Nuclear accident emergency protection guidance system, method, program, medium, and electronic device
The nuclear accident emergency protection guidance system utilizes data acquisition and support vector machine models to provide users with personalized protection action plans, solving the problem of inappropriate timing of protection actions during nuclear accidents and improving protection effectiveness and resource utilization efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA INST FOR RADIATION PROTECTION
- Filing Date
- 2025-12-28
- Publication Date
- 2026-05-15
AI Technical Summary
After a nuclear accident, the public cannot accurately judge the timing of protective actions, leading to increased dose accumulation or waste of resources. Existing technologies that rely on users to make their own decisions or take unified actions cannot meet individual needs.
The nuclear accident emergency protection guidance system uses a data acquisition module to obtain environmental meteorological data and user location information. It then uses a consequence assessment component and a support vector machine prediction model to provide personalized protection action plans, including recommendations for evacuation, sheltering, taking iodine, or waiting.
It enables personalized emergency protection guidance for each user, improving the accuracy of protective actions and the efficiency of resource utilization, and reducing the accumulation of radiation dose.
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Figure CN122047985A_ABST
Abstract
Description
Technical Field
[0001] This specification relates to the field of nuclear protection technology, and in particular to a nuclear accident emergency protection guidance system, method, procedure, medium and electronic equipment. Background Technology
[0002] Following a nuclear accident, the public's ability to initiate protective measures at the appropriate time is a key factor in determining their exposure levels and health risks. Incorrect timing (too early or too late) can lead to increased cumulative dose or wasted resources.
[0003] In existing technologies, decisions are usually made by the user or by broadcast instructions to users in a certain area to act in unison.
[0004] However, users in different locations have different physical conditions and face different environments, and using the above method may result in users receiving a greater amount of radiation. Summary of the Invention
[0005] This specification provides a nuclear accident emergency protection guidance system, method, procedure, medium, and electronic equipment to at least partially solve the aforementioned problems existing in the prior art.
[0006] The following technical solution is adopted in this specification: This manual provides a nuclear accident emergency response guidance system. It includes a data acquisition module, a preprocessing module, a data processing module, and an output module; The data acquisition module is used to acquire environmental meteorological data and / or environmental radiation data and / or user location information and / or map information; The preprocessing module is used to input the environmental meteorological data and / or environmental radiation data and / or user location information and / or map information into the consequence assessment component to determine the dynamic tensor output by the consequence assessment component; the dynamic tensor includes real-time dose distribution map, regional risk level assessment, source term release pattern, building concealment value, iodine supply accessibility, user location information, and user dose data; The data processing module is used to input the dynamic tensor into the support vector machine prediction model; the support vector machine prediction model is used to classify the risk status of the dynamic tensor to determine whether and when to initiate protective actions such as evacuation, concealment, iodine administration, or waiting. The output module is used to determine the user protection action plan output by the support vector machine prediction model; the user protection action plan includes the start time of escape, escape route, stay location and stay time, and hiding location and hiding time.
[0007] Preferably, the support vector machine prediction model is trained through supervised learning, and the training data includes historical nuclear accident data and / or simulated nuclear accident data.
[0008] Preferably, the support vector machine prediction model uses a radial basis function kernel.
[0009] Preferably, the output module is further configured to correct the output of the support vector machine prediction model based on preset wind speed thresholds and radiation dose thresholds, so as to generate the final user protection instructions.
[0010] Preferably, the nuclear accident emergency protection guidance system is applied to the planning of an individual user's escape zone from radiation.
[0011] Preferably, the input features of the support vector machine prediction model include dynamic tensor data at different times within a predetermined time window, and the support vector machine prediction model is used to perform temporal identification and classification of risks based on this data.
[0012] On the other hand, this specification also provides guidance on nuclear accident emergency response, including: Acquire environmental meteorological data, environmental radiation data, user location information, and map information; Based on the consequences assessment component, a dynamic tensor is determined according to the environmental meteorological data and / or environmental radiation data and / or user location information and / or map information; the dynamic tensor includes real-time dose distribution map, regional risk level assessment, source term release pattern, building concealment value, iodine supply accessibility, user location information, and user dose data; The dynamic tensor is input into the trained support vector machine prediction model, and the prediction model is used to determine whether and when to initiate protective actions such as evacuation, concealment, iodine administration, or waiting. Based on the output of the prediction model, a user protection action plan is determined; the user protection action plan includes the start time of escape, escape route, location and duration of stay, and location and duration of concealment.
[0013] On the other hand, the computer-readable storage medium provided in this specification stores a computer program that, when executed by a processor, implements the nuclear accident emergency protection guidance method provided in the above aspect.
[0014] On the other hand, this specification provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the nuclear accident emergency protection guidance method provided in one aspect above.
[0015] On the other hand, this specification provides a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to implement the nuclear accident emergency protection guidance method provided in the above-mentioned aspect.
[0016] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects: Using the above method, the nuclear accident emergency protection guidance system includes a data acquisition module, a preprocessing module, a data processing module, and an output module. The data acquisition module acquires environmental meteorological data and / or environmental radiation data and / or user location information and / or map information. The preprocessing module inputs this environmental meteorological data and / or environmental radiation data and / or user location information and / or map information into the consequence assessment component to determine the dynamic tensor output by the consequence assessment component. This dynamic tensor includes real-time dose distribution maps, regional risk level assessments, source term release patterns, building concealment values, iodine supply accessibility, user location information, and user dose data. The data processing module inputs this dynamic tensor into a support vector machine (SVM) prediction model. This SVM prediction model assesses the current risk status and determines whether and when users need to perform protective actions such as evacuation, concealment, iodine administration, or waiting. The output module determines the user protection action plan output by the SVM prediction model. This user protection action plan includes the start time of evacuation, escape route, location and duration of stay, and concealment location and duration.
[0017] It is evident that this nuclear accident emergency protection guidance system, based on the dynamic tensor output by the consequence assessment component and combined with the support vector machine prediction model, achieves personalized emergency protection guidance for each user. Attached Figure Description
[0018] The accompanying drawings, which are included to provide a further understanding of this specification and form part of this specification, illustrate exemplary embodiments and are used to explain this specification, but do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the architecture of a nuclear accident emergency protection guidance system described in this specification; Figure 2 A schematic diagram illustrating the prediction performance of a support vector machine model in protection timing classification, as provided in one embodiment of this specification; Figure 3 A flowchart illustrating a nuclear accident emergency response guidance method provided as an embodiment of this specification; Figure 4 The embodiment provided in this specification corresponds to Figure 1 A schematic diagram of an electronic device. Detailed Implementation
[0019] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this specification will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.
[0020] In the description of this invention, it should be noted that the term "or" is generally used to include the meaning of "and / or" unless otherwise expressly stated in the content.
[0021] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. Furthermore, in the description of this application, the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.
[0022] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
[0023] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.
[0024] Figure 1 This is a schematic diagram of the architecture of a nuclear accident emergency protection guidance system described in this specification, such as... Figure 1 As shown, it includes a data acquisition module, a preprocessing module, a data processing module, and an output module.
[0025] Preferably, the data acquisition module is used to acquire environmental meteorological data and / or environmental radiation data and / or user location information and / or map information, etc.
[0026] The environmental meteorological data includes temperature changes, weather changes, wind force and speed, etc. The environmental radiation data includes changes in radiation values. The map information includes geographic environmental maps, building maps, and aerial views, etc.
[0027] Preferably, the data acquisition module includes sensors, communication terminals, etc. The sensors are used to acquire data such as temperature, humidity, and bird's-eye view, while the communication terminal can connect to the Internet to acquire weather forecasts, geographical maps, building maps, etc.
[0028] Preferably, the preprocessing module is used to input the environmental meteorological data and / or environmental radiation data and / or user location information and / or map information into the consequence assessment component to determine the dynamic tensor output by the consequence assessment component. This dynamic tensor includes a real-time dose distribution map, regional risk level assessment, source term release patterns, building concealment values, iodine supply accessibility, user location information, and user dose data.
[0029] The technology related to the consequence assessment component has been developed to a relatively mature level, and will not be elaborated upon here.
[0030] Preferably, the data processing module is used to input the dynamic tensor into a support vector machine prediction model. The support vector machine prediction model is used to analyze the risk state represented by the dynamic tensor and determine whether and when to initiate protective actions such as evacuation, concealment, iodine administration, or waiting.
[0031] Preferably, the output module is used to determine the user protection action plan output by the support vector machine prediction model. The user protection action plan includes the start time of escape, escape route, location and duration of stay, and hiding location and duration of hide.
[0032] Preferably, to improve the robustness of the model in diverse environments, this specification also proposes: Emergency resource constraints are embedded into the input structure of the predictive model, including but not limited to "path cost from the user to the nearest iodine supply point" and "building protection capability score." This score, based on factors such as building type, number of floors, and the structure of the surrounding area, is quantified into a numerical input through a rule-based function to help determine whether immediate shelter conditions are met. This feature enhances the executability of protective action recommendations.
[0033] The system constructs a multi-stage Support Vector Machine (SVM) sub-model group, corresponding to the initial stage, high-risk diffusion stage, and tail stage of a nuclear accident, respectively. By setting a sliding time window mechanism, the system can automatically select the sub-model that best matches the current time-series features, thereby improving the response capability and classification accuracy at different stages of the accident's evolution.
[0034] Using the above method, the nuclear accident emergency protection guidance system provides personalized emergency protection guidance solutions for each user based on the dynamic tensor output by the consequence assessment component and combined with the support vector machine prediction model.
[0035] Preferably, the output module is used to send the user protection action plan to the user's communication terminal.
[0036] Preferably, this nuclear accident emergency protection guidance system is applied to the planning of an individual user's escape route from the radiation zone. Further, the specific structure and training method of the support vector machine prediction model in the aforementioned data processing module are described below.
[0037] In mathematical terms, Support Vector Machine (SVM) is a typical supervised learning classification model that classifies data by finding a hyperplane in the feature space that maximizes the classification margin. The decision function of the SVM classification model can be expressed as: in, The feature vector input to the model, This is the feature mapping function (used when a kernel function is employed to map the input to a higher-dimensional space). Let T be the weight vector, and T be the transpose sign. This is the bias term. The model performs binary classification of the input based on the sign of the decision function, i.e., when... When the value is 0, it is considered a positive class. When the value is less than 0, it is classified as a negative class.
[0038] To obtain the optimal classification hyperplane, SVM needs to maximize the classification margin and minimize misclassified points during training. Its optimization objective can be described as follows: in, Let T be the weight vector, and T be the transpose sign. This is a bias term. For the first The slack variables corresponding to each sample are used to allow some samples to violate the interval requirement. Weight vector norm, This is the penalty coefficient, used to balance the increase in the classification margin with the reduction in classification errors. The number of training samples is given. `st` is constrained by... ∈{+1, -1}, For the first The class label (positive or negative) of each sample. This is a feature mapping function used to map input samples to a high-dimensional space.
[0039] In this specific implementation, the radial basis function kernel (RBF kernel) is preferably used as the kernel function of the SVM, and its expression is as follows: in The output value of the kernel function represents the sample. and "Similarity" or "correlation" in a high-dimensional mapping space These are kernel function parameters used to control the function curvature of the RBF kernel. Representing vectors and The squared Euclidean distance between two samples represents the degree of difference between them in the input space. Using the RBF kernel function can handle the non-linear relationship between input features and output classification, improving the model's classification accuracy in complex environments.
[0040] To enable the support vector machine (SVM) prediction model to accurately identify the risk state in a nuclear accident scenario, the model's input features need to be designed appropriately. The multidimensional information contained in the dynamic tensor should be converted into feature vectors usable by the model as input. In this embodiment, the design of the input feature dimensions may include the following aspects: Radiation dose rate and cumulative dose at the user's current location; The building concealment value of the user's location (the radiation shielding factor of the building). The distance and relative location between the user and the source of the incident (e.g., whether the user is downwind or upwind). Local environmental meteorological data, such as real-time wind speed, wind direction, precipitation, and weather conditions; The trend of radiation dose change within a certain time range (based on the source term release law and the future dose distribution change predicted by meteorological forecasts); Iodine availability, such as whether the user has taken the recommended dose of iodine tablets or the user's distance from the nearest iodine tablet supply point; The time elapsed after the accident or other time factors.
[0041] By extracting and combining the aforementioned features, the Support Vector Machine (SVM) model can acquire comprehensive spatiotemporal state information, thereby accurately classifying risks. During the training phase, data from historical nuclear accident cases and simulated accident data can be collected to construct the training set. Each training sample consists of the aforementioned feature vector and its corresponding optimal protection decision category label. Label determination can be based on historical experience or authoritative guidelines, such as judging the optimal action timing based on the dose consequences of initiating evacuation or concealment at different times during an accident. Supervised learning of the SVM model is then performed using the prepared training data, and the model parameters are adjusted by iteratively optimizing the aforementioned objective function. and This enables the model to reproduce the optimal protection strategy contained in the training samples with high accuracy. The training process can be implemented using existing SVM solving algorithms (such as the Sequence Minimum Optimization (SMO) algorithm).
[0042] In practical applications, the real-time acquired dynamic tensor data is processed to generate feature vectors, which are then input into a trained support vector machine (SVM) prediction model. The model outputs a classification result corresponding to one of the predefined protective behavior categories (evacuation, concealment, iodine administration, and waiting). Given that there are four decision categories for protective behavior in this embodiment, a "one-to-many" multi-classification strategy (transforming the problem into a combination of four binary SVMs) or other SVM methods capable of directly handling multi-class classification can be used to achieve the classification decision. The category output by the model represents the optimal type of protective measure recommended under the current circumstances, as well as the corresponding timing for initiating the action. For example, when the model outputs "wait," it means that immediate evacuation or other actions are not advisable, and observation should be maintained; while an output of "evacuation" indicates that evacuation should begin immediately.
[0043] It is important to note that the model's output is not the sole basis for decision-making. To improve the reliability of decisions, this system combines machine learning predictions with traditional rule-based thresholds: on the one hand, the model helps identify optimal action times in complex environments; on the other hand, safety thresholds are used as a baseline to validate and correct the model's results. For example, if the model suggests the user wait, but the system detects that the radiation dose rate at the user's current location has exceeded a preset safety threshold, the system will ignore the model's waiting suggestion and directly issue an immediate shelter or evacuation order. Similarly, if the model predicts immediate evacuation, but the wind direction and speed indicate that the radioactive cloud has not yet reached the user's area, the system can allow for an appropriate delay in evacuation according to a preset plan to avoid potential additional risks from premature action. Through this rule-based correction mechanism, the final generated protection instructions can balance model predictions with industry safety standards, ensuring users receive robust and reliable emergency protection guidance.
[0044] Figure 2 A schematic diagram illustrating the prediction performance of a support vector machine model in protection timing classification, as provided in one embodiment of this specification, is shown below. Figure 2 As shown in the figure, this diagram illustrates the model's classification confusion matrix for the two types of behavior ("waiting" and "protective behavior"). On the test sample set, the model effectively identifies situations requiring protective action, demonstrating good classification accuracy and practicality.
[0045] Preferably, the support vector machine prediction model employs a radial basis function kernel for nonlinear classification.
[0046] Preferably, to improve the robustness of the model in diverse environments, this specification also proposes: Emergency resource constraints are embedded into the input structure of the predictive model, including but not limited to "path cost from the user to the nearest iodine supply point" and "building protection capability score." This score, based on factors such as building type, number of floors, and the structure of the surrounding area, is quantified into a numerical input through a rule-based function to help determine whether immediate shelter conditions are met. This feature enhances the executability of protective action recommendations.
[0047] The system constructs a multi-stage SVM sub-model group, corresponding to the initial stage, high-risk diffusion stage, and tail stage of a nuclear accident, respectively. By setting a sliding time window mechanism, the system can automatically select the sub-model that best matches the current time-series features, improving the response capability and classification accuracy at different stages of the accident's evolution.
[0048] Preferably, the input features of the support vector machine prediction model include dynamic tensor data at different times within a predetermined time window, so as to achieve temporal identification and classification of risks.
[0049] Preferably, the output module is equipped with a threshold rule judgment mechanism, which is used to comprehensively judge the output results of the prediction model based on preset wind speed threshold and radiation dose threshold, and generate user protection instructions.
[0050] Preferably, the nuclear accident emergency protection guidance system is applied to the planning of an individual user's escape zone from radiation.
[0051] Furthermore, to improve the robustness of the model in diverse environments, this specification also proposes: Emergency resource constraints are embedded into the input structure of the predictive model, including but not limited to "path cost from the user to the nearest iodine supply point" and "building protection capability score." This score, based on factors such as building type, number of floors, and the structure of the surrounding area, is quantified into a numerical input through a rule-based function to help determine whether immediate shelter conditions are met. This feature enhances the executability of protective action recommendations.
[0052] The system constructs a multi-stage SVM sub-model group, corresponding to the initial stage, high-risk diffusion stage, and tail stage of a nuclear accident, respectively. By setting a sliding time window mechanism, the system can automatically select the sub-model that best matches the current time-series features, improving the response capability and classification accuracy at different stages of the accident's evolution.
[0053] The above describes one or more embodiments of the nuclear accident emergency protection guidance system provided in this specification. Based on the same idea, this specification also provides a nuclear accident emergency protection guidance method.
[0054] Figure 3 A flowchart illustrating a nuclear accident emergency response guidance method provided as an embodiment of this specification, as shown below. Figure 3As shown, the nuclear accident emergency response guidelines include the following steps.
[0055] S200 acquires environmental meteorological data and / or environmental radiation data and / or user location information and / or map information.
[0056] Preferably, the method can be executed by a nuclear accident emergency protection guidance system provided in one or more embodiments of this specification, or by electronic devices such as servers and communication terminals.
[0057] S202: Based on the consequences assessment component, determine the dynamic tensor according to the environmental meteorological data and / or environmental radiation data and / or user location information and / or map information.
[0058] Preferably, the dynamic tensor includes a real-time dose distribution map, regional risk level assessment, source term release patterns, building concealment values, iodine supply accessibility, user location information, and user dose data.
[0059] S204: Input the dynamic tensor into the trained support vector machine prediction model, and use the prediction model to determine whether and when to initiate protective actions such as evacuation, concealment, iodine administration, or waiting.
[0060] S206: Based on the output of this prediction model, determine the user protection action plan.
[0061] Preferably, the user protection action plan includes the start time of escape, escape route, location and duration of stay, and location and duration of concealment.
[0062] Preferably, after step S206, the nuclear accident emergency protection guidance system can send the user's protection action plan to the user's communication terminal.
[0063] It should be noted that all actions involving the acquisition of signals, information, or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where the application is located, and with the authorization granted by the owner of the relevant device.
[0064] This specification also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 3 Provides guidance on emergency response to nuclear accidents.
[0065] This specification also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 3 Provides guidance on emergency response to nuclear accidents.
[0066] This specification also provides a computer program product in which instructions, when executed by the processor of an electronic device, cause the electronic device to perform the above-described functions. Figure 3 Provides guidance on emergency response to nuclear accidents.
[0067] This instruction manual also provides Figure 4 The diagram shows a schematic structural representation of the electronic device. Figure 4 At the hardware level, the electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for the business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to achieve the above-mentioned functions. Figure 3 The aforementioned nuclear accident emergency protection guidelines. Of course, besides software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution entity of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.
[0068] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.
[0069] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.
[0070] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.
[0071] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.
[0072] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0073] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0074] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0076] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0077] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0078] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0079] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0080] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0081] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.
[0082] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.
[0083] The above description is merely an embodiment of this specification and is not intended to limit this specification. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims of this application.
Claims
1. A nuclear accident emergency protection guidance system, characterized in that, It includes a data acquisition module, a preprocessing module, a data processing module, and an output module; The data acquisition module is used to acquire environmental meteorological data and / or environmental radiation data and / or user location information and / or map information; The preprocessing module is used to input the environmental meteorological data and / or environmental radiation data and / or user location information and / or map information into the consequence assessment component to determine the dynamic tensor output by the consequence assessment component; the dynamic tensor includes real-time dose distribution map, regional risk level assessment, source term release pattern, building concealment value, iodine supply accessibility, user location information, and user dose data; The data processing module is used to input the dynamic tensor into the support vector machine prediction model; the support vector machine prediction model is used to classify the risk status of the dynamic tensor to determine whether and when to initiate protective actions such as evacuation, concealment, iodine administration, or waiting. The output module is used to determine the user protection action plan output by the support vector machine prediction model; the user protection action plan includes the start time of escape, escape route, stay location and stay time, and hiding location and hiding time.
2. The nuclear accident emergency protection guidance system according to claim 1, characterized in that, The support vector machine prediction model is trained through supervised learning, and the training data includes historical nuclear accident data and / or simulated nuclear accident data.
3. The nuclear accident emergency protection guidance system according to claim 1, characterized in that, The support vector machine prediction model uses a radial basis function kernel.
4. The nuclear accident emergency protection guidance system according to claim 1, characterized in that, The output module is also used to correct the output of the support vector machine prediction model according to preset wind speed threshold and radiation dose threshold, so as to generate the final user protection instructions.
5. The nuclear accident emergency protection guidance system according to claim 1, characterized in that, The nuclear accident emergency protection guidance system is used for planning the escape from radiation zones for individual users.
6. The nuclear accident emergency protection guidance system according to claim 1, characterized in that, The input features of the support vector machine prediction model include dynamic tensor data at different times within a predetermined time window, and the support vector machine prediction model is used to perform temporal identification and classification of risks based on this.
7. A method for guiding emergency response to nuclear accidents, characterized in that, include: Acquire environmental meteorological data, environmental radiation data, user location information, and map information; Based on the consequences assessment component, a dynamic tensor is determined according to the environmental meteorological data and / or environmental radiation data and / or user location information and / or map information; the dynamic tensor includes real-time dose distribution map, regional risk level assessment, source term release pattern, building concealment value, iodine supply accessibility, user location information, and user dose data; The dynamic tensor is input into the trained support vector machine prediction model, and the prediction model is used to determine whether and when to initiate protective actions such as evacuation, concealment, iodine administration, or waiting. Based on the output of the prediction model, a user protection action plan is determined; the user protection action plan includes the start time of escape, escape route, location and duration of stay, and location and duration of concealment.
8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method described in claim 7.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method described in claim 7.
10. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device causes the electronic device to perform the method as described in claim 7.