Internet of Things big data integrated waterlogging disaster monitoring and early warning evaluation method

Abstract

The invention relates to the technical field of disasters in agricultural production, in particular to an internet of things big data integrated waterlogging disaster monitoring and early warning evaluation method. According to the method, on the basis of geographic information data, soil information data, meteorological data, satellite remote sensing data such as MODIS, historical statistical data and data such as a waterlogging disaster model library, the sensitivity, disaster-inducing factor dangerousness and disaster-bearing body vulnerability of rice, wheat and rape in different disaster-pregnant environments are calculated, and then a waterlogging disaster comprehensive risk assessment model is constructed. And performing level risk zoning. Scientific calculation is carried out through a PIE-Engine calculation service platform, a four-pre prevention and control technology platform for crop waterlogging disasters is constructed, real-time monitoring, early warning and intelligent decision support are achieved, the accuracy and timeliness of disaster prevention and control are remarkably improved, and agricultural production safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of disaster technology in agricultural production, and more specifically, to a waterlogging disaster monitoring and early warning assessment method integrating Internet of Things and big data. Background Art

[0002] The risk assessment of farmland waterlogging disaster is a quantitative description of the extent of waterlogging disaster and its consequences, and is the basis for determining the loss of grain production and economic losses caused by waterlogging disaster. Waterlogging disaster risk assessment is generally divided into mid-disaster assessment and post-disaster assessment. The mid-disaster assessment estimates the impact of the ongoing waterlogging stress on crop growth, the possible recovery of crops in the later period, and the possible impact on the final yield, which can provide real-time guidance for disaster prevention and early warning. In fact, a large number of experimental research works that have been carried out through potted plants or field flooding and waterlogging are part of the mid-disaster assessment. However, in the actual production and crop growth process, crops may encounter multiple or continuous waterlogging stress, and often encounter alternating dry and wet, drought and flood cross stress. The post-disaster recovery ability of crops is affected by many factors such as the degree of waterlogging stress, crop development period, later meteorological conditions, and field management level. Therefore, the complexity of plant growth, the variability of crop growth environment and meteorological conditions, bring difficulties to the mid-disaster assessment of waterlogging disasters, especially to the dynamic assessment of waterlogging disasters.

[0003] At present, although a large number of potted or field experimental observation studies have been carried out, there is still a lack of assessment methods and evaluation standards for the impact of different waterlogging degrees on the later growth of crops. Post-disaster assessment mainly estimates the impact of waterlogging disasters on the final yield of crops. Post-disaster assessment methods include: field surveys, remote sensing estimation, models, etc. Farmland waterlogging disaster risk analysis is an assessment of the probability of possible waterlogging disasters and their consequences, and proposes waterlogging risk zoning and disaster reduction measures to reduce risks. In the practice of farmland waterlogging disaster risk assessment, the adaptive ability to the complexity of the occurrence and development of waterlogging disasters is weak, and there is a lack of waterlogging disaster risk assessment technology for the entire process of "occurrence-development-extinction", which makes the dynamic assessment of waterlogging disaster risks difficult and the accuracy low, and it is impossible to provide timely and effective waterlogging spatiotemporal information intelligent services for waterlogging disaster prevention and emergency disaster reduction decisions.

[0004] With the development of Internet of Things technology and big data analysis methods, the integration and analysis of multi-source data to achieve real-time monitoring, early warning assessment and intelligent decision-making of waterlogging disasters has become a research hotspot in agricultural disaster management. However, the existing technology still lacks a comprehensive method that effectively integrates geographic information data, soil information data, meteorological data, satellite remote sensing data and historical statistical data to accurately assess the waterlogging disaster risks of different crops and conduct graded risk zoning. In summary, in the face of the above-mentioned practical needs, research status and development trends, a three-dimensional dynamic monitoring and assessment method and system for waterlogging disasters is constructed to provide technical support for crop disaster reduction and production protection under complex underlying surface conditions in the middle and lower reaches of the Yangtze River. Summary of the invention

[0005] In view of the above problems, the present invention proposes a waterlogging disaster monitoring and early warning assessment method based on the integration of Internet of Things and big data, aiming to improve the disaster response capability in agricultural production and ensure food security. Based on geographic information data, soil information data, meteorological data, satellite remote sensing data such as MODIS, historical statistical data (planted area, yield), and waterlogging disaster model library and other data, the sensitivity of different disaster-prone environments, the risk of disaster-causing factors, and the vulnerability of disaster-bearing bodies of rice, wheat, and rapeseed are calculated, and then a comprehensive risk assessment model for waterlogging of rice, wheat, and rapeseed is constructed, and a graded risk zoning is performed, and then the calculation of crop waterlogging disaster parameters is completed through the PIE-Engine computing service platform.

[0006] According to a first aspect of an embodiment of the present invention, a method for monitoring and early warning of waterlogging disasters integrating Internet of Things and big data is provided.

[0007] In one or more embodiments, preferably, the IoT big data integrated waterlogging disaster monitoring and early warning assessment method includes:

[0008] Acquire multi-source data through IoT devices and sensors, establish a data integration platform, and clean, process and store multi-source data, including geographic information data, soil information data, meteorological data, satellite remote sensing data and historical statistical data;

[0009] Calculate the disaster-prone environmental sensitivity, disaster-causing factor risk and disaster-bearing body vulnerability of rice, wheat and rapeseed based on the multi-source data;

[0010] A comprehensive risk assessment model for waterlogging disasters is constructed using disaster factors, and risk zoning is performed based on the geographic information system;

[0011] Upload disaster-related parameters to the computing service platform for model simulation, and then optimize and update the waterlogging disaster risk assessment model;

[0012] A technical platform for the prevention, forecasting, early warning and pre-control of crop waterlogging disasters is constructed based on the waterlogging disaster risk assessment model and graded risk zoning.

[0013] In one or more embodiments, preferably, multi-source data is acquired through IoT devices and sensors, a data integration platform is established, and the multi-source data is cleaned, processed and stored. The multi-source data includes geographic information data, soil information data, meteorological data, satellite remote sensing data and historical statistical data. Specifically, the meteorological data, soil moisture data and terrain data are acquired in real time using IoT devices, and the meteorological data, soil moisture data and terrain data are integrated and analyzed with satellite remote sensing data and historical planting data.

[0014] In one or more embodiments, preferably, the disaster factor calculation step includes using historical data to perform model training and optimization to improve the accuracy of calculating the sensitivity of the disaster-prone environment, the hazard of the disaster-causing factors, and the vulnerability of the disaster-bearing body.

[0015] In one or more embodiments, preferably, the step of constructing a comprehensive risk assessment model for waterlogging disasters using the disaster factors and performing risk zoning based on a geographic information system includes using a waterlogging disaster model library and map to assess the comprehensive risk of waterlogging disasters for rice, wheat and rapeseed, and generating a disaster risk map.

[0016] In one or more embodiments, preferably, the step of uploading disaster-related parameters to a computing service platform, performing model simulation, and then optimizing and updating the waterlogging disaster risk assessment model includes completing waterlogging disaster parameter calculation and model simulation through cloud computing, and updating the disaster risk assessment model in real time.

[0017] In one or more embodiments, preferably, the step of constructing a technical platform for the prevention, forecasting, warning and pre-control of crop waterlogging disasters based on the waterlogging disaster risk assessment model and grade risk zoning includes establishing a disaster warning system, an emergency response system and a post-disaster recovery support system based on the results provided by the risk assessment model to achieve all-round prevention and control of waterlogging disasters.

[0018] In one or more embodiments, preferably, the IoT big data integrated waterlogging disaster monitoring and early warning assessment method also includes using big data analysis to generate disaster response recommendations and management strategies.

[0019] In one or more embodiments, preferably, the data integration platform of the waterlogging disaster monitoring and early warning assessment method integrating Internet of Things and big data adopts distributed database technology for efficient processing and rapid access to data, and supports the storage and analysis of massive data.

[0020] In one or more embodiments, preferably, the IoT big data integrated waterlogging disaster monitoring and early warning assessment method also supports real-time monitoring and dynamic adjustment, and dynamically updates disaster assessment results based on the latest meteorological and soil data.

[0021] According to a second aspect of an embodiment of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the methods described in the first aspect of the embodiment of the present invention.

[0022] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0023] In the scheme of the present invention, based on geographic information data, soil information data, meteorological data, satellite remote sensing data such as MODIS, historical statistical data (planted area, yield), and waterlogging disaster model library and other data, the sensitivity of different disaster-prone environments, the danger of disaster-causing factors, and the vulnerability of disaster-bearing bodies of rice, wheat and rapeseed are calculated, and then a comprehensive risk assessment model for waterlogging damage of rice, wheat and rapeseed is constructed, and hierarchical risk zoning is carried out, and finally the calculation of crop waterlogging disaster parameters is completed for risk assessment and early warning.

[0024] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0025] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 The present invention provides a flow chart of a method for monitoring and early warning of waterlogging disasters based on the integration of Internet of Things and big data.

[0028] Figure 2 It is a schematic diagram of the "four-precaution" prevention and control platform for crop waterlogging disasters in the present invention.

[0029] Figure 3 It is a schematic diagram of the PI E-Engin cloud computing service platform module in the present invention.

[0030] Figure 4 This is a schematic diagram of the crop waterlogging real-time monitoring subsystem in the present invention.

[0031] Figure 5 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0032] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0034] The present invention mainly constructs a waterlogging disaster monitoring and early warning assessment method integrated with the Internet of Things big data. Based on geographic information data, soil information data, meteorological data, satellite remote sensing data such as MODIS, historical statistical data (planted area, yield), and waterlogging disaster model library and other data, the sensitivity of different disaster-prone environments, the risk of disaster-causing factors, and the vulnerability of disaster-bearing bodies of rice, wheat, and rapeseed are calculated, and then a comprehensive waterlogging risk assessment model for rice, wheat, and rapeseed is constructed, and hierarchical risk zoning is performed. Then, the scientific calculation of crop waterlogging disaster parameters is completed through the PIE-Engine computing service platform.

[0035] The detailed technical scheme of the present invention is as follows:

[0036] Step 1: Develop a processing module that integrates projection processing, spatiotemporal interpolation, outlier processing, and boundary clipping in the three-dimensional dynamic monitoring and intelligent assessment system of waterlogging disasters to pre-process the driving data entering the system. Construct a model library of waterlogging disasters for different varieties of rice, wheat, and rapeseed (waterlogging-tolerant and waterlogging-intolerant varieties) and different growth stages (jointing-booting stage, heading-maturity stage for rice and wheat, and budding stage, flowering-maturity stage for rapeseed).

[0037] Step 2: Based on geographic information data, soil information data, meteorological data, satellite remote sensing data such as MODIS, historical statistical data (planted area, yield), and waterlogging disaster model library, calculate the sensitivity of different disaster-prone environments, the risk of disaster-causing factors, and the vulnerability of disaster-bearing bodies of rice, wheat, and rapeseed, and then build a comprehensive risk assessment model for waterlogging of rice, wheat, and rapeseed, and perform graded risk zoning. The calculation formula of the overall waterlogging disaster risk assessment model is as follows:

[0038] D r =D s ×D h ×D v (1)

[0039] Among them, Dr is the risk assessment value, Ds, Dh, and Dv represent the vulnerability of the disaster-prone body, the hazard of the disaster-causing factor, and the sensitivity assessment value of the disaster-prone environment, respectively.

[0040] 1) Assessment of the vulnerability of disaster-prone bodies. The comprehensive assessment model of the vulnerability of disaster-prone bodies includes the physical exposure of disaster-prone bodies and the regional disaster response capacity:

[0041] D v =w3×V e +w4×(1-V d ) (2)

[0042] In the formula, the exposure of the disaster-prone body (Ve) is expressed by the ratio of the winter wheat or rapeseed planting area in each county of the study area to the cultivated land area extracted by MODIS; the regional disaster resilience (Vd) is the ratio of the crop yield per unit area of ​​the evaluation unit to the average yield per unit area of ​​each evaluation unit in the entire study area, and w3 and w4 are the preset weight ratios.

[0043] 2) Hazard evaluation of disaster-causing factors. In natural disaster risk assessment, climate probability is often used to describe the possibility of disasters. First, calculate the Q value of waterlogging damage, analyze the probability distribution of the Q value, and finally calculate the hazard of the disaster-causing factors:

[0044]

[0045] In the formula, To represent the integral of QW at different locations, Qw is the waterlogging index of different levels of crops.

[0046] 3) Evaluation of disaster-prone environmental sensitivity. The comprehensive evaluation model of disaster-prone environmental sensitivity is:

[0047]

[0048] Where, L l is the low-lying analysis index, Sw is the waterlogged soil index, Slope is the slope, w1 and w2 are the weights of different factors obtained by the hierarchical analysis method.

[0049] 4) Calculation of spatial distribution of soil waterlogging damage index: using standardized soil sand percentage data (SNDDPPT) and clay percentage data (CLYPPT) at different depths (0-5cm, 5-15cm, 15-30cm) with a resolution higher than 1km, extracting clay and sand percentage data at different depths, and calculating the average value. The formula for soil waterlogging damage index expressed in clay and sand percentage is as follows:

[0050]

[0051] In the formula, Pclay is the percentage of clay particles, Psand is the percentage of sand particles, and the smaller the Sw value is, the sandier the soil is and the better the soil permeability is. Conversely, the larger the value is, the stronger the soil viscosity is, the worse the soil permeability is, and the more likely it is to cause waterlogging.

[0052] Based on the established waterlogging disaster monitoring index model for rice, wheat and rapeseed, the relationship between the cumulative index value and the yield loss rate on the growing season scale is fitted to establish a loss assessment model for waterlogging disasters. Then, according to the actual time of waterlogging disasters, the cumulative values ​​of waterlogging disaster indicators before this time in the growing seasons of different crops are calculated and substituted into the established waterlogging disaster loss assessment model to dynamically assess the yield loss rate caused by waterlogging disasters.

[0053] From crop sowing (transplanting) to the end of the growth period, the cumulative sum of all waterlogging index DWI values ​​is the accumulated waterlogging index AWI (Accumulated Waterlogging Index), and the calculation formula is:

[0054]

[0055] Where DWIi is the index of days of waterlogging damage to wheat from sowing to harvesting. It represents the cumulative sum of the DWI values ​​on all affected days.

[0056] For different types of crops, the DWI and yield reduction rate of 3 / 4 waterlogging stress samples in the experiment were randomly selected for regression fitting to obtain the damage assessment curves of different crops, and then the regression curves were verified using the growth test data.

[0057] For the verified disaster loss curve, the AWI from the day of crop emergence (transplanting) to the actual occurrence of waterlogging disaster is calculated, and then brought into the curve to dynamically calculate the predictive evaluation of the yield disaster loss rate.

[0058] According to different types of output data, ArcGIS is used to produce a thematic map output template file library that integrates unified base map, scale, boundary, legend, color tone, grade classification standard and other elements. Then, a specific interface is set in the three-dimensional dynamic monitoring and intelligent assessment system of waterlogging disasters to uniformly produce thematic maps for the output data.

[0059] The algorithm is developed, compiled and module-encapsulated by using a unified programming language and development environment, and then integrated into the three-dimensional dynamic monitoring and intelligent assessment system of waterlogging disasters using a unified interface calling method, so that the system structure can be simpler and the operation efficiency can be more efficient.

[0060] Step 3: Develop a technical platform for the “four-precaution” prevention and control of crop waterlogging disasters. Figure 2 As shown, Figure 2 This is a schematic diagram of the "four-prediction" prevention and control platform for crop waterlogging disasters in the present invention. The "four-prediction" prevention and control technology platform mainly includes a forecast module, an early warning module, a rehearsal module, and a plan module. The specific modules are as follows:

[0061] 1) Forecast: a. Meteorological forecast information display, by gathering the precipitation forecast map released by the meteorological department for free sharing through the Internet collected by the meteorological bureau or the system, continuously strengthen the monitoring, reporting and analysis of real-time rain and water information, and use hydrological and meteorological coupling, big data, artificial intelligence and other technologies to provide forecasts of heavy rain, strong winds, blizzards and other meteorological information and three-dimensional hologram display, and display the dynamic changes of meteorological information in real time through dynamic playback. b. Refined rainfall grid forecast information display, through 5km refined rainfall grid query, provide 5km spatial resolution, 1 hour time resolution, and 7 days of forecast time. The function can realize the query and animation playback of rainfall grid forecast map. c. Short-term forecast information display, by clicking on the map to select the short-term forecast location, the rainfall trend of the place in the next 2 hours can be predicted in real time. Provide the function of forecasting the rainfall trend in the next 2 hours and displaying the three-dimensional hologram. d. Display of waterlogging forecast information to achieve real-time tracking of waterlogging information; relying on system integration models and waterlogging forecast and warning information, it can predict waterlogging risks in advance and improve risk identification and warning capabilities.

[0062] 2) Early warning: a. Real-time early warning: by connecting to the IoT sensing monitoring platform, the early warning data can be quickly identified and extracted through the early warning threshold rules, and the early warning information can be summarized on the large-screen visualization homepage. The early warning data includes rain warning, water warning, waterlogging warning, dangerous warning, water quality warning, water volume warning, engineering operation warning, etc. And realize the functions of early warning information statistics and early warning point viewing. b. Forecast and early warning: by connecting to hydrological and meteorological forecast simulation models, the forecast information of rainfall, water conditions, etc. in the next 8 to 12 hours can be quickly extracted, organized and statistically analyzed, and future forecast and early warning information can be identified, supporting the display and release of forecast and early warning information. c. Rehearsal: for early warning risks, intelligent simulation is carried out based on forecast information. By integrating and coupling intelligent simulation results, the simulation results of the water and rain operation process are simulated and displayed with three-dimensional simulation rendering technology, including rainfall process, runoff generation and confluence process, flood evolution process, waterlogging inundation process, etc., to provide support for waterlogging command and dispatch and command and drainage operation.

[0063] 3) Plans: a. Dispatch plans, combined with the rehearsal results and the existing plan structured results, generate reference plans for command and dispatch. Supports preview and download of plans. b. Dispatch instruction management, supports pushing the identified dispatch plans and instructions to the members of the flood control dispatch working group in the form of SMS, guides flood control personnel to execute dispatch instructions, and supports flood control personnel to upload the execution results of instructions to the system. Managers can check the person who completed the dispatch instruction, the completion time, the completion status, etc. The system realizes the SMS sending of dispatch instructions by connecting to the SMS gateway / SMS machine. System staff can select the recipient, SMS template or customize the recipient and edit the SMS content according to the actual situation to issue SMS dispatch instructions.

[0064] 4) Disaster rescue: Support APP to receive dangerous information, and through integration with GIS map, realize the rapid location and display of event information for the staff of the disaster prevention and control office to view, including disaster risk reporting, dangerous information, and disposal measures.

[0065] Step 4: Use the PI E-Engin ne platform computing service platform to complete the scientific calculation of crop waterlogging disaster parameters. Figure 3The figure is a schematic diagram of the PI E-Engin cloud computing service platform module in the present invention. The PI E-Engin remote sensing cloud platform is developed based on cloud computing, Internet of Things, big data, artificial intelligence and other technologies. Relying on the basic environment of the cloud platform, it integrates and runs the sustainable and updated spatiotemporal high-precision data and the three-dimensional dynamic monitoring and evaluation index model of waterlogging disasters in a standardized manner, and builds a remote sensing application service platform covering the entire process of "collection, storage, calculation, management and use" of spatiotemporal data. It provides users with a one-stop remote sensing application service of "cloud + terminal", giving full play to the advantages of high efficiency, low threshold, low cost and easy access, and providing online multi-source remote sensing satellite image data services, remote sensing data production and processing services, and remote sensing intelligent interpretation and analysis services.

[0066] By combining massive satellite remote sensing images, geographic element data and other spatial data, the multi-source database of spatiotemporal collaborative observation, the spatiotemporal high-precision standardized data product library, the waterlogging disaster monitoring and assessment indicator model library, and the waterlogging disaster monitoring and assessment thematic map product library are integrated. Based on the platform, algorithm models can be studied at any scale and interactive programming verification can be adopted to achieve three-dimensional dynamic monitoring and intelligent assessment of waterlogging disasters. Flexible and elastic computing services are a subversive tool compared to traditional remote sensing processing methods.

[0067] Step 5. The real-time monitoring subsystem for crop waterlogging is a comprehensive monitoring solution developed for different stages of waterlogging disasters, including four modules: a waterlogging monitoring map, regular monitoring before disasters, dynamic monitoring during disasters, and intelligent evaluation after disasters. Figure 4 This is a schematic diagram of the crop waterlogging real-time monitoring subsystem in the present invention, and the specific core structure is as follows:

[0068] The waterlogging monitoring map is an important part of the crop waterlogging real-time monitoring subsystem. It uses geographic information system (GIS) technology to integrate various monitoring data on a single map, clearly showing information such as the affected area, crop distribution, and water distribution. Through this map, decision makers can intuitively understand the distribution and impact range of waterlogging disasters, which helps to formulate disaster response strategies and resource allocation. In addition, the waterlogging monitoring map can also realize the superposition and comparison of multi-source data, helping decision makers to comprehensively assess the disaster situation, accurately judge the disaster level, and take timely response measures to minimize the losses caused by the disaster.

[0069] The specific process includes: 1) Identification and positioning of disaster-affected areas. A waterlogging monitoring map should be able to accurately identify and locate the disaster-affected areas. Through marks or color blocks on the map, the scope and distribution of the waterlogged areas can be clearly displayed, so that decision makers can intuitively understand the extent of the disaster and take timely countermeasures. 2) Crop planting distribution monitoring. In addition to the disaster-affected areas, a waterlogging monitoring map should also be able to display the distribution of crop planting. Through image analysis or remote sensing data, the distribution of crops, including wheat, rice, rapeseed and other information, is marked on the map, which helps to assess the extent of crop damage and losses. 3) Water body distribution and water level monitoring. A waterlogging monitoring map should also include information on water body distribution and water level monitoring. This can be obtained through satellite remote sensing data or real-time water level monitoring station data to analyze the distribution of water bodies, water level change trends, etc., to help decision makers better understand the formation mechanism and development trend of waterlogging disasters. 4) Multi-source data overlay and comparison. This function not only includes integrating different types of monitoring data on one map, but also should be able to overlay and compare data. By overlaying data from different time points or different sources on the map, the development and changes of the disaster situation can be intuitively compared, providing more comprehensive information support for decision makers. 5) Disaster assessment and level judgment. Based on the information on the map, a waterlogging monitoring map should also have the function of assessing and level judgment of the disaster situation. According to factors such as the scope of the disaster-stricken area, the degree of crop damage, and the distribution of water bodies, the disaster level is automatically or manually divided and evaluated to provide a reference for the formulation of disaster response strategies. 6) Display of response measures and resource allocation. Finally, a waterlogging monitoring map should also include the display of response measures and resource allocation. Marking information such as the disaster level, disaster-stricken area, and emergency material storage points on the map will help decision makers to allocate resources and organize rescue in a timely manner to minimize the losses caused by disasters.

[0070] A waterlogging monitoring map can comprehensively display the situation of waterlogging disasters, help decision makers more accurately assess the disaster situation and formulate response strategies, thereby minimizing the losses caused by the disaster.

[0071] Pre-disaster regular monitoring is the regular monitoring stage before a disaster occurs. Through continuous monitoring of indicators such as crop growth status, soil moisture, and surface water conditions, a baseline state of crop growth is established. This helps to promptly detect abnormal conditions, such as stunted crop growth and excessive soil moisture, and provides basic data support for disaster prevention and early warning. By comparing and analyzing with historical data, abnormal conditions can be identified in a timely manner, and corresponding management measures can be taken to reduce the risk of disasters.

[0072] The specific process includes: 1) Crop growth status monitoring. Crop growth status is one of the core contents of routine monitoring before disasters. By monitoring crop growth, growth rate, leaf color, plant height and other indicators, we can fully understand the growth status and health of crops. This includes monitoring and comparing the growth characteristics of different crop varieties so as to detect abnormal conditions in time and take corresponding measures. 2) Soil moisture monitoring. Soil moisture is one of the important factors affecting crop growth. By monitoring soil moisture, we can understand the moisture content and distribution of the soil and detect excessive soil moisture or drought in time. This can be monitored in real time through equipment such as soil moisture sensors, or remote sensing technology can be used to obtain spatial distribution information of soil moisture. 3) Surface water monitoring. Surface water monitoring refers to monitoring the water level, flow rate, water quality and other conditions of surface water. For areas where waterlogging disasters may occur, it is necessary to focus on monitoring the surface water situation, detect the rise of surface water level and overflow in time, and prevent the occurrence of waterlogging disasters. This can be monitored through hydrological stations, water level meters, water quality monitoring equipment, etc., or satellite remote sensing technology can be used to obtain spatial distribution information of surface water conditions. 4) Comparative analysis of historical data. Regular monitoring before a disaster needs to be combined with historical data for comparative analysis. By reviewing and analyzing historical monitoring data, we can understand the long-term trend and seasonal change patterns of indicators such as crop growth, soil moisture, and surface water conditions, and identify the characteristics and patterns of abnormal situations. This helps to timely discover and warn of possible disaster risks. 5) Identification and warning of abnormal situations. The ultimate goal of regular monitoring before a disaster is to timely discover abnormal situations and issue warnings. When the monitoring data shows that it is inconsistent with historical data or exceeds the normal range, the system should be able to automatically issue a warning signal to remind relevant departments or decision makers to pay attention to possible disaster risks and take corresponding prevention and response measures to reduce the losses caused by disasters.

[0073] Dynamic monitoring during a disaster is a real-time monitoring phase during a disaster. It mainly monitors the crop damage, surface water level changes and other dynamic information in real time, and combines meteorological forecast data to analyze the development trend of the disaster. Monitoring at this stage helps to grasp the development of the disaster in a timely manner, predict the possible development trend of the disaster, and provide a scientific basis for disaster response and relief work. Through dynamic monitoring during a disaster, relevant departments can quickly adjust their response strategies and take targeted rescue actions to minimize the losses caused by the disaster. The specific process includes: 1) Monitoring of crop damage. During a disaster, it is crucial to monitor the crop damage in real time. This includes monitoring the lodging, waterlogging, water stress and other conditions of crops, as well as the extent to which crops are affected by pests and diseases. Through real-time monitoring data collected, the extent of crop damage can be understood in a timely manner, providing an important basis for post-disaster rescue and recovery. 2) Monitoring of surface water level changes. Changes in surface water levels have an important impact on the development and spread of disasters. By monitoring the changes in surface water levels in real time, information such as the direction of movement, flow rate and depth of the water body can be obtained, and the scope and extent of possible flooding can be predicted. This can be monitored through hydrological stations, water level gauges and other equipment, or by using remote sensing technology to obtain spatial distribution information of water bodies. 3) Combined with meteorological forecast data, dynamic monitoring during disasters also needs to be analyzed in combination with meteorological forecast data. Meteorological factors are one of the important factors affecting the occurrence and development of disasters, including rainfall, wind direction and speed, temperature, etc. By obtaining and analyzing meteorological forecast data in real time, it is possible to predict meteorological changes and disaster risks that may occur in the future, which helps to adjust response strategies and take preventive measures in a timely manner. 4) Analysis of disaster development trends. Based on real-time monitoring data and meteorological forecast data, disaster development trend analysis is one of the important contents of dynamic monitoring during disasters. By analyzing the changing trends and development trends of the disaster situation, the possible development direction, scope and intensity of the disaster can be predicted, providing a scientific basis for disaster response and rescue work. 5) Adjustment of response strategies and rescue actions. The ultimate goal of dynamic monitoring during disasters is to provide timely information support to relevant departments and rescue personnel, help them adjust their response strategies and take targeted rescue actions. Through real-time monitoring data and development trend analysis results, disaster response strategies can be adjusted in a timely manner, the implementation of rescue operations can be guided, and the losses caused by disasters can be minimized.

[0074] Post-disaster intelligent assessment is the assessment phase after a disaster, which mainly uses intelligent algorithms to assess the losses caused by the disaster. By analyzing disaster survey data and post-disaster monitoring data, the intelligent assessment system can quickly and accurately assess key indicators such as the affected area, crop losses, and land degradation, and provide guidance and support for post-disaster recovery and reconstruction. Through post-disaster intelligent assessment, relevant departments can promptly understand the actual impact of the disaster, formulate targeted recovery and reconstruction plans, and help the affected areas resume normal production and living order as soon as possible.

[0075] The specific process includes: 1) Refinement of loss assessment indicators. In post-disaster intelligent assessment, loss assessment indicators need to be refined to a more specific level. In addition to the affected area and crop losses, the degree of land degradation, damage to buildings, damage to water conservancy facilities, casualties and other losses can also be assessed. Through the detailed assessment of different loss indicators, we can have a more comprehensive understanding of the impact of disasters and provide more targeted support for post-disaster recovery and reconstruction. 2) Application of intelligent algorithms. Post-disaster intelligent assessment requires the use of intelligent algorithms to quickly and accurately assess the losses caused by disasters. Common intelligent algorithms include machine learning algorithms, deep learning algorithms, and data mining algorithms. Through the analysis of disaster survey data and post-disaster monitoring data, intelligent algorithms can automatically identify and extract key information, realize intelligent assessment of various loss indicators, and greatly improve the efficiency and accuracy of assessment. 3) Data fusion and multi-source information integration. Post-disaster intelligent assessment needs to make full use of multi-source data for comprehensive assessment. This includes the use of multiple data sources such as remote sensing image data, ground survey data, satellite monitoring data, and manual survey data to achieve data fusion and information integration. Through the comprehensive analysis of multi-source information, the losses caused by disasters can be more comprehensively assessed, providing more scientific and accurate guidance for post-disaster recovery and reconstruction. 4) Visualization of assessment results and report generation. The results of post-disaster intelligent assessment need to be presented to relevant departments and decision makers in an intuitive form. Therefore, the assessment system should have the function of visualizing the results, and can present the assessment results in the form of charts, maps, reports, etc. This helps decision makers to understand the disaster and losses more intuitively, so as to formulate corresponding recovery and reconstruction plans. 5) Predictive assessment and risk analysis. Post-disaster intelligent assessment can also perform predictive assessment and risk analysis. By analyzing the post-disaster loss indicators, it is possible to predict the disaster risks that may occur in the future, and take corresponding preventive and response measures in advance. At the same time, it is also possible to analyze the risks and potential impacts of different recovery and reconstruction plans, and provide a scientific basis for decision-making on post-disaster recovery and reconstruction.

[0076] The crop waterlogging real-time monitoring subsystem can provide comprehensive monitoring and assessment before, during and after waterlogging disasters occur, enabling relevant departments to respond to disasters in a timely manner and reduce the losses caused by disasters. The subsystem provides scientific disaster monitoring and assessment results to decision makers by comprehensively utilizing multi-source monitoring data and intelligent algorithms, helping them to formulate reasonable disaster prevention and mitigation measures and response strategies. The establishment of the crop waterlogging real-time monitoring subsystem can improve the efficiency and accuracy of disaster response, effectively reduce agricultural production risks, and ensure food security and farmers' livelihood stability.

[0077] According to a second aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0078] According to a third aspect of an embodiment of the present invention, an electronic device is provided. Figure 5 It is a structural diagram of an electronic device in one embodiment of the present invention. Figure 5 The electronic device shown is a general disaster monitoring and early warning assessment device. Figure 5 The electronic device may be a smart phone, a tablet computer, or the like. The electronic device 500 includes a processor 501 and a memory 502. The processor 501 is electrically connected to the memory 502.

[0079] The processor 501 is the control center of the electronic device 500. It uses various interfaces and lines to connect various parts of the entire electronic device. It executes various functions of the electronic device and processes data by running or calling computer programs stored in the memory 502, and calling data stored in the memory 502, thereby monitoring the electronic device as a whole.

[0080] In this embodiment, the processor 501 in the electronic device 500 will load the instructions corresponding to the processes of one or more computer programs into the memory 502 according to the following steps, and the processor 501 will run the computer programs stored in the memory 502 to implement various functions.

[0081] In some embodiments, the electronic device 500 may further include: a display 503, a radio frequency circuit 504, an audio circuit 505, a wireless fidelity module 506, and a power supply 507. The display 503, the radio frequency circuit 504, the audio circuit 505, the wireless fidelity module 506, and the power supply 507 are electrically connected to the processor 501, respectively.

[0082] The display 503 may be used to display information input by a user or information provided to a user and various graphical user interfaces, which may be composed of graphics, text, icons, videos, and any combination thereof. The display 503 may include a display panel, and in some embodiments, the display panel may be configured in the form of a liquid crystal display (LCD) or an organic light-emitting diode (OLED).

[0083] The radio frequency circuit 504 can be used to send and receive radio frequency signals, so as to establish wireless communication with a network device or other electronic devices through wireless communication, and to send and receive signals between the network device or other electronic devices.

[0084] The audio circuit 505 can be used to provide an audio interface between a user and the electronic device through a speaker and a microphone.

[0085] The wireless fidelity module 506 can be used for short-range wireless transmission, and can help users to send and receive emails, browse websites, and access streaming media, etc. It provides users with wireless broadband Internet access.

[0086] The power supply 507 can be used to supply power to various components of the electronic device 500. In some embodiments, the power supply 507 can be logically connected to the processor 501 through a power management system, so that the power management system can manage charging, discharging, power consumption and other functions.

[0087] although Figure 5 Not shown, the electronic device 500 may also include a camera, a Bluetooth module, etc., which will not be described in detail here.

[0088] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0089] In the scheme of the present invention, based on geographic information data, soil information data, meteorological data, satellite remote sensing data such as MODIS, historical statistical data (planted area, yield), and waterlogging disaster model library and other data, the sensitivity of different disaster-prone environments, the danger of disaster-causing factors, and the vulnerability of disaster-bearing bodies of rice, wheat and rapeseed are calculated, and then a comprehensive risk assessment model for waterlogging damage of rice, wheat and rapeseed is constructed, and hierarchical risk zoning is carried out, and finally the calculation of crop waterlogging disaster parameters is completed for risk assessment and early warning.

[0090] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0091] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0094] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A waterlogging disaster monitoring and early warning assessment method based on the integration of Internet of Things and big data, characterized in that: The method includes: Acquire multi-source data through IoT devices and sensors, establish a data integration platform, and clean, process and store multi-source data, including geographic information data, soil information data, meteorological data, satellite remote sensing data and historical statistical data; Calculate the disaster-prone environmental sensitivity, disaster-causing factor risk and disaster-bearing body vulnerability of rice, wheat and rapeseed based on the multi-source data; A comprehensive risk assessment model for waterlogging disasters is constructed using disaster factors, and risk zoning is performed based on the geographic information system; Upload disaster-related parameters to the computing service platform for model simulation, and then optimize and update the waterlogging disaster risk assessment model; A technical platform for the prevention, forecasting, early warning and pre-control of crop waterlogging disasters is constructed based on the waterlogging disaster risk assessment model and graded risk zoning.

2. The method for monitoring and early warning of waterlogging disasters based on the Internet of Things and big data integration as claimed in claim 1, characterized in that: The method obtains multi-source data through IoT devices and sensors, establishes a data integration platform, and cleans, processes and stores the multi-source data. The multi-source data includes geographic information data, soil information data, meteorological data, satellite remote sensing data and historical statistical data. Specifically, it uses IoT devices to obtain meteorological data, soil moisture data and terrain data in real time, and integrates and analyzes the meteorological data, soil moisture data and terrain data with satellite remote sensing data and historical planting data.

3. The method for monitoring and early warning of waterlogging disasters based on the Internet of Things and big data integration as claimed in claim 1, characterized in that: The disaster factor calculation step includes using historical data to train and optimize the model to improve the accuracy of calculating the sensitivity of the disaster-prone environment, the danger of disaster-causing factors and the vulnerability of the disaster-bearing body.

4. The method for monitoring and early warning of waterlogging disasters based on the Internet of Things and big data integration as claimed in claim 1, characterized in that: The step of constructing a comprehensive risk assessment model for waterlogging disasters using the disaster factors and performing risk zoning based on a geographic information system includes using a waterlogging disaster model library and a map to assess the comprehensive risk of waterlogging disasters for rice, wheat and rapeseed, and generating a disaster risk map.

5. The method for monitoring and early warning of waterlogging disasters based on the Internet of Things and big data integration as claimed in claim 1, characterized in that: The steps of uploading disaster-related parameters to the computing service platform, performing model simulation, and then optimizing and updating the waterlogging disaster risk assessment model include completing waterlogging disaster parameter calculation and model simulation through cloud computing, and updating the disaster risk assessment model in real time.

6. The method for monitoring and early warning of waterlogging disasters based on the Internet of Things and big data integration as claimed in claim 1, characterized in that: The steps of constructing a technical platform for the prevention, forecasting, early warning and pre-control of crop waterlogging disasters based on the waterlogging disaster risk assessment model and graded risk zoning include establishing a disaster early warning system, an emergency response system and a post-disaster recovery support system based on the results provided by the risk assessment model to achieve all-round prevention and control of waterlogging disasters.

7. The method for monitoring and early warning of waterlogging disasters based on the Internet of Things and big data integration as claimed in claim 1, characterized in that: It also includes the use of big data analysis to generate disaster response recommendations and management strategies.

8. The method for monitoring and early warning of waterlogging disasters based on the Internet of Things and big data integration as claimed in claim 1, characterized in that: The data integration platform adopts distributed database technology for efficient data processing and rapid access, and supports the storage and analysis of massive data.

9. The method for monitoring and early warning of waterlogging disasters based on the Internet of Things and big data integration as claimed in claim 1, characterized in that: It also supports real-time monitoring and dynamic adjustment, and dynamically updates disaster assessment results based on the latest meteorological and soil data.

10. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1-9.

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