Dammed lake outburst risk assessment method and system

Abstract

The invention is suitable for the field of risk assessment, and provides a barrier lake outburst risk assessment method and system, and the system comprises a data retention module, a risk judgment module, a multi-channel data collection module, and a warning spreading module. According to the scheme, the dependence and processing capacity of a monitoring system on traditional data are enhanced, and more importantly, when conventional monitoring is limited, the monitoring system can depend on informal data such as social media information to obtain necessary information. Besides, the scheme can amplify tiny signals through a data processing means with discrimination, help a decision maker to make detailed judgment, ensure that a disaster response strategy is as detailed and accurate as possible, and effectively improve the timeliness and the suitability of disaster management and emergency response.

Description

Technical Field

[0001] The invention belongs to the field of risk assessment, and in particular relates to a method and system for assessing the risk of a barrier lake burst. Background Art

[0002] The risk assessment of a barrier lake outburst is a process of predicting and quantifying the potential impact of the outburst of a natural dam formed in a river by landslides, debris flows, earthquakes or other geological events, i.e. a barrier lake. The risk assessment of a barrier lake outburst is multidisciplinary, involving geology, hydrology, hydraulics, engineering and environmental science, and aims to reduce the threat of potential disasters to people's lives and property.

[0003] The risk assessment process of barrier lake outburst relies on cross-disciplinary professional cooperation, and the response capacity of the assessment and early warning system is improved through the continuous development of high-tech and innovative methods. Through these means, the serious threat posed by the outburst of barrier lakes can be identified and reduced, providing a solid foundation for protecting people's lives and property safety.

[0004] Disaster monitoring relies on data sources such as sensor networks and satellite images. Data such as lake level and flow rate provided by digital flow mapping or field measurement points become barriers when communications are disconnected. This limits the real-time monitoring and understanding of the dynamic changes of the barrier lake. When an incident occurs, timely information exchange is crucial for disaster assessment and response. However, when traditional communication methods or data transmission mechanisms encounter infrastructure damage, they will not be able to convey important information to decision makers, including the scale of the disaster, the safety status of the affected area, the deployment of rescue forces, and other vital information. Summary of the invention

[0005] The purpose of the present invention is to provide a method for assessing the risk of a barrier lake burst, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0006] The present invention is achieved by providing a method for assessing the risk of a barrier lake outburst, the method comprising: Monitor various data of the barrier lake in real time, establish a data repository, and store the monitored data in the data repository; Establish a risk analysis model and combine the real-time data of various barrier lakes to determine whether the current barrier lake has the risk of bursting; When a collapse risk is identified, obtain information about the area where a collapse may occur or has occurred, and obtain disaster information about the area in real time. If real-time disaster information cannot be obtained, collect all social media information about the current area, extract features from social media information based on the disaster situation, and then obtain the current disaster information about the area. Combined with available real-time data, assess the current disaster risk level. Based on the assessed level of disaster risk, warning notices are prepared and sent to the affected areas.

[0007] As a further solution of the present invention, the real-time monitoring of various data of the barrier lake and the establishment of a data repository to store the monitored data in the data repository specifically include: Build a data transmission channel, and obtain various data of the barrier lake monitored in real time by sensors through the data transmission channel, and pre-process all the collected data; A data repository is established to store the preprocessed data in the data repository, and a time tag indicating when the data was collected is added to the data during data storage.

[0008] As a further solution of the present invention, the risk analysis model is established, and combined with the real-time data of various barrier lakes obtained, to determine whether the current barrier lake has a risk of bursting, specifically including: Set indicators related to the outburst of barrier lakes and establish a risk analysis model using the relevant indicators as input sources; Extract data with the same time tag and matching relevant indicators from the data repository, substitute it into the risk analysis model, and calculate the risk level of the current time tag; Set a burst risk threshold and compare the risk level with the burst risk threshold.

[0009] As a further solution of the present invention, the feature extraction of social media information based on the disaster situation is performed to obtain the current disaster situation information in the area, and the current disaster risk level is evaluated in combination with the available real-time data, which specifically includes: When the risk level exceeds the flood risk threshold, determine the area where flooding may occur or has occurred, and extract all the data of the area and the smoothness of data transmission in the area from the data repository; When the transmission smoothness of the area is low, so that the data of the area cannot be transmitted to the data storage, all social platform information of the area is intercepted, including text, pictures and videos; Taking disaster-related content as the main feature, all intercepted social platform information is used to extract disaster features, including collecting geographic location and time information through text mining and natural language processing; and obtaining disaster information through image recognition; Based on the acquired disaster characteristics and the latest data extracted from the area, determine the level of collapse that the current area may face or is facing.

[0010] As a further solution of the present invention, the method of formulating a warning notice according to the assessment level of the disaster risk and sending the warning notice to the affected area specifically includes: Develop matching warning information and emergency plans for each level of failure; Obtain the final level of collapse and disseminate the corresponding warning information and emergency plans for the level of collapse.

[0011] Another object of the present invention is to provide a barrier lake outburst risk assessment system, the system comprising: The data retention module is used to monitor various data of the barrier lake in real time and establish a data repository to store the monitored data in the data repository; The risk judgment module is used to establish a risk analysis model and combine the real-time data of various barrier lakes to determine whether the current barrier lake has a risk of bursting; The multi-channel data collection module is used to obtain information about areas where a collapse may occur or has occurred, and obtain disaster information about the area in real time when a collapse risk is identified. If real-time disaster information cannot be obtained, all social media information about the current area is collected, and features are extracted from social media information based on the disaster situation, thereby obtaining disaster information about the current area, and combining the available real-time data to assess the current disaster risk level; The warning dissemination module is used to formulate warning notices according to the assessment level of disaster risk and send warning notices to the affected areas.

[0012] As a further solution of the present invention, the data retention module includes: An information collection unit is used to build a data transmission channel, and obtain various data of the barrier lake monitored in real time by sensors through the data transmission channel, and pre-process all the collected data; The data storage unit is used to set up a data storage library, store the pre-processed data in the data storage library, and add a time tag when the data is collected to the data during data storage.

[0013] As a further solution of the present invention, the risk judgment module includes: The model building unit is used to set indicators related to the outburst of the barrier lake and to establish a risk analysis model using the relevant indicators as input sources; The level determination unit is used to extract data with the same time tag and matching the relevant indicators from the data repository, and substitute it into the risk analysis model to calculate the risk level of the current time tag; The risk comparison unit is used to set a burst risk threshold and compare the risk level with the burst risk threshold.

[0014] As a further solution of the present invention, the multi-channel data acquisition module includes: A signal patency determination unit is used to determine the area where a collapse may occur or has occurred when the risk level exceeds the collapse risk threshold, and extract all data of the area and the transmission patency of the data in the area from the data storage library; A multi-channel collection unit is used to intercept all social platform information in the area, including text, pictures and videos, when the transmission smoothness of the area is low and the data in the area cannot be transmitted to the data storage library; The multi-channel information recognition unit is used to extract disaster characteristics from all intercepted social platform information, taking disaster-related content as the main feature, including collecting geographic location and time information through text mining and natural language processing; and obtaining disaster information through image recognition; The collapse risk assessment unit is used to determine the collapse level that the current area may face or is facing based on the acquired disaster characteristics and the latest data extracted from the area.

[0015] As a further solution of the present invention, the warning dissemination module includes: The program planning unit is used to develop matching warning information and emergency plans for each breach level; The information dissemination unit is used to obtain the final collapse level and disseminate the corresponding warning information and emergency plan of the collapse level.

[0016] The beneficial effects of the present invention are: This solution not only enhances the monitoring system's reliance on and processing capabilities of traditional data, but more importantly, when conventional monitoring is limited, it can rely on more informal data, such as social media intelligence, to obtain necessary information. In addition, the solution can amplify tiny signals through discerning data processing methods, helping decision makers make detailed judgments, ensuring that disaster response strategies are as detailed and accurate as possible, and effectively improving the timeliness and appropriateness of disaster management and emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of a method for assessing the risk of a barrier lake outburst provided by an embodiment of the present invention; Figure 2 A flowchart of real-time monitoring of various data of a barrier lake provided by an embodiment of the present invention, establishing a data repository, and storing the monitored data in the data repository; Figure 3 A flow chart of establishing a risk analysis model provided by an embodiment of the present invention, combining various acquired real-time data of the barrier lake, and determining whether the current barrier lake has a risk of bursting; Figure 4 A flow chart for evaluating the level of current disaster risk provided by an embodiment of the present invention; Figure 5A flowchart of formulating a warning notification according to the disaster risk assessment level and sending the warning notification to the affected area provided in an embodiment of the present invention; Figure 6 A structural block diagram of a barrier lake outburst risk assessment system provided by an embodiment of the present invention; Figure 7 A structural block diagram of a data storage module provided in an embodiment of the present invention; Figure 8 A structural block diagram of a risk judgment module provided by an embodiment of the present invention; Fig. 9 A structural block diagram of a multi-channel data acquisition module provided by an embodiment of the present invention; Fig.10 This is a structural block diagram of the warning dissemination module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0020] Figure 1 A flow chart of a method for assessing the risk of a barrier lake outburst provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, a method for assessing the risk of a barrier lake outburst comprises: S100, real-time monitoring of various data of the barrier lake, and establishing a data repository to store the monitored data in the data repository; This step will build an automated monitoring network related to the barrier lake topic, which consists of various types of sensors that can regularly collect data on important parameters such as water level, flow, osmotic pressure, etc. These data are transmitted in real time through a series of pre-defined data transmission channels.

[0021] The data is then screened and preprocessed for the first time, aiming to smooth the data, reduce noise interference, and provide signal integration and recognition enhancement operations. Ensuring the reliability of information communicated in a timely manner is the cornerstone of decision-making in subsequent steps. After the data is submitted to the core processing unit, it is then stored and indexed in a carefully designed data repository. In addition to being given an accurate time label to indicate the specific time sequence, each data point is also used to depict the continuous scenario of changes in the time dimension, so that the decision support system involved can understand the time correlation. The database system itself must support fast reading and writing, as well as disaster recovery and online migration capabilities, to further ensure the long-term security, stability and accessibility of the data.

[0022] S200, establishing a risk analysis model, combining the acquired real-time data of the barrier lake, and determining whether the barrier lake currently has a risk of bursting; This step defines a series of indicators that are closely related to the breach of the barrier lake, such as water level, volume flow rate, crack size, soil moisture, etc. The selection of these indicators not only relies on historical cases and theoretical research, but also takes into account the most advanced geological data analysis to ensure that the selected indicators can fully and accurately represent the current state of the barrier lake. With the help of artificial intelligence, machine learning and multidimensional statistical models, the risk analysis model is built on a robust algorithm that can extract valuable risk signs from complex and multivariate data.

[0023] From the set data repository, we accurately locate the records that are synchronized with the required risk indicators, and process and analyze these data streams. Then, the matching data is input into the model to comprehensively evaluate the risk level at the corresponding moment. Through the continuous iterative learning process, the model is continuously optimized to improve the prediction accuracy.

[0024] Finally, by setting multiple risk thresholds in detail, regional risks can be subdivided into multiple levels, such as normal, caution, warning and emergency. When the risk level predicted by the model exceeds a certain threshold, the early warning system will automatically start, creating a favorable window period to prepare for possible disasters.

[0025] S300, when a collapse risk is identified, obtain information about a region where a collapse may occur or has occurred, and obtain disaster information about the region in real time. If real-time disaster information cannot be obtained, collect all social media information about the current region, extract features from the social media information based on the disaster situation, and then obtain the current disaster information about the region. Combined with available real-time data, evaluate the current disaster risk level. When the calculated risk level exceeds the preset breach risk threshold, the system enters an emergency state. The system will quickly lock and identify the potential breach area of ​​the barrier lake, and retrieve all relevant real-time data in the area, such as meteorological, hydrological, geological and other information, and evaluate the accessibility (patency) of this information. The assessment of patency is critical because it is related to whether accurate information can be successfully collected and disseminated.

[0026] In response to data transmission interruptions caused by communication barriers and other factors, this method will initiate an alternative plan to collect information on social media platforms in the region as a data source, which may include text, pictures and videos uploaded by the public. Social media has become a key source of supplementary information, especially when formal monitoring methods are blocked.

[0027] Advanced text mining technology and natural language processing algorithms are used to process text information in social media and extract appropriate time and location data; image recognition technology is also used to analyze uploaded pictures and videos to obtain real-time, geographically located disaster data. This series of information retrieval and processing techniques can greatly improve our understanding of the true situation of the incident.

[0028] Finally, an activation function based on multiple weights and input data is used to evaluate and predict the risk of collapse, ensuring that the disaster response strategy is as detailed and accurate as possible, effectively improving the timeliness and appropriateness of disaster management and emergency response.

[0029] S400, formulates a warning notice based on the assessment level of disaster risk and sends the warning notice to the affected area.

[0030] Figure 2 A flowchart of real-time monitoring of various data of a barrier lake provided by an embodiment of the present invention, establishing a data repository, and storing the monitored data in the data repository is shown in FIG. Figure 2 As shown, the real-time monitoring of various data of the barrier lake and the establishment of a data repository to store the monitored data in the data repository specifically include: S110, building a data transmission channel, and obtaining various data of the barrier lake monitored in real time by sensors through the data transmission channel, and preprocessing all the collected data; S120, establishing a data repository, storing the preprocessed data in the data repository, and adding a time tag when the data is collected to the data during data storage; In the step of preprocessing all the collected data, the relationship between the corresponding process is: ; in, Indicates at time After preprocessing, Indicates sensor time The original data, Indicates sensor In time The mean of the collected data on Indicates sensor The standard deviation of the collected data, Represents the total number of sensors, Represents a constant that prevents the denominator from going to zero.

[0031] Specifically, by combining the cosine function and the high-order exponential function, the nonlinear changes and outliers in the data can be better captured; by using a complex combination of the sine function and power, the impact of noise on data processing can be reduced to ensure the smoothness and stability of the data.

[0032] Figure 3 A flow chart of establishing a risk analysis model provided by an embodiment of the present invention, combining various real-time data of the barrier lake obtained, and determining whether the current barrier lake has a risk of bursting, such as Figure 3 As shown, the risk analysis model is established, and combined with the real-time data of various barrier lakes obtained, it is determined whether the current barrier lake has a risk of bursting, specifically including: S210, set indicators related to the outburst of barrier lakes, and establish a risk analysis model using the relevant indicators as input sources; S220, extracting data with the same time tag and matching the relevant indicators from the data repository, and substituting the data into the risk analysis model to calculate the risk level of the current time tag; S230, setting a burst risk threshold, and comparing the risk level with the burst risk threshold; In the step of extracting data with the same time tag and matching the relevant indicators from the data repository and substituting it into the risk analysis model to calculate the risk level of the current time tag, the risk level is further determined by calculating and obtaining a comprehensive risk score. The specific steps of calculating and obtaining the comprehensive risk score are as follows: The standardized risk factor is calculated using the maximum and minimum values ​​of the indicator. The corresponding process relationship is: ; in, represents the standardized risk factor, Indicators In time The measured value on Indicators The minimum value of Indicators The maximum value of The standardized risk factor is nonlinearly scaled to obtain the nonlinearly scaled risk factor. The corresponding process relationship is: ; in, represents the exponent used to adjust the nonlinear effects, represents the risk factor after nonlinear scaling; Based on the maximum and minimum values ​​of the indicator, the inverse tangent conversion operation is performed to obtain the factor after the inverse tangent conversion. The relationship between the corresponding process is: ; in, Represents a constant used to prevent operation errors. Represents the factor after arc tangent transformation; Based on the risk factors after nonlinear scaling and the factors after arctangent transformation, the comprehensive risk score is obtained by weighted summation. The relationship between the corresponding process is: ; in, Indicates time The comprehensive risk score of Indicators The weight of Indicates the total number of indicators.

[0033] Specifically, the inverse tangent function and nonlinear scaling are introduced to handle extreme changes in data, making the risk score more sensitive to abnormal situations.

[0034] Figure 4 A flowchart for evaluating the current disaster risk level provided by an embodiment of the present invention, such as Figure 4 As shown, the feature extraction of social media information based on the disaster situation is performed to obtain the current disaster situation information in the area, and the current disaster risk level is evaluated in combination with the available real-time data, specifically including: S310, when the risk level exceeds the flood risk threshold, determine the area where flooding may occur or has occurred, and extract all data of the area and the smoothness of transmission of all data of the area from the data repository; S320, when the transmission smoothness of the area is low, so that the data of the area cannot be transmitted to the data storage repository, intercept all social platform information of the area, including text, pictures and videos; S330, taking disaster-related content as the main feature, extracting disaster features from all intercepted social platform information, including collecting geographic location and time information through text mining and natural language processing; and obtaining disaster information through image recognition; S340, judging the level of collapse that the current area may face or is facing based on the acquired disaster characteristics and the latest data extracted for the area; In the step of determining the level of collapse that the current area may face or is facing based on the acquired disaster characteristics and the latest data extracted from the area, the overall disaster severity score is obtained through the social platform information data. The specific steps for obtaining the overall disaster severity score are as follows: Based on the information data of social platforms, text features and image features are extracted and obtained; The text features are used to calculate the scores related to the text features. The corresponding relationship is: ; in, represents the score related to the text feature, Indicates The weight of the text features, represents the total frequency of all text features, Indicates time Previous The frequency of occurrence of a text feature, represents the total number of text features, represents the index used to adjust the impact of text features; The image features are used to calculate the scores related to the image features. The relationship between the corresponding process is: ; in, represents the score associated with the image features, represents the total number of image features, represents the weight of the image feature, Indicates time The feature values ​​extracted from the image are Indicates time Previous The frequency of occurrence of image features, represents the parameters used to adjust the impact of image features, represents a constant to avoid the denominator from going to zero; The scores related to text features and image features are added together to obtain the total disaster severity score. The corresponding relationship is: ; in, Indicates time The overall disaster severity score.

[0035] In the step of judging the level of collapse that the current area may face or is facing based on the acquired disaster characteristics and the latest data extracted from the area, the corresponding process has the following relationship: ; in, Indicates the level of collapse, represents a positive number used for stable calculations, Indicates time The smoothness of data transmission; Specifically, through the sinusoidal function and nonlinear features, the complex interactions between features can be captured; at the same time, by combining text and image features, the formula provides a multi-dimensional disaster information extraction method; In the step of judging the level of collapse that the current area may face or is facing based on the acquired disaster characteristics and the latest data extracted from the area, the corresponding process has the following relationship: ; in, Indicates the level of collapse, represents a positive number used for stable calculations, Indicates time The smoothness of data transmission.

[0036] Specifically, by combining the comprehensive risk score and the total disaster severity score, a comprehensive assessment of the breach level can be made; at the same time, the complex relationship between the scores is captured by using the hyperbolic cosine function and the cubic root to better reflect the actual situation.

[0037] Figure 5 A flowchart of formulating a warning notification according to the disaster risk assessment level and sending the warning notification to the affected area is provided in an embodiment of the present invention, such as Figure 5 As shown, according to the assessment level of disaster risk, a warning notice is formulated and sent to the affected area, specifically including: S410, formulate matching warning information and emergency plans for each breach level; S420, obtaining the final collapse level, and disseminating the corresponding warning information and emergency plan of the collapse level.

[0038] Figure 6 The structural block diagram of the barrier lake outburst risk assessment system provided by the embodiment of the present invention is as follows: Figure 6 As shown, a barrier lake outburst risk assessment system comprises: The data storage module 100 is used to monitor various data of the barrier lake in real time, establish a data storage library, and store the monitored data in the data storage library; This module will build an automated monitoring network related to the topic of barrier lakes, which consists of various types of sensors that can regularly collect data on important parameters such as water level, flow, osmotic pressure, etc. These data are transmitted in real time through a series of pre-defined data transmission channels.

[0039] The data is then screened and preprocessed for the first time, aiming to smooth the data, reduce noise interference, and provide signal integration and recognition enhancement operations. Ensuring the reliability of information communicated in a timely manner is the cornerstone of decision-making in subsequent steps. After the data is submitted to the core processing unit, it is then stored and indexed in a carefully designed data repository. In addition to being given an accurate time label to indicate the specific time sequence, each data point is also used to depict the continuous scenario of changes in the time dimension, so that the decision support system involved can understand the time correlation. The database system itself must support fast reading and writing, as well as disaster recovery and online migration capabilities, to further ensure the long-term security, stability and accessibility of the data.

[0040] The risk judgment module 200 is used to establish a risk analysis model and determine whether the current barrier lake has a risk of bursting in combination with the acquired real-time data of the barrier lake; This module defines a series of indicators closely related to the outburst of barrier lakes, such as water level, volume flow rate, crack size, soil moisture, etc. The selection of these indicators not only relies on historical cases and theoretical research, but also takes into account the most advanced geological data analysis to ensure that the selected indicators can fully and accurately represent the current state of barrier lakes. With the help of artificial intelligence, machine learning and multidimensional statistical models, the risk analysis model is built on a robust algorithm that can extract valuable risk signs from complex and multivariate data.

[0041] From the set data repository, we accurately locate the records that are synchronized with the required risk indicators, and process and analyze these data streams. Then, the matching data is input into the model to comprehensively evaluate the risk level at the corresponding moment. Through the continuous iterative learning process, the model is continuously optimized to improve the prediction accuracy.

[0042] Finally, by setting multiple risk thresholds in detail, regional risks can be subdivided into multiple levels, such as normal, caution, warning and emergency. When the risk level predicted by the model exceeds a certain threshold, the early warning system will automatically start, creating a favorable window period to prepare for possible disasters.

[0043] The multi-channel data collection module 300 is used to obtain information about the area where a collapse may occur or has occurred, and obtain disaster information about the area in real time when a collapse risk is identified. When the real-time disaster information cannot be obtained, all social media information about the current area is collected, and features are extracted from the social media information based on the disaster situation, thereby obtaining the current disaster information about the area, and combining the available real-time data to assess the current disaster risk level. The module enters an emergency state when the calculated risk level exceeds the preset breach risk threshold. The system will quickly lock and identify the potential breach area of ​​the barrier lake, and retrieve all relevant real-time data in the area, such as meteorological, hydrological, geological and other information, and evaluate the accessibility (patency) of this information. The assessment of patency is critical because it is related to whether accurate information can be successfully collected and disseminated.

[0044] In response to data transmission interruptions caused by communication barriers and other factors, the module will initiate an alternative plan to collect information from social media platforms in the region as a data source, which may include text, pictures and videos uploaded by the public. Social media has become a key source of supplementary information, especially when formal monitoring methods are blocked.

[0045] Advanced text mining technology and natural language processing algorithms are used to process text information in social media and extract appropriate time and location data; image recognition technology is also used to analyze uploaded pictures and videos to obtain real-time, geographically located disaster data. This series of information retrieval and processing techniques can greatly improve our understanding of the true situation of the incident.

[0046] Finally, an activation function based on multiple weights and input data is used to evaluate and predict the risk of collapse, ensuring that the disaster response strategy is as detailed and accurate as possible, effectively improving the timeliness and appropriateness of disaster management and emergency response.

[0047] The warning dissemination module 400 is used to formulate a warning notice according to the assessment level of the disaster risk and send the warning notice to the affected area.

[0048] Figure 7 The structural block diagram of the data retention module provided by the embodiment of the present invention is as follows: Figure 7 As shown, the data retention module includes: The information collection unit 110 is used to build a data transmission channel, and obtain various data of the barrier lake monitored in real time by sensors through the data transmission channel, and pre-process all the collected data; The data storage unit 120 is used to establish a data storage library, store the pre-processed data in the data storage library, and add a time tag when the data is collected to the data when the data is stored.

[0049] Figure 8 The structure diagram of the risk judgment module provided by the embodiment of the present invention is as follows: Figure 8 As shown, the risk judgment module includes: The model building unit 210 is used to set indicators related to the outburst of the barrier lake and establish a risk analysis model using the relevant indicators as input sources; The level determination unit 220 is used to extract data with the same time tag and matching the relevant indicators from the data repository, and substitute it into the risk analysis model to calculate the risk level of the current time tag; The risk comparison unit 230 is used to set a burst risk threshold and compare the risk level with the burst risk threshold.

[0050] Fig. 9 The structural block diagram of the multi-channel data acquisition module provided by the embodiment of the present invention is as follows: Fig. 9 As shown, the multi-channel data acquisition module includes: The signal smoothness determination unit 310 is used to determine the area where a collapse may occur or has occurred when the risk level exceeds the collapse risk threshold, and extract all the data of the area and the smoothness of the data transmission in the area from the data storage library; The multi-channel collection unit 320 is used to intercept all social platform information in the area, including text, pictures and videos, when the transmission smoothness of the area is low, so that the data in the area cannot be transmitted to the data storage library; The multi-channel information identification unit 330 is used to extract disaster characteristics from all intercepted social platform information with disaster-related content as the main feature, including collecting geographic location and time information through text mining and natural language processing; and obtaining disaster information through image recognition; The flood risk assessment unit 340 is used to determine the flood level that the current area may face or is facing based on the acquired disaster characteristics and the latest data extracted from the area.

[0051] Fig.10 A structural block diagram of the warning dissemination module provided by an embodiment of the present invention, such as Fig.10 As shown, the warning dissemination module includes: A program planning unit 410 is used to formulate matching warning information and emergency plans for each failure level; The information dissemination unit 420 is used to obtain the final collapse level and disseminate the warning information and emergency plan corresponding to the collapse level.

[0052] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0053] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0054] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0055] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0056] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for assessing the risk of a barrier lake outburst, characterized in that: The method comprises the following steps: Monitor various data of the barrier lake in real time, establish a data repository, and store the monitored data in the data repository; Establish a risk analysis model and combine the real-time data of various barrier lakes to determine whether the current barrier lake has the risk of bursting; When a collapse risk is identified, obtain information about the area where a collapse may occur or has occurred, and obtain disaster information about the area in real time. If real-time disaster information cannot be obtained, collect all social media information about the current area, extract features from social media information based on the disaster situation, and then obtain the current disaster information about the area. Combined with available real-time data, assess the current disaster risk level. Prepare warning notices based on the assessed level of disaster risk and send them to affected areas; The real-time monitoring of various data of the barrier lake and the establishment of a data repository to store the monitored data in the data repository specifically include: Build a data transmission channel, and obtain various data of the barrier lake monitored in real time by sensors through the data transmission channel, and pre-process all the collected data; A data repository is established to store the preprocessed data in the data repository, and a time tag indicating when the data was collected is added to the data during data storage.

2. The barrier lake outburst risk assessment method according to claim 1, characterized in that: In the step of preprocessing all the collected data, the relationship between the corresponding process is: ； in, Indicates at time After preprocessing, Indicates sensor In time The original data on Indicates sensor In time The mean of the collected data on Indicates sensor The standard deviation of the collected data, Represents the total number of sensors, Represents a constant that prevents the denominator from going to zero.

3. The barrier lake outburst risk assessment method according to claim 2, characterized in that: The risk analysis model is established, and combined with the real-time data of various barrier lakes obtained, to determine whether the current barrier lake has a risk of bursting, specifically including: Set indicators related to the outburst of barrier lakes and establish a risk analysis model using the relevant indicators as input sources; Extract data with the same time tag and matching relevant indicators from the data repository, substitute it into the risk analysis model, and calculate the risk level of the current time tag; Set a burst risk threshold and compare the risk level with the burst risk threshold.

4. The barrier lake outburst risk assessment method according to claim 3, characterized in that: In the step of extracting data with the same time tag and matching the relevant indicators from the data repository and substituting it into the risk analysis model to calculate the risk level of the current time tag, the risk level is further judged by calculating and obtaining the comprehensive risk score. The specific steps of calculating and obtaining the comprehensive risk score are as follows: The standardized risk factor is calculated using the maximum and minimum values ​​of the indicator. The corresponding process relationship is: ； in, represents the standardized risk factor, Indicator In time The measured value on Indicator The minimum value of Indicator The maximum value of The standardized risk factor is nonlinearly scaled to obtain the nonlinearly scaled risk factor. The corresponding process relationship is: ； in, represents the exponent used to adjust the nonlinear effects, represents the risk factor after nonlinear scaling; Based on the maximum and minimum values ​​of the indicator, the inverse tangent conversion operation is performed to obtain the factor after the inverse tangent conversion. The relationship between the corresponding process is: ； in, Represents a constant used to prevent operation errors. Represents the factor after arc tangent transformation; Based on the risk factors after nonlinear scaling and the factors after arctangent transformation, the comprehensive risk score is obtained by weighted summation. The relationship between the corresponding process is: ； in, Indicates time The comprehensive risk score of Indicator The weight of Indicates the total number of indicators.

5. The barrier lake outburst risk assessment method according to claim 4, characterized in that: The feature extraction of social media information based on the disaster situation is performed to obtain the current disaster situation information in the area, and the current disaster risk level is evaluated in combination with the available real-time data. Specifically, the following sub-steps are included: When the risk level exceeds the flood risk threshold, determine the area where flooding may occur or has occurred, and extract all the data of the area and the smoothness of the transmission of all the data of the area from the data repository; When the transmission smoothness of the area is low, so that the data of the area cannot be transmitted to the data storage, all social platform information of the area is intercepted, including text, pictures and videos; Taking disaster-related content as the main feature, all intercepted social platform information is used to extract disaster features, including collecting geographic location and time information through text mining and natural language processing; and obtaining disaster information through image recognition; Based on the acquired disaster characteristics and the latest data extracted from the area, determine the level of collapse that the current area may face or is facing.

6. The barrier lake outburst risk assessment method according to claim 5, characterized in that: In the step of determining the level of collapse that the current area may face or is facing based on the acquired disaster characteristics and the latest data extracted from the area, the overall disaster severity score is obtained through the social platform information data. The specific steps for obtaining the overall disaster severity score are as follows: Based on the information data of social platforms, text features and image features are extracted and obtained; The text features are used to calculate the scores related to the text features. The corresponding relationship is: ； in, represents the score related to the text feature, Indicates The weight of the text features, represents the total frequency of all text features, Indicates time Previous The frequency of occurrence of a text feature, represents the total number of text features, represents the index used to adjust the impact of text features; The image features are used to calculate the scores related to the image features. The relationship between the corresponding process is: ； in, represents the score associated with the image features, represents the total number of image features, represents the weight of the image feature, Indicates time The feature values ​​extracted from the image are Indicates time Previous The frequency of occurrence of image features, represents the parameters used to adjust the impact of image features, represents a constant to avoid the denominator from going to zero; The scores related to text features and image features are added together to obtain the total disaster severity score. The corresponding relationship is: ； in, Indicates time The overall disaster severity score.

7. The method for assessing the risk of a barrier lake outburst according to claim 6, characterized in that: In the step of judging the level of collapse that the current area may face or is facing based on the acquired disaster characteristics and the latest data extracted from the area, the corresponding process has the following relationship: ； in, Indicates the level of collapse, represents a positive number used to stabilize calculations, Indicates time The smoothness of data transmission.

8. The barrier lake outburst risk assessment method according to claim 7, characterized in that: According to the assessment level of disaster risk, a warning notice is formulated and sent to the affected areas, including: Develop matching warning information and emergency plans for each level of failure; Obtain the final level of collapse and disseminate the corresponding warning information and emergency plans for the level of collapse.

9. A barrier lake outburst risk assessment system, characterized in that: The barrier lake outburst risk assessment method according to any one of claims 1 to 8 is applied, wherein the system comprises: The data retention module is used to monitor various data of the barrier lake in real time and establish a data repository to store the monitored data in the data repository; The risk judgment module is used to establish a risk analysis model and combine the real-time data of various barrier lakes to determine whether the current barrier lake has a risk of bursting; The multi-channel data collection module is used to obtain information about areas where a collapse may occur or has occurred, and obtain disaster information about the area in real time when a collapse risk is identified. If real-time disaster information cannot be obtained, all social media information about the current area is collected, and features are extracted from social media information based on the disaster situation, thereby obtaining disaster information about the current area, and combining the available real-time data to assess the current disaster risk level; The warning dissemination module is used to formulate warning notices according to the assessment level of disaster risk and send warning notices to the affected areas.

10. The barrier lake outburst risk assessment system according to claim 9, characterized in that: The data retention module includes: An information collection unit is used to build a data transmission channel, and obtain various data of the barrier lake monitored in real time by sensors through the data transmission channel, and pre-process all the collected data; A data storage unit is used to set up a data storage library, store the pre-processed data in the data storage library, and add a time tag when the data is collected to the data during data storage; The risk judgment module includes: The model building unit is used to set indicators related to the outburst of the barrier lake and to establish a risk analysis model using the relevant indicators as input sources; The level determination unit is used to extract data with the same time tag and matching the relevant indicators from the data repository, and substitute it into the risk analysis model to calculate the risk level of the current time tag; The risk comparison unit is used to set the burst risk threshold and compare the risk level with the burst risk threshold; The multi-channel data acquisition module includes: A signal patency determination unit is used to determine the area where a collapse may occur or has occurred when the risk level exceeds the collapse risk threshold, and extract all data of the area and the transmission patency of the data in the area from the data storage library; The multi-channel collection unit is used to intercept all social platform information in the area, including text, pictures and videos, when the transmission smoothness of the area is low and the data in the area cannot be transmitted to the data storage library; The multi-channel information recognition unit is used to extract disaster characteristics from all intercepted social platform information, taking disaster-related content as the main feature, including collecting geographic location and time information through text mining and natural language processing; and obtaining disaster information through image recognition; The flood risk assessment unit is used to determine the flood level that the current area may face or is facing based on the acquired disaster characteristics and the latest data extracted from the area; The warning dissemination module includes: The program planning unit is used to develop matching warning information and emergency plans for each breach level; The information dissemination unit is used to obtain the final collapse level and disseminate the corresponding warning information and emergency plan of the collapse level.

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