Nuclear power plant accident detection method, device and system

Abstract

The invention relates to a nuclear power plant accident detection method, device and system. The method comprises the following steps: acquiring operation data of a nuclear power plant, the operation data comprising equipment operation data and state information, inputting the equipment operation data and the state information into a dynamic simulation model to obtain a first simulation result, performing similarity matching on the first simulation result and an accident model in a preset accident model library to obtain a similarity value, under the condition that the similarity value meets a preset condition, abnormal data are selected from the operation data; and updating the operation data by 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 early warning information based on the second simulation result. According to the scheme, the loss caused by a secondary accident is reduced.

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] In the related technologies, the accident model monitoring and diagnosis system of nuclear power plants is an integrated system that integrates multiple functions such as data acquisition, status monitoring, fault diagnosis, and predictive analysis. These systems can collect the operating data of nuclear power equipment in real time and analyze and process it through advanced algorithms and models. However, the traditional detection method only monitors and diagnoses one accident, but if an accident occurs, it is easy to cause a secondary accident and cause serious losses. Summary of the invention

[0003] In order 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 a nuclear power plant accident is provided, comprising:

[0005] Acquiring operation data of a nuclear power plant; the operation data includes equipment operation 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 operation 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, the selecting abnormal data from the operating data includes:

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

[0014] In the case where 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 meets the preset condition, after selecting the abnormal data from the operation data, the method further includes:

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

[0017] Performing data enhancement processing on the abnormal data in a data enhancement processing manner matching the data type; wherein the probability of the abnormal data after the data enhancement processing causing an accident is greater than that of 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 operation data is updated using the abnormal data after enhancement processing 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 the similarity value, the method further includes:

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

[0022] Comparing the concentration value of the leaked harmful 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 operation data includes:

[0025] When the similarity value after the weighted processing meets a preset condition, abnormal data is selected from the operation 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] When the similarity value satisfies a preset condition, determining whether there is a permanent deformation or internal fracture of the device according to the operation data;

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

[0029] Generate a stress distribution diagram using the stress distribution data, and select the maximum stress value in the stress distribution diagram;

[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 results according to the severity of the second simulation results 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, used to acquire operation data of the nuclear power plant; the operation data includes equipment operation data and status information;

[0036] A first simulation unit, used for inputting the equipment operation data and the status information into a dynamic simulation model to obtain a first simulation result;

[0037] A matching unit, used for performing 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 operation 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, used for inputting 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, there is provided a computer-readable storage medium 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 embodiment of the present disclosure may include the following beneficial effects: by obtaining the operation data of the nuclear power plant, the operation data includes the equipment operation data and status information, inputting the equipment operation data and status information into the dynamic simulation model, obtaining the first simulation result, matching the first simulation result with the accident model in the preset accident model library for similarity, obtaining the similarity value, and selecting abnormal data from the operation data when the similarity value meets the preset conditions; using the abnormal data to update the operation data, obtaining the secondary accident simulation data, inputting the secondary accident simulation data into the dynamic simulation model, obtaining the second simulation result, and generating and outputting the secondary warning information based on the second simulation result. By using the relevant data of the primary accident to simulate the secondary accident, the secondary accident can be warned in a timely and effective manner, reducing the 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 present 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 present invention is a flow chart of a method for detecting a nuclear power plant accident according to an exemplary embodiment.

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

[0050] Figure 3 It is a block diagram showing a device 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 shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

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

[0053] It should be understood that although the terms first, second, third, etc. may be used to describe various information in the disclosed embodiments, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the disclosed embodiments, 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" or "when" or "in response to determination".

[0054] In addition, various forms of processes shown in the embodiments of the present disclosure may be used to reorder, add or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present application can be achieved, and this document does not limit this.

[0055] In the related technologies, the accident model monitoring and diagnosis system of nuclear power plants is an integrated system that integrates multiple functions such as data acquisition, status monitoring, fault diagnosis, and predictive analysis. These systems can collect the operating data of nuclear power equipment in real time and analyze and process it through advanced algorithms and models. However, the traditional detection method only monitors and diagnoses one accident, but if an accident occurs, it is easy to cause a secondary accident and cause serious losses.

[0056] In order to solve the above problems, the present disclosure provides a nuclear power plant accident detection method, device and system, which obtains the operation data of the nuclear power plant, including equipment operation data and status information, inputs the equipment operation data and status information into a dynamic simulation model, obtains a first simulation result, matches the first simulation result with the accident model in the preset accident model library for similarity, obtains a similarity value, and selects abnormal data from the operation data when the similarity value meets the preset conditions; updates the operation data using the abnormal 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 the secondary accident using the relevant data of the primary accident, the secondary accident can be warned in a timely and effective manner, reducing the losses caused by the secondary accident.

[0057] Figure 1 is a flow chart of 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, obtaining operation 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 be information such as season, weather, ambient temperature, etc.

[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 may be input into a dynamic simulation model, and the dynamic simulation model may use the equipment operation data and status information to simulate the equipment operation process in a 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-constructed, in which process parameters and result parameters of multiple different accidents are stored. The above parameters may be generated based on actual historical accidents or based on accident simulations.

[0067] In one embodiment, the first simulation result is respectively calculated with a similarity value of 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 also be included:

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

[0070] It is understandable that if harmful substances are leaked in the first simulation result, the impact of harmful substances on the secondary accident is crucial. The greater the concentration of the leaked harmful 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 value of the leaked hazardous substance is greater than or equal to the preset concentration value, weighted processing is performed on the similarity value to obtain the weighted similarity value.

[0073] In one embodiment, the concentration value of the leaked hazardous substance is compared with the 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 safety range. Therefore, the similarity value is weighted to obtain the weighted similarity value. That is, the similarity value is added to the preset value to obtain the weighted similarity value.

[0074] As an example, the above 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 greater the preset value.

[0075] In the embodiment of the present application, when the similarity value meets the preset condition, selecting abnormal data from the operation 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 meets 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 state 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] The operating data are compared with the corresponding normal threshold values ​​respectively;

[0083] In the case where 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 normal threshold value may be preset. 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 operation data.

[0087] It is understandable that if the equipment suffers 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 together with their drive rods to be ejected from the reactor. Such 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 permanent deformation or internal fracture, obtaining stress distribution data of the device.

[0089] Step b3, generating a stress distribution diagram using the stress distribution data, and selecting 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 diagram can be used to determine the maximum stress value and the specific position corresponding to the maximum stress value. This position is the position where permanent deformation or internal fracture occurs, and this position has a greater risk of causing a secondary accident.

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

[0093] Step c1, determining the data type of the abnormal data.

[0094] Step c2: perform data enhancement processing on the abnormal data according to a 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 secondary accident simulation data is performed using the enhanced abnormal 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 the data enhancement processing.

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

[0100] Step 106, inputting 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 uses the operating data carrying the abnormal data to simulate the secondary accident 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 results according to the severity of the second simulation results to obtain classification results;

[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, by obtaining the operation data of the nuclear power plant, the operation data includes equipment operation data and status information, inputting the equipment operation data and status information into the dynamic simulation model, obtaining the first simulation result, matching the first simulation result with the accident model in the preset accident model library for similarity, obtaining the similarity value, and selecting abnormal data from the operation data when the similarity value meets the preset conditions; updating the operation data with the abnormal data to obtain secondary accident simulation data, inputting the secondary accident simulation data into the dynamic simulation model, obtaining the 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, the secondary accident can be warned in a timely and effective manner, 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 update 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 used 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] A selection unit 204, configured to select abnormal data from the operation data when the similarity value satisfies a preset condition;

[0113] An updating unit 205 is used to update the operation 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 used 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 used to:

[0117] The operating data are compared with the corresponding normal threshold values ​​respectively;

[0118] In the case where 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 device may further include:

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

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

[0122] The updating unit 205 may be specifically used for:

[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 device may further include:

[0125] The acquiring unit is further used to acquire the 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 for comparing the concentration value of the leaked harmful substance with a preset concentration value;

[0127] A weighting unit, used 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 device may further include:

[0130] The acquiring unit is further used to acquire the 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 used to perform weighted processing on the similarity value when the concentration value of the leaked harmful substance is greater than or equal to the preset concentration value to obtain the weighted similarity value;

[0133] The selection unit 204 can be specifically used for:

[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 used 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 exists, 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 used to:

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

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

[0143] Regarding the device 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 operation data of the nuclear power plant, the operation data includes equipment operation data and status information, inputting the equipment operation data and status information into the dynamic simulation model, obtaining the first simulation result, matching the first simulation result with the accident model in the preset accident model library for similarity, obtaining the similarity value, and selecting abnormal data from the operation data when the similarity value meets the preset conditions; updating the operation data with the abnormal data to obtain secondary accident simulation data, inputting the secondary accident simulation data into the dynamic simulation model, obtaining the second simulation result, and generating and outputting secondary warning information based on the second simulation result. By simulating the secondary accident using the relevant data of the primary accident, the secondary accident can be warned in a timely and effective manner, reducing the losses caused by the secondary accident.

[0145] Figure 33 is a block diagram of a device for a nuclear power plant accident detection method according to an exemplary embodiment. For example, the device 300 may be an electronic device, such as a mobile phone, a computer, a digital broadcast terminal, a message transceiver device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0146] Reference Figure 3 , the device 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 operations, and recording operations. The processing component 302 may include one or more processors 320 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 302 may include one or more modules to facilitate the interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate the 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 for 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 touch, slide, and gestures on the touch panel. The touch sensor may 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 may receive external multimedia data. Each front camera and rear camera may 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), and when the device 300 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 304 or sent via the communication component 316. In some embodiments, the audio component 310 also 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 keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0153] The sensor assembly 314 includes one or more sensors for providing various aspects of status assessment for 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 the position change 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 the temperature change 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 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, and the instructions can be executed by the processor 320 of the device 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 device 300 .

[0158] Those skilled in the art will readily appreciate other embodiments of the present invention 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 present invention and include common knowledge or customary techniques in the art that are not disclosed in this disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0159] It should be understood that the present invention is not limited to the exact construction that has been 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 operation data of a nuclear power plant; the operation data includes equipment operation 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 operation 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 where 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 operation data, the method further includes: Determine the data type of the abnormal data; Performing data enhancement processing on the abnormal data in a data enhancement processing manner matching the data type; wherein the probability of the abnormal data after the data enhancement processing causing an accident is greater than that of 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 operation data is updated using the abnormal data after enhancement processing to obtain secondary accident simulation data.

4. The nuclear power plant accident detection method according to claim 1, characterized in that: After the similarity matching of the first simulation result with the accident model in the preset accident model library is performed to obtain the similarity value, the method further includes: In the case where the first simulation result includes the concentration of the leaked harmful substance, obtaining the concentration value of the leaked harmful substance; Comparing the concentration value of the leaked harmful 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 operation data includes: When the similarity value after the weighted processing meets a preset condition, abnormal data is selected from the operation 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 operation data includes: When the similarity value satisfies a preset condition, determining whether there is a permanent deformation or internal fracture of the device according to the operation data; In response to determining that permanent deformation or internal fracture of the device occurs, obtaining stress distribution data of the device; Generate a stress distribution diagram using the stress distribution data, and select the maximum stress value in the stress distribution diagram; 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 results according to the severity of the second simulation results 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, used to acquire operation data of the nuclear power plant; the operation data includes equipment operation data and status information; A first simulation unit, used for inputting the equipment operation data and the status information into a dynamic simulation model to obtain a first simulation result; A matching unit, used for performing 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 operation 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, used for inputting 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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