Nuclear power plant serious accident teaching evaluation system and method based on human body signs

Abstract

The invention provides a nuclear power plant serious accident teaching evaluation system and method based on human body signs, and the system comprises a bioelectrical sensing module which is used for capturing the physiological data of a worker when a nuclear power plant serious accident is simulated; the data processing and analyzing module is used for processing and analyzing the physiological data so as to evaluate the physiological reaction of the worker in the accident and generate an evaluation result; the communication module is used for transmitting the evaluation result to terminal equipment of a corresponding worker; and the user interface module is used for allowing a worker to check and analyze the evaluation result. Based on the scheme provided by the invention, the physiological reaction of the worker in the simulated nuclear power plant accident can be monitored and recorded in real time, the worker is helped to learn and evaluate the reaction and expression of the worker in the accident, subsequent teaching and evaluation are facilitated, the coping ability of the worker in the real accident in the future is improved, and the practical value is important.

Description

Technical Field

[0001] The present application relates to the field of nuclear power plant accident simulation teaching, and in particular to a nuclear power plant severe accident teaching and evaluation system and method based on human body signs. Background Art

[0002] Power plant accidents are complex and dangerous situations that require workers to handle them efficiently and accurately. However, due to the uniqueness and complexity of accidents, it is difficult to train in a real environment.

[0003] Existing severe accident teaching techniques utilize software simulations, utilizing a nuclear power plant severe accident analysis program to present data to trainees via a server screen display. This teaching method simulates real-life accident scenarios, allowing employees to experience and learn how to respond to severe accidents firsthand. Simulation drills can be conducted using simulation software or in specialized training facilities. Alternatively, under the guidance of professionals, employees can practice operating emergency equipment and responding to radioactive leaks, enhancing their practical skills.

[0004] However, the practical operation process places high demands on the functionality of technical equipment, and during simulation, the existing technical equipment is unable to achieve the mental and physical signs that trainees feel when they are on site and monitor them. Summary of the Invention

[0005] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0006] To this end, the first purpose of this application is to propose a nuclear power plant severe accident teaching and evaluation system based on human vital signs to monitor and record the physiological reactions of workers in simulated nuclear power plant accidents in real time, and help users evaluate the performance of workers in accidents.

[0007] The second object of this application is to propose a method.

[0008] The third objective of this application is to provide an electronic device.

[0009] The fourth object of this application is to provide a computer-readable storage medium.

[0010] A fifth object of this application is to provide a computer program product.

[0011] To achieve the above objectives, the first embodiment of the present application proposes a nuclear power plant severe accident teaching and assessment system based on human body signs, comprising:

[0012] A bioelectric sensing module, used to capture physiological data of workers during simulations of severe accidents at nuclear power plants;

[0013] a data processing and analysis module, configured to process and analyze the physiological data to evaluate the physiological response of the worker in the accident and generate an evaluation result;

[0014] A communication module, configured to transmit the evaluation result to a terminal device of the corresponding staff member;

[0015] A user interface module is used to allow the staff to view and analyze the evaluation results.

[0016] Optionally, the bioelectric sensing module includes:

[0017] a skin contact sensor for capturing an electrocardiogram signal of the worker;

[0018] non-invasive sensors for capturing respiratory and body temperature data of the worker;

[0019] An invasive sensor is used to capture blood pressure data of the worker.

[0020] Optionally, the data processing and analysis module includes:

[0021] A heart rate variability analysis module, used for processing and analyzing the electrocardiogram signal of the staff member;

[0022] A breathing analysis module, used for processing and analyzing the breathing data of the staff member;

[0023] A body temperature analysis module, used to process and analyze the body temperature data of the staff member;

[0024] The blood pressure analysis module is used to process and analyze the blood pressure data of the staff member.

[0025] Optionally, the data processing and analysis module uses a machine learning algorithm to process and analyze the physiological data.

[0026] Optionally, the terminal device includes:

[0027] computer;

[0028] cell phone;

[0029] Tablet device.

[0030] To achieve the above objectives, the second embodiment of the present application proposes a teaching and assessment method for severe accidents in nuclear power plants based on human body signs, comprising:

[0031] Capturing physiological data from workers during simulations of severe accidents at nuclear power plants;

[0032] Processing and analyzing the physiological data to evaluate the worker's physiological response during the accident and generating an evaluation result;

[0033] Transmitting the evaluation results to the terminal devices of the corresponding staff members;

[0034] The staff is allowed to review and analyze the evaluation results.

[0035] To achieve the above-mentioned purpose, a third embodiment of the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0036] The memory stores computer-executable instructions;

[0037] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the second aspects above.

[0038] To achieve the above-mentioned purpose, the fourth embodiment of the present application proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method as described in any one of the second aspects above.

[0039] To achieve the above-mentioned purpose, the fifth embodiment of the present application proposes a computer program product, including a computer program, which implements the method as described in any one of the above-mentioned second aspects when executed by a processor.

[0040] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0041] It can monitor and record the physiological reactions of workers in simulated nuclear power plant accidents in real time, helping workers learn and evaluate their own reactions and performances in accidents, facilitating subsequent teaching and evaluation, and improving their ability to respond to real accidents in the future. It has important practical value.

[0042] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0044] Figure 1 This is a block diagram of a nuclear power plant severe accident teaching and evaluation system based on human body signs according to an embodiment of the present application;

[0045] Figure 2is a block diagram of a data processing and analysis module according to an embodiment of the present application;

[0046] Figure 3 This is a flow chart of a teaching and assessment method for severe accidents in nuclear power plants based on human body signs according to an embodiment of the present application;

[0047] Figure 4 It is a block diagram of an electronic device. DETAILED DESCRIPTION

[0048] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0049] The following describes a system and method for teaching and evaluating severe accidents in nuclear power plants based on human body signs according to an embodiment of the present application with reference to the accompanying drawings.

[0050] Figure 1 FIG. 1 is a block diagram of a nuclear power plant severe accident teaching and evaluation system based on human body signs according to an embodiment of the present application. Figure 1 As shown, the system includes the following modules:

[0051] The bioelectric sensing module 100 is used to capture the physiological data of workers when simulating a severe accident in a nuclear power plant.

[0052] In the embodiment of the present application, when simulating a real accident scene to allow employees to personally experience and learn how to deal with serious accidents, the physiological data of the staff are collected through various sensors in the bioelectric sensing module 100.

[0053] In this application, the worker's electrocardiogram signal is captured by a skin contact sensor, the worker's respiration and body temperature data are captured by a non-invasive sensor, and the worker's blood pressure data is captured by an invasive sensor.

[0054] It can be understood that the electrocardiogram signal can reflect the activity state and physiological pressure of the heart.

[0055] It should be noted that this application does not specifically limit the models of skin contact sensors, non-invasive sensors and invasive sensors. In the field of nuclear power plant accident simulation, the use of sensors with similar functions to collect users' physiological data is within the scope of protection of this application.

[0056] The data processing and analysis module 200 is used to process and analyze physiological data to evaluate the physiological response of the worker in the accident and generate evaluation results.

[0057] In the embodiment of the present application, the data processing and analysis module 200 uses a machine learning algorithm to process and analyze physiological data, and is specifically applied to Figure 2 In the modules shown, Figure 2 As shown, the data processing and analysis module 200 also includes the following modules:

[0058] Heart rate variability analysis module 210, used to process and analyze the staff's electrocardiogram signals;

[0059] A breathing analysis module 220 is used to process and analyze the breathing data of the staff member;

[0060] Body temperature analysis module 230, used to process and analyze the body temperature data of staff members;

[0061] The blood pressure analysis module 240 is used to process and analyze the blood pressure data of the staff.

[0062] In the embodiment of the present application, the final generated evaluation result can reflect the physiological reaction and stress level of the staff in the accident.

[0063] In the embodiments of this application, a machine learning algorithm is a computer program that learns from data. The computer gathers information from the data and uses it to better complete a task. This algorithm requires sufficient training to ensure accuracy. Supervised learning, unsupervised learning, reinforcement learning, and logistic regression are some of the most popular machine learning algorithms currently available.

[0064] It is understandable that in the relevant technology, machine learning algorithms are currently a relatively mature type of algorithms, and their training processes are also well known to people in this field. In the embodiments of this application, there is no specific limitation on the type and training process of the machine learning algorithm.

[0065] The communication module 300 is used to transmit the evaluation results to the terminal device of the corresponding staff member.

[0066] In the embodiment of the present application, the communication module 300 transmits the evaluation results to the terminal device of the corresponding staff member through a wireless network or other means.

[0067] It is understandable that terminal devices include but are not limited to computers, mobile phones and tablet devices. Any device with communication functions can serve as a terminal device for staff in a certain sense, and this application does not make specific restrictions here.

[0068] The user interface module 400 is used to allow staff to view and analyze the evaluation results.

[0069] In an embodiment of the present application, the user interface 400 can be an app or a web interface installed by the staff on the terminal device. By granting the staff the authority to access the user interface, the staff can view and analyze the evaluation results.

[0070] It is understood that the assessment results viewed by staff can be real-time data or historical data for the convenience of teaching and assessment.

[0071] The embodiments of the present application can monitor and record the physiological reactions of workers in a simulated nuclear power plant accident in real time, helping workers learn and evaluate their own reactions and performance in the accident, facilitating subsequent teaching and evaluation, and improving their ability to respond to real accidents in the future, which has important practical value.

[0072] Figure 3 This is a flow chart of a method for teaching and evaluating severe accidents in nuclear power plants based on human body signs according to an embodiment of the present application, including:

[0073] Step 101 , capturing physiological data of workers during a simulation of a severe accident at a nuclear power plant;

[0074] Step 102: Process and analyze the physiological data to evaluate the physiological response of the worker during the accident and generate an evaluation result;

[0075] Step 103: Transmit the evaluation results to the terminal device of the corresponding staff member;

[0076] Step 104 allows staff to view and analyze the evaluation results.

[0077] Optionally, capture physiological data of workers during simulations of severe accidents at nuclear power plants, including:

[0078] Capture workers' ECG signals, respiratory and body temperature data, and blood pressure data.

[0079] Regarding the steps in the above embodiments, the specific manner of performing the operations in each step has been described in detail in the embodiments of the system and will not be elaborated here.

[0080] Figure 4A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0081] like Figure 4 As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the device 700 can also be stored in the RAM 703. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0082] Various components in device 700 are connected to I / O interface 705, including an input unit 706, such as a keyboard, mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, optical disk, etc.; and a communication unit 709, such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0083] The computing unit 701 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as the voice command response method. For example, in some embodiments, the voice command response method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the voice command response method described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform the voice command response method in any other appropriate manner (e.g., by means of firmware).

[0084] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0085] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0086] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0087] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0088] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0089] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. This client-server relationship is established by computer programs running on the respective computers, establishing a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host, a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and VPS services ("Virtual Private Servers" or simply "VPS"). The server may also be a server in a distributed system or a server integrated with blockchain.

[0090] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of this disclosure can be achieved, and this document is not limited here.

[0091] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A teaching and evaluation system for severe accidents in nuclear power plants based on human body signs, characterized in that: include: Bioelectric sensing module, used to capture the physiological data of workers during simulation of severe accidents in nuclear power plants; A data processing and analysis module, used to process and analyze the physiological data to evaluate the physiological response of the worker in the accident and generate an evaluation result; A communication module, used for transmitting the evaluation result to the terminal device of the corresponding staff member; A user interface module is used to allow the staff to view and analyze the evaluation results.

2. The system according to claim 1, characterized in that The bioelectric sensing module comprises: A skin contact sensor for capturing an electrocardiogram signal of the worker; non-invasive sensors for capturing respiratory and temperature data of said worker; An invasive sensor is used to capture blood pressure data of the worker.

3. The system according to claim 2, characterized in that The data processing and analysis module comprises: A heart rate variability analysis module, used for processing and analyzing the electrocardiogram signal of the staff member; A breathing analysis module, used for processing and analyzing the breathing data of the staff member; A body temperature analysis module, used to process and analyze the body temperature data of the staff member; The blood pressure analysis module is used to process and analyze the blood pressure data of the staff member.

4. The system according to claim 1, characterized in that The data processing and analysis module uses a machine learning algorithm to process and analyze the physiological data.

5. The system according to claim 1, characterized in that The terminal device comprises: computer; cell phone; Tablet device.

6. A teaching and evaluation method for severe accidents in nuclear power plants based on human body signs, characterized in that: include: Capturing physiological data from workers during simulations of severe accidents at nuclear power plants; Processing and analyzing the physiological data to evaluate the physiological response of the worker in the accident and generate an evaluation result; Transmitting the evaluation results to the terminal devices of the corresponding staff members; The staff is allowed to review and analyze the evaluation results.

7. The method according to claim 6, characterized in that The method captures the physiological data of workers when simulating a severe accident at a nuclear power plant, including: The staff member's electrocardiogram signal, respiration and body temperature data, and blood pressure data are captured.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 6 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 6 to 7 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 6 to 7 when being executed by a processor.

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