near coordinates (4.0, 2.0, 0.8) and (4.5, 2.0, 1.0), respectively. Since the anesthesiologist needs to move between the two devices, the extended task execution domain will cover the space of the nurse's routine operating position connecting these two devices. All task items and their corresponding task execution domains or extended task execution domains are summarized to form a set of nursing tasks to be executed. The nursing task set is stored in the form of a structured list. Each record in the list includes the task item name, the list of bound target objects, and the spatial coordinate boundaries of the task execution domain or the extended task execution domain. In some embodiments, the binding relationship of the target objects is stored in the operating room supplies database. The database records the unique number, name, type, and static or dynamic coordinate range of each item or device in a three-dimensional spatial coordinate system. It is understandable that the definition of the task execution domain considers not only the static position of the target object but also the dynamic space occupancy during operation. For example, when operating the "instrument table," the space occupied by the nurse's body and the range of arm movement are taken into account. The dynamic task execution domain is defined by uniformly buffering outward by 0.6 meters from the static spatial range of the target object. Data comparison shows the change in spatial range before and after buffering. For example, the diagonal length of the static cube space of the "instrument table" is 1.5 meters, and the diagonal length of the task execution domain after buffering expands to 2.7 meters. Optionally, for mobile target objects, their task execution domain is dynamically updated based on real-time positioning information. For example, when the position of the "emergency medicine cart" changes in the operating room, the system obtains its real-time coordinates through positioning tags and defines a cube with a side length of 0.8 meters centered on these coordinates as the task execution domain at the current moment. The set of nursing tasks to be executed, which is composed of these tasks, is re-parsed and updated when the surgical procedure transitions between stages. For example, when transitioning from the "operation start stage" to the "intraoperative operation stage," the system matches a new task requirement template and generates a set of nursing tasks corresponding to the new stage.

[0093] In one embodiment of the present invention, for each task in the nursing task set, the three-dimensional spatial coordinate range of its task execution domain is extracted, and for the personal activity map, historical activity heatmaps of nurses at different levels in each functional area are extracted. The spatial overlap between the three-dimensional spatial coordinate range of the task execution domain and the historical activity heatmap is calculated as a spatial coupling factor, and the frequency of occurrence of behavioral patterns similar to the current task in the personal activity map is analyzed as an empirical coupling factor. The spatial coupling factor and the empirical coupling factor are combined and weighted to obtain a specific coupling degree value for the nurse and the task. All the calculated coupling degree values ​​are arranged into a matrix by traversing all nurses and all tasks to generate a coupling degree matrix.

[0094] In practice, for each task in the nursing task set, the three-dimensional spatial coordinate range of its task execution domain is extracted. For example, the nursing task set includes a task called "passing the scalpel". The task execution domain of this task is defined as a cubic space with a side length of 0.4 meters centered at coordinates (5.2, 4.1, 1.0). Its coordinate range is [5.0, 5.4] on the X-axis, [3.9, 4.3] on the Y-axis, and [0.8, 1.2] on the Z-axis. For extracting the personal activity map, the historical activity heat map of nursing staff at different levels in various functional areas is generated by statistically analyzing the cumulative dwell time of nursing staff in multiple past surgeries within a spatial grid. The spatial grid divides the entire three-dimensional space of the operating room into cubic units with a side length of 0.2 meters. For example, the historical activity heat map of nursing staff with the number N004 in the core sterile area under priority 5 shows that the cumulative dwell time at coordinate grid unit (5,4,1) is 850 seconds, and the cumulative dwell time at coordinate grid unit (5,4.2,1) is 720 seconds.

[0095] In some embodiments, the spatial overlap between the three-dimensional spatial coordinate range of the task execution domain and the historical activity heatmap is used as a spatial coupling factor. The overlap is obtained by calculating the ratio of the sum of the corresponding unit values ​​of all spatial grid cells covered by the task execution domain in the historical activity heatmap of the corresponding nurse to the total number of grid cells covered by the task execution domain. For example, the task execution domain of the task "passing the scalpel" covers 8 grid cells with a side length of 0.2 meters. The historical cumulative dwell time values ​​of nurse N004 in these grid cells are 850, 720, 600, 500, 300, 200, 150, and 100 seconds, respectively, with a total of 3420 seconds. The total number of grid cells is 8, and the spatial coupling factor is calculated to be 427.5 seconds / cell. Data comparison shows the differences in spatial coupling factors between different tasks and the same caregiver. For example, the task execution domain of the task "replacing the negative pressure suction bottle" is located in the waste treatment area. The total cumulative residence time of its covered grid cells in the caregiver's N004 historical activity heat map is only 450 seconds, and the calculated spatial coupling factor is 56.25 seconds / cell.

[0096] It is understandable that the frequency of occurrence of behavioral patterns similar to the current task in the personal activity map is used as an experiential coupling factor. Behavioral patterns are categorized according to task nature; for example, the task of "passing a scalpel" is categorized as the "precision instrument passing" behavioral pattern. The system retrieves the number of times the "precision instrument passing" behavioral pattern occurred in all historical tasks recorded in nurse N004's personal activity map. Assuming a total of 200 historical task executions, and the "precision instrument passing" behavioral pattern appeared 45 times, the experiential coupling factor is calculated to be 0.225. The spatial coupling factor and the experiential coupling factor are combined and weighted to obtain the specific coupling degree value between the nurse and the task, using the following formula:

[0097]

[0098] in: This indicates the specific coupling value between the nursing staff and the task. This represents the spatial coupling factor after normalization. This represents the empirical coupling factor. and It is a preset weighting coefficient and satisfies .

[0099] In practice, all nursing staff and all tasks are iterated through, and all the calculated coupling values ​​are arranged into a matrix to generate a coupling matrix. Assuming there are 3 nursing staff in the operating room, numbered N004, N005, and N006, and the current nursing task set contains 4 tasks, the calculated coupling matrix is ​​a 3x4 matrix. The matrix elements represent the coupling value between the i-th nursing staff and the j-th task. For example, the value 0.85 in the first row and first column of the matrix represents the coupling value between nursing staff N004 and the task "passing the scalpel", and the value 0.62 in the first row and second column represents the coupling value between nursing staff N004 and the task "changing the negative pressure suction bottle".

[0100] In some embodiments, the spatial coupling factor calculation considers the time decay of historical activity heatmaps, assigning higher weight to recent historical data. For example, an exponentially weighted moving average method is used to update the dwell time value within a grid cell, making the weight of activity data from one year ago lower than that from one week ago. It is understood that the classification of behavioral patterns follows the standard classification of operating room nursing operations, which divides nursing operations into several major categories and subcategories, including "instrument delivery," "medication preparation," "vital sign monitoring," "sterile area maintenance," and "waste disposal." The calculation of the experience coupling factor considers not only frequency but also historical success rate data of behavioral pattern execution, extracted from surgical records. Optionally, the sum of the weighting coefficients can be adjusted through the system configuration interface to adapt to different operating room management strategies; for example, a strategy emphasizing spatial familiarity could be set to 0.7. Set to 0.3. Parallel computation is used when calculating the coupling degree value, with each nurse-task pair calculated as an independent thread to improve matrix generation speed. The generated coupling degree matrix is ​​recalculated when the task set or nurse composition changes, such as when a new nurse joins the schedule or a new task is generated at a new stage of the surgical procedure.

[0101] In one embodiment of the invention, each nurse is assigned a task execution order from their candidate task subset. Based on a three-dimensional spatial coordinate system, the shortest spatial movement path for each nurse is planned, starting from their current position and proceeding sequentially to each task execution domain. All nurses' shortest spatial movement paths are superimposed and projected onto the three-dimensional spatial coordinate system. The presence of intersections or adjacent points in the spatial coordinates of different nurses' movement paths within the same time slice is detected. If intersections or adjacent points exist, the estimated personnel density and spatial margin at these intersections or adjacent points within the same time slice are calculated. The level of mutual interference between different nurses' task execution paths is quantitatively assessed based on the estimated personnel density and spatial margin as the degree of spatial interference. A spatial interference threshold is set to identify path conflict points where the degree of spatial interference exceeds the threshold, along with the tasks and nurses involved. The candidate task subsets of nurses involved in path conflicts are adjusted by either changing the task execution order or replacing them with other candidate tasks with suboptimal coupling but no path conflict. After adjustments, path planning and spatial interference calculations are performed again to form a new task allocation scheme. This adjustment and recalculation process is repeated iteratively until the spatial interference at all path conflict points is below the spatial interference threshold or the preset maximum number of iterations is reached. The final task allocation scheme that meets the conditions is solidified into a preliminary nursing task assignment plan, which clearly defines the specific task sequence that each nurse must perform within a specific time window.

[0102] In practice, each nurse is assigned a task execution order from their candidate task subset. For example, the candidate task subset for nurse N007 includes task A "passing hemostats" and task B "replenishing sterile gauze". The system determines the execution order as task A takes precedence over task B based on the task's preset urgency or dependency. The candidate task subset for nurse N008 includes task C "adjusting light position" and task D "counting instruments". The execution order is determined as task C takes precedence over task D. Based on a three-dimensional spatial coordinate system, the system plans the shortest spatial movement path for nurses to proceed sequentially to each task execution domain from their current position. The current coordinates of nurse N007 are (2.0, 2.0, 1.0), the center coordinates of the task execution domain for task A are (5.0, 4.0, 1.1), and the center coordinates of the task execution domain for task B are (6.0, 6.0, 0.9). The system uses the A* search algorithm to plan a continuous polyline path from the starting point to task A and then to task B in a three-dimensional grid map that considers equipment obstacles. The current coordinates of nurse N008 are (1.0, 7.0, 1.0), and their paths to tasks C and D are planned in the same way.

[0103] In some embodiments, the shortest spatial movement paths of all caregivers are superimposed and projected onto a three-dimensional spatial coordinate system. The projection discretizes the time dimension into time slices with 0.5-second intervals, and the estimated three-dimensional coordinates of the caregivers along their paths are calculated within each time slice. The system detects whether there are intersections or adjacent points in the spatial coordinates of the movement paths of different caregivers within the same time slice. An intersection is defined as the estimated coordinates of different paths being completely identical within the same time slice, and an adjacent point is defined as the Euclidean distance between the estimated coordinates of different paths within the same time slice being less than 0.5 meters. Data comparison reveals potential conflicts in the path planning results. For example, at time slice t=45, the coordinates of the path points for caregiver N007 moving from task A to task B are (5.6, 4.8, 1.0), and the coordinates of the path points for caregiver N008 moving from task C to task D are (5.5, 4.9, 1.0). The distance between the two points is 0.14 meters, and they are identified as adjacent points. It is understandable that if there are intersections or adjacent points, the estimated personnel density and space margin at the intersections or adjacent points of the paths in the same time slice are calculated. The estimated personnel density is obtained by counting the number of nursing staff expected to be simultaneously located in a spherical space with a radius of 1 meter centered on the conflict point within the conflict time slice. The space margin is obtained by calculating the difference between the volume of the spherical space, the volume occupied by the fixed equipment in the space, and the volume occupied by the estimated nursing staff mannequin.

[0104] Optionally, the degree of spatial interference is calculated using the formula:

[0105]

[0106] in: Indicates the degree of spatial interference. This indicates the estimated number of caregivers simultaneously located in the conflict zone during the conflict period. This represents the average equivalent area occupied by a single caregiver on a horizontal surface. This represents the effective horizontal area available for free movement at the conflict point after deducting fixed obstacles. See Table 1 for path conflict point data in some embodiments.

[0107] Table 1: Spatial Interference Data of Path Conflict Points

[0108]

[0109] In practical implementation, a spatial interference threshold is set to identify path conflict points where the degree of spatial interference exceeds the threshold, as well as the tasks and nursing staff involved. For example, if the spatial interference threshold is set to 0.3, then the spatial interference degree of conflict point P1 in Table 1 is 0.199, which is below the threshold, while the spatial interference degree of conflict point P2 is 0.446, which exceeds the threshold. The system identifies that conflict point P2 involves nursing staff N007 and N008 and their currently assigned task sequences. The candidate task subsets of nursing staff involved in path conflicts are adjusted by changing the task execution order or replacing them with other candidate tasks with suboptimal coupling but no path conflict. For example, in the candidate task subset of nursing staff N007, in addition to tasks A and B, there is also task E "record vital signs" with a slightly lower coupling degree. The system attempts to change the task sequence of nursing staff N007 from AB to BA. If the spatial interference degree still exceeds the threshold after recalculation after the change, the system further attempts to replace task B with task E.

[0110] In some embodiments, after adjustment, path planning and spatial interference degree calculation are re-performed to form a new task allocation scheme. For example, after replacing the task of nursing staff N007 with task E, its new path no longer passes through the vicinity of coordinates (3.10, 3.20, 1.0). The estimated number of people recalculated in time slice 62 decreases to 1, the effective horizontal area increases to 2.01, and the newly calculated spatial interference degree is 0.124, which is lower than the threshold of 0.3. The adjustment and recalculation process is iterated until the spatial interference degree of all path conflict points is lower than the spatial interference threshold, or the preset iteration limit is reached. For example, the system sets the iteration limit to 10 times, and the above adjustment meets the conditions after the second iteration. The final task allocation scheme that meets the conditions is solidified into a preliminary nursing task assignment plan. The nursing task assignment plan clarifies the specific task sequence that each nurse needs to perform within a specific time window. For example, the assignment plan is recorded in the form of a timeline: Nursing staff N007 performs task A from 10:05:00 to 10:05:45 and task E from 10:06:00 to 10:06:30; Nursing staff N008 performs task C from 10:05:10 to 10:05:40 and task D from 10:05:50 to 10:06:40.

[0111] It is understandable that the spatial interference threshold can be dynamically adjusted based on the size of the operating room and the number of personnel. For example, a higher threshold can be used for large operating rooms, and a lower threshold for small operating rooms. During the iteration process, any changes to the task execution order must adhere to the preconditions between tasks. For instance, if task B depends on the completion of task A, the order cannot be changed so that B precedes A. Optionally, the candidate source for replacement tasks is the tasks ranked after the optimal task in the individual coupling degree matrix of the nursing staff. The system attempts to replace tasks one by one in descending order of coupling degree value until a task that satisfies the spatial interference constraints is found. Obstacle information in path planning includes 3D models of the operating table, large equipment, and fixed furniture, which are mapped to obstruction cells in a 3D raster map. The estimated volume occupied by the nursing staff's human body model is approximated using a standard ellipsoidal model, with its size parameters set based on the average body size of the nursing staff. In practice, the standard ellipsoid model is used to approximate the volume occupied by the human body model of nursing staff. This model simplifies the human body into a three-dimensional ellipsoid, and its size parameters are set according to the average body shape data of the nursing staff group. For example, the height, shoulder width and chest thickness of nursing staff in industry standards are referenced. The major axis of the ellipsoid corresponds to the height direction of the human body, and the minor axis corresponds to the shoulder width and chest thickness direction. The semi-axis length of the ellipsoid is determined by taking the average value of the historical body shape data of nursing staff in the operating room, thereby generating a geometric body in the three-dimensional coordinate system that can represent the space occupied by a typical nursing staff.

[0112] See Figure 4 During the configuration phase of the operating room nursing task system, the effective area (unit: m²) of five functional areas, including the core sterile area and instrument preparation area, and the corresponding spatial interference thresholds were simultaneously presented. Specifically, the effective area of ​​each functional area showed a fluctuating distribution: the effective area of ​​the core sterile area was 8.5 m², corresponding to a spatial interference threshold of 0.25; the effective area of ​​the instrument preparation area increased to 12.3 m², and the spatial interference threshold increased accordingly to 0.3; the effective area of ​​the drug supply area decreased to 9.8 m², and the threshold dropped to 0.28; the effective area of ​​the waste disposal area further decreased to 7.2 m², and the threshold dropped accordingly to 0.22; the effective area of ​​the personnel passage reached 15.6 m², and the corresponding spatial interference threshold increased significantly to 0.35. The logic of the diagram is consistent with the design principle of "spatial interference threshold must match the spatial conditions of functional areas" in the intelligent allocation of operating room nursing tasks: the more spacious the functional area (such as personnel passage), the higher the redundancy of personnel activities it can accommodate, and therefore the higher the spatial interference threshold; the relatively compact area (such as the waste disposal area) has limited space for personnel activities, and the corresponding interference threshold is lower, so as to ensure the spatial orderliness of nursing tasks in different areas.

[0113] In one embodiment of the invention, the current operating mode, estimated end time, and fault status of key surgical equipment are read via an IoT interface as equipment operating status data. The real-time location, sterilization status, and current usage status of surgical instruments are obtained via RFID or computer vision technology as real-time surgical instrument usage data. Each task's required equipment or instruments are checked against the task sequence in the nursing task assignment plan. If the required equipment is faulty, occupied, or not available or in standby condition, the task is determined to be unavailable at the current time. All unavailable tasks are recorded, their reasons for unavailability are marked, and their estimated availability time is identified. Based on the resource availability verification results, the nursing task assignment plan is fine-tuned in the time dimension to postpone unavailable tasks until their required resources become available. For tasks that must be executed immediately due to resource issues, other available nursing personnel with currently accessible resources are searched in the coupling matrix for replacement assignment. The task assignment plan, after time fine-tuning and personnel replacement, is then integrated and formatted according to the timeline. Generate a structured task assignment list that includes nursing staff identification, task content, execution location, start time, and end time, and send the structured task assignment list to the corresponding display terminal in the operating room or the nursing staff's mobile terminal.

[0114] In practice, the current operating mode, estimated end time, and fault status of key surgical equipment are read through the IoT interface as equipment operating status data. The IoT interface is connected to the equipment monitoring system in the operating room and polls the system once per second. For example, the query objects include anesthesia machines, electrosurgical units, monitors, and infusion pumps. The data read is in JSON string format and contains the device ID, operating status enumeration value, current mode, estimated remaining time of the current task, and error code. The data packet for the anesthesia machine with device ID AM-01 shows: operating status "running", current mode "capacity control", estimated remaining time 120 seconds, and no error code. By using radio frequency identification (RFID) or computer vision technology, the real-time location, sterilization status, and current usage status of surgical instruments are obtained as real-time usage data. RFID technology is achieved by reading passive RFID tags attached to instrument trays, with readers installed on the ceilings of various functional areas in the operating room. Computer vision technology uses high-definition cameras deployed near the instrument table and operating table to capture instrument images and uses a trained convolutional neural network model for identification and status classification. For example, the RFID tag ID of the instrument "curved hemostat" is bound to the database, and the system reads its current location as "Instrument Table Area A" and its status as "sterilized and ready for use." Through visual recognition, the system confirms that a "tissue scissors" is in the doctor's hand, and its status is marked as "in use."

[0115] In some embodiments, the system checks the equipment or instruments required for each task against the task sequence in the nursing task assignment plan. The nursing task assignment plan is stored in list form, and each item in the list includes a task ID, task name, planned executor, planned start time, planned end time, and a list of required resource IDs. For example, the task "Adjust anesthesia machine parameters" with task ID T005 is planned to be executed by nurse N009 at 10:15:00, and the list of required resource IDs includes anesthesia machine AM-01. Based on the list of resource IDs, the system searches for the current status of equipment AM-01 in the equipment operation status data and the current status of the required instruments in the real-time surgical instrument usage data. If the equipment required for a task is faulty or occupied, or if the required instrument is not in an available or standby position, the task is determined to be unreachable at the current time. For example, when checking task T005, if the equipment operation status data shows that the current operation status of the anesthesia machine AM-01 is "faulty" and the error code is "E101 air pressure sensor abnormal", the system determines that the resource for task T005 is unreachable. Similarly, when checking task T006 "passing needle holder", if the real-time usage data of the surgical instruments shows that the status of the required needle holder is "cleaning" and the location is in the "waste treatment area cleaning tank", the system determines that the instrument is not in an available or standby position, and the resource for task T006 is unreachable.

[0116] Understandably, the system records all tasks where resources are unreachable, marking the reasons for their unreachability and their estimated availability time. Internally, the system maintains a resource-unreachable task record table. For example, a record is created for task T005, marked with the reason "Equipment Failure: Anesthesia Machine AM-01," and the estimated availability time is marked as "Unknown." A record is created for task T006, marked with the reason "Instrument Status Unavailable: Needle Holder," and the estimated availability time is estimated to be 1800 seconds after the current time based on the cleaning and disinfection standard procedure. Based on the resource reachability verification results, the nursing task assignment plan is fine-tuned in the time dimension, postponing tasks with unreachable resources until their required resources become available. The fine-tuning algorithm uses a time shift strategy. For example, task T006 was originally scheduled to start at 10:20:00, but since its required equipment is expected to be available at 10:35:00, the system postpones the planned start time of task T006 to 10:35:00, and the planned times of subsequent tasks are postponed accordingly.

[0117] Optionally, for tasks that must be executed immediately due to resource constraints, the system searches the coupling matrix for other nurses who can perform the task and whose resources are currently available, and then replaces them with other nurses. Tasks that must be executed immediately are identified by surgical procedure logic or emergency instructions from doctors. For example, task T007 "Emergency Medication" is marked as high priority and cannot be delayed, but the current inventory of the specific emergency medication required by the originally assigned nurse N010 is 0. The system queries the coupling matrix for the coupling degree values ​​of all nurses except N010 for task T007, and simultaneously checks whether these nurses can currently obtain alternative medications or whether the original medications have been replenished. Assuming that nurse N011's coupling degree value for task T007 is second only to N010, and the system detects that the medication status in the medicine cabinet in N011's area is "available," then the system replaces the assignee of task T007 from N010 to N011. The task assignment plan, after time fine-tuning and personnel replacement, is then integrated and formatted according to the timeline.

[0118] In practice, timeline integration sorts the adjusted discrete task items according to their planned start times, ensuring that tasks for the same caregiver do not overlap in time. The formatting process converts the data into a clearly structured tabular format. A structured task assignment list is generated, containing caregiver identification, task content, execution location, start time, and end time. This structured task assignment list is described in XML format, with the root element being... <schedule>Each task is a <task>Element, including child elements <nurseid> 、 <taskcontent> 、 <location> 、 <starttime>and <endtime>For example, a <task>The element's content is: N011 Emergency Drug Supply Area - First Aid Cabinet B10:18:30 10:19:10. The structured task assignment list is sent to the corresponding display terminal or nursing staff mobile terminal in the operating room. The display terminal refers to the LCD screen installed on the operating room wall. The system pushes the XML data to the display screen's backend service via the local area network, where the service parses and renders it into an intuitive Gantt chart or list view. The nursing staff mobile terminal refers to a smart bracelet worn on the wrist or a handheld tablet. Data is transmitted wirelessly; simplified text reminders are displayed on the bracelet, and a complete task list and navigation prompts are displayed on the tablet.

[0119] In some embodiments, the calculation of the expected available time points follows the formula:

[0120]

[0121] in: Indicates the expected time point when the resource will be available. Indicates the current system time. This indicates the estimated recovery time based on the resource type and current status, for equipment failures. The maintenance assessment time is provided by the equipment system's self-diagnostic module or set to a default value; for instrument cleaning, The duration is set according to the standard cleaning and disinfection process. When assigning replacements, the query of the coupling matrix needs to be combined with the current workload of the nursing staff, and only nursing staff with idle time periods within the current planned task time window will be queried.

[0122] Understandably, the determination of resource unavailability is continuously monitored. When equipment recovers from a malfunction or the instrument status changes to "sterilized and ready for use," the system automatically updates the resource status and reassesses the accessibility of related postponed tasks. The structured task assignment list is version-coded before being sent to ensure that the information displayed on the terminal is the latest version. Upon receiving a new task assignment list, the mobile terminal will vibrate or make an audible alert to remind nursing staff to check it.

[0123] See Figure 5 In the coupling degree calculation stage of intelligent allocation of operating room nursing tasks, a matrix intuitively presents the coupling degree matching relationship between different nurses and various nursing tasks. Specifically, the row dimension of the matrix is ​​the nurse ID, and the column dimension is the task name. The values ​​in the matrix, combined with the color scale on the right (the higher the coupling degree value, the better the matching degree), quantify the degree of fit between nurses and tasks. For example, the coupling degree value of nurse N002 with the "passing needle holder" task is 0.85, and the coupling degree values ​​of nurses N003 and N009 with "adjusting electrosurgical parameters" and "adjusting anesthesia machine parameters" are 0.90, respectively. These high coupling degree items reflect that the corresponding nurses have better overall performance in terms of spatial coupling factor (spatial overlap between the task execution domain and the historical activity heatmap) and experience coupling factor (frequency of similar behavior patterns) in this type of task. On the other hand, the coupling degree value of N004 with each task is in the range of 0.10-0.15, indicating that its fit with the current task set is relatively low. As one of the core bases for task assignment, the matrix can be further combined with spatial interference analysis, resource accessibility verification, and other steps to further screen and optimize the candidate task subset for nursing staff, and finally form a reasonable task allocation scheme.

[0124] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0125] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.< / task> < / endtime> < / starttime> < / location> < / taskcontent> < / nurseid> < / task> < / schedule>

Claims

1. A method for intelligent allocation of operating room nursing tasks, characterized in that, include: A three-dimensional spatial coordinate system is established in the operating room, dividing the operating room into multiple functional areas with a hierarchical structure; Capture the position and posture information of the nursing staff in the three-dimensional spatial coordinate system; Based on the location information and posture information, and combined with the hierarchical structure of the multiple functional areas, a personal activity map is generated for each nursing staff member. Extract the stage features of the current surgical procedure, and match the preset task requirement template according to the stage features; Based on the task requirement template, the set of nursing tasks to be executed and their corresponding spatial execution domains are parsed out; The coupling degree is calculated between the spatial execution domain of the nursing task set and the personal activity map to generate a coupling degree matrix; Based on the coupling degree matrix, a subset of candidate tasks is selected for each nurse from the set of nursing tasks; For the subset of candidate tasks, the path movement of nursing staff in a three-dimensional spatial coordinate system during task execution is simulated, and the degree of spatial interference between tasks is calculated. The candidate task subset is modified based on the degree of spatial interference to form a preliminary nursing task assignment plan; Acquire operating status data of equipment and real-time usage data of surgical instruments in the operating room, perform resource accessibility verification on the nursing task assignment plan, and output the final task allocation list.

2. The intelligent allocation method for operating room nursing tasks as described in claim 1, characterized in that, The division of the operating room into multiple functional areas with a hierarchical structure includes: Identify the core location of the operating table within the operating room space and define the core sterile zone with the operating table as the center. Around the core sterile area, based on the fixed layout of surgical equipment, instrument tables, and medicine cabinets, an instrument preparation area, a medicine supply area, and a waste disposal area are divided. Outside the equipment preparation area, drug supply area and waste disposal area, define personnel access and temporary supply channels; Different spatial access priorities are assigned to the core sterile area, instrument preparation area, drug supply area, waste disposal area, and personnel passage and temporary supply passage, forming the hierarchical structure.

3. The intelligent allocation method for operating room nursing tasks as described in claim 1, characterized in that, Based on the location information and posture information, and combined with the hierarchical structure of the multiple functional areas, a personal activity map is generated for each caregiver, including: Continuously record the location information of nursing staff over a period of time to form the original movement trajectory; By analyzing the body orientation and hand gestures in the posture information, the potential operational intentions of the nursing staff can be identified; The original movement trajectory is associated with the potential operational intent, and different behavioral segments in the trajectory corresponding to observation, preparation, operation, and movement are marked. The labeled behavioral fragments are mapped to the multiple functional areas, and the dwell time and behavioral patterns of nursing staff are statistically analyzed under different access priority levels in each functional area. Based on the aforementioned stay duration and behavioral patterns, a personal activity map containing spatiotemporal behavioral characteristics is constructed, indexed by the nursing staff's identity.

4. The intelligent allocation method for operating room nursing tasks as described in claim 1, characterized in that, The process of parsing the set of nursing tasks to be executed and their corresponding spatial execution domains based on the task requirement template includes: Read the standard task items, task execution order, and task prerequisites defined in the task requirement template; The standard task items are bound to specific items and equipment in the operating room to determine the target object to be operated on for each task; Based on the actual storage or installation location of the target object in the three-dimensional spatial coordinate system, a task execution domain is defined for each task, and the task execution domain is a three-dimensional spatial range. Considering the associated operations during task execution, the spatial ranges of multiple related target objects are merged to form an extended task execution domain; All task items and their corresponding task execution domains or extended task execution domains are aggregated to form the set of nursing tasks to be executed.

5. The intelligent allocation method for operating room nursing tasks as described in claim 1, characterized in that, The step of calculating the coupling degree between the spatial execution domain of the nursing task set and the personal activity graph to generate a coupling degree matrix includes: For each task in the set of nursing tasks, extract the three-dimensional spatial coordinate range of its task execution domain; For the aforementioned personal activity map, extract the historical activity heat map of nursing staff at different levels in each functional area; The spatial overlap between the three-dimensional spatial coordinate range of the task execution domain and the historical activity heatmap is calculated and used as a spatial coupling factor. Analyze the frequency of occurrence of behavioral patterns similar to the current task in the personal activity graph, and use it as an experience coupling factor; By combining the spatial coupling factor and the empirical coupling factor, a specific coupling degree value for nursing staff and tasks is obtained through weighted calculation. By iterating through all nursing staff and all tasks, all calculated coupling values ​​are arranged into a matrix to generate the coupling matrix.

6. The intelligent allocation method for operating room nursing tasks as described in claim 1, characterized in that, For the subset of candidate tasks, the simulation of the path movement of nursing staff in a three-dimensional spatial coordinate system during task execution, and the calculation of the spatial interference between tasks, include: Assign a task execution order from each nurse's subset of candidate tasks; Based on the three-dimensional spatial coordinate system, plan the shortest spatial movement path for nursing staff to move sequentially to each task execution domain from their current position. The shortest spatial movement paths of all nursing staff are superimposed and projected in a three-dimensional spatial coordinate system; Detect whether there are intersections or adjacent points on the spatial coordinates of the movement paths of different nursing staff in the same time slice; If there are intersections or adjacent points, then the estimated personnel density and space margin at the intersections or adjacent points of the paths are further calculated under the same time slice. Based on the estimated personnel density and space margin, the level of mutual interference between different nursing staff task execution paths is quantitatively assessed as the degree of spatial interference.

7. The intelligent allocation method for operating room nursing tasks as described in claim 6, characterized in that, The step of modifying the candidate task subset based on the degree of spatial interference to form a preliminary nursing task assignment plan includes: Set a spatial interference threshold, and identify path conflict points where the degree of spatial interference exceeds the spatial interference threshold, as well as the tasks and caregivers involved; Adjust the subset of candidate tasks for nursing staff that involve path conflicts by changing the task execution order or replacing them with other candidate tasks that have suboptimal coupling but no path conflicts. After the adjustment, the path planning and spatial interference were recalculated to form a new task allocation scheme; The adjustment and recalculation process is repeated iteratively until the spatial interference of all path conflict points is lower than the spatial interference threshold, or the preset maximum number of iterations is reached. The final task allocation scheme that meets the conditions is solidified into the preliminary nursing task assignment plan, which clarifies the specific task sequence that each nurse needs to perform within a specific time window.

8. The intelligent allocation method for operating room nursing tasks as described in claim 1, characterized in that, The process of acquiring operating room equipment status data and real-time surgical instrument usage data, and performing resource accessibility verification on the nursing task assignment plan, includes: The current operating mode, estimated end time and fault status of key surgical equipment are read through the Internet of Things interface as equipment operating status data. By using radio frequency identification or computer vision technology, the real-time location of surgical instruments, whether they have been sterilized and prepared, and their current usage status can be obtained as real-time usage data of surgical instruments; Check the equipment or instruments required for each task against the task sequence in the nursing task assignment plan. If the equipment required for the task is faulty or occupied, or the required equipment is not in an available or standby position, then the task is determined to be unreachable at the current moment. Record all tasks for which resources are unreachable, and mark the reasons for their unreachability and the expected time when they will be available.

9. The intelligent allocation method for operating room nursing tasks as described in claim 8, characterized in that, The final task assignment list output includes: Based on the results of the resource accessibility check, the nursing task assignment plan is fine-tuned in the time dimension, postponing tasks that are not accessible due to unavailable resources until the resources they require become available. For tasks that must be performed immediately due to resource issues, alternative caregivers who can perform the task and whose current resources are available are searched in the coupling matrix and reassigned. The task assignment plan, after time adjustments and personnel replacements, is integrated and formatted according to the timeline; Generate a structured task assignment list that includes nursing staff identification, task content, execution location, start time, and end time; The structured task assignment list is sent to the corresponding display terminal in the operating room or the mobile terminal of the nursing staff.

10. An intelligent operating room nursing task allocation system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent allocation method for operating room nursing tasks as described in any one of claims 1 to 9.

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