Method for optimizing medical building space layout based on patient flow line and operation and maintenance cooperation

Abstract

The application discloses a kind of based on patient streamline and operation and maintenance cooperation's medical building space layout optimization method, 1, multi-source data acquisition and processing: collection target hospital historical operation data;2, construct patient streamline double-target optimization model: objective function 1: patient total medical treatment passage time is shortest;Objective function 2: the lowest risk of cross infection in whole hospital;3, generate initial layout scheme 4, facility cooperation preset design;5, scheme evaluation and iterative optimization;6, dynamic self-adapting adjustment in operation and maintenance stage.The application has the following remarkable beneficial effects: 1, improve patient treatment passage efficiency;2, reduce the risk of cross infection in hospital;3, realize the intelligent integrated design of medical building;4, give building space full life cycle flexibility, effectively prolong the service life of building, reduce the transformation cost of full life cycle, with remarkable social and economic benefits.

Description

Technical Field

[0001] This invention relates to medical building design, and in particular to a method for optimizing the spatial layout of medical buildings based on patient flow and operation and maintenance collaboration. Background Technology

[0002] Currently, medical building design generally suffers from the problem of "emphasizing function over experience," and the spatial layout of buildings is disconnected from the subsequent intelligent operation and maintenance system, resulting in the following technical defects: Disorganized patient flow: An unreasonable functional layout of outpatient, medical technology, and inpatient departments can lead to patients moving inefficiently between different departments, causing traffic congestion, increasing distances, and resulting in a poor medical experience.

[0003] Low space utilization: The functional zoning of departments is fixed at the design stage, lacking spatial flexibility to cope with changes in the disease spectrum or sudden public health events (such as respiratory infectious disease outbreaks), and building renovation is difficult and costly.

[0004] "Post-construction" of smart facilities: The pipeline channels and equipment installation points of facilities such as dynamic guide screens, logistics robots, and telemedicine were not reserved in advance. Secondary modification and installation after the building is completed not only damages the finished surface of the building, but also increases construction costs and reduces system efficiency.

[0005] High operation and maintenance costs: The lack of dynamic prediction and guidance of people and goods flow leads to congestion during peak hours and low operation and maintenance management efficiency. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a method for optimizing the spatial layout of medical buildings based on patient flow and operation and maintenance collaboration, so as to optimize the medical space layout during the design phase of medical building schemes and achieve dynamic adjustment during the operation phase.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: The medical building space layout optimization method based on patient flow and operation and maintenance collaboration described in this invention includes the following steps: S1, Multi-source data acquisition and processing: Collect historical operational data from the target hospital, including: 1. Outpatient volume, medical examination appointment volume, and inpatient bed turnover rate for each department; 2. Patient movement trajectory data: Data collected from in-hospital turnstiles, location beacons, and continuous spatial coordinates, time periods, and points of stay throughout the entire process of a patient's journey from entrance, waiting, examination, medication dispensing to discharge; 3. Frequency of interdepartmental logistics transfer: Data on the origin, destination, time period, single transport volume, and daily round-trip frequency of four types of automated / manual logistics: hospital waste transfer, medicine and consumable delivery, specimen delivery, and linen recycling. Historical operational data was cleaned and standardized to build a "medical behavior-space demand" database, providing a data foundation for subsequent spatial layout optimization; S2, Construct a dual-objective optimization model for patient flow: The dual-objective optimization model includes: Objective function 1: Minimize the total patient travel time to medical care, expressed as MinΣ(N i ×L ij ×C j ); Objective function 2: Minimize the risk of cross-infection throughout the hospital, expressed as MinΣ(R k ×D k ×T k ); in: N i : The total number of patients in category i, where i and j are used to distinguish different medical departments; L ij The actual walking distance between the corresponding departments represents the total actual walking distance of the i-th type of patient from the starting department i to the target department j on the building plane; C j The congestion coefficient of patients in the actual route, with a value range of 1-3; the corridor pedestrian density is 1 for <1 person / ㎡, 2 for 1-2 people / ㎡, and 3 for >2 people / ㎡; R k Infection risk coefficient: 0.8-1.0 for high-risk areas, 0.3-0.5 for medium-risk areas, and below 0.1 for low-risk areas; D k People density represents the number of patients simultaneously present in a given area (k units), expressed in person / m². T k : Stay time, representing the average length of time a patient stays in risk area k, in minutes; The goal of minimizing the total patient travel time is defined as minimizing the total walking distance between medical departments. The goal of minimizing the risk of cross-infection throughout the hospital is to minimize the instantaneous population density in high-risk areas. First, retrieve the database for "Medical Behavior - Spatial Requirements" from step S1; Then, an initial candidate department layout is generated (based on area requirements and column grid placement). The following three constraints must be forcibly loaded during the solution of the bi-objective optimization model: 1. Clean and contaminated flow separation constraint: The flow lines of clean items (clean consumables, sterile instruments, meal preparation, etc.) and contaminated items (medical waste, contaminated linens, etc.) must be independent of each other in terms of spatial path and must not share the same passage. They must be provided with their own dedicated horizontal and vertical traffic channels, or take spatial isolation measures such as staggered operation at different times; this constraint is judged by the "no intersection of flow lines" in the building plan topology. 2. Department Proximity Constraints: Based on the frequency of medical collaboration between departments and patient referral traffic data, a walking distance limit threshold is set for related departments (such as the emergency department and radiology department ≤ 50m apart, the operating room and intensive care unit (ICU) on the same floor or adjacent floors, the outpatient pharmacy and the cashier, etc.). In the layout scheme, if the actual walking distance between related departments exceeds the upper limit threshold, it is determined to be an invalid layout and is excluded. 3. Structural column grid adaptation constraints: The floor plan layout boundaries of each department must be adapted to the modular system of the building's structural load-bearing column grid to ensure that the area and shape of each department can be fully positioned within the structural grid, avoiding invalid and fragmented spaces across the column grid; Among the candidate layout schemes that meet the above three constraints, two indicators are comprehensively evaluated: walking distance to medical care and population density in high-risk areas, and several better schemes are selected as the initial layout scheme set. S3, Generate the initial layout scheme: Using optimization algorithms (such as genetic algorithms or simulated annealing algorithms), based on the bi-objective optimization model and constraints constructed in step S2, the initial layout scheme set is iteratively solved to automatically generate multiple initial layout schemes that satisfy the constraints; each generated initial layout scheme contains the following three spatial association information: 1. Area Information: The building area and usable area occupied by each department in the building plan; 2. Location Information: The specific coordinates of each department on the building's floor plan and the floor it is located on; 3. Interconnectivity: Topological adjacency between departments (whether they share walls, vertical correspondence between upper and lower floors, etc.), connectivity (whether they are directly connected by level doors, vertically connected by dedicated elevators, etc.), and visual accessibility (whether they can be directly seen in the corridor). The initial layout scheme set generated above is output in the form of a building floor plan; providing a spatial carrier for subsequent pre-installation of facilities. S4, Facility Collaborative Pre-configuration Design: Based on the initial layout scheme generated in step S3, the facility coordination pre-configuration scheme design is carried out simultaneously, and the specific steps include: S4.1 Wayfinding point planning: Based on the patient flow prediction results in step S2, dynamic wayfinding information screens are deployed at the nodes along the path that patients must pass through. At the same time, according to the probability distribution of patients going to each department at different times at each of the path nodes, the data interface and control logic of the wayfinding content are preset so that the wayfinding system can automatically switch the guidance information according to the real-time congestion situation during the operation phase. S4.2, Logistics Robot Path Planning: Based on the data on the frequency and volume of inter-departmental logistics transmission collected in step S1, a dedicated operating channel for the logistics robots throughout the hospital is planned. This dedicated channel is physically separated from the main pedestrian flow path in terms of floor plan layout to avoid conflicts between the logistics robots and pedestrian flow during operation. At the same time, the installation points and power supply capacity of charging piles are preset at appropriate locations on the operating path of the logistics robots on each floor. S4.3, Flexible Space Calibration: Based on the heat map data of patient flow density distribution in step S3, identify public areas in the building where people are prone to gather (such as outpatient hall, waiting area, rest area, medication waiting area, etc.), and define these public areas as "flexible spaces"; in the architectural design scheme, the following hardware foundations are preset for the above-mentioned flexible spaces: 1. Movable lightweight partition slide rail channels pre-embedded in structural floor slabs or ceilings; 2. Reserve quick-connect ports for both high and low voltage electrical connections (to meet the needs of temporarily adding medical equipment and information terminals); 3. Reserve quick-connect interface for drainage (to meet the needs of temporarily adding handwashing facilities or medical gas terminals); The aforementioned pre-set hardware infrastructure provides hardware support for the rapid switching of the building between "normal mode" and "public health emergency mode," avoiding damage to the main structure of the building caused by secondary demolition and alteration during the operation phase; S5, Solution Evaluation and Iterative Optimization: Using digital twin technology, the collaborative pre-defined scheme generated in step S4 is simulated in multiple scenarios, including: Weekday scenario: Average daily patient volume based on historical data; Peak period scenario: Peak patient flow based on historical data; Public health emergency scenario: Simulating a situation where there is a surge in fever patients and a need to rapidly expand the isolation and waiting area; Through simulation, the three core indicators of each collaborative pre-built solution are quantitatively evaluated under the above three scenarios: 1. Patient flow efficiency (average walking distance and waiting time to seek medical treatment); 2. Building space utilization rate (area utilization rate and vacancy rate of each functional area); 3. Facility coverage (percentage of wayfinding points covering path length, connectivity of logistics robot channels, etc.); Based on the simulation evaluation results, the constraint parameters of the bi-objective optimization model in step S2 (such as the department proximity threshold, the population density limit, etc.) are adjusted. Steps S3 to S5 are repeated to form a closed-loop iterative process of "generation-simulation-feedback-regeneration" until the optimal layout scheme that meets the preset evaluation criteria in all scenarios is obtained. Step S6, Dynamic Adaptive Adjustment during the Operation and Maintenance Phase: After the target medical building is constructed and put into use according to the optimal layout scheme, the system continuously collects real-time data on pedestrian density, turnstile access, and departmental queuing and calling systems. When the system detects that the congestion index of a certain area exceeds a preset threshold, or when the demand in a certain department surges due to a seasonal disease outbreak, the system automatically triggers the following dynamic adjustment mechanism: 1. Soft adjustment mechanism (information guidance): The system pushes diversion guidance information to patients through the hospital's official information release platform (including in-hospital dynamic guidance screens, mobile APP and WeChat mini program), guiding patients to adjacent empty examination departments or suggesting that they visit the hospital at different times to avoid the current peak hours, thereby realizing the spatial and temporal redistribution of the flow of people; 2. Hard adjustment mechanism (space conversion): When the soft adjustment mechanism is insufficient to alleviate congestion, the system sends a control command to the electric drive device pre-embedded in the elastic space, automatically activating the pre-set movable lightweight partition, completing the re-division of the physical space partition in a short time, realizing the functional conversion of the waiting area into a temporary triage area, or expanding the waiting capacity of a specific department.

[0008] Compared with the prior art, the present invention has the following significant advantages: 1. Improve patient access efficiency: By shortening the average walking distance between major departments from the architectural design stage, the system reduces disorderly movement and unnecessary waiting time, significantly improving the patient experience.

[0009] 2. Reduce the risk of cross-infection within the hospital: By forcibly constraining the spatial topology of clean and dirty flow, and combining it with quantitative control of the population density in high-risk areas, the physical spatial separation of people and materials, and clean and dirty materials is achieved from the perspective of building plan design, effectively reducing the probability of pathogen transmission within the hospital.

[0010] 3. Achieve intelligent and integrated design for medical buildings: Breaking through the traditional construction model of "adding intelligence after the building is completed", the pre-design of intelligent operation and maintenance facilities such as dynamic wayfinding, logistics robot channels, and flexible space interfaces is completed simultaneously in the scheme design stage. This avoids secondary renovation construction such as wall grooving, floor drilling, and pipeline installation in the later stage. It realizes physical integration and data connection between building space and intelligent system from the design source, which significantly reduces the total construction cost and improves the overall energy efficiency of the system.

[0011] 4. Improving the flexibility of building space throughout its entire life cycle: By identifying flexible spaces and reserving hardware interfaces in advance, building spaces are made physically adaptable to changes in disease patterns, innovations in medical processes, and public health emergencies, effectively extending the lifespan of buildings and reducing the cost of renovation throughout their entire life cycle, resulting in significant social and economic benefits. Attached Figure Description

[0012] Figure 1 This is an overall flowchart of the present invention.

[0013] Figure 2 This is a flowchart of the patient flow optimization model described in this invention. Detailed Implementation

[0014] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. These embodiments are implemented based on the technical solution of the present invention, and detailed implementation methods and specific operation processes are given. However, the scope of protection of the present invention is not limited to the following embodiments.

[0015] like Figure 1 , 2 As shown, the medical building space layout optimization method based on patient flow and operation and maintenance collaboration of the present invention includes the following steps: S1, Multi-source data acquisition and processing: Historical operational data of the target hospital will be collected. The target hospital refers to an existing operating hospital of the same size as the medical building to be optimized. The historical operational data covers an operating period of 12 to 36 consecutive months and includes: 1. Outpatient volume, medical examination appointment volume, and inpatient bed turnover rate for each department; 2. Patient movement trajectory data: Data collected from in-hospital turnstiles, location beacons, and continuous spatial coordinates, time periods, and points of stay throughout the entire process of a patient's journey from entrance, waiting, examination, medication dispensing to discharge; 3. Frequency of interdepartmental logistics transfer: Data on the origin, destination, time period, single transport volume, and daily round-trip frequency of four types of automated / manual logistics: hospital waste transfer, medicine and consumable delivery, specimen delivery, and linen recycling. Historical operational data is cleaned to remove equipment failure null values, extreme abnormal passenger flow, duplicate and redundant records, etc.; then standardization processing is carried out: unifying the granularity of statistical time periods, unifying the building space coordinate system, unifying the various data units, eliminating the differences in the caliber of different business systems, and building a "medical behavior-space demand" database to provide a data foundation for subsequent spatial layout optimization; S2, Construct a dual-objective optimization model for patient flow: The dual-objective optimization model includes: Objective function 1: Minimize the total patient travel time to medical care, expressed as MinΣ(N i ×L ij ×C j ); Objective function 2: Minimize the risk of cross-infection throughout the hospital, expressed as MinΣ(R k ×D k ×T k ); in: N i : The total number of patients in category i, where i and j are used to distinguish different medical departments; L ij The actual walking distance between the corresponding departments represents the total actual walking distance of the i-th type of patient from the starting department i to the target department j on the building plane; C j The congestion coefficient of patients in the actual route, with a value range of 1-3; the corridor pedestrian density is 1 for <1 person / ㎡, 2 for 1-2 people / ㎡, and 3 for >2 people / ㎡; R k Infection risk coefficient: 0.8-1.0 for high-risk areas, including fever clinics and respiratory wards; 0.3-0.5 for medium-risk areas, including general outpatient departments and medical examination waiting areas; and below 0.1 for low-risk areas, including administrative offices, logistics warehouses, and underground parking garages. D k People density represents the number of patients simultaneously present in a given area (k units), expressed in person / m². T k : Stay time, representing the average length of time a patient stays in risk area k, in minutes; The goal of minimizing the total travel time for patients is to minimize the total walking distance between medical departments (outpatient, medical technology, and inpatient). The goal of minimizing the risk of cross-infection throughout the hospital is to minimize the instantaneous population density in high-risk areas. First, retrieve the database for "Medical Behavior - Spatial Requirements" from step S1; Then, an initial candidate department layout is generated (based on area requirements and column grid placement). The following three constraints must be forcibly loaded during the solution of the bi-objective optimization model: 1. Clean and contaminated flow separation constraint: The flow lines of clean items (clean consumables, sterile instruments, meal preparation, etc.) and contaminated items (medical waste, contaminated linens, etc.) must be independent of each other in terms of spatial path and must not share the same passage. They must be provided with their own dedicated horizontal and vertical traffic channels, or take spatial isolation measures such as staggered operation at different times; this constraint is judged by the "no intersection of flow lines" in the building plan topology. 2. Department Proximity Constraints: Based on the frequency of medical collaboration between departments and patient referral traffic data, a walking distance limit threshold is set for related departments (such as the emergency department and radiology department ≤ 50m apart, the operating room and intensive care unit (ICU) on the same floor or adjacent floors, the outpatient pharmacy and the cashier, etc.). In the layout scheme, if the actual walking distance between related departments exceeds the upper limit threshold, it is determined to be an invalid layout and is excluded. 3. Structural Column Grid Adaptation Constraints: The floor plan layout boundaries of each department must adapt to the modular system of the building's structural load-bearing column grid (e.g., the outpatient and medical technology area uses an 8.4m×8.4m column grid, while the inpatient ward uses a 7.8m×7.8m or 8.1m×8.1m column grid, etc.) to ensure that the area and shape of each department can be completely positioned within the structural grid, avoiding invalid and fragmented spaces that cross the column grid; Among the candidate layout schemes that meet the above three constraints, two indicators are comprehensively evaluated: walking distance to medical care and population density in high-risk areas, and several better schemes are selected as the initial layout scheme set. S3, Generate the initial layout scheme: Using a genetic algorithm or simulated annealing algorithm, based on the bi-objective optimization model and constraints constructed in step S2, the initial layout scheme set is iteratively solved to automatically generate multiple initial layout schemes that satisfy the constraints; each generated initial layout scheme contains the following three spatial association information: 1. Area Information: The building area and usable area occupied by each department in the building plan; 2. Location Information: The specific coordinates of each department on the building's floor plan and the floor it is located on; 3. Interconnectivity: Topological adjacency between departments (whether they share walls, vertical correspondence between upper and lower floors, etc.), connectivity (whether they are directly connected by level doors, vertically connected by dedicated elevators, etc.), and visual accessibility (whether they can be directly seen in the corridor). The initial layout scheme set generated above is output in the form of a building floor plan; providing a spatial carrier for subsequent pre-installation of facilities. S4, Facility Collaborative Pre-configuration Design: Based on the initial layout scheme generated in step S3, a facility coordination pre-configuration scheme design is carried out simultaneously, specifically: S4.1 Wayfinding Point Planning: Based on the patient flow prediction results in step S2, dynamic wayfinding information screens are deployed at the path nodes that patients must pass through (i.e., physical locations where directional choices are required, such as elevator lobbies, main corridor intersections, and floor transition points). At the same time, according to the probability distribution of patients going to each department at different times at each of the aforementioned path nodes, the data interface and control logic of the wayfinding content are pre-set so that the wayfinding system can automatically switch guidance information according to real-time congestion during the operation phase (such as displaying dynamic prompts such as "There are many people waiting in the radiology department, it is recommended to go to the second floor for blood collection and then return"). S4.2, Logistics Robot Path Planning: Based on the data on the frequency and volume of inter-departmental logistics transmission collected in step S1, a dedicated operating channel for the hospital's logistics robots is planned. This dedicated channel is physically separated from the main pedestrian paths (such as medical staff corridors, logistics elevators, and equipment shaft passages) in terms of floor plan layout to avoid conflicts between the logistics robots and pedestrian traffic during operation. At the same time, the installation points and power supply capacity of charging piles are preset at suitable locations on the operating paths of the logistics robots on each floor. S4.3, Flexible Space Calibration: Based on the heat map data of patient flow density distribution in step S3, identify public areas in the building where people are prone to gather, including the outpatient hall, waiting area, rest area, and medication waiting area; define these public areas where people are prone to gather as "flexible spaces"; in the architectural design scheme, the following hardware foundations are preset for the above-mentioned flexible spaces: 1. Movable lightweight partition slide rail channels pre-embedded in structural floor slabs or ceilings; 2. Reserve quick-connect ports for both high-voltage and low-voltage electrical connections to meet the needs of temporarily added medical equipment and information terminals; 3. Reserve quick-connect interfaces for drainage to meet the needs of temporarily adding handwashing facilities or medical gas terminals; The aforementioned pre-set hardware infrastructure provides hardware support for the rapid switching of the building between "normal mode" and "public health emergency mode," avoiding damage to the main structure of the building caused by secondary demolition and alteration during the operation phase; The specific implementation method for the rapid switch from the "normal mode" to the "public health emergency mode" is explained as follows: 1. Normal daily medical treatment mode: The movable lightweight partition is stored and hidden in the side wall of the flexible space. The partition is closed along the pre-embedded floor / ceiling sliding track. The power supply, drainage and medical gas quick plug ports are protected by protective covers and are not exposed. The flexible space is used as a general lobby, waiting area and medicine dispensing area, which fully meets the daily medical treatment passage and waiting needs of the hospital.

[0016] 2. Public Health Emergency Mode Switching Process: When a public health event occurs and flexible spaces need to be converted into temporary isolation waiting areas or temporary screening areas, there is no need to excavate or demolish building floors or walls. Workers slide movable lightweight partitions along pre-embedded rail channels, using the rails to position the partitions and quickly divide the large space into multiple independent isolation units. Then, protective covers are lifted, and temporary medical equipment and information terminals are plugged into the quick-connect ports for both high and low voltage electrical connections. Temporary handwashing facilities and medical gas terminals are connected to the water supply, drainage, and medical gas quick-connect interfaces. All assembly work involves only equipment plugging and partition sliding, requiring no civil engineering construction.

[0017] 3. Restore from emergency mode to normal mode: After the emergency response is completed, remove the external temporary equipment, disconnect all kinds of quick interfaces, and cover the port protective covers; retract the lightweight partition along the sliding track to the side wall, and restore the flexible space to a complete public waiting area.

[0018] The entire switchover relies on the pre-embedded sliding rail channels and various quick interfaces from the early civil engineering stage. Unlike the existing technology that requires on-site trenching, wiring, pipe connection, and wall demolition for emergency renovations, this method can shorten the emergency renovation period and avoid damage to the main building structure.

[0019] S5, Solution Evaluation and Iterative Optimization: Using digital twin technology, the collaborative pre-defined scheme generated in step S4 is simulated in multiple scenarios, including: Weekday scenario: Average daily patient volume based on historical data; Peak period scenario: Peak patient flow based on historical data (such as Monday mornings, peak seasons for seasonal illnesses); Public health emergency scenario: Simulating a situation where there is a surge in fever patients and a need to rapidly expand the isolation and waiting area; Through simulation, the three core indicators of each collaborative pre-built solution are quantitatively evaluated under the above three scenarios: 1. Patient flow efficiency (average walking distance and waiting time to seek medical treatment); 2. Building space utilization rate (area utilization rate and vacancy rate of each functional area); 3. Facility coverage (percentage of wayfinding points covering path length, connectivity of logistics robot channels, etc.); Based on the simulation evaluation results, the constraint parameters of the bi-objective optimization model in step S2 (such as the department proximity threshold, the population density limit, etc.) are adjusted. Steps S3 to S5 are repeated to form a closed-loop iterative process of "generation-simulation-feedback-regeneration" until the optimal layout scheme that meets the preset evaluation criteria in all scenarios is obtained. Step S6, Dynamic Adaptive Adjustment during the Operation and Maintenance Phase: After the target medical building is constructed and put into use according to the optimal layout scheme, the system continuously collects real-time data on pedestrian density, turnstile access, and departmental queuing and calling systems. When the system detects that the congestion index of a certain area exceeds a preset threshold, or when the demand in a certain department surges due to a seasonal disease outbreak, the system automatically triggers the following dynamic adjustment mechanism: 1. Soft adjustment mechanism (information guidance): The system pushes diversion guidance information to patients through the hospital's official information release platform (including in-hospital dynamic guidance screens, mobile APP and WeChat mini program), guiding patients to adjacent empty examination departments or suggesting that they visit the hospital at different times to avoid the current peak hours, thereby realizing the spatial and temporal redistribution of the flow of people; 2. Hard adjustment mechanism (space conversion): When the soft adjustment mechanism is insufficient to alleviate congestion, the system sends a control command to the electric drive device pre-embedded in the elastic space, automatically activating the pre-set movable lightweight partition, completing the re-division of the physical space partition in a short time, realizing the functional conversion of the waiting area into a temporary triage area, or expanding the waiting capacity of a specific department.

[0020] Finally, it should be emphasized that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still make modifications to the technical solutions described in the foregoing embodiments without creative effort, or make equivalent substitutions for some of the technical features. Therefore, any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the spatial layout of medical buildings based on patient flow and operation and maintenance collaboration, characterized in that, Includes the following steps: S1, Multi-source data acquisition and processing: Collect historical operational data from the target hospital, including:

1. Outpatient volume, medical examination appointment volume, and inpatient bed turnover rate for each department; 2. Patient movement trajectory data: Data collected from in-hospital turnstiles, location beacons, and continuous spatial coordinates, time periods, and points of stay throughout the entire process of a patient's journey from entrance, waiting, examination, medication dispensing to discharge; 3. Frequency of interdepartmental logistics transfer: Data on the origin, destination, time period, single transport volume, and daily round-trip frequency of four types of automated / manual logistics: hospital waste transfer, medicine and consumable delivery, specimen delivery, and linen recycling. Historical operational data was cleaned and standardized to construct a "medical behavior-spatial demand" database; S2, Construct a dual-objective optimization model for patient flow: The dual-objective optimization model includes: Objective function 1: Minimize the total patient travel time to medical care, expressed as MinΣ(N i ×L ij ×C j ); Objective function 2: Minimize the risk of cross-infection throughout the hospital, expressed as MinΣ(R k ×D k ×T k ); in: N i : The total number of patients in category i, where i and j are used to distinguish different medical departments; L ij The actual walking distance between the corresponding departments represents the total actual walking distance of the i-th type of patient from the starting department i to the target department j on the building plane; C j The congestion coefficient of patients in the actual route, with a value range of 1-3; the corridor pedestrian density is 1 for <1 person / ㎡, 2 for 1-2 people / ㎡, and 3 for >2 people / ㎡; R k Infection risk coefficient: 0.8-1.0 for high-risk areas and below 0.1 for low-risk areas; D k People density represents the number of patients simultaneously present in a given area (k units), expressed in person / m². T k : Stay time, representing the average length of time a patient stays in risk area k, in minutes; The goal of minimizing the total patient travel time is defined as minimizing the total walking distance between medical departments. The goal of minimizing the risk of cross-infection throughout the hospital is to minimize the instantaneous population density in high-risk areas. First, retrieve the "Medical Behavior - Spatial Requirements" database from step S1; Then, an initial candidate department layout is generated; The following three constraints are loaded during the solution process of the bi-objective optimization model:

1. Clean and contaminated flow separation constraint: The flow lines of clean items and contaminated items must be independent of each other in terms of spatial path, with dedicated horizontal and vertical traffic channels set up for each, or spatial isolation measures such as staggered operation at different times should be adopted; this constraint is judged by the "no intersection of flow lines" in the building plan topology.

2. Department Proximity Constraint: Based on the frequency of medical collaboration between departments and patient referral traffic data, a maximum walking distance threshold is set for related departments; in the layout scheme, if the actual walking distance between related departments exceeds the maximum walking distance threshold, the layout is deemed invalid and excluded.

3. Structural column grid adaptation constraints: The floor plan layout boundaries of each department must be adapted to the modular system of the building's structural load-bearing column grid; Among the candidate layout schemes that meet the above three constraints, two indicators are comprehensively evaluated: walking distance to medical care and population density in high-risk areas, and several schemes are selected as the initial layout scheme set. S3, Generate the initial layout scheme: Using an optimization algorithm, based on the bi-objective optimization model and constraints constructed in step S2, the initial layout scheme set is iteratively solved to automatically generate multiple initial layout schemes that satisfy the constraints; each generated initial layout scheme contains the following three spatial association information items:

1. Area Information: The building area and usable area occupied by each department in the building plan; 2. Location Information: The specific coordinates of each department on the building's floor plan and the floor it is located on; 3. Interconnectivity: Topological adjacency, connectivity, and visual accessibility between departments; S4, Facility Collaborative Pre-configuration Design: Based on the initial layout scheme generated in step S3, the facility collaborative pre-configuration scheme design is carried out simultaneously; S5, Solution Evaluation and Iterative Optimization: Using digital twin technology, the collaborative pre-set scheme generated in step S4 is simulated in multiple scenarios; based on the simulation evaluation results, the constraint parameters of the dual-objective optimization model in step S2 are adjusted, and steps S3 to S5 are repeated to form a closed-loop iterative process of "generation-simulation-feedback-regeneration" until the optimal layout scheme that meets the preset evaluation criteria in all scenarios is obtained. S6, dynamic adaptive adjustment during the operation and maintenance phase: After the target medical building is constructed and put into use according to the optimal layout scheme, the system continuously collects real-time data on pedestrian density, turnstile access, and departmental queuing and calling systems. When the system detects that the congestion index of a certain area exceeds a preset threshold, or when the demand in a certain department surges due to a seasonal disease outbreak, the system automatically triggers the following dynamic adjustment mechanism: Soft adjustment mechanism: The system pushes diversion and guidance information to patients through the hospital's official information release platform, guiding patients to adjacent empty examination departments or suggesting that they visit the hospital at different times to avoid the current peak hours, thereby realizing the spatial and temporal redistribution of the flow of people; 2. Hard adjustment mechanism: When the soft adjustment mechanism is insufficient to alleviate congestion, the system sends a control command to the electric drive device pre-embedded in the elastic space, automatically activating the pre-set movable lightweight partition, completing the re-division of the physical partition of the space, realizing the functional transformation of the waiting area into a temporary triage area, or expanding the waiting capacity of specific departments.

2. The method for optimizing the spatial layout of medical buildings based on patient flow and operation and maintenance collaboration as described in claim 1, characterized in that, In step S3, the optimization algorithm is a genetic algorithm or a simulated annealing algorithm; the topological adjacency relationship between the departments is: whether they share a wall or are vertically connected between upper and lower floors; the connectivity relationship is: whether they are directly connected by a level door or vertically connected by a dedicated elevator; the visual accessibility relationship is: whether they can be directly seen in the corridor.

3. The method for optimizing the spatial layout of medical buildings based on patient flow and operation and maintenance collaboration as described in claim 1, characterized in that, In step S4, the facility collaborative pre-configuration design includes: S4.1 Wayfinding point planning: Based on the patient flow prediction results in step S2, dynamic wayfinding information screens are deployed at the nodes along the path that patients must pass through. At the same time, according to the probability distribution of patients going to each department at different times at each of the path nodes, the data interface and control logic of the wayfinding content are preset so that the wayfinding system can automatically switch the guidance information according to the real-time congestion situation during the operation phase. S4.2, Logistics Robot Path Planning: Based on the data on the frequency and volume of inter-departmental logistics transmission collected in step S1, a dedicated operating channel for logistics robots throughout the hospital is planned; this dedicated channel is physically separated from the main pedestrian flow path in terms of plan layout. S4.3, Flexible Space Calibration: Based on the heat map data of patient flow density distribution in step S3, identify public areas in the building where people are prone to gather, and define these public areas as "flexible spaces"; in the building design scheme, the following hardware foundations are preset for the above-mentioned flexible spaces:

1. Movable lightweight partition slide rail channels pre-embedded in structural floor slabs or ceilings; 2. Reserve ports for quick connection of strong and weak current wires; 3. Reserve a quick-connect interface for drainage.

4. The method for optimizing the spatial layout of medical buildings based on patient flow and operation and maintenance collaboration as described in claim 1, characterized in that, In step S5, the multi-scenario simulation includes: Weekday scenario: Average daily patient volume based on historical data; Peak period scenario: Peak patient flow based on historical data; Public health emergency scenario: Simulating a situation where there is a surge in fever patients and the need to rapidly expand the isolation and waiting area.

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