Training guidance method and system for nuclear power plant operator, electronic equipment and storage medium

Abstract

The invention provides a training guidance method and system for nuclear power plant operators, electronic equipment and a storage medium, and the method comprises the steps: building a model operation scene of a nuclear power plant, and simulating an abnormal operation state of the nuclear power plant in the simulation operation scene; obtaining process data and decision-making data of decision-making processing on the abnormal operation state by nuclear power plant operators; evaluating the cooperation and communication capabilities of the nuclear power plant operators based on the process data to obtain a first evaluation result; evaluating the risk decision-making ability of the nuclear power plant operator based on the decision-making data to obtain a second evaluation result; and generating first training guidance information based on the first evaluation result and the second evaluation result. Through the technical scheme of the invention, the operation level of the nuclear power plant operator can be accurately evaluated, and the operation management capability of the nuclear power plant operator is improved.

Description

Technical Field

[0001] The present application relates to the field of nuclear power technology, and in particular to a training and guidance method, system, electronic equipment and storage medium for nuclear power plant operators. Background Art

[0002] In the related art, when training nuclear power plant operators, the operators generally execute designated operating procedures through a simulator to conduct simulated teaching and training, but it is impossible to monitor and evaluate the actual performance and psychological state of the operators when facing emergencies. Summary of the invention

[0003] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0004] In a first aspect, the present application proposes a training and guidance method for nuclear power plant operators, comprising: establishing a model operation scenario of a nuclear power plant, and simulating the abnormal operation state of the nuclear power plant under the simulated operation scenario; obtaining process data and decision data for decision-making by the nuclear power plant operators on the abnormal operation state; evaluating the collaboration and communication capabilities of the nuclear power plant operators based on the process data to obtain a first evaluation result; evaluating the risk decision-making capabilities of the nuclear power plant operators based on the decision data to obtain a second evaluation result; and generating first training guidance information based on the first evaluation result and the second evaluation result.

[0005] In one implementation, the evaluating the collaboration and communication capabilities of the nuclear power plant operators based on the process data to obtain a first evaluation result includes: inputting the process data into a pre-trained first evaluation model to obtain the first evaluation result; wherein the first evaluation model has pre-learned the mapping relationship between the relevant data of the nuclear power plant operators in the decision-making process and different evaluation results.

[0006] In an optional implementation, the first evaluation model is a neural network expert system, and the expert knowledge base in the neural network expert system stores first rule knowledge of processing rules corresponding to different abnormal operating states of the nuclear power plant.

[0007] In an optional implementation, the process data includes at least one of the following: duration data used for the decision processing; voice data of the decision processing; behavior data of the decision processing; and operation data performed during the decision processing.

[0008] In one implementation, the risk decision-making ability of the nuclear power plant operator is evaluated based on the decision data to obtain a second evaluation result, including: inputting the decision data into a pre-trained second evaluation model to obtain the second evaluation result; wherein the second evaluation model has pre-learned the mapping relationship between different decision data and evaluation results.

[0009] In an optional implementation, the second evaluation model is a neural network expert system, and the expert knowledge base in the neural network expert system stores second rule knowledge of diagnostic decision rules corresponding to different abnormal operating states of the nuclear power plant.

[0010] In one implementation, the method further includes: obtaining physiological data of a nuclear power plant operator during a decision-making process for the abnormal operating state; and generating second training guidance information based on the physiological data, the first assessment result, and the second assessment result.

[0011] On the second aspect, the present application proposes a training and guidance system for nuclear power plant operators, including: a simulation operation module: used to establish a model operation scenario of a nuclear power plant, and simulate the abnormal operation state of the nuclear power plant under the simulation operation scenario; a data acquisition module: used to obtain process data and decision data of the nuclear power plant operator for decision-making on the abnormal operation state; a team collaboration and communication evaluation module: used to evaluate the collaboration and communication capabilities of the nuclear power plant operator based on the process data, and obtain a first evaluation result; a risk analysis and decision evaluation module: used to evaluate the risk decision-making capabilities of the nuclear power plant operator based on the decision data, and obtain a second evaluation result; a guidance module: used to generate first training guidance information based on the first evaluation result and the second evaluation result.

[0012] In one implementation, the team collaboration and communication assessment module is specifically used to: input the process data into a pre-trained first assessment model to obtain the first assessment result; wherein the first assessment model has pre-learned the relationship between the relevant data of the nuclear power plant operator in the decision-making process and different assessment results.

[0013] In an optional implementation, the first evaluation model is a neural network expert system, and the expert knowledge base in the neural network expert system stores first rule knowledge of processing rules corresponding to different abnormal operating states of the nuclear power plant.

[0014] In an optional implementation, the process data includes at least one of the following: duration data used for the decision processing; voice data of the decision processing; behavior data of the decision processing; and operation data performed during the decision processing.

[0015] In one implementation, the risk analysis and decision evaluation module is specifically used to: input the decision data into a pre-trained second evaluation model to obtain the second evaluation result; wherein the second evaluation model has pre-learned the relationship between different decision data and evaluation results.

[0016] In an optional implementation, the second evaluation model is a neural network expert system, and the expert knowledge base in the neural network expert system stores second rule knowledge of diagnostic decision rules corresponding to different abnormal operating states of the nuclear power plant.

[0017] In one implementation, the system also includes: a physiological data monitoring module: used to obtain the physiological data of the nuclear power plant operator during the decision-making process of the abnormal operating state; the guidance module is also used to: generate second training guidance information based on the physiological data, the first evaluation result and the second evaluation result.

[0018] In a third aspect, the present application proposes an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the training and guidance method for nuclear power plant operators as described in the first aspect.

[0019] In a fourth aspect, the present application proposes a computer-readable storage medium for storing instructions, which, when executed, enables the method described in the first aspect to be implemented.

[0020] In a fifth aspect, the present application proposes a computer program product, including a computer program, which, when executed by a processor, implements the steps of the training and guidance method for nuclear power plant operators as described in the first aspect.

[0021] The training guidance method, system, electronic device and storage medium for nuclear power plant operators provided in this application obtain process data and decision data of nuclear power plant operators in the process of decision-making and processing abnormal operating states in simulated operating scenarios, evaluate nuclear power plant operators based on the above data, and generate corresponding training guidance information based on the evaluation results. It can accurately evaluate the operating level of nuclear power plant operators and improve the operation management capabilities of nuclear power plant operators.

[0022] 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 the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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:

[0024] Figure 1 It is a flowchart of a training and guidance method for nuclear power plant operators provided in an embodiment of the present application;

[0025] Figure 2 It is a flow chart of another method for training and guiding nuclear power plant operators provided in an embodiment of the present application;

[0026] Figure 3 It is a flowchart of another method for training and guiding nuclear power plant operators provided in an embodiment of the present application;

[0027] Figure 4 is a schematic diagram of a training and guidance system for nuclear power plant operators provided in an embodiment of the present application;

[0028] Figure 5 is a schematic diagram of another training and guidance system for nuclear power plant operators provided in an embodiment of the present application;

[0029] Figure 6 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] Embodiments of the present application are described in detail below, and 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.

[0031] The following describes the training and guidance method and system for nuclear power plant operators in an embodiment of the present application with reference to the accompanying drawings.

[0032] Figure 1 1 is a flow chart of a training and guidance method for nuclear power plant operators provided in an embodiment of the present application. Figure 1 As shown, the method may include but is not limited to the following steps:

[0033] Step S101: Establish a model operation scenario of a nuclear power plant, and simulate the abnormal operation state of the nuclear power plant under the simulated operation scenario.

[0034] For example, based on pre-established nuclear power plant simulator software, a simulated operation scenario of the nuclear power plant is established, and the abnormal operation state of the nuclear power plant is simulated in the simulated operation scenario.

[0035] Among them, in the embodiment of the present application, the above-mentioned abnormal operating state includes at least one of the following: operating accident, equipment failure.

[0036] Step S102: Acquire process data and decision data of the nuclear power plant operator's decision-making process on abnormal operating status.

[0037] In the embodiment of the present application, the above-mentioned nuclear power plant operator may be an operator in the main control room of the nuclear power plant.

[0038] In the embodiment of the present application, there may be multiple nuclear power plant operators.

[0039] For example, obtaining process data on the behavior of nuclear power plant operators in the process of making decisions on abnormal operating conditions, as well as relevant data on decision-making processes.

[0040] In some embodiments of the present application, the above-mentioned decision data includes at least one of the following: the fault type obtained by the decision of the nuclear power plant operator; the disposal measures implemented by the nuclear power plant operator; and the decision-making principle of the nuclear power plant operator.

[0041] Step S103: Evaluate the collaboration and communication capabilities of nuclear power plant operators during the decision-making process based on the process data to obtain a first evaluation result.

[0042] For example, based on the acquired process data, the cooperation and communication capabilities of nuclear power plant operators in the process of making decisions on operational accidents are evaluated to obtain a first evaluation result.

[0043] In an embodiment of the present application, the above-mentioned process data includes at least one of the following: duration data used for decision processing; voice data of decision processing; behavior data of decision processing; and operation data performed during decision processing.

[0044] The behavioral data for the above-mentioned decision-making process may include, but are not limited to, body movement data, facial expression data, etc. of nuclear power plant operators during the decision-making process.

[0045] For example, the above process data can be identified to obtain semantic data, semantic emotion data, body movement data, facial expression data, and feature data such as the time taken to perform corresponding operations after receiving collaboration requests from other nuclear power plant operators during the communication and decision-making process of nuclear power plant operators with other nuclear power plant operators, thereby evaluating the collaboration and communication capabilities of nuclear power plant operators based on the above feature data.

[0046] Among them, in an embodiment of the present application, the above-mentioned first evaluation result may include at least one of the following: communication ability evaluation result: whether the nuclear power plant operator can express his own opinions and views clearly and accurately, and listen to the opinions and suggestions of other operators; collaboration ability evaluation result: whether the nuclear power plant operator can effectively collaborate and cooperate with other operators to complete work tasks together; leadership ability evaluation result: whether the nuclear power plant operator can play a leading role, lead other operators to complete tasks together, and provide effective guidance and support to other operators; task allocation ability evaluation result: whether the nuclear power plant operator can reasonably allocate work tasks according to the ability evaluation results and expertise of team members, and ensure the smooth completion of tasks; problem-solving ability evaluation result: whether the nuclear power plant operator can effectively solve problems and conflicts between team members, and can propose effective solutions.

[0047] As an example, voice data of nuclear power plant operators during decision-making can be obtained, emotion recognition and text recognition can be performed on the voice data to obtain corresponding emotion information and text information, and whether the emotional state of the nuclear power plant operators during communication with other nuclear power plant operators is normal can be evaluated based on the emotion information. Semantic recognition can be performed based on the text information to evaluate the effectiveness of the terms used by the nuclear power plant operators during communication with other nuclear power plant operators, thereby evaluating the communication ability of the nuclear power plant operators.

[0048] As another example, voice data and behavior data of a first nuclear power plant operator during a decision-making process may be obtained, semantic recognition may be performed on the voice data, and instruction information in the voice data may be obtained, where the instruction information may be information instructing a second nuclear power plant operator to perform a first operation. Based on the voice data and behavior data of the second nuclear power plant operator during the decision-making process, the operation time of the second nuclear power plant operator in performing the first operation indicated in the above instruction information may be determined, thereby determining the collaboration efficiency between the first nuclear power plant operator and the second nuclear power plant operator.

[0049] In some embodiments of the present application, the first evaluation result may be one of a plurality of different preset levels, or the first evaluation result may be an evaluation score.

[0050] Step S104: Evaluate the risk decision-making ability of the nuclear power plant operator based on the decision data to obtain a second evaluation result.

[0051] For example, based on the acquired decision data, the risk analysis and decision-making processing capabilities of nuclear power plant operators when abnormal operating conditions occur are evaluated to evaluate whether the nuclear power plant operators can correctly handle the abnormal operating conditions they face according to appropriate processing rules, thereby obtaining a second evaluation result.

[0052] Among them, in an embodiment of the present application, the above-mentioned second evaluation result includes at least one of the following: risk identification capability: that is, whether the nuclear power plant operator can accurately identify the operating failure corresponding to the abnormal operating state, and predict the possible impact and consequences of the operating failure; risk assessment capability: that is, whether the nuclear power plant operator can use scientific methods to conduct qualitative and quantitative assessments of risks, and be able to formulate corresponding risk control measures; decision-making capability: that is, whether the nuclear power plant operator can make corresponding correct decisions and measures in response to operating failures; risk management capability: that is, the nuclear power plant operator can effectively manage various risks in the operation of the nuclear power plant, and be able to formulate corresponding risk management systems and processes.

[0053] In some embodiments of the present application, the second evaluation result may be an evaluation level, or the second evaluation result may be an evaluation score.

[0054] Step S105: Generate first training guidance information based on the first evaluation result and the second evaluation result.

[0055] For example, based on the first assessment result and the second assessment result, it is determined that the performance of the nuclear power plant operator does not meet the preset standards when making decisions on abnormal operating conditions (for example, the assessment level is lower than the preset level threshold, or the assessment score is less than or equal to the preset score threshold), and based on the poor performance indicator, targeted guidance information is generated to guide the nuclear power plant operators to improve their collaboration, communication and risk decision-making capabilities.

[0056] As an example, if the first assessment result indicates that the communication ability of nuclear power plant operators is poor during decision-making for abnormal operating conditions, first training guidance information can be generated to guide nuclear power plant operators to improve their communication ability.

[0057] As another example, if the second evaluation result indicates that the nuclear power plant operator made an incorrect judgment on the type of fault that caused the abnormal operating state, relevant training guidance information on the correct fault type corresponding to the abnormal operating state can be generated.

[0058] By implementing the embodiments of the present application, process data and decision data of nuclear power plant operators in the process of making decisions on abnormal operating states in simulated operating scenarios can be obtained, so as to evaluate the nuclear power plant operators based on the above data, and generate corresponding training guidance information based on the evaluation results. It is possible to accurately evaluate the operating level of nuclear power plant operators and improve the operation management capabilities of nuclear power plant operators.

[0059] In some embodiments of the present application, before evaluating a nuclear power plant operator based on process data and decision data, the process data and decision data may be processed, and a first evaluation result may be obtained based on the processed process data, and a second evaluation result may be obtained based on the processed decision data.

[0060] Among them, in an embodiment of the present application, the above-mentioned data processing may include at least one of the following: data cleaning, data normalization, feature extraction, pattern recognition, batch processing, real-time processing and streaming processing.

[0061] In one implementation, the acquired process data may be processed based on a pre-trained neural network model to obtain a first evaluation result. As an example, see Figure 2 , Figure 2 FIG. 1 is a flow chart of another method for training and guiding nuclear power plant operators provided in an embodiment of the present application. Figure 2 As shown, the method may include but is not limited to the following steps:

[0062] Step S201: Establish a model operation scenario of a nuclear power plant, and simulate the abnormal operation state of the nuclear power plant under the simulated operation scenario.

[0063] In the embodiments of the present application, step S201 can be implemented by any of the methods in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0064] Step S202: Acquire process data and decision data of the nuclear power plant operator's decision-making process on abnormal operating status.

[0065] In the embodiments of the present application, step S202 can be implemented by any of the methods in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0066] Step S203: inputting the process data into a pre-trained first evaluation model to obtain a first evaluation result.

[0067] Among them, the first evaluation model has pre-learned the mapping relationship between the relevant data of the nuclear power plant operators in the decision-making process and different evaluation results.

[0068] In an optional implementation, the first evaluation model is a neural network expert system, and the expert knowledge base in the neural network expert system stores first rule knowledge of processing rules corresponding to different abnormal operating states of the nuclear power plant.

[0069] For example, the first evaluation model is a neural network model mixed with an expert system. The knowledge base of the expert system stores rule knowledge corresponding to the correct process for handling different types of operating failures. Therefore, after obtaining the process data, the first evaluation model can obtain the corresponding rule knowledge from the expert system based on the actual operating failure corresponding to the process data, and combine the process data of nuclear power plant operators in making decisions on abnormal operating conditions to perform adaptive reasoning evaluation.

[0070] Among them, in an embodiment of the present application, the above-mentioned first evaluation model can be trained by the following steps: pre-acquiring multiple groups of different training process data, and manually evaluating each group of training process data to obtain corresponding evaluation results, using the training process data as input data and the corresponding evaluation results as output data to train the initial evaluation model, using the back propagation algorithm to adjust the model coefficients of the initial evaluation model, and calculating the accuracy, precision, recall rate and other evaluation indicators of the initial evaluation model after adjustment, until the evaluation indicators meet the preset indicator threshold to complete the model training, and generate the first evaluation model based on the model parameters of the trained initial model.

[0071] Step S204: Evaluate the risk decision-making ability of the nuclear power plant operator based on the decision data to obtain a second evaluation result.

[0072] In the embodiments of the present application, step S204 can be implemented by any of the methods in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0073] Step S205: Generate first training guidance information based on the first evaluation result and the second evaluation result.

[0074] In the embodiments of the present application, step S205 can be implemented by any of the methods in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0075] By implementing the embodiments of the present application, process data and decision data of nuclear power plant operators in the process of making decisions on abnormal operating states in simulated operating scenarios can be obtained, so as to evaluate the nuclear power plant operators based on the above data, and generate corresponding training guidance information based on the evaluation results. It is possible to accurately evaluate the operating level of nuclear power plant operators and improve the operation management capabilities of nuclear power plant operators.

[0076] In one implementation, the acquired decision data may be processed based on a pre-trained neural network model to obtain a second evaluation result. As an example, see Figure 3 , Figure 3FIG. 1 is a flow chart of another method for training and guiding nuclear power plant operators provided in an embodiment of the present application. Figure 3 As shown, the method may include but is not limited to the following steps:

[0077] Step S301: Establish a model operation scenario of a nuclear power plant, and simulate the abnormal operation state of the nuclear power plant under the simulated operation scenario.

[0078] In the embodiments of the present application, step S301 can be implemented in any of the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0079] Step S302: Acquire process data and decision data of the nuclear power plant operator for making decisions on abnormal operating conditions.

[0080] In the embodiments of the present application, step S302 can be implemented by any of the methods in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0081] Step S303: Evaluate the collaboration and communication capabilities of nuclear power plant operators during the decision-making process based on the process data to obtain a first evaluation result.

[0082] In the embodiments of the present application, step S303 can be implemented by any of the methods in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0083] Step S304: input the decision data into a pre-trained second evaluation model to obtain a second evaluation result.

[0084] Among them, the second evaluation model has pre-learned the mapping relationship between different decision data and evaluation results.

[0085] Among them, in an embodiment of the present application, the above-mentioned second evaluation model can be trained by the following steps: pre-acquiring multiple groups of different training decision data, and manually evaluating each group of training decision data to obtain corresponding evaluation results, using the training decision data as input data and the corresponding evaluation results as output data to train the initial evaluation model, using the back propagation algorithm to adjust the model coefficients of the initial evaluation model, and calculating the accuracy, precision, recall rate and other evaluation indicators of the initial evaluation model after adjustment, until the evaluation indicators meet the preset indicator threshold to complete the model training, and generate a second evaluation model based on the model parameters of the trained initial model.

[0086] In an optional implementation, the second evaluation model is a neural network expert system, and the expert knowledge base in the neural network expert system stores second rule knowledge of diagnostic decision rules corresponding to different abnormal operating states of the nuclear power plant.

[0087] For example, the second evaluation model is a neural network model mixed with an expert system. The knowledge base of the expert system stores rule knowledge such as actual fault types, handling measures, and generated operating risks corresponding to processing a variety of different preset abnormal operating states. Therefore, after obtaining the decision data, the second evaluation model can obtain corresponding rule knowledge from the expert system based on the abnormal operating states faced by nuclear power plant operators, thereby performing adaptive reasoning evaluation on the decision data of nuclear power plant operators in making decisions on different abnormal operating states.

[0088] Step S305: Generate first training guidance information based on the first evaluation result and the second evaluation result.

[0089] In the embodiments of the present application, step S305 can be implemented in any of the ways in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0090] By implementing the embodiments of the present application, process data and decision data of nuclear power plant operators in the process of making decisions on abnormal operating states in simulated operating scenarios can be obtained, so as to evaluate the nuclear power plant operators based on the above data and pre-trained neural network models, and generate corresponding training guidance information based on the evaluation results. It is possible to accurately evaluate the operating level of nuclear power plant operators and improve the operation management capabilities of nuclear power plant operators.

[0091] In some embodiments of the present application, the above method may further include the following steps: obtaining physiological data of nuclear power plant operators during decision-making on abnormal operating conditions; generating second training guidance information based on the physiological data, the first evaluation results and the second evaluation results.

[0092] In an embodiment of the present application, the above physiological data includes at least one of the following: heart rate, blood pressure, and respiratory rate.

[0093] For example, the physiological data of nuclear power plant operators during the process of making decisions on abnormal operating conditions is obtained, and combined with the first assessment result and the second assessment result, the impact of the psychological stress level and emotional state of the nuclear power plant operators during the decision-making process on the assessment results is determined, so as to generate second training guidance information to help improve the psychological quality of nuclear power plant operators when the first assessment result and / or the second assessment result do not meet the preset conditions.

[0094] As an example, if the physiological data of a nuclear power plant operator is significantly higher than the normal physiological level during the decision-making process, and the first evaluation result and / or the second evaluation result of the nuclear power plant operator do not meet the preset conditions, it can be determined that the nuclear power plant operator has a high psychological stress level and an unstable emotional state when making decisions on abnormal operating conditions, which affects the nuclear power plant operator's accurate decision-making. Then, the second training guidance information can be generated to help the nuclear power plant operator stabilize his or her psychological and emotional state.

[0095] See also Figure 4 , Figure 4 Schematic diagram of a training and guidance system for nuclear power plant operators provided in an embodiment of the present application. Figure 4 As shown, the system 400 includes: a simulation operation module 401: used to establish a model operation scenario of a nuclear power plant, and simulate the abnormal operation state of the nuclear power plant under the simulation operation scenario; a data acquisition module 402: used to obtain process data and decision data of nuclear power plant operators for decision-making on abnormal operation states; a team collaboration and communication evaluation module 403: used to evaluate the collaboration and communication capabilities of nuclear power plant operators in the decision-making process based on process data, and obtain a first evaluation result; a risk analysis and decision evaluation module 404: used to evaluate the risk decision-making capabilities of nuclear power plant operators based on decision data, and obtain a second evaluation result; a guidance module 405: used to generate first training guidance information based on the first evaluation result and the second evaluation result.

[0096] In one implementation, the team collaboration and communication evaluation module 403 is specifically used to: input process data into a pre-trained first evaluation model to obtain a first evaluation result; wherein the first evaluation model has pre-learned the relationship between relevant data of nuclear power plant operators in the decision-making process and different evaluation results.

[0097] In an optional implementation, the first evaluation model is a neural network expert system, and the expert knowledge base in the neural network expert system stores first rule knowledge of processing rules corresponding to different abnormal operating states of the nuclear power plant.

[0098] In an optional implementation, the process data includes at least one of the following: duration data used for decision processing; voice data of decision processing; behavior data of decision processing; and operation data performed during decision processing.

[0099] In one implementation, the risk analysis and decision evaluation module 404 is specifically used to: input the decision data into a pre-trained second evaluation model to obtain a second evaluation result; wherein the second evaluation model has pre-learned the relationship between different decision data and the evaluation results.

[0100] In an optional implementation, the second evaluation model is a neural network expert system, and the expert knowledge base in the neural network expert system stores second rule knowledge of diagnostic decision rules corresponding to different abnormal operating states of the nuclear power plant.

[0101] In one implementation, the system further includes: a physiological data monitoring module. As an example, see Figure 5 , Figure 5 FIG. 1 is a schematic diagram of another training and guidance system for nuclear power plant operators provided in an embodiment of the present application. Figure 5 As shown, the system 500 further includes: a physiological data monitoring module 506 for obtaining physiological data of nuclear power plant operators during decision-making and processing of abnormal operating conditions; and a guidance module 505 for generating second training guidance information based on the physiological data, the first evaluation result and the second evaluation result. Figure 5 Modules 501 to 505 in Figure 4 Modules 401 to 405 in the embodiment have the same structure and function.

[0102] Through the system of the embodiment of the present application, the process data and decision data of the nuclear power plant operator in the process of making decisions on abnormal operating states in the simulated operation scenario can be obtained, so as to evaluate the nuclear power plant operator based on the above data and the pre-trained neural network model, and generate corresponding training guidance information based on the evaluation results. It is possible to accurately evaluate the operation level of the nuclear power plant operator and improve the operation management ability of the nuclear power plant operator.

[0103] It should be noted that the above explanation of the embodiment of the training and guidance method for nuclear power plant operators is also applicable to the training and guidance system for nuclear power plant operators of this embodiment, and will not be repeated here.

[0104] In order to implement the above embodiment, the present application also proposes an electronic device. Figure 6 , Figure 6 Schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 6 As shown, the electronic device 600 includes: a processor 601, and a memory 602 communicatively connected to the processor 601; the memory 602 stores computer-executable instructions; the processor 601 executes the computer-executable instructions stored in the memory to implement the method provided in the aforementioned embodiment.

[0105] In order to implement the above embodiments, the present application also 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 methods provided by the above embodiments.

[0106] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0107] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0108] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0109] This application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, this application is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, risks can be minimized by limiting data collection and deleting data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of users.

[0110] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0111] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0112] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0113] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0114] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0115] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0116] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0117] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A training and guidance method for nuclear power plant operators, characterized in that: include: Establishing a model operation scenario of a nuclear power plant, and simulating an abnormal operation state of the nuclear power plant under the simulated operation scenario; Acquiring process data and decision data of decision-making processing by nuclear power plant operators on the abnormal operating state; Evaluate the collaboration and communication capabilities of the nuclear power plant operators based on the process data to obtain a first evaluation result; Evaluate the risk decision-making ability of the nuclear power plant operator based on the decision data to obtain a second evaluation result; First training guidance information is generated based on the first evaluation result and the second evaluation result.

2. The method according to claim 1, characterized in that The step of evaluating the cooperation and communication capabilities of the nuclear power plant operators based on the process data to obtain a first evaluation result includes: The process data is input into a pre-trained first evaluation model to obtain the first evaluation result; wherein the first evaluation model has pre-learned the mapping relationship between the relevant data of the nuclear power plant operator in the decision-making process and different evaluation results.

3. The method according to claim 2, characterized in that The first evaluation model is a neural network expert system, and the expert knowledge base in the neural network expert system stores first rule knowledge of processing rules corresponding to different abnormal operating states of the nuclear power plant.

4. The method according to claim 2, characterized in that The process data includes at least one of the following: Duration data of the decision-making process; Voice data processed by the decision making; Behavioral data processed by the decision; Data on operations performed during the decision-making process.

5. The method according to claim 1, characterized in that The step of evaluating the risk decision-making ability of the nuclear power plant operator based on the decision data to obtain a second evaluation result includes: The decision data is input into a pre-trained second evaluation model to obtain the second evaluation result; wherein the second evaluation model has pre-learned the mapping relationship between different decision data and evaluation results.

6. The method according to claim 5, characterized in that The second evaluation model is a neural network expert system, and the expert knowledge base in the neural network expert system stores second rule knowledge of diagnosis decision rules corresponding to different abnormal operating states of the nuclear power plant.

7. The method according to claim 1, characterized in that The method further comprises: Acquiring physiological data of nuclear power plant operators during decision-making and processing of the abnormal operating state; Second training guidance information is generated based on the physiological data, the first assessment result, and the second assessment result.

8. A training and guidance system for nuclear power plant operators, characterized in that: include: Simulation operation module: used to establish a model operation scenario of a nuclear power plant and simulate the abnormal operation state of the nuclear power plant under the simulation operation scenario; Data acquisition module: used to obtain process data and decision data of the nuclear power plant operators for making decisions on the abnormal operating state; A team collaboration and communication evaluation module: used to evaluate the collaboration and communication capabilities of the nuclear power plant operators based on the process data to obtain a first evaluation result; Risk analysis and decision assessment module: used to assess the risk decision-making ability of the nuclear power plant operator based on the decision data to obtain a second assessment result; Guidance module; Used to generate first training guidance information based on the first evaluation result and the second evaluation result.

9. 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 1 to 7.

10. 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 1 to 7 when executed by a processor.

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