Nuclear power plant accident detection method, device and system

Abstract

The present disclosure relates to a nuclear power plant accident detection method, device, and system. The method comprises: obtaining nuclear power plant operating data, including equipment operating data and status information; inputting the equipment operating data and status information into a dynamic simulation model to obtain a first simulation result; performing a similarity match between the first simulation result and an accident model in a preset accident model library to obtain a similarity value; and selecting abnormal data from the operating data when the similarity value meets preset conditions; updating the operating data using the abnormal data to obtain secondary accident simulation data; inputting the secondary accident simulation data into the dynamic simulation model to obtain a second simulation result; and generating and outputting secondary warning information based on the second simulation result. This solution reduces losses caused by secondary accidents.

Description

Technical Field

[0001] The present disclosure relates to the field of nuclear power technology, and in particular to a method, device, and system for detecting accidents in nuclear power plants. Background Art

[0002] Among related technologies, nuclear power plant accident model monitoring and diagnosis systems are integrated systems that combine multiple functions, including data acquisition, condition monitoring, fault diagnosis, and predictive analysis. These systems collect real-time operating data from nuclear power equipment and analyze and process it using advanced algorithms and models. However, traditional detection methods only monitor and diagnose a single accident. If an accident occurs, it can easily trigger a secondary incident, resulting in serious losses. Summary of the Invention

[0003] To overcome the problems existing in the related art, the present disclosure provides a nuclear power plant accident detection method, device and system.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for detecting an accident in a nuclear power plant is provided, comprising:

[0005] Acquiring operating data of a nuclear power plant; the operating data includes equipment operating data and status information;

[0006] Inputting the equipment operation data and the status information into a dynamic simulation model to obtain a first simulation result;

[0007] Performing similarity matching between the first simulation result and an accident model in a preset accident model library to obtain a similarity value;

[0008] When the similarity value satisfies a preset condition, selecting abnormal data from the operating data;

[0009] Using the abnormal data to update the operating data, to obtain secondary accident simulation data;

[0010] Inputting the secondary accident simulation data into the dynamic simulation model to obtain a second simulation result;

[0011] Based on the second simulation result, secondary warning information is generated and output.

[0012] In some embodiments of the present application, selecting abnormal data from the operating data includes:

[0013] comparing the operating data with corresponding normal thresholds respectively;

[0014] In the case that there is operating data that does not fall within the normal threshold range, the operating data is determined to be abnormal data.

[0015] In some embodiments of the present application, when the similarity value satisfies a preset condition, after selecting abnormal data from the operating data, the method further includes:

[0016] Determining the data type of the abnormal data;

[0017] performing data enhancement processing on the abnormal data according to a data enhancement processing method that matches the data type; wherein the probability of the abnormal data causing an accident after the data enhancement processing is greater than the abnormal data before the data enhancement processing;

[0018] The method of updating the operating data using the abnormal data to obtain secondary accident simulation data includes:

[0019] The enhanced abnormal data is used to update the operating data to obtain secondary accident simulation data.

[0020] In some embodiments of the present application, after performing similarity matching between the first simulation result and the accident model in the preset accident model library to obtain a similarity value, the method further includes:

[0021] In a case where the first simulation result includes the concentration of the leaked harmful substance, obtaining a concentration value of the leaked harmful substance;

[0022] Comparing the concentration value of the leaked hazardous substance with a preset concentration value;

[0023] When the concentration value of the leaked hazardous substance is greater than or equal to the preset concentration value, weighting the similarity value to obtain a weighted similarity value;

[0024] When the similarity value satisfies a preset condition, selecting abnormal data from the operating data includes:

[0025] When the similarity value after the weighted processing meets a preset condition, abnormal data is selected from the operating data.

[0026] In some embodiments of the present application, when the similarity value satisfies a preset condition, selecting abnormal data from the operating data includes:

[0027] If the similarity value satisfies a preset condition, determining whether the device has permanent deformation or internal fracture according to the operating data;

[0028] In response to determining that permanent deformation or internal fracture of the device occurs, obtaining stress distribution data of the device;

[0029] generating a stress distribution map using the stress distribution data, and selecting a maximum stress value in the stress distribution map;

[0030] The abnormal data is obtained based on the maximum stress value and the stress distribution diagram.

[0031] In some embodiments of the present application, generating and outputting secondary warning information based on the second simulation result includes:

[0032] Classifying the second simulation result according to the severity of the second simulation result to obtain a classification result;

[0033] Generate and output secondary warning information that matches the level of the classification result.

[0034] According to a second aspect of an embodiment of the present disclosure, there is provided a nuclear power plant accident detection device, comprising:

[0035] An acquisition unit, configured to acquire operating data of a nuclear power plant; the operating data includes equipment operating data and status information;

[0036] a first simulation unit, configured to input the device operation data and the status information into a dynamic simulation model to obtain a first simulation result;

[0037] a matching unit, configured to perform similarity matching between the first simulation result and an accident model in a preset accident model library to obtain a similarity value;

[0038] a selection unit, configured to select abnormal data from the operating data when the similarity value satisfies a preset condition;

[0039] an updating unit, configured to update the operating data using the abnormal data to obtain secondary accident simulation data;

[0040] a second simulation unit, configured to input the secondary accident simulation data into the dynamic simulation model to obtain a second simulation result;

[0041] A generating unit is used to generate and output secondary warning information based on the second simulation result.

[0042] According to a third aspect of an embodiment of the present disclosure, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in any one of the first aspects is implemented.

[0043] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the first aspects is implemented.

[0044] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method as described in any one of the first aspects when executed by a processor.

[0045] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: by obtaining operating data of a nuclear power plant, including equipment operating data and status information, inputting the equipment operating data and status information into a dynamic simulation model to obtain a first simulation result, performing a similarity match between the first simulation result and an accident model in a preset accident model library to obtain a similarity value, and selecting abnormal data from the operating data when the similarity value meets preset conditions; using the abnormal data to update the operating data to obtain secondary accident simulation data, inputting the secondary accident simulation data into the dynamic simulation model to obtain a second simulation result, and generating and outputting secondary warning information based on the second simulation result. By simulating secondary accidents using relevant data from the primary accident, timely and effective warnings can be issued for secondary accidents, reducing losses caused by the secondary accident.

[0046] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0048] Figure 1 The figure is a flow chart showing a method for detecting an accident in a nuclear power plant according to an exemplary embodiment.

[0049] Figure 2 The figure is a block diagram of a nuclear power plant accident detection device according to an exemplary embodiment.

[0050] Figure 3 The present invention is a block diagram showing an apparatus for a nuclear power plant accident detection method according to an exemplary embodiment. DETAILED DESCRIPTION

[0051] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0052] The terms used in the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure. The singular forms "a", "an" and "the" used in the present disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0053] It should be understood that although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the embodiments of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0054] Furthermore, the various forms of processes shown in the embodiments of this disclosure may be used to reorder, add, or delete steps. For example, the steps described in this application may be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this application can be achieved. This is not a limitation herein.

[0055] Among related technologies, nuclear power plant accident model monitoring and diagnosis systems are integrated systems that combine multiple functions, including data acquisition, condition monitoring, fault diagnosis, and predictive analysis. These systems collect real-time operating data from nuclear power equipment and analyze and process it using advanced algorithms and models. However, traditional detection methods only monitor and diagnose a single accident. If an accident occurs, it can easily trigger a secondary incident, resulting in serious losses.

[0056] To address the above-mentioned issues, the present disclosure provides a nuclear power plant accident detection method, device, and system. The method obtains operating data from the nuclear power plant, including equipment operating data and status information, inputs the equipment operating data and status information into a dynamic simulation model, obtains a first simulation result, performs a similarity match between the first simulation result and an accident model in a preset accident model library, obtains a similarity value, and selects abnormal data from the operating data when the similarity value meets preset conditions. The abnormal data is used to update the operating data to obtain secondary accident simulation data, inputs the secondary accident simulation data into the dynamic simulation model, obtains a second simulation result, and generates and outputs secondary warning information based on the second simulation result. By simulating secondary accidents using relevant data from the primary accident, a timely and effective warning of the secondary accident can be issued, reducing the losses caused by the secondary accident.

[0057] Figure 1 FIG. 1 is a flow chart showing a method for detecting a nuclear power plant accident according to an exemplary embodiment. Figure 1 As shown, it should be noted that the nuclear power plant accident detection method of the embodiment of the present application is applied to the nuclear power plant accident detection device. Figure 1 As shown, the method may include the following steps:

[0058] Step 101: Acquire operating data of a nuclear power plant.

[0059] The operation data includes equipment operation data and status information.

[0060] It is understandable that it is necessary to first obtain the operating data and status information of the nuclear power equipment in the nuclear power plant, wherein the status information may include information such as season, weather, and ambient temperature.

[0061] Step 102: Input the equipment operation data and status information into the dynamic simulation model to obtain a first simulation result.

[0062] In one embodiment, the dynamic simulation model can be implemented using existing technologies, which will not be described in detail here.

[0063] In an embodiment of the present application, the equipment operation data and status information can be input into a dynamic simulation model. The dynamic simulation model uses the equipment operation data and status information to simulate the equipment operation process in the nuclear power plant to obtain a first simulation result.

[0064] As an example, the first simulation result may include data generated during the operation of the device and operation result data.

[0065] Step 103 : performing similarity matching between the first simulation result and the accident model in the preset accident model library to obtain a similarity value.

[0066] In some embodiments, an accident model library may be pre-built, which stores process parameters and result parameters of multiple different accidents. The above parameters may be generated based on actual historical accidents or based on accident simulations.

[0067] In one embodiment, a similarity value is calculated between the first simulation result and each accident model in a preset accident model library, so as to determine the possibility that the first simulation result is the result of an accident.

[0068] In some embodiments of the present application, after step 103, the following steps may be further included:

[0069] Step a1: when the first simulation result includes the concentration of the leaked harmful substance, obtaining the concentration value of the leaked harmful substance.

[0070] It is understandable that if hazardous substances are leaked in the first simulation result, the impact of hazardous substances on the secondary accident is crucial. The greater the concentration of the leaked hazardous substances, the higher the possibility of causing a secondary accident.

[0071] Step a2: comparing the concentration value of the leaked hazardous substance with a preset concentration value.

[0072] Step a3: When the concentration of the leaked hazardous substance is greater than or equal to a preset concentration, weighted processing is performed on the similarity value to obtain a weighted similarity value.

[0073] In one embodiment, the concentration value of the leaked hazardous substance is compared with a preset concentration value. If the concentration value of the leaked hazardous substance is greater than or equal to the preset concentration value, it indicates that the leaked hazardous substance has exceeded the safe range. Therefore, the similarity value is weighted to obtain a weighted similarity value. In other words, the similarity value is added to the preset value to obtain the weighted similarity value.

[0074] As an example, the above-mentioned preset value may be pre-set according to actual conditions, and the preset value may correspond to the concentration value of the leaked harmful substance. The higher the concentration value of the leaked harmful substance, the larger the preset value.

[0075] In an embodiment of the present application, when the similarity value satisfies a preset condition, selecting abnormal data from the operating data includes:

[0076] When the similarity value after weighted processing meets the preset conditions, abnormal data is selected from the running data.

[0077] It is understandable that determining whether the preset conditions are met based on the weighted similarity value can increase the consideration of the factor of hazardous substance leakage, thereby increasing the rationality of the judgment result.

[0078] Step 104 : When the similarity value satisfies a preset condition, abnormal data is selected from the operating data.

[0079] In one embodiment, the preset condition may be that the similarity value is greater than or equal to a preset similarity threshold. When the similarity value is greater than or equal to the preset similarity threshold, it indicates that the current operating status of the nuclear power plant is very likely to cause an accident, and therefore abnormal data needs to be selected from the operating data.

[0080] In some embodiments of the present application, when the similarity value meets a preset condition, early warning information for an accident can be generated based on the abnormal data.

[0081] In some embodiments of the present application, step 104 may specifically include the following steps:

[0082] Compare the operating data with the corresponding normal thresholds respectively;

[0083] If there is operating data that does not fall within the normal threshold range, the operating data is determined to be abnormal data.

[0084] In one embodiment, the above-mentioned normal threshold value may be pre-set, and different operating data may correspond to different normal threshold values.

[0085] In some embodiments of the present application, step 104 may specifically include the following steps:

[0086] Step b1: When the similarity value meets the preset conditions, determine whether the device has permanent deformation or internal fracture based on the operating data.

[0087] It is understandable that if the equipment experiences permanent deformation or internal fracture during an accident, the possibility of causing a secondary accident is very high. For example, if a control rod mechanism pressure shell is mechanically damaged, the pressure of the reactor coolant system will cause a bundle of control rod assemblies along with their drive rods to be ejected from the reactor. This mechanical damage will lead to the rapid introduction of positive reactivity in the core, causing a sharp increase in the core nuclear power and an unfavorable core power distribution, which may cause local fuel rod damage.

[0088] Step b2: in response to determining that the device has undergone permanent deformation or internal fracture, obtaining stress distribution data of the device.

[0089] Step b3: Generate a stress distribution diagram using the stress distribution data, and select the maximum stress value in the stress distribution diagram.

[0090] Step b4: obtaining abnormal data based on the maximum stress value and the stress distribution diagram.

[0091] In one embodiment, the generated stress distribution map can be used to determine the maximum stress value and the specific location corresponding to the maximum stress value. This location is where permanent deformation or internal fracture occurs, and this location has a greater risk of causing a secondary accident.

[0092] In some embodiments of the present application, after step 104, the following steps may be further included:

[0093] Step c1: determine the data type of the abnormal data.

[0094] Step c2: perform data enhancement processing on the abnormal data according to the data enhancement processing method that matches the data type.

[0095] Among them, the probability of abnormal data causing an accident after data enhancement processing is greater than that of abnormal data before data enhancement processing.

[0096] The abnormal data is used to update the operating data to obtain the secondary accident simulation data, including: using the enhanced abnormal data to update the operating data to obtain the secondary accident simulation data.

[0097] It is understandable that in some cases, the abnormal data is only slightly abnormal, or the degree of abnormality is not enough to quickly cause a secondary accident, but there is still a risk of causing a secondary accident. Therefore, the abnormal data can be enhanced according to the data enhancement processing method that matches the data type, that is, the abnormal data is added or subtracted from the first numerical value according to the preset rules. The first data value is calculated based on the data enhancement method, so as to highlight the abnormal data. The enhanced abnormal data is used to simulate the secondary accident data, which can highlight the risks brought by the abnormal data, so that the secondary accident can be prevented in time before the secondary accident occurs.

[0098] For example, when the data type is temperature, a data enhancement processing method corresponding to the temperature is determined, and the temperature value is added to the first value to obtain the temperature value after data enhancement processing.

[0099] Step 105: Update the operating data using the abnormal data to obtain secondary accident simulation data.

[0100] Step 106: Input the secondary accident simulation data into the dynamic simulation model to obtain a second simulation result.

[0101] In one embodiment, the operating data carrying the abnormal data is input into the secondary accident simulation data, and the secondary accident simulation data simulates the secondary accident using the operating data carrying the abnormal data to obtain a second simulation result.

[0102] Step 107: Generate and output secondary warning information based on the second simulation result.

[0103] In one embodiment, the secondary warning information may include simulation process information and simulation result information, and may also include secondary accident severity information.

[0104] In some embodiments of the present application, step 107 may specifically include the following steps:

[0105] Classifying the second simulation result according to the severity of the second simulation result to obtain a classification result;

[0106] Generate and output secondary warning information that matches the level of the classification result.

[0107] According to the nuclear power plant accident detection method proposed in the embodiment of the present application, the operating data of the nuclear power plant is obtained, and the operating data includes equipment operating data and status information. The equipment operating data and status information are input into a dynamic simulation model to obtain a first simulation result. The first simulation result is matched with the accident model in a preset accident model library for similarity to obtain a similarity value. When the similarity value meets the preset conditions, abnormal data is selected from the operating data; the operating data is updated with the abnormal data to obtain secondary accident simulation data, and the secondary accident simulation data is input into the dynamic simulation model to obtain a second simulation result. Based on the second simulation result, secondary warning information is generated and output. By using the relevant data of the primary accident to simulate the secondary accident, it is possible to timely and effectively warn of the secondary accident, thereby reducing the losses caused by the secondary accident.

[0108] Figure 2 FIG. 1 is a block diagram of a nuclear power plant accident detection device according to an exemplary embodiment. Figure 2 The device includes an acquisition unit 201, a first simulation unit 202, a matching unit 203, a selection unit 204, an updating unit 205, a second simulation unit 206 and a generation unit 207.

[0109] The acquisition unit 201 is used to acquire the operation data of the nuclear power plant; the operation data includes equipment operation data and status information;

[0110] The first simulation unit 202 is used to input the equipment operation data and status information into the dynamic simulation model to obtain a first simulation result;

[0111] A matching unit 203 is configured to perform similarity matching between the first simulation result and an accident model in a preset accident model library to obtain a similarity value;

[0112] The selection unit 204 is configured to select abnormal data from the operating data when the similarity value satisfies a preset condition;

[0113] An updating unit 205 is used to update the operating data using the abnormal data to obtain secondary accident simulation data;

[0114] The second simulation unit 206 is used to input the secondary accident simulation data into the dynamic simulation model to obtain a second simulation result;

[0115] The generating unit 207 is configured to generate and output secondary warning information based on the second simulation result.

[0116] In some embodiments of the present application, the selection unit 204 may be specifically configured to:

[0117] Compare the operating data with the corresponding normal thresholds respectively;

[0118] If there is operating data that does not fall within the normal threshold range, the operating data is determined to be abnormal data.

[0119] In some embodiments of the present application, the apparatus may further include:

[0120] A unit for determining the data type of abnormal data;

[0121] an enhancement processing unit, configured to perform data enhancement processing on the abnormal data in a data enhancement processing manner that matches the data type; wherein the probability of the abnormal data causing an accident after the data enhancement processing is greater than that of the abnormal data before the data enhancement processing;

[0122] The updating unit 205 may be specifically configured to:

[0123] The enhanced abnormal data is used to update the operating data to obtain the secondary accident simulation data.

[0124] In some embodiments of the present application, the apparatus may further include:

[0125] The acquiring unit is further configured to acquire a concentration value of the leaked harmful substance when the first simulation result includes the concentration of the leaked harmful substance;

[0126] A comparison unit, used to compare the concentration value of the leaked hazardous substance with a preset concentration value;

[0127] A weighting unit, configured to perform weighted processing on the similarity value when the concentration value of the leaked hazardous substance is greater than or equal to a preset concentration value, to obtain a weighted similarity value;

[0128] The updating unit 205 can be specifically used to update the operating data using the abnormal data after the enhanced processing to obtain the secondary accident simulation data.

[0129] In some embodiments of the present application, the apparatus may further include:

[0130] The acquiring unit is further configured to acquire a concentration value of the leaked harmful substance when the first simulation result includes the concentration of the leaked harmful substance;

[0131] The comparison unit is also used to compare the concentration value of the leaked harmful substance with the preset concentration value;

[0132] The weighting unit is further configured to perform weighted processing on the similarity value when the concentration value of the leaked hazardous substance is greater than or equal to a preset concentration value to obtain a weighted similarity value;

[0133] The selection unit 204 may be specifically configured to:

[0134] When the similarity value after weighted processing meets the preset conditions, abnormal data is selected from the running data.

[0135] In some embodiments of the present application, the selection unit 204 may be specifically configured to:

[0136] When the similarity value meets the preset conditions, determine whether the equipment has permanent deformation or internal fracture based on the operating data;

[0137] In response to determining that permanent deformation or internal fracture of the device occurs, obtaining stress distribution data of the device;

[0138] Generate a stress distribution diagram using stress distribution data, and select the maximum stress value in the stress distribution diagram;

[0139] Based on the stress maximum value and stress distribution diagram, abnormal data are obtained.

[0140] In some embodiments of the present application, the generating unit 207 may be specifically configured to:

[0141] Classifying the second simulation result according to the severity of the second simulation result to obtain a classification result;

[0142] Generate and output secondary warning information that matches the level of the classification result.

[0143] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0144] According to the nuclear power plant accident detection device proposed in the embodiment of the present application, by obtaining the operating data of the nuclear power plant, the operating data includes equipment operating data and status information, inputting the equipment operating data and status information into a dynamic simulation model to obtain a first simulation result, performing a similarity match between the first simulation result and an accident model in a preset accident model library to obtain a similarity value, and when the similarity value meets a preset condition, selecting abnormal data from the operating data; using the abnormal data to update the operating data to obtain secondary accident simulation data, inputting the secondary accident simulation data into the dynamic simulation model to obtain a second simulation result, and generating and outputting secondary warning information based on the second simulation result. By using the relevant data of the primary accident to simulate the secondary accident, it is possible to timely and effectively warn of the secondary accident, thereby reducing the losses caused by the secondary accident.

[0145] Figure 3This is a block diagram illustrating an apparatus for a nuclear power plant accident detection method according to an exemplary embodiment. For example, apparatus 300 may be an electronic device, such as a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0146] Reference Figure 3 , apparatus 300 may include one or more of the following components: a processing component 302 , a memory 304 , a power component 306 , a multimedia component 308 , an audio component 310 , an input / output (I / O) interface 312 , a sensor component 314 , and a communication component 316 .

[0147] The processing component 302 generally controls the overall operation of the device 300, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 302 may include one or more processors 320 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 302 may include one or more modules to facilitate interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate interaction between the multimedia component 308 and the processing component 302.

[0148] The memory 304 is configured to store various types of data to support operations on the device 300. Examples of such data include instructions for any application or method operating on the device 300, contact data, phone book data, messages, pictures, videos, etc. The memory 304 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0149] The power component 306 provides power to the various components of the device 300. The power component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device 300.

[0150] The multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 includes a front camera and / or a rear camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0151] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC) that is configured to receive external audio signals when the device 300 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 304 or transmitted via the communication component 316. In some embodiments, the audio component 310 further includes a speaker for outputting audio signals.

[0152] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0153] The sensor assembly 314 includes one or more sensors for providing various aspects of the status assessment of the device 300. For example, the sensor assembly 314 can detect the open / closed state of the device 300, the relative positioning of components, such as the display and keypad of the device 300. The sensor assembly 314 can also detect changes in the position of the device 300 or a component of the device 300, the presence or absence of user contact with the device 300, the orientation or acceleration / deceleration of the device 300, and temperature changes of the device 300. The sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 314 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0154] The communication component 316 is configured to facilitate wired or wireless communication between the device 300 and other devices. The device 300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0155] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0156] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, which can be executed by the processor 320 of the apparatus 300 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0157] In an exemplary embodiment, a computer program product is also provided, comprising a computer program, which implements the above method when executed by the processor 320 of the apparatus 300 .

[0158] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0159] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for detecting an accident in a nuclear power plant, characterized in that: include: Acquiring operating data of a nuclear power plant; the operating data includes equipment operating data and status information; Inputting the equipment operation data and the status information into a dynamic simulation model to obtain a first simulation result; Performing similarity matching between the first simulation result and an accident model in a preset accident model library to obtain a similarity value; When the similarity value satisfies a preset condition, selecting abnormal data from the operating data; Using the abnormal data to update the operating data, to obtain secondary accident simulation data; Inputting the secondary accident simulation data into the dynamic simulation model to obtain a second simulation result; Based on the second simulation result, secondary warning information is generated and output.

2. The nuclear power plant accident detection method according to claim 1, characterized in that: The selecting abnormal data from the operating data includes: comparing the operating data with corresponding normal thresholds respectively; In the case that there is operating data that does not fall within the normal threshold range, the operating data is determined to be abnormal data.

3. The nuclear power plant accident detection method according to claim 1, characterized in that: When the similarity value satisfies a preset condition, after selecting abnormal data from the operating data, the method further includes: Determining the data type of the abnormal data; performing data enhancement processing on the abnormal data according to a data enhancement processing method that matches the data type; wherein the probability of the abnormal data causing an accident after the data enhancement processing is greater than the abnormal data before the data enhancement processing; The method of updating the operating data using the abnormal data to obtain secondary accident simulation data includes: The enhanced abnormal data is used to update the operating data to obtain secondary accident simulation data.

4. The nuclear power plant accident detection method according to claim 1, characterized in that: After performing similarity matching between the first simulation result and the accident model in the preset accident model library to obtain a similarity value, the method further includes: In a case where the first simulation result includes the concentration of the leaked harmful substance, obtaining a concentration value of the leaked harmful substance; Comparing the concentration value of the leaked hazardous substance with a preset concentration value; When the concentration value of the leaked hazardous substance is greater than or equal to the preset concentration value, weighting the similarity value to obtain a weighted similarity value; When the similarity value satisfies a preset condition, selecting abnormal data from the operating data includes: When the similarity value after the weighted processing meets a preset condition, abnormal data is selected from the operating data.

5. The nuclear power plant accident detection method according to claim 1, characterized in that: When the similarity value satisfies a preset condition, selecting abnormal data from the operating data includes: If the similarity value satisfies a preset condition, determining whether the device has permanent deformation or internal fracture according to the operating data; In response to determining that permanent deformation or internal fracture of the device occurs, obtaining stress distribution data of the device; generating a stress distribution map using the stress distribution data, and selecting a maximum stress value in the stress distribution map; The abnormal data is obtained based on the maximum stress value and the stress distribution diagram.

6. The nuclear power plant accident detection method according to claim 1, characterized in that: The generating and outputting secondary warning information based on the second simulation result includes: Classifying the second simulation result according to the severity of the second simulation result to obtain a classification result; Generate and output secondary warning information that matches the level of the classification result.

7. A nuclear power plant accident detection device, characterized in that: include: An acquisition unit, configured to acquire operating data of a nuclear power plant; the operating data includes equipment operating data and status information; a first simulation unit, configured to input the device operation data and the status information into a dynamic simulation model to obtain a first simulation result; a matching unit, configured to perform similarity matching between the first simulation result and an accident model in a preset accident model library to obtain a similarity value; a selection unit, configured to select abnormal data from the operating data when the similarity value satisfies a preset condition; an updating unit, configured to update the operating data using the abnormal data to obtain secondary accident simulation data; a second simulation unit, configured to input the secondary accident simulation data into the dynamic simulation model to obtain a second simulation result; A generating unit is used to generate and output secondary warning information based on the second simulation result.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that The computer program implements the method according to any one of claims 1 to 6 when executed by a processor.

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