A hospital environment disinfection risk prediction and management system and method

Abstract

This invention discloses a hospital environmental disinfection risk prediction and management system and method, belonging to the field of risk prediction and management technology. It utilizes an environmental sensing module to collect environmental parameters and disinfection equipment status in real time, providing accurate and real-time data support. A digital twin processing module is used to construct a digital twin model, facilitating visualization of the disinfection status of various areas within the hospital. Subsequently, a dual-channel risk prediction model is used to analyze multimodal environmental parameters, predicting the contamination risk of each area and generating corresponding equipment control commands. This enables corresponding disinfection strategies and real-time early warnings, significantly improving the accuracy and practicality of the prediction results. This enhances the dynamic decision-making capability and traceability of hospital disinfection operations, improves disinfection efficiency and risk prevention effectiveness, and facilitates application and promotion.

Description

Technical Field

[0001] This invention belongs to the field of risk prediction and management technology, specifically relating to a hospital environment disinfection risk prediction and management system and method. Background Technology

[0002] Hospital environmental disinfection is a crucial step in preventing nosocomial infections, ensuring medical quality, and protecting patient safety. It plays a vital role in blocking the transmission chain of pathogens. However, current hospital environmental disinfection systems still face a series of shortcomings, including: The mainstream method for environmental monitoring still relies on manual, periodic inspections and contact plate sampling. This method is time-consuming, labor-intensive, and suffers from significant feedback delays, providing only static microbial data at discrete points. It cannot achieve continuous and quantitative assessment of the entire ward or key contamination levels, leaving disinfection decisions without real-time and accurate data support, often leading to the dilemma of over-disinfection or under-disinfection; Existing technologies cannot achieve dynamic twinning and transmission simulation, and existing digital twin models are further limited. The current model focuses on static geometry and equipment display, and cannot access and integrate real-time environmental microbial data and personnel and logistics information. Therefore, it cannot dynamically map the real pollution state to the virtual space, nor can it simulate the real-time spread of pathogens. The real-time early warning function is also poor. The interior of a hospital is a complex interconnected space composed of wards, corridors, elevators and other areas. The spread of pathogens depends on the connection relationship between these spatial nodes and the flow path of personnel. Most existing prediction models are based on time series or point data analysis, which cannot effectively integrate the spatial topology of the building and make it difficult to accurately simulate the dynamic process of pollution sources spreading through the spatial network. This results in low accuracy and practicality of the prediction results.

[0003] Therefore, how to provide an effective technical solution to address the problems of insufficient real-time and accurate data support for disinfection decisions, poor real-time early warning functions, and low accuracy and practicality of prediction results in existing technologies has become an urgent technical challenge to be solved. Summary of the Invention

[0004] The purpose of this invention is to provide a hospital environment disinfection risk prediction and management system and method to solve the above-mentioned problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a hospital environment disinfection risk prediction and management system, including an environment sensing module, a digital twin processing module, an intelligent analysis module, and an equipment control module. The environment sensing module is communicatively connected to the digital twin processing module and the intelligent analysis module, respectively. The digital twin processing module is communicatively connected to the intelligent analysis module, and the intelligent analysis module is communicatively connected to the equipment control module. The environmental sensing module is used to acquire multimodal environmental parameters inside the hospital. The multimodal environmental parameters include at least one of the following: ambient temperature, humidity, air particulate matter concentration, PM2.5 concentration, volatile organic compound concentration, formaldehyde concentration, carbon dioxide concentration, microbial aerosol concentration, and disinfection equipment operating status parameters. The multimodal environmental parameters are then uploaded to the digital twin processing module and the intelligent analysis module. The digital twin processing module is used to acquire the building digital model of the hospital building, construct an initial digital twin model of the hospital environment based on the building digital model, and map the multimodal environment parameters to the initial digital twin model of the hospital environment to obtain the digital twin model. The intelligent analysis module is used to analyze multimodal environmental parameters using a pre-built dual-channel risk prediction model to obtain the pollution risk index of each area of ​​the hospital environment. When the pollution risk index exceeds the preset risk threshold, it generates equipment control instructions and sends the equipment control instructions to the equipment control module. The equipment control module is used to control the disinfection equipment to perform disinfection according to the equipment control instructions.

[0006] In one possible design, the environmental sensing module includes environmental monitoring sensors and disinfection equipment; The environmental monitoring sensors include a temperature and humidity sensor, an air particulate matter sensor, a harmful gas sensor, and a microbial aerosol collection device. The temperature and humidity sensor is used to detect the ambient temperature and humidity. The air particulate matter sensor is used to detect the concentration of air particulate matter in the air. The harmful gas sensor is used to detect the concentration of volatile organic compounds, formaldehyde, and carbon dioxide. The microbial aerosol collection device is used to collect the concentration of microbial aerosols. The disinfection equipment is used to collect operating status parameters of the disinfection equipment.

[0007] In one possible design, the digital twin processing module is also used to extract spatial structure information from the building's digital model, including room connectivity, room structure, and door and window locations, and to generate a spatial topology map based on the room connectivity, room structure, and door and window locations.

[0008] In one possible design, a data transmission module is also included, which is used to transmit multimodal environmental parameters to the digital twin processing module and the intelligent analysis module via a wireless or wired network protocol.

[0009] Secondly, the present invention provides a method for predicting and managing disinfection risks in hospital environments, including: Acquire multimodal environmental parameters within the hospital, wherein the multimodal environmental parameters include at least one of the following: ambient temperature, humidity, air particulate matter concentration, PM2.5 concentration, volatile organic compound concentration, formaldehyde concentration, carbon dioxide concentration, microbial aerosol concentration, and disinfection equipment operating status parameters; Obtain the digital model of the hospital building, construct an initial digital twin model of the hospital environment based on the digital model of the building, and map the multimodal environment parameters to the initial digital twin model of the hospital environment to obtain the digital twin model. A pre-built dual-channel risk prediction model is used to analyze multimodal environmental parameters to obtain the pollution risk index of each area of ​​the hospital environment. When the pollution risk index exceeds the preset risk threshold, equipment control instructions are generated. The disinfection equipment is controlled according to the equipment control instructions to carry out disinfection.

[0010] In one possible design, the dual-channel risk prediction model includes a data-driven prediction layer and a physical diffusion prediction layer; the analysis of multimodal environmental parameters using the pre-built dual-channel risk prediction model yields pollution risk indices for various areas of the hospital environment, including: The system acquires personnel activity parameters and regional spatial attribute information, inputs multimodal environmental parameters, personnel activity parameters, and regional spatial attribute information into the data-driven prediction layer, and outputs the risk assessment value of the corresponding area of ​​the hospital environment in the current time period. The initial pollution source and ventilation conditions are obtained and input into the physical diffusion prediction layer. The standardized risk value is output, which is used to characterize the spread of pollution risk in the building over time and space. By weighting and fusing the risk assessment value and the standardized risk value, the pollution risk index of each area of ​​the hospital environment is obtained.

[0011] In one possible design, the risk assessment value and the standardized risk value are weighted and fused to obtain the pollution risk index for each area of ​​the hospital environment, including: Acquire data availability from the environmental perception module, error rate of the data-driven prediction layer, and regional ventilation characteristics of various areas within the hospital environment; Based on the data availability of the environmental perception module, the error rate of the data-driven prediction layer, and the regional ventilation characteristics of various areas of the hospital environment, the initial fusion weights are adjusted using preset weighting rules to obtain the fusion weights. The pollution risk index for each area of ​​the hospital environment is obtained by weighting and fusing the risk assessment value and the standardized risk value based on the fusion weight.

[0012] In one possible design, after obtaining the pollution risk index for each area of ​​the hospital environment, the following is also included: The pollution risk index of each area of ​​the hospital environment is mapped to the corresponding spatial area in the digital twin model; The mapped pollution risk index is rendered to obtain a risk heatmap.

[0013] Thirdly, the present invention provides a computer device comprising a memory, a processor, and a transceiver connected in sequence and communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the hospital environment disinfection risk prediction and management method as described in the second aspect above.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the hospital environment disinfection risk prediction and management method as described in the second aspect above.

[0015] Fifthly, the present invention provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the hospital environment disinfection risk prediction and management method as described in the second aspect above.

[0016] The beneficial effects of this invention are as follows: This invention discloses a hospital environment disinfection risk prediction and management system and method. The system acquires multimodal environmental parameters within the hospital through an environmental sensing module. Subsequently, a digital twin processing module acquires a digital model of the hospital building and constructs an initial digital twin model of the hospital environment based on the building's digital model. The multimodal environmental parameters are mapped into this initial digital twin model to obtain the digital twin model. An intelligent analysis module uses a pre-constructed dual-channel risk prediction model to analyze the multimodal environmental parameters, obtaining a pollution risk index for each area of ​​the hospital environment. When the pollution risk index exceeds a preset risk threshold, an equipment control command is generated and sent to the equipment control module. The equipment control module then controls the disinfection equipment to perform disinfection according to the command. This invention uses an environmental sensing module to collect environmental parameters and the status of disinfection equipment in real time, providing accurate and real-time data support. It also constructs a digital twin model to facilitate visualization of the disinfection status of various areas within the hospital. Subsequently, a dual-channel risk prediction model is used to analyze multimodal environmental parameters, predict the pollution risk of each area, and generate corresponding equipment control commands. This enables corresponding disinfection strategies and real-time early warnings, significantly improving the accuracy and practicality of the prediction results. This enhances the dynamic decision-making capability and traceability of hospital disinfection operations, improves disinfection efficiency and risk prevention effectiveness, and facilitates application and promotion. Attached Figure Description

[0017] Figure 1A block diagram of the hospital environment disinfection risk prediction and management system provided in an embodiment of the present invention; Figure 2 A flowchart illustrating the hospital environment disinfection risk prediction and management method provided in this embodiment of the invention. Detailed Implementation

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0019] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0020] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0021] Example: like Figure 1 As shown, the first aspect of this embodiment provides a hospital environment disinfection risk prediction and management system, including an environment sensing module, a digital twin processing module, an intelligent analysis module, and an equipment control module. The environment sensing module is communicatively connected to the digital twin processing module and the intelligent analysis module, respectively. The digital twin processing module is communicatively connected to the intelligent analysis module, and the intelligent analysis module is communicatively connected to the equipment control module. The environmental sensing module is used to acquire multimodal environmental parameters inside the hospital. The multimodal environmental parameters include at least one of the following: ambient temperature, humidity, air particulate matter concentration, PM2.5 concentration, volatile organic compound concentration, formaldehyde concentration, carbon dioxide concentration, microbial aerosol concentration, and disinfection equipment operating status parameters. The multimodal environmental parameters are then uploaded to the digital twin processing module and the intelligent analysis module. The digital twin processing module is used to acquire the building digital model of the hospital building, construct an initial digital twin model of the hospital environment based on the building digital model, and map the multimodal environment parameters to the initial digital twin model of the hospital environment to obtain the digital twin model. The intelligent analysis module is used to analyze multimodal environmental parameters using a pre-built dual-channel risk prediction model to obtain the pollution risk index of each area of ​​the hospital environment. When the pollution risk index exceeds the preset risk threshold, it generates equipment control instructions and sends the equipment control instructions to the equipment control module. The equipment control module is used to control the disinfection equipment to perform disinfection according to the equipment control instructions.

[0022] Based on the above disclosure, this embodiment provides a hospital environment disinfection risk prediction and management system. It acquires multimodal environmental parameters collected by sensors installed in various key areas within the hospital through an environmental sensing module. These parameters are then uploaded to a digital twin processing module and an intelligent analysis module. The digital twin processing module obtains a digital model of the hospital building and constructs an initial digital twin model of the hospital environment based on this model. The multimodal environmental parameters are mapped to this initial digital twin model, resulting in a digital twin model. The intelligent analysis module analyzes the multimodal environmental parameters using a pre-built dual-channel risk prediction model to obtain a pollution risk index for each area of ​​the hospital environment. When the pollution risk index exceeds a preset risk threshold, a device control command is generated and sent to the device control module. The device control module then controls the disinfection equipment to perform disinfection according to the command. This achieves intelligent sensing, simulation, and risk prediction of the hospital disinfection process, effectively improving the automation level and decision-making accuracy of disinfection management.

[0023] In a preferred embodiment, the environmental sensing module includes an environmental monitoring sensor and a disinfection device; The environmental monitoring sensors include a temperature and humidity sensor, an air particulate matter sensor, a harmful gas sensor, and a microbial aerosol collection device. The temperature and humidity sensor is used to detect the ambient temperature and humidity. The air particulate matter sensor is used to detect the concentration of air particulate matter in the air. The harmful gas sensor is used to detect the concentration of volatile organic compounds, formaldehyde, and carbon dioxide. The microbial aerosol collection device is used to collect the concentration of microbial aerosols. The disinfection equipment is used to collect operating status parameters of the disinfection equipment.

[0024] It should be noted that the environmental perception module also includes a QR code device, which is used to uniquely identify disinfection equipment, environmental monitoring sensors and / or environmental areas. Specifically, it can be understood as a kind of digital tag to assist in equipment management, data binding and disinfection record traceability. The information collected by the device is used as system management data to participate in the display of digital twin models or disinfection record management. Disinfection equipment includes, but is not limited to, high-energy pulse disinfection equipment, plasma disinfection equipment or disinfection robots, etc., which are air disinfection equipment or surface disinfection equipment that coexist with human beings.

[0025] In a preferred embodiment, the intelligent analysis module is further configured to extract spatial structure information from the building digital model, the spatial structure information including room connectivity, room structure and door and window positions, and generate a spatial topology map based on the room connectivity, room structure and door and window positions.

[0026] It should be noted that the building digital model is specifically the BIM model of the hospital building. Since the hospital building contains a variety of spatial structural information, such as room connectivity, room structure, door and window positions, lobby, corridors, and ventilation openings, a spatial topology map is constructed based on the hospital building's BIM (Building Information Modeling) model and CAD drawings. After construction, manual calibration is performed to ensure that the spatial topology map is consistent with the actual spatial structure. The spatial topology map is used to characterize the propagation paths and influence relationships of pollutants or microorganisms between different areas. In the spatial topology map, each node corresponds to an independent spatial area, and the connection relationship between nodes reflects the passage of people or air exchange channels.

[0027] In a preferred embodiment, the system further includes a data transmission module for transmitting multimodal environmental parameters to the digital twin processing module and the intelligent analysis module via a wireless or wired network protocol.

[0028] It should be noted that the data transmission module uploads multimodal environmental parameters to the digital twin processing module and intelligent analysis module via network protocols such as WIFI, NBIoT, or Ethernet. Within the local area network, it can also use low-power short-range wireless communication protocols such as ZigBee, Bluetooth, or LoRa to communicate with the gateway and then access the digital twin processing module and intelligent analysis module via TCP / IP protocol.

[0029] like Figure 2 As shown, the second aspect of this embodiment provides a method for predicting and managing the disinfection risks in a hospital environment, including the following steps: S1. Obtain multimodal environmental parameters within the hospital, wherein the multimodal environmental parameters include at least one of the following: ambient temperature, humidity, air particulate matter concentration, PM2.5 concentration, volatile organic compound concentration, formaldehyde concentration, carbon dioxide concentration, microbial aerosol concentration, and disinfection equipment operating status parameters; S2. Obtain the digital model of the hospital building, construct an initial digital twin model of the hospital environment based on the digital model of the building, and map the multimodal environment parameters to the initial digital twin model of the hospital environment to obtain the digital twin model; S3. Use a pre-built dual-channel risk prediction model to analyze multimodal environmental parameters and obtain the pollution risk index of each area of ​​the hospital environment. When the pollution risk index exceeds the preset risk threshold, generate equipment control instructions. It should be noted that the dual-channel risk prediction model includes a data-driven prediction layer and a physical diffusion prediction layer. The data-driven prediction layer is built upon a recurrent neural network. The data-driven prediction layer and the physical diffusion prediction layer reflect the form and propagation process of pollution risk from different perspectives, and are fused at the output stage to significantly improve the stability and reliability of the overall risk assessment results. The data-driven prediction layer is used to compensate for corresponding real-world situations, such as sensor noise or local anomalies, difficulty in accurately describing the impact of human behavior, and time lag in disinfection effects. The physical diffusion prediction layer is built upon a spatial topology map and a diffusion model. It reflects the natural decay or purification process of pollution in each area, the pollution exchange relationship between adjacent areas, and pollution sources introduced by human activities or emergencies. The diffusion model works by simulating both forward and reverse diffusion processes to generate high-quality data from random noise.

[0030] Among them, a pre-built dual-channel risk prediction model was used to analyze multimodal environmental parameters to obtain the pollution risk index of various areas of the hospital environment, including: S31. Obtain personnel activity parameters and regional spatial attribute information, input multimodal environmental parameters, personnel activity parameters and regional spatial attribute information into the data-driven prediction layer, and output the risk assessment value of the corresponding area of ​​the hospital environment in the current time period; S32. Obtain the initial pollution source and ventilation conditions, input the initial pollution source and ventilation conditions into the physical diffusion prediction layer, and output a standardized risk value. The standardized risk value is used to characterize the spread of pollution risk in the building over time and space. S33. The risk assessment value and the standardized risk value are weighted and integrated to obtain the pollution risk index of each area of ​​the hospital environment.

[0031] In practical implementation, the concentration of microbial aerosols in the multimodal environmental parameters reflects the potential pollution level; personnel activity parameters include data such as population density and entry / exit frequency; disinfection equipment operation status parameters include whether the equipment is in operation and the duration of operation; and regional attribute information includes room type, area, and floor height. The multimodal environmental parameters, personnel activity parameters, and regional attribute information are input into the data-driven prediction layer, which outputs the risk assessment value of the corresponding area of ​​the hospital environment in the current time period. The initial pollution source and ventilation conditions are input into the physical diffusion prediction layer, which outputs standardized risk values. The physical diffusion prediction layer outputs physical quantities such as pollutant concentrations. Using a preset nonlinear mapping function, these physical quantities are converted into standardized risk values ​​through nonlinear mapping. For example, an S-shaped function conforming to the infection transmission law can be used for conversion, reflecting the characteristics of gradual risk at low concentrations, a sharp increase in risk in the critical range, and risk saturation at high concentrations. This allows physical quantities of different dimensions to be uniformly mapped to standardized risk values ​​between 0 and 1. The nonlinear mapping function can be flexibly configured according to actual needs such as ward type and interference control standards; this embodiment does not impose specific limitations.

[0032] Specifically, in step S33, the risk assessment value and the standardized risk value are weighted and fused to obtain the pollution risk index for each area of ​​the hospital environment, including: S33.1. Obtain the data availability of the environmental perception module, the error rate of the data-driven prediction layer, and the regional ventilation characteristics of various areas of the hospital environment; S33.2. Based on the data availability of the environmental perception module, the error rate of the data-driven prediction layer, and the regional ventilation characteristics of various areas of the hospital environment, the initial fusion weights are adjusted using preset weighting rules to obtain the fusion weights; S33.3. Based on the fusion weight, the risk assessment value and the standardized risk value are weighted and fused to obtain the pollution risk index of each area of ​​the hospital environment.

[0033] It should be noted that the basis for judging data availability is the missing rate of sensor data. If the missing rate of sensor data exceeds the preset missing threshold, the weight of the data-driven prediction layer is reduced. The basis for judging the error rate of the data-driven prediction layer is the average absolute error within the preset time window. If the average absolute error exceeds the preset error threshold, the weight of the data-driven prediction layer is reduced. For areas with good ventilation characteristics, the weight of the physical diffusion prediction layer is increased. The preset weighting rule is set according to the actual situation, and this embodiment does not impose specific limitations. Furthermore, the initial fusion weights can be adjusted using the preset weighting rule, or a neural network model can be used to learn and generate fusion weights. The risk assessment value and the standardized risk value are then weighted and fused according to the fusion weights to obtain the pollution risk index of each area.

[0034] Furthermore, in key areas of the hospital, such as the ICU or operating room, airflow field characteristic parameters can be obtained in advance through offline CFD (Computational Fluid Dynamics) simulation and used to correct the correlation coefficients in the physical diffusion prediction layer, thereby improving the accuracy of local area modeling.

[0035] Furthermore, after obtaining the pollution risk index for each area of ​​the hospital environment, it also includes: The pollution risk index of each area of ​​the hospital environment is mapped to the corresponding spatial area in the digital twin model; The mapped pollution risk index is rendered to obtain a risk heatmap.

[0036] The process involves rendering the mapped pollution risk index using a preset risk color rule. For example, the preset risk color rule can be rendered using colors corresponding to different pollution risk indices, such as red, yellow, and green. The specific colors can be set manually and are not limited here. After rendering, the corresponding spatial areas in the digital twin model are rendered with the corresponding colors and are dynamically updated in real time.

[0037] S4. Control the disinfection equipment to perform disinfection according to the equipment control instructions.

[0038] In one possible design, changes in monitoring data before and after disinfection are used as the basis for evaluating the actual disinfection effect. The parameters of the dual-channel risk prediction model are updated or corrected based on the evaluation basis, so that the prediction results are more in line with the actual operation. The process of obtaining the evaluation basis involves obtaining changes in environmental parameters and microbial-related indicators before and after disinfection, and calculating a comprehensive disinfection effect score based on the changes in multiple indicators.

[0039] This embodiment provides a computer device, including a memory, a processor, and a transceiver connected in sequence. The memory stores a computer program, the transceiver sends and receives messages, and the processor reads the computer program to execute the hospital environment disinfection risk prediction and management method described in the second aspect. Specifically, the memory may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; the processor may include, but is not limited to, an STM32F105 series microprocessor. Furthermore, the computer device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0040] The working process, working details and technical effects of the aforementioned computer equipment provided in the third aspect of this embodiment can be found in the hospital environment disinfection risk prediction and management method described in the second aspect, and will not be repeated here.

[0041] The fourth aspect of this embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the hospital environment disinfection risk prediction and management method described in the second aspect is performed. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0042] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in the fourth aspect of this embodiment can be found in the hospital environment disinfection risk prediction and management method described in the second aspect, and will not be repeated here.

[0043] The fifth aspect of this embodiment provides a computer program product, including a computer program or instructions, which, when executed by a computer, are used to implement the hospital environment disinfection risk prediction and management method as described in the second aspect.

[0044] The working process, working details, and technical effects of the aforementioned computer program product provided in this embodiment can be found in the hospital environment disinfection risk prediction and management method described in the second aspect, and will not be repeated here.

[0045] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A hospital environment disinfection risk prediction and management system, characterized in that, It includes an environmental perception module, a digital twin processing module, an intelligent analysis module, and an equipment control module. The environmental perception module is communicatively connected to the digital twin processing module and the intelligent analysis module, respectively. The digital twin processing module is communicatively connected to the intelligent analysis module, and the intelligent analysis module is communicatively connected to the equipment control module. The environmental sensing module is used to acquire multimodal environmental parameters inside the hospital. The multimodal environmental parameters include at least one of the following: ambient temperature, humidity, air particulate matter concentration, PM2.5 concentration, volatile organic compound concentration, formaldehyde concentration, carbon dioxide concentration, microbial aerosol concentration, and disinfection equipment operating status parameters. The multimodal environmental parameters are then uploaded to the digital twin processing module and the intelligent analysis module. The digital twin processing module is used to acquire the building digital model of the hospital building, construct an initial digital twin model of the hospital environment based on the building digital model, and map the multimodal environment parameters to the initial digital twin model of the hospital environment to obtain the digital twin model. The intelligent analysis module is used to analyze multimodal environmental parameters using a pre-built dual-channel risk prediction model to obtain the pollution risk index of each area of ​​the hospital environment. When the pollution risk index exceeds the preset risk threshold, it generates equipment control instructions and sends the equipment control instructions to the equipment control module. The equipment control module is used to control the disinfection equipment to perform disinfection according to the equipment control instructions.

2. The hospital environment disinfection risk prediction and management system according to claim 1, characterized in that, The environmental sensing module includes environmental monitoring sensors and disinfection equipment; The environmental monitoring sensors include a temperature and humidity sensor, an air particulate matter sensor, a harmful gas sensor, and a microbial aerosol collection device. The temperature and humidity sensor is used to detect the ambient temperature and humidity. The air particulate matter sensor is used to detect the concentration of air particulate matter in the air. The harmful gas sensor is used to detect the concentration of volatile organic compounds, formaldehyde, and carbon dioxide. The microbial aerosol collection device is used to collect the concentration of microbial aerosols. The disinfection equipment is used to collect operating status parameters of the disinfection equipment.

3. The hospital environment disinfection risk prediction and management system according to claim 1, characterized in that, The intelligent analysis module is also used to extract spatial structure information from the building digital model. The spatial structure information includes room connectivity, room structure, and door and window positions, and generates a spatial topology map based on the room connectivity, room structure, and door and window positions.

4. The hospital environment disinfection risk prediction and management system according to claim 1, characterized in that, It also includes a data transmission module, which is used to transmit multimodal environmental parameters to the digital twin processing module and the intelligent analysis module via wireless or wired network protocols.

5. A method for predicting and managing hospital environmental disinfection risks, applied to the hospital environmental disinfection risk prediction and management system as described in any one of claims 1 to 4, characterized in that, include: Acquire multimodal environmental parameters within the hospital, wherein the multimodal environmental parameters include at least one of the following: ambient temperature, humidity, air particulate matter concentration, PM2.5 concentration, volatile organic compound concentration, formaldehyde concentration, carbon dioxide concentration, microbial aerosol concentration, and disinfection equipment operating status parameters; Obtain the digital model of the hospital building, construct an initial digital twin model of the hospital environment based on the digital model of the building, and map the multimodal environment parameters to the initial digital twin model of the hospital environment to obtain the digital twin model. A pre-built dual-channel risk prediction model is used to analyze multimodal environmental parameters to obtain the pollution risk index of each area of ​​the hospital environment. When the pollution risk index exceeds the preset risk threshold, equipment control instructions are generated. The disinfection equipment is controlled according to the equipment control instructions to carry out disinfection.

6. The method for predicting and managing hospital environmental disinfection risks according to claim 5, characterized in that, The dual-channel risk prediction model includes a data-driven prediction layer and a physical diffusion prediction layer. The pre-built dual-channel risk prediction model is used to analyze multimodal environmental parameters to obtain pollution risk indices for various areas of the hospital environment, including: The system acquires personnel activity parameters and regional spatial attribute information, inputs multimodal environmental parameters, personnel activity parameters, and regional spatial attribute information into the data-driven prediction layer, and outputs the risk assessment value of the corresponding area of ​​the hospital environment in the current time period. The initial pollution source and ventilation conditions are obtained and input into the physical diffusion prediction layer. The standardized risk value is output, which is used to characterize the spread of pollution risk in the building over time and space. By weighting and fusing the risk assessment value and the standardized risk value, the pollution risk index of each area of ​​the hospital environment is obtained.

7. The method for predicting and managing hospital environmental disinfection risks according to claim 6, characterized in that, The risk assessment value and the standardized risk value are weighted and fused to obtain the pollution risk index for each area of ​​the hospital environment, including: Acquire data availability from the environmental perception module, error rate of the data-driven prediction layer, and regional ventilation characteristics of various areas within the hospital environment; Based on the data availability of the environmental perception module, the error rate of the data-driven prediction layer, and the regional ventilation characteristics of various areas of the hospital environment, the initial fusion weights are adjusted using preset weighting rules to obtain the fusion weights. The pollution risk index for each area of ​​the hospital environment is obtained by weighting and fusing the risk assessment value and the standardized risk value based on the fusion weight.

8. The method for predicting and managing hospital environmental disinfection risks according to claim 5, characterized in that, After obtaining the pollution risk index for each area of ​​the hospital environment, the following is also included: The pollution risk index of each area of ​​the hospital environment is mapped to the corresponding spatial area in the digital twin model; The mapped pollution risk index is rendered to obtain a risk heatmap.

9. A computer device, characterized in that, The device includes a memory, a processor, and a transceiver that are sequentially and communicatively connected. The memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the hospital environment disinfection risk prediction and management method as described in any one of claims 5 to 8.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the hospital environment disinfection risk prediction and management method as described in any one of claims 5 to 8.

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