Disinfection robot system based on digital twin kernel

Abstract

The application provides a disinfection and sterilization robot system based on a digital twin kernel, and relates to the technical field of medical automation. After the user interaction module obtains the disinfection and sterilization instruction of the user, the positioning and navigation module compares and analyzes the double models of the digital twin kernel model and the whole life cycle BIM model to obtain position information and navigation information according to the instruction, and then transmits the information to the disinfection and sterilization robot module and cooperates with the dark surface judgment module, so that the disinfection and sterilization robot moves to the object or area to be disinfected and sterilized to perform the disinfection and sterilization work of the bright surface and the dark surface. The navigation and positioning of the disinfection and sterilization robot are more accurate, the problems of robot self-loss and inaccurate indoor navigation and positioning are solved, the user can remotely control the disinfection and sterilization robot more accurately to perform comprehensive disinfection and sterilization work, manual disinfection and sterilization is replaced, and therefore the efficiency and safety of hospital disinfection and sterilization are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of medical automation technology, and more specifically, to a disinfection robot system based on a digital twin kernel. Background Technology

[0002] my country is currently experiencing a surge in the informatization, automation, and intelligentization of hospitals. Traditional hospital operation and maintenance methods, characterized by low efficiency, strong subjectivity, low automation, and a passive approach, can no longer meet the needs of national development and people's lives. Efficient and intelligent management of medical equipment has become an urgent requirement, placing higher demands on overall intelligent hospital management and remote automated control of medical equipment. For example, the tedious and time-consuming task of daily disinfection in hospitals is currently mostly carried out manually. Medical staff need to invest considerable time and energy manually spraying disinfectant on hospital equipment or areas, which is not only inefficient and incomplete but also increases the risk of bacterial and viral infections for staff. Therefore, hospitals urgently need a safer and more efficient disinfection method. Summary of the Invention

[0003] The purpose of this invention is to provide a disinfection robot system based on a digital twin kernel. After obtaining disinfection commands from the user through a user interaction module, the positioning and navigation module performs a comparative analysis of the digital twin kernel model and the full lifecycle BIM model to obtain location and navigation information. This information is then transmitted to the disinfection robot module, which, in conjunction with the dark area judgment module, moves the disinfection robot to the object or area to be disinfected to perform disinfection work on both the visible and dark areas. This improves the accuracy of the disinfection robot's navigation and positioning, solving the problems of robot self-disorientation and insufficient indoor navigation and positioning. It allows users to remotely control the disinfection robot more precisely for comprehensive disinfection operations, replacing manual disinfection and effectively improving the efficiency and safety of hospital disinfection.

[0004] The embodiments of the present invention are implemented as follows:

[0005] This application provides a disinfection robot system based on a digital twin kernel, including:

[0006] The system comprises a user interaction module, a positioning and navigation module, and a disinfection robot module. The user interaction module provides a visual interface for users to interact with the disinfection robot module and manages user identity and permissions, as well as data storage and management. The positioning and navigation module obtains location and navigation information for the disinfection robot module through comparative analysis of a digital twin kernel model and a full-lifecycle BIM model. The disinfection robot module disinfects and sterilizes designated objects or areas based on user disinfection commands obtained from the user interaction module and the location and navigation information provided by the positioning and navigation module.

[0007] In some embodiments of the present invention, the full lifecycle BIM model is a three-dimensional point cloud model constructed using Revit, and the digital twin kernel model is an information model constructed using Unity.

[0008] In some embodiments of the present invention, the digital twin kernel model includes pre-set positioning beacon information and navigation information generated based on the Simulex analysis engine, wherein the navigation information includes path information and time information.

[0009] In some embodiments of the present invention, the system further includes a dark surface judgment module, which performs a stacked comparison based on the digital twin kernel model, the full life cycle BIM model and the collected real-world image information to make a dark surface judgment on the object or area to be disinfected, and feeds back the judgment result to the disinfection robot module.

[0010] In some embodiments of the present invention, the system further includes an environmental dynamic monitoring module for dynamically collecting and monitoring data on people flow, equipment, and air in the disinfection environment.

[0011] In some embodiments of the present invention, the system further includes an early warning and auxiliary decision-making module, which is used to issue early warnings for exceeding standards based on the data on people flow, equipment and air in the disinfection environment collected by the environmental dynamic monitoring module, and to suggest solutions to the user.

[0012] In some embodiments of the present invention, the user interaction module includes a local and / or cloud database for storing model data of the digital twin kernel model, the full lifecycle BIM model, monitoring data of the environmental dynamic monitoring module, and collected real-world image information.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0014] This invention proposes a disinfection robot system based on a digital twin kernel. After obtaining disinfection commands from the user through a user interaction module, the positioning and navigation module performs a comparative analysis of the digital twin kernel model and the full lifecycle BIM model to obtain location and navigation information. This information is then transmitted to the disinfection robot module, in conjunction with a dark area detection module, enabling the disinfection robot to move to the object or area to be disinfected and perform disinfection work on both visible and dark areas. This system improves the accuracy of the disinfection robot's navigation and positioning, solving the problems of robot self-disorientation and insufficient indoor navigation and positioning. It allows users to remotely control the disinfection robot more precisely for comprehensive disinfection operations, replacing manual disinfection and effectively improving the efficiency and safety of hospital disinfection. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the structure of an embodiment of a disinfection robot system based on a digital twin kernel according to the present invention;

[0017] Figure 2 This is a flowchart illustrating the positioning and navigation logic of the positioning and navigation module 2 in this embodiment of the invention.

[0018] Figure 3 This is a flowchart illustrating the collision detection logic of the positioning and navigation module 2 in this embodiment of the invention.

[0019] Figure 4 This is a flowchart of the dark side determination process of the dark side determination module 4 in an embodiment of the present invention.

[0020] Icons: 1. User interaction module; 2. Location and navigation module; 3. Disinfection robot module; 4. Dark area detection module; 5. Environmental dynamic monitoring module; 6. Early warning and decision support module. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0022] Example 1

[0023] Please see Figure 1-4 This application provides a disinfection robot system based on a digital twin kernel. After obtaining the user's disinfection instructions through the user interaction module, the positioning and navigation module performs a comparative analysis of the digital twin kernel model and the full lifecycle BIM model to obtain location and navigation information. This information is then transmitted to the disinfection robot module and, in conjunction with the dark area judgment module, the disinfection robot moves to the object or area to be disinfected to perform disinfection work on both the visible and dark areas. This makes the navigation and positioning of the disinfection robot more accurate, solving the problems of robot self-disorientation and insufficient indoor navigation and positioning. It allows users to remotely control the disinfection robot more precisely to carry out comprehensive disinfection operations, replacing manual disinfection and thus effectively improving the efficiency and safety of hospital disinfection and sterilization.

[0024] like Figure 1 As shown, the above-mentioned disinfection robot system based on a digital twin kernel includes:

[0025] The system comprises a user interaction module 1, a positioning and navigation module 2, and a disinfection robot module 3. The user interaction module 1 provides a visual interface for users to interact with the disinfection robot module and manages user identity and permissions, as well as data storage and management. The positioning and navigation module 2 obtains location and navigation information for the disinfection robot module through comparative analysis of a digital twin kernel model and a full-lifecycle BIM model. The disinfection robot module 3 disinfects and sterilizes designated objects or areas to be disinfected based on user disinfection commands obtained from the user interaction module 1 and the location and navigation information provided by the positioning and navigation module 2.

[0026] The full lifecycle BIM model of the positioning and navigation module 2 is a 3D point cloud model constructed using Revit. Specifically, it can be derived from 2D CAD drawings of the hospital as a whole and its various parts (such as room and equipment layouts), and then used to construct the 3D point cloud model using Revit. This includes architectural and MEP (Mechanical, Electrical, and Plumbing) models with a precision of LOD 4.0 or higher. The architectural model includes structural and non-structural models, while the MEP model includes electrical, plumbing, and HVAC models. The structural model contains, but is not limited to, mechanical calculation reports, seismic test reports, wind resistance test reports, and strength test reports. The non-structural model contains, but is not limited to, interior and exterior decoration models and equipment models. The electrical model contains, but is not limited to, cable trays, pipelines, and electrical appliances. The plumbing model contains, but is not limited to, functional units such as water supply, drainage, hot water, rainwater, sewage, consumption, and energy conservation. The HVAC model contains, but is not limited to, air conditioning, fire protection, and HVAC water units. The data in the full lifecycle BIM model includes data from the planning and design phase, including but not limited to text files, graphic files, and other documents; data from the construction phase, including but not limited to forms, procurement documents, and other documents; and data from the operation and maintenance phase, including but not limited to usage status, changes and maintenance status, and information on both the client and contractor. This provides a relatively comprehensive and complete full lifecycle data for the hospital, offering a data foundation for comprehensive disinfection and sterilization within the hospital.

[0027] The digital twin kernel model of the positioning and navigation module 2 is an information model of each component extracted and constructed from a 3D point cloud model using the C# programming language in Unity. It includes pre-set positioning beacon information and navigation information generated based on the Simulex analysis engine. The navigation information includes path information and time information. First, the positioning beacon information of each location requiring disinfection in the hospital environment can be collected and preset as parameters in the digital twin kernel model. Then, the user's disinfection commands are obtained, and the navigation information required by the disinfection robot module 3, such as path information and time information, is generated using Revit based on the Simulex analysis engine. The positioning and navigation module 2 then obtains accurate location and navigation information through data comparison and analysis of the digital twin kernel model and the full life cycle BIM model. This enables the disinfection robot module 3 to move to the location specified by the navigation information to perform disinfection and sterilization. This directly eliminates the cumbersome process of existing robot positioning methods such as WLAN positioning, infrared positioning, iBeacon positioning, DR positioning, SLAM positioning, or other algorithms such as E-OTD, PNP, EKF, and AMCL, which may result in accumulated errors, insufficient accuracy, or the need for global motion followed by internal modeling. This improves the positioning and navigation speed of the disinfection robot, thereby increasing the efficiency of disinfection of the hospital environment and equipment.

[0028] Specifically, the positioning and navigation module 2 obtains static constant data including location information and dynamic variable data including navigation information through comparative analysis of the digital twin kernel model and the full lifecycle BIM model. The static constant data provides the disinfection robot with real-time location coordinates of the device or area. These coordinates are composed of (X, Y, Z) and form a three-dimensional data set. This data set consists of multiple sets, which respectively locate the position coordinates of the disinfection robot itself and the coordinates of the diluted disinfectant spray nozzle. The dynamic variable data provides the disinfection robot with navigation route coordinates (X, Y, Z). In these coordinates, X, Y, and Z are all variables. When Z is a constant and X and Y are variables, it indicates that the robot is working on the same floor. The path parameters for moving from point P1 to point P2 are generated by Revit based on the Simulex analysis engine. When X and Y are constants and Z is a variable, it indicates that the robot is moving between floors in the elevator. Furthermore, through the digital twin kernel model, pre-set positioning beacon information can be provided for each device or area requiring disinfection within the hospital, giving it static constant data such as locatable coordinates. For moving people and equipment, collision detection is performed using a combination of static constant data and dynamic variable data. When the coordinate data (X,Y,Z) of a moving person or equipment shows that X and Y coincide and Z is the same, it indicates that the two physical entities are on the same floor and will collide. In this case, the Simulex analysis engine will re-analyze, calculate, and modify the path data to the object or area to be disinfected to prevent other objects from colliding with the disinfection robot. When the coordinate data (X,Y,Z) of a moving person or equipment shows that X and Y do not coincide or Z is different, it indicates that the two physical entities will not collide or are on different floor levels. The Simulex analysis engine will not modify the path data, and the disinfection robot will continue to move according to the acquired path parameters. The entire positioning, navigation, and collision detection logic flow of the positioning and navigation module 2 is as follows: Figure 2 , Figure 3 As shown. Through the model analysis and positioning and navigation logic of the above-mentioned positioning and navigation module 2, the navigation and positioning of the disinfection robot can be made more accurate, solving the problem of the robot getting lost and enabling users to remotely control the disinfection robot to perform operations more precisely.

[0029] Furthermore, the aforementioned disinfection robot system based on a digital twin kernel also includes a dark-side judgment module 4. This module 4 performs a stacked comparison of the digital twin kernel model, the full lifecycle BIM model, and the collected real-world image information from the positioning and navigation module 2 to determine the dark side of the object or area to be disinfected, and then feeds the judgment result back to the disinfection robot module 3. The real-world image information originates from data collected by comprehensive surveillance covering the hospital area. The data from the full lifecycle BIM model and the digital twin kernel model both originate from the model database of the user interaction module 1. Specifically, the user interaction module 1 stores the model data of the digital twin kernel model and the full lifecycle BIM model, as well as the collected real-world image information, including local and / or cloud databases. The dark-side judgment module 4 stacks and compares the available space and the actual disinfection space using the real-world image information, the full lifecycle BIM model, the monitoring data from the environmental dynamic monitoring module, and the digital twin kernel model. The intersection of the available space and the actual disinfection space is the bright side, and the remaining portion is the dark side. When the available space and the actual disinfection space volume are the same, the dark surface judgment module 4 determines that there is no dark surface to be disinfected, and all disinfection tasks have been completed. When the available space and the actual disinfection space volume are different, the dark surface judgment module 4 determines that there is a dark surface to be disinfected. Then, based on the judgment result, a movement command is issued to the disinfection robot module 3. The disinfection robot module 3 causes the disinfection robot to move to the location of the dark surface to be disinfected according to the navigation information and location information to continue to complete the remaining disinfection work. This process is repeated until the judgment result is that there is no dark surface to be disinfected. This realizes remote control of the disinfection robot to complete the disinfection tasks of all visible and dark surfaces, replacing manual disinfection, thereby effectively improving the efficiency and safety of hospital disinfection and sterilization. The entire process is as follows: Figure 4 As shown.

[0030] In addition, the aforementioned disinfection robot system based on a digital twin kernel also includes an environmental dynamic monitoring module 5 and an early warning and auxiliary decision-making module 6. The environmental dynamic monitoring module 5 is used to dynamically collect and monitor data on people flow, equipment, and air quality in the disinfection environment. The environmental dynamic monitoring module 5 can monitor VOC content, temperature and humidity, smoke concentration, pressure and pressure difference, aerosol concentration, power consumption, active virus content, and functional room usage according to functional room categories. The data comes from collectors throughout the environment and is displayed as a percentage in the user interaction module 1's visual interface. The functional room usage display shows the current people flow and available space, categorized as no available, available, or vacant. Equipment monitoring is categorized by equipment use, such as disinfection robots, transport robots, medical guidance robots, radioactive equipment, and air conditioning equipment. All data are displayed in the user interaction module 1's visual interface in the form of digital twin kernel model data, and can be customized according to the customer's equipment type.

[0031] The aforementioned early warning and auxiliary decision-making module 6 is used to issue early warnings for exceeding standards based on the data on people flow, equipment, and air quality in the disinfection environment collected by the environmental dynamic monitoring module, and to provide solutions to the user. When the dynamic environmental monitoring data in the environmental dynamic monitoring module 5 exceeds the threshold, the early warning and auxiliary decision-making module 6 is triggered, and an early warning and auxiliary decision-making window pops up to display the variables exceeding the threshold and to provide the user with corresponding solutions. The digital twin kernel model display area in the visualization interface of the user interaction module 1 is displayed in red accordingly. The user interaction module 1 proposes a solution to the user by searching for similar cases across the entire network and relevant national and local regulations. When the user adopts the solution suggested by the auxiliary suggestion, the model is automatically trained to provide the correct solution. When the user does not adopt the solution suggested by the auxiliary suggestion, a new solution is entered into the database to continue training the early warning and auxiliary decision-making model. The suggested solution includes the handling method and the handling location. The handling method includes cause analysis and solution measures. The cause analysis is accurate to the equipment circuit loop, power supply equipment, power supply line, and alarm control point of the alarm device or environment. The handling location is accurate to the location of the maintenance port that can be indicated to maintenance personnel, and the route to the maintenance port. Thus, through the cooperation of the environmental dynamic monitoring module 5 and the early warning and auxiliary decision-making module 6, dangers can be predicted and resolved in advance, fully protecting the life, health and safety of staff.

[0032] It should be noted that the technical content not specifically described in the embodiments of the present invention can be implemented by existing related technologies and belongs to the prior art, and will not be described again in the embodiments of the present invention.

[0033] In the embodiments provided in this application, it should be understood that the disclosed systems, modules, and methods can also be implemented in other ways. The system and module embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, modules, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0034] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0035] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A disinfection robot system based on a digital twin kernel, characterized in that, include: The system comprises a user interaction module, a positioning and navigation module, and a disinfection robot module. The user interaction module provides a visual interface for users to interact with the disinfection robot module and manages user identity and permissions, as well as data storage and management. The positioning and navigation module obtains location and navigation information for the disinfection robot module through comparative analysis of a digital twin kernel model and a full lifecycle BIM model. The disinfection robot module disinfects and sterilizes designated objects or areas according to user disinfection commands obtained from the user interaction module and the location and navigation information provided by the positioning and navigation module. The system also includes a dark area judgment module, which performs a stacked comparison based on the digital twin kernel model, the full life cycle BIM model, and the collected real-world image information to make a dark area judgment on the object or area to be disinfected, and feeds back the judgment result to the disinfection robot module.

2. The disinfection robot system based on a digital twin kernel as described in claim 1, characterized in that, The full lifecycle BIM model is a 3D point cloud model built using Revit, and the digital twin kernel model is an information model built using Unity.

3. The disinfection robot system based on a digital twin kernel as described in claim 2, characterized in that, The digital twin kernel model includes pre-set positioning beacon information and navigation information generated based on the Simulex analysis engine. The navigation information includes path information and time information.

4. The disinfection robot system based on a digital twin kernel as described in claim 1, characterized in that, The system also includes an environmental dynamic monitoring module, which is used to dynamically collect and monitor data on people flow, equipment, and air in the disinfection environment.

5. A disinfection robot system based on a digital twin kernel as described in claim 4, characterized in that, The system also includes an early warning and decision support module, which is used to issue early warnings for exceeding standards based on the data on people flow, equipment, and air quality in the disinfection environment collected by the environmental dynamic monitoring module, and to suggest solutions to the user.

6. The disinfection robot system based on a digital twin kernel as described in claim 5, characterized in that, The user interaction module includes a local and / or cloud database for storing model data of the digital twin kernel model, the full lifecycle BIM model, monitoring data from the environmental dynamic monitoring module, and collected real-world image information.

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