A hospital airborne infection risk real-time dynamic evaluation method, device, medium and product

Abstract

The application discloses a hospital airborne infection risk real-time dynamic evaluation method, device, medium and product, relates to the public health technical field, and the method comprises the following steps: determining a plurality of personnel behavior parameters based on a personnel quantity index; adopting a machine learning algorithm to perform feature extraction on video monitoring data of the hospital, and generating a target detection image; obtaining a tracking target based on a personnel ID and a target bounding box in the target detection image by using a Byte Track algorithm; matching the tracking target and the target detection image; converting a multi-target tracking result into conversion data by using a perspective transformation method; determining an infection probability based on the plurality of personnel behavior parameters and the conversion data by adopting an improved Wells-Riley model; and performing real-time dynamic evaluation on a hospital airborne infection risk according to the infection probability. The application can effectively evaluate the hospital airborne infection risk, thereby providing effective technical reference for timely preventing and reducing the possibility of nosocomial infection.

Description

Technical Field

[0001] The present application relates to the field of public health technology, and in particular to a method, equipment, medium and product for real-time dynamic assessment of hospital airborne infection risks. Background Art

[0002] In recent years, public awareness of respiratory diseases has increased significantly. Respiratory diseases are caused by pathogens that enter the human body through the nose, throat, trachea, and bronchi. They are primarily transmitted through droplets from patients or infected individuals. Different respiratory diseases have different clinical manifestations. While treating patients, medical staff also face a significant risk of nosocomial infections. Nosocomial infections, also known as hospital-acquired infections, refer to non-primary infections acquired during hospital stays and treatment.

[0003] As a place with a high density of people and frequent movement, hospitals have a high risk of nosocomial infection, especially when facing a complex and diverse group of people, this risk is further increased. Factors such as the number of people in the hospital space, the duration of contact, the method of contact, and the distance of contact will have a significant impact on the probability of infection. Although existing research has provided a certain basis for the monitoring of nosocomial infection, there are still many problems that need to be solved: (1) Many studies focus on examining the risk of a single area, or only consider the impact of a single behavior or static state of a person in a single space on the assessment of infection risk. (2) Although some studies have introduced a distance index to quantify the impact of social distance on exposure, it is actually still related to the density of people and fine-grained behavior. Previous studies have failed to analyze the infection risk of different locations in the space through specific spatial location information and partition information. (3) Some studies usually only assume that the risk of virus transmission will decrease as the distance between individuals increases, ignoring the characteristics of droplets and aerosols containing viruses spreading in the air.

[0004] Therefore, traditional management methods cannot effectively assess the risk of airborne infections in hospitals, and cannot provide effective technical references for timely prevention and reduction of the possibility of nosocomial infections. Summary of the Invention

[0005] The purpose of this application is to provide a real-time dynamic assessment method, equipment, medium and product for hospital airborne infection risk, which can effectively assess the hospital airborne infection risk and provide an effective technical reference for timely prevention and reduction of the possibility of hospital infection.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In the first aspect, the present application provides a real-time dynamic assessment method for hospital airborne infection risk, comprising:

[0008] Determining multi-personnel behavior parameters based on a personnel quantity index; the personnel quantity index is the total number of personnel in the monitoring area; the multi-personnel behavior parameters include contact time, contact distance, contact method, and number of personnel;

[0009] Obtaining video surveillance data from the hospital and extracting features from the video surveillance data using a machine learning algorithm to generate a target detection image; the target detection image includes a person ID and a target bounding box;

[0010] Using the Byte Track algorithm, a tracking target is obtained based on the person ID and the target bounding box;

[0011] Matching the tracking target with the target detection image to obtain a multi-target tracking result;

[0012] Performing coordinate transformation on the multi-target tracking results using a perspective transformation method to obtain transformation data;

[0013] Determining the infection probability based on the multiple personnel behavior parameters and the conversion data using a modified Wells-Riley model;

[0014] Based on the infection probability, a real-time dynamic assessment of the hospital airborne infection risk is performed.

[0015] Optionally, the machine learning algorithm is a YOLOv8 algorithm.

[0016] Optionally, using a perspective transformation method to perform coordinate transformation on the multi-target tracking result to obtain transformation data includes:

[0017] Obtaining the spatiotemporal coordinate information of all human targets in the multi-target tracking result;

[0018] A perspective transformation formula is used to determine a conversion matrix, and the conversion matrix is ​​used to convert the spatiotemporal coordinate information of all personnel targets into spatial coordinates to obtain the conversion data.

[0019] Optionally, the spatial coordinates obtained by conversion are expressed as:

[0020]

[0021] Where (u, v) represents the Cartesian coordinates of each frame of the multi-target tracking result, (u, v, 1) represents the homogeneous coordinates, M represents the transformation matrix, and a ij (i, j = 1, 2, 3) are the elements of the transformation matrix M, and (x, y, z) represent the spatial coordinates.

[0022] Optionally, the construction process of the improved Wells-Riley model includes:

[0023] A Wells-Riley model is obtained, and the correction coefficient and ventilation rate are added as new parameters to the Wells-Riley model to obtain the improved Wells-Riley model; the correction coefficient includes a contact distance correction coefficient, a contact mode correction coefficient, and a mask wearing correction coefficient.

[0024] Optionally, the infection probability determination formula is expressed as:

[0025]

[0026] Where, P is the infection probability; P d is the relationship between the number of droplets exposed in the air and the distance, and is the correction coefficient of the contact distance. d represents the distance; I is the number of infected people; q is the virus generation rate of the infected people; S is the contact correction coefficient for the inhalation of the virus by the people, θ1 is the spatial position angle between the infected person and the people in the same monitoring area, d m is the diameter of the mouth when coughing; p is the respiratory rate of the person; k a k is the correction factor when the infected person wears a mask b is the correction factor when the person wears a mask; t is the duration of contact between the infected person and the people in the same monitoring area; Q is the ventilation rate; E z is the air distribution efficiency.

[0027] Optionally, based on the infection probability, a real-time dynamic assessment of hospital airborne infection risk is performed, including:

[0028] Based on the infection probability, a high-risk infection area is determined, and people in the high-risk infection area are evaluated and dynamically monitored in real time; the high-risk infection area is a monitoring area where the infection probability is higher than a set threshold.

[0029] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-mentioned methods for real-time dynamic assessment of hospital airborne infection risks.

[0030] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for real-time dynamic assessment of hospital airborne infection risks.

[0031] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for real-time dynamic assessment of hospital airborne infection risks.

[0032] According to the specific embodiments provided in this application, this application has the following technical effects:

[0033] This application provides a real-time dynamic assessment method, device, medium, and product for hospital airborne infection risk. By using a machine learning algorithm to extract features from video surveillance data, computer vision-based capture of personnel behavior trajectories can be achieved, providing fine-grained behavioral information for short-range encounters. By employing an improved Wells-Riley model, infection probability is determined based on multiple personnel behavior parameters and the converted data. Based on the infection probability, real-time dynamic assessment of hospital airborne infection risk can be effectively achieved, thereby providing an effective technical reference for timely prevention and reduction of the possibility of nosocomial infection, effectively reducing the risk of infection. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 A flowchart of a real-time dynamic assessment method for hospital airborne infection risk provided by one embodiment of the present application;

[0036] Figure 2 A schematic diagram of contact distance provided in an embodiment of the present application;

[0037] Figure 3 A schematic diagram of a contact method provided in one embodiment of the present application;

[0038] Figure 4 A schematic diagram of contact time provided in one embodiment of the present application;

[0039] Figure 5 A schematic diagram of the number of personnel provided in an embodiment of the present application;

[0040] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0041] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0042] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0043] In one exemplary embodiment, as shown in Figure 1 , a hospital airborne infection risk real-time dynamic evaluation method is provided, which is executed by a computer device, specifically, can be executed by a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiments of the present application, the method is applied to the server as an example, which includes the following steps 100 to 106. Wherein:

[0044] Step 100: determining a multi-person behavior parameter based on a personnel quantity index. The personnel quantity index is the total number of personnel in the monitoring area. The multi-person behavior parameter includes contact time, contact distance, contact mode and personnel quantity.

[0045] Wherein, the fine-grained behavior contained in the trajectory in the personnel flow process is analyzed, and the parameters of contact distance, contact mode, contact time and personnel quantity are introduced, as shown in Figures 2 to 5 . As shown in Figure 3 , the contact mode can include but is not limited to face-to-face contact, face-to-back contact and face-to-side contact. Wherein, Figure 2 part (a) is a contact distance spatial distribution diagram, Figure 2 part (b) is a contact distance spatial coordinate diagram.

[0046] Step 101: obtaining video monitoring data of the hospital, and adopting a machine learning algorithm to extract features of the video monitoring data, and generating a target detection image. The target detection image contains personnel ID and target bounding box. Wherein, the video monitoring data of the hospital can be obtained by a camera.

[0047] Step 102: obtaining a tracking target based on personnel ID and target bounding box by using Byte Track algorithm.

[0048] Step 103: matching the tracking target and the target detection image to obtain a multi-target tracking result. This step can realize tracking of the target under multiple machine vision (such as camera).

[0049] Step 104: Use the perspective transformation method to perform coordinate transformation on the multi-target tracking results to obtain transformation data.

[0050] Step 105: Using the improved Wells-Riley model, the infection probability is determined based on the multi-person behavioral parameters and the transformed data.

[0051] Step 106: Based on the infection probability, a real-time dynamic assessment of the hospital airborne infection risk is performed.

[0052] In another exemplary embodiment of the present application, the implementation process of the above step 101 is described by taking the YOLOv8 algorithm as an example of the machine learning algorithm used. Based on the video surveillance data obtained in the hospital, the YOLOv8 algorithm is used to identify relevant personnel and perform feature extraction to generate personnel IDs and target bounding boxes. Specifically:

[0053] (1) Obtain video frames from the hospital’s surveillance data and input them into the YOLOv8 algorithm.

[0054] (2) Use YOLOv8 algorithm for human target detection and feature extraction.

[0055] (3) Generate a unique ID and bounding box for the target.

[0056] In another exemplary embodiment of the present application, the implementation process of step 104 can be described as follows:

[0057] (1) Obtain the spatiotemporal coordinate information of all human targets in the multi-target tracking results and express it in three-dimensional coordinates (u, v, t).

[0058] (2) Use the perspective transformation formula to determine the transformation matrix, and use the transformation matrix to convert the spatiotemporal coordinate information of all personnel targets into spatial coordinates to obtain the transformation data. Among them, the pixel coordinates are converted into actual coordinates, and the transformation matrix M coordinates are solved using the perspective transformation formula. The coordinate perspective transformation formula is as follows:

[0059]

[0060] Where (u, v) represents the Cartesian coordinates of each frame of the multi-target tracking result. (u, v, 1) represents the homogeneous coordinates. To unify the standards, the bottom midpoint of the target tracking box in the YOLOv8 algorithm is used as the coordinate point of the personnel in the row to draw the bird's-eye view. M represents the transformation matrix. ij (i, j = 1, 2, 3) are the elements of the transformation matrix M. (x, y, z) represent spatial coordinates, which are homogeneous coordinates after transformation, i.e., coordinates in the bird's-eye view. Its Cartesian coordinates (x', y') satisfy the following equation:

[0061]

[0062] In another exemplary embodiment of the present application, in step 105, the construction process of the improved Wells-Riley model includes: adding four new parameters, namely, contact distance, contact mode, correction coefficient of wearing a mask, and ventilation rate, to the traditional Wells-Riley model to obtain an improved Wells-Riley model.

[0063] Relevant calculation parameters are obtained from the video data to perform real-time calculation of infection probability. The infection probability refers to the probability of infection of susceptible individuals, which is expressed as:

[0064]

[0065] Where P is the infection probability. d is the relationship between the number of droplets exposed in the air and the distance, is the contact distance correction coefficient, d represents the distance. I represents the number of infected people, which defaults to 1. q represents the virus generation rate of infected people, which is set to 142. S represents the correction factor for the inhalation of viruses by people, i.e., the correction factor for the contact mode of the susceptible person inhaling the virus, which is related to the angle and distance. θ1 is the spatial position angle between the infected person and the people in the same monitoring area. m is the diameter of the mouth during coughing, set to 0.02m. p is the respiratory rate of the person, set to 0.96. a k is the correction factor when the infected person wears a mask b k is the correction factor when people wear masks a and k b Both are 0.5. t is the duration of contact between the infected person and people in the same monitoring area. Q is the ventilation rate. E z is the air distribution efficiency.

[0066] In another exemplary embodiment of the present application, the implementation process of step 106 can be described as follows:

[0067] (1) Personnel behavior analysis:

[0068] Based on the spatial and temporal coordinates of individuals, the distribution of the number of individuals within each monitoring area is analyzed. Based on the spatial and temporal trajectory information of high-risk individuals, the time, method, and distance of contact between high-risk individuals and others are extracted to analyze their behavior.

[0069] (2) Implementation assessment of airborne infection risk:

[0070] High-risk infection areas and infected populations are assessed and monitored in real time based on the infection probability of susceptible individuals within each monitoring area. High-risk infection areas are identified based on infection probability, and individuals within these areas are assessed and monitored in real time. High-risk infection areas are monitored areas where the infection probability exceeds a set threshold.

[0071] Based on the above description, compared with the existing methods, the main advantages of this application are:

[0072] 1. Infection risk analysis based on fine-grained behavioral information: This application uses computer vision-based personnel behavior trajectory capture to provide fine-grained behavioral information for short-distance contacts. This approach surpasses previous research methods that rely on shallow behavioral indicators. In addition, the camera-based personnel behavior trajectory capture is used to assess the risk of infection, combining on-site personnel mask wearing and on-site ventilation environment measurements to measure the ventilation rate. On-site measurements of ventilation rates reflect the objective physical environment, while maintaining social distance and wearing masks are subjective controls. In this case, by real-time monitoring of personnel activities and gatherings, based on fine-grained personnel contact analysis, immediate measures can be taken to increase ventilation rates or broadcast alarms to strengthen mask wearing, which can effectively reduce the probability of infection.

[0073] 2. Risk assessment of multiple people and multiple spaces: The method proposed in this application can work collaboratively between multiple cameras (i.e., machine vision). When a dangerous person disappears from the view of one camera and enters another camera, the method provided in this application can still be tracked and identified, greatly reducing the workload of medical staff.

[0074] 3. Identifying infection risk in spatiotemporal dimensions: The advantage of the proposed machine vision-based behavior trajectory capture method is that it can capture contact information and calculate risk in spatiotemporal dimensions. The proposed trajectory capture method can analyze exposure time and its spatial distribution, as well as changes in infection risk over time.

[0075] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6As shown in the figure. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store hospital airborne infection risk real-time dynamic assessment data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to realize a hospital airborne infection risk real-time dynamic assessment method.

[0076] Those skilled in the art can understand that, Figure 6 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement. In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to realize the steps in each of the method embodiments described above.

[0077] In one exemplary embodiment, a computer readable storage medium is provided, storing a computer program, which is executed by a processor to realize the steps in each of the method embodiments described above.

[0078] In one exemplary embodiment, a computer program product is provided, including a computer program, which is executed by a processor to realize the steps in each of the method embodiments described above.

[0079] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0080] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0081] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0082] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0083] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A real-time dynamic assessment method for hospital airborne infection risk, characterized in that: The real-time dynamic assessment method for hospital airborne infection risk includes: Determining multi-personnel behavior parameters based on a personnel quantity index; the personnel quantity index is the total number of personnel in the monitoring area; the multi-personnel behavior parameters include contact time, contact distance, contact method, and number of personnel; Obtaining video surveillance data from the hospital and extracting features from the video surveillance data using a machine learning algorithm to generate a target detection image; the target detection image includes a person ID and a target bounding box; Using the Byte Track algorithm, a tracking target is obtained based on the person ID and the target bounding box; Matching the tracking target with the target detection image to obtain a multi-target tracking result; Performing coordinate transformation on the multi-target tracking results using a perspective transformation method to obtain transformation data; An improved Wells-Riley model is used to determine the infection probability based on the multi-personnel behavioral parameters and the converted data; wherein the construction process of the improved Wells-Riley model includes: obtaining a Wells-Riley model, and adding a correction coefficient and a ventilation rate as new parameters to the Wells-Riley model to obtain the improved Wells-Riley model; the correction coefficient includes a contact distance correction coefficient, a contact mode correction coefficient, and a mask wearing correction coefficient; Based on the infection probability, a real-time dynamic assessment of the hospital airborne infection risk is performed.

2. The real-time dynamic assessment method for hospital airborne infection risk according to claim 1, characterized in that: The machine learning algorithm is the YOLOv8 algorithm.

3. The real-time dynamic assessment method for hospital airborne infection risk according to claim 1, characterized in that: The coordinate transformation of the multi-target tracking result is performed using a perspective transformation method to obtain transformation data, including: Obtaining the spatiotemporal coordinate information of all human targets in the multi-target tracking result; A perspective transformation formula is used to determine a conversion matrix, and the conversion matrix is ​​used to convert the spatiotemporal coordinate information of all personnel targets into spatial coordinates to obtain the conversion data.

4. The real-time dynamic assessment method for hospital airborne infection risk according to claim 3, characterized in that: The spatial coordinates obtained by conversion are expressed as: Where (u, v) represents the Cartesian coordinates of each frame of the multi-target tracking result, (u, v, 1) represents the homogeneous coordinates, M represents the transformation matrix, and a ij (i, j = 1, 2, 3) are the elements of the transformation matrix M, and (x, y, z) represent the spatial coordinates.

5. The real-time dynamic assessment method for hospital airborne infection risk according to claim 1, characterized in that: The formula for determining the infection probability is expressed as: Where, P is the infection probability; P d is the relationship between the number of droplets exposed in the air and the distance, and is the correction coefficient of the contact distance. d represents the distance; I is the number of infected people; q is the virus generation rate of the infected people; S is the contact correction coefficient for the inhalation of the virus by the people, θ1 is the spatial position angle between the infected person and the people in the same monitoring area, d m is the diameter of the mouth when coughing; p is the respiratory rate of the person; k a k is the correction factor when the infected person wears a mask b is the correction factor when the person wears a mask; t is the duration of contact between the infected person and the people in the same monitoring area; Q is the ventilation rate; E z is the air distribution efficiency.

6. The real-time dynamic assessment method for hospital airborne infection risk according to claim 1, characterized in that: Based on the infection probability, a real-time dynamic assessment of hospital airborne infection risk is conducted, including: Based on the infection probability, a high-risk infection area is determined, and people in the high-risk infection area are evaluated and dynamically monitored in real time; the high-risk infection area is a monitoring area where the infection probability is higher than a set threshold.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the real-time dynamic assessment method for hospital airborne infection risk according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the real-time dynamic assessment method for hospital airborne infection risk according to any one of claims 1 to 6 is implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the real-time dynamic assessment method for hospital airborne infection risk according to any one of claims 1 to 6 is implemented.

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