Farmland waterlogging disaster monitoring cloud platform

Abstract

The invention discloses a farmland waterlogging disaster monitoring cloud platform with a data base plate, a model platform and a knowledge base. According to the platform, a farmland waterlogging disaster data base plate based on a space-air-ground intelligent sensing network is constructed based on a space-air-ground space-time high-precision standardized data set, an agricultural knowledge base composed of ten databases is integrated, and the farmland waterlogging disaster data base plate is constructed by utilizing technologies such as machine learning and deep learning. Model platforms such as an agricultural disaster prevention and control platform, an agricultural disaster monitoring platform, an agricultural disaster evaluation platform and an agricultural disaster early warning platform are constructed, and a farmland waterlogging disaster monitoring cloud platform with a data base plate, a model platform and a knowledge base is formed. Through combination of the data base plate, the model platform and the knowledge base, the platform can realize accurate monitoring, intelligent early warning and scientific evaluation of farmland waterlogging disasters, and provides efficient disaster management and prevention and control solutions for agricultural production.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural disaster monitoring and prevention and control, and more specifically, to a farmland waterlogging disaster monitoring cloud platform. Background Art

[0002] Waterlogging monitoring is an important guarantee for waterlogging removal, waterlogging reduction and disaster reduction. The monitoring of farmland waterlogging disasters is mainly based on meteorological stations, satellite remote sensing and field survey data, and the occurrence and severity of disasters are judged and analyzed according to the corresponding indicators of waterlogging disasters. Among them, waterlogging disaster monitoring indicators and levels are the key to realizing the "four prevention" prevention and control technology of crop waterlogging disasters. Existing studies have shown that disaster-causing factors such as precipitation in the middle and lower reaches of the Yangtze River are significantly correlated with the crop yield reduction rate. The national and industry standards formed provide a reference for waterlogging disaster monitoring and evaluation. In recent years, the disaster-pregnant environment, formation mechanism, evolution law, disaster risk and disaster loss of crop waterlogging disasters have continued to present new characteristics and new problems.

[0003] Traditional waterlogging disaster monitoring methods mainly rely on manual observation and simple meteorological data analysis. Due to the limitations of data accuracy and multi-source data fusion technology, they often have low timeliness and accuracy, and are difficult to meet the needs of dynamic and accurate monitoring and intelligent assessment of crop waterlogging disasters under complex underlying surface conditions. With the development of the Internet of Things, remote sensing technology, big data and artificial intelligence technology, it is possible to build an intelligent and information-based farmland waterlogging disaster monitoring and prevention and control system. The existing technology still lacks a comprehensive cloud platform that can integrate multiple data sources and technical means for monitoring, assessment, prevention and control, and early warning of waterlogging disasters. It is urgent to couple the new generation of "satellite screening-low-altitude detailed inspection-ground detailed inspection" spatiotemporal collaborative monitoring methods, carry out three-dimensional dynamic monitoring technology and product research and development of crop waterlogging disasters, and build a multi-source data fusion crop waterlogging disaster intelligent assessment system. It is urgent to build a cloud platform for monitoring farmland waterlogging disasters. Based on the high-precision standardized space-time data set of air, space, and land, a data base for farmland waterlogging disasters based on the air-space-ground intelligent perception network is constructed. Combined with an agricultural knowledge base consisting of 10 databases, as well as machine learning, deep learning and other technologies, model platforms such as agricultural disaster prevention and control platforms, agricultural disaster monitoring platforms, agricultural disaster assessment platforms, and agricultural disaster early warning platforms are constructed. Summary of the invention

[0004] In view of the above problems, the present invention proposes a farmland waterlogging disaster monitoring cloud platform to solve the problem of insufficient farmland waterlogging disaster monitoring and prevention in the prior art. The platform integrates high-precision standardized data sets of space, space, and time, intelligent perception network, agricultural knowledge base, and advanced machine learning and deep learning models to form a comprehensive platform integrating monitoring, early warning, evaluation, and prevention and control. The technical problem to be solved by the present invention is to solve how to establish a waterlogging disaster monitoring cloud platform. The present invention discloses a farmland waterlogging disaster monitoring cloud platform. Based on high-precision standardized data sets of space, space, and time, a farmland waterlogging disaster data base based on the space, space, and time intelligent perception network is constructed, and an agricultural knowledge base composed of 10 databases is combined with machine learning, deep learning and other technologies to construct agricultural disaster prevention and control platforms, agricultural disaster monitoring platforms, agricultural disaster assessment platforms, agricultural disaster early warning platforms and other model platforms to form a "data base + model platform + knowledge base" farmland waterlogging disaster monitoring cloud platform.

[0005] According to a first aspect of an embodiment of the present invention, a cloud platform for monitoring farmland waterlogging disasters is provided.

[0006] In one or more embodiments, preferably, the farmland waterlogging disaster monitoring cloud platform includes:

[0007] The data base is used to provide basic data for farmland waterlogging disaster analysis and monitoring. The basic data in the data base specifically integrates satellite remote sensing data, drone aerial data, ground sensor data and meteorological data;

[0008] Agricultural knowledge base, used to provide information and data support related to agricultural production, including crop growth database, meteorological database, soil database, hydrological database, historical disaster database, agricultural management database, pest and disease database, fertilization management database, irrigation management database and agricultural product market database;

[0009] The model platform is used for real-time monitoring, early warning, assessment and prevention of farmland waterlogging disasters. Based on machine learning and deep learning, it builds agricultural disaster prevention and control platforms, agricultural disaster monitoring platforms, agricultural disaster assessment platforms and agricultural disaster early warning platforms.

[0010] In one or more embodiments, preferably, the farmland waterlogging disaster monitoring cloud platform is characterized in that the data backplane includes an air-space-ground intelligent sensing network for real-time collection and transmission of soil moisture, precipitation, temperature and humidity of farmland.

[0011] In one or more embodiments, preferably, the farmland waterlogging disaster monitoring cloud platform is characterized in that the agricultural knowledge base supports dynamic updating and management of data, and is used to store and call data generated in the agricultural production process.

[0012] In one or more embodiments, preferably, the farmland waterlogging disaster monitoring cloud platform is characterized in that the model platform further comprises:

[0013] Agricultural disaster monitoring platform, used for real-time monitoring and visualization of field waterlogging conditions, remote sensing image processing and spatiotemporal data analysis based on the data base;

[0014] Agricultural disaster early warning platform, which is used to generate early warning information of waterlogging disasters through meteorological prediction models and waterlogging disaster models combined with big data analysis methods;

[0015] Agricultural disaster assessment platform, which uses monitoring data and historical disaster data to assess the impact, loss extent and risk level of waterlogging disasters;

[0016] The agricultural disaster prevention and control platform is used to formulate prevention and control strategies and emergency plans based on early warning and assessment results, including engineering measures, agronomic measures and ecological measures.

[0017] In one or more embodiments, preferably, the cloud platform is integrated through cloud computing technology to achieve multi-user online access and provide data query, monitoring and early warning, risk assessment and prevention and control strategy recommendations.

[0018] In one or more embodiments, preferably, the monitoring, early warning, assessment and prevention and control functions of the farmland waterlogging disaster monitoring cloud platform are configured with the function of modular configuration and expansion according to actual needs.

[0019] In one or more embodiments, preferably, the platform has self-learning capabilities and automatically optimizes the accuracy of disaster monitoring and early warning models through continuous monitoring and historical data accumulation.

[0020] In one or more embodiments, preferably, the platform is capable of data interaction with external data sources and systems for data interconnection and comprehensive analysis.

[0021] In one or more embodiments, preferably, the field waterlogging disaster monitoring cloud platform provides a user interface through a mobile terminal and a PC terminal, supporting users to view real-time monitoring data, receive early warning information, and obtain prevention and control strategies.

[0022] In one or more embodiments, preferably, the field waterlogging disaster monitoring cloud platform also includes an integrated disaster data analysis tool for analyzing historical data and current data to provide future trend forecasts and decision support.

[0023] 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.

[0024] According to a third 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.

[0025] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0026] In the scheme of the present invention, based on the high-precision standardized space-time data set of air-space-ground-earth-space, a farmland waterlogging disaster data base based on the air-space-ground-earth-space intelligent perception network is constructed, and an agricultural knowledge base composed of 10 databases, as well as machine learning, deep learning and other technologies are combined to construct agricultural disaster prevention and control platforms, agricultural disaster monitoring platforms, agricultural disaster assessment platforms, agricultural disaster early warning platforms and other model platforms, forming a farmland waterlogging disaster monitoring cloud platform of "data base + model platform + knowledge base".

[0027] 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.

[0028] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] 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.

[0030] Figure 1 It is a structural diagram of a farmland waterlogging disaster monitoring cloud platform according to an embodiment of the present invention.

[0031] Figure 2 It is a schematic diagram of an agricultural multimodal large model according to an embodiment of the present invention.

[0032] Figure 3 It is a framework diagram of a waterlogging disaster monitoring cloud platform according to an embodiment of the present invention.

[0033] Figure 4 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0034] 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.

[0035] 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.

[0036] The present invention discloses a farmland waterlogging disaster monitoring cloud platform. Based on the high-precision standardized data set of space, air, ground and time, a farmland waterlogging disaster data base based on the space, ground and intelligent perception network is constructed, and an agricultural knowledge base composed of 10 databases, as well as machine learning, deep learning and other technologies are used to construct model platforms such as agricultural disaster prevention and control platforms, agricultural disaster monitoring platforms, agricultural disaster assessment platforms, and agricultural disaster early warning platforms, forming a farmland waterlogging disaster monitoring cloud platform of "data base + model platform + knowledge base".

[0037] like Figure 1 As shown, the farmland waterlogging disaster monitoring cloud platform comprises:

[0038] The data base 101 is used to provide basic data for farmland waterlogging disaster analysis and monitoring. The basic data in the data base specifically integrates satellite remote sensing data, drone aerial data, ground sensor data and meteorological data;

[0039] Agricultural knowledge base 102, used to provide information and data support related to agricultural production, including crop growth database, meteorological database, soil database, hydrological database, historical disaster database, agricultural management database, pest and disease database, fertilization management database, irrigation management database and agricultural product market database;

[0040] Model platform 103 is used for real-time monitoring, early warning, assessment and prevention of farmland waterlogging disasters. It builds agricultural disaster prevention and control platform, agricultural disaster monitoring platform, agricultural disaster assessment platform and agricultural disaster early warning platform based on machine learning and deep learning.

[0041] The detailed technical scheme of the present invention is as follows:

[0042] Step 1: Use Beidou / GNSS (Global Navigation Satellite System) high-precision satellite positioning technology to monitor farmland waterlogging disasters. Its basic working principle is to deploy Beidou monitoring terminals in high-risk areas of farmland waterlogging, and cooperate with diversified data collection equipment such as integrated meteorological stations, video monitoring terminals, soil moisture meters, rain gauges, and water level meters. Before the occurrence of farmland waterlogging disasters, when the water level around the sensors originally installed in a certain area changes, the satellite can sense the water level changes. The background system will combine multiple monitoring data and realize all-weather and fully automatic online monitoring through intelligent comprehensive analysis of big data on the cloud platform. At the same time, the biggest advantage of the Beidou satellite signal transmission channel is stability. It is not restricted by factors such as weather and terrain. The stable Beidou satellite signal transmission channel ensures the timeliness of early warning. When the original GPRS signal of the monitoring point is unstable or poor, the GPRS+Beidou satellite dual-channel transmission will ensure signal transmission to the greatest extent, improve the accuracy of data forecasts and the smoothness of reception, and provide technical support for early warning work.

[0043] Specifically, the execution process is:

[0044] Get the original water level;

[0045] Collect current water level;

[0046] Use the first calculation formula to calculate the water level difference:

[0047] Determine whether the water level difference satisfies the second calculation formula, if so, continue to execute, if not, issue an early warning;

[0048] Determine the packet loss rate of the current GPRS signal. When the packet loss rate exceeds the preset unstable calculation formula, start the Beidou satellite signal transmission;

[0049] The first calculation formula is:

[0050] △L=Lt-L0

[0051] Among them, △L is the water level difference, Lt is the current water level, and L0 is the original water level;

[0052] The second calculation formula is:

[0053] △L≤T

[0054] Where, T is the time comparison margin;

[0055] The instability calculation formula:

[0056] DB>Y

[0057] Among them, DB is the packet loss rate, and Y is the preset maximum packet loss amount.

[0058] Step 2: Based on Beidou / GNSS (Global Navigation Satellite System), combined with UAV remote sensing technology and ground observation stations, build an air-ground-space intelligent perception network for farmland waterlogging disasters. Through this perception network, field survey data, remote sensing data, meteorological data, soil data, vegetation coverage data, geographic data, crop growth data, surface runoff, three-dimensional terrain data and other data can be obtained, thus forming a data base for farmland waterlogging disasters.

[0059] Specifically, suppose that n different data types are collected, and the weight of each data is wi;

[0060] (where i = 1, 2, ..., n), the comprehensive data index I can be calculated by weighted summation: Among them, di represents the specific value of the i-th data type.

[0061] Data from different sources are integrated and processed to form a comprehensive data base for subsequent analysis and decision support.

[0062] Step 3: Build a knowledge platform library. The knowledge platform library mainly includes the global food and agricultural statistics database, the global agricultural experts and germplasm resource library, the national agricultural science database, the major foreign pesticide database, the agricultural land area database, the agricultural resource zoning database, the modern agricultural technology knowledge base, the space-air database, the crop phenotype database, the crop pest and disease database, etc. The above 10 databases basically cover most of the agricultural knowledge bases in the agricultural field.

[0063] Step 4: Build the model platform. Figure 2 As shown, Figure 2 The schematic diagram of the agricultural multimodal large model of an embodiment of the present invention mainly involves the agricultural multimodal large model, machine learning, deep learning, artificial intelligence, crop growth model, etc. The agricultural multimodal large model, machine learning, deep learning, artificial intelligence, crop growth model and other technologies are combined to form an agricultural disaster prevention and control platform, an agricultural disaster monitoring platform, an agricultural disaster assessment platform, and an agricultural disaster early warning platform.

[0064] Among them, the agricultural multimodal big model is mainly based on the agricultural multimodal big model (Chat-Agricul ter) with a three-layer architecture of "1+2+N". Chat-Agricul ter is a human-machine dialogue intelligent collaboration system based on the agricultural multimodal big model, which provides multimodal knowledge question and answer, image data retrieval and task collaboration in the remote sensing space-time field. It has the characteristics of high production efficiency, resource allocation optimization, data visualization, etc. "1" refers to 1 agricultural knowledge base, that is, a large number of multimodal samples and knowledge bases in the agricultural field; "2" refers to 2 types of base models, mainly including mechanism models and AI-driven base models; "N" refers to N agricultural disaster scenario models, that is, for agricultural disasters, agricultural resources, agricultural environment, agricultural ecology, agricultural culture and other business fields. Specifically, the collected data is analyzed and pattern recognized through machine learning and deep learning technology to establish a prediction model, such as a crop growth model, which is used to predict the growth of crops and possible risks.

[0065] The specific calculation method of the crop growth model is as follows:

[0066] Calculating the growth rate of the crop using the third calculation formula;

[0067] R(t)=f(B(t),E(t))

[0068] Where R(t) is the plant growth rate, f() is a function used to describe biological growth, E(t) is the environmental factor, including temperature, humidity and light intensity, and B(t) is the function of the initial biomass B0, specifically B0×e kt . Where k is the coefficient obtained through historical data analysis.

[0069] This model shows that if there are no external constraints (such as resource limitations or pests and diseases), the biomass of crops will increase exponentially. However, in reality, crop growth is affected by many factors, so more complex models are needed to accurately describe it.

[0070] Step 5: Combine the data base, knowledge platform library, and model platform obtained above to build a cloud platform for monitoring farmland waterlogging disasters based on the integration of "data base + knowledge platform library + model platform". The specific cloud platform architecture is as follows: Figure 3 shown.

[0071] 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.

[0072] According to a third aspect of an embodiment of the present invention, an electronic device is provided. Figure 4It is a structural diagram of an electronic device in one embodiment of the present invention. Figure 4 The electronic device shown is a general-purpose farmland waterlogging disaster monitoring device. The electronic device can be a smart phone, a tablet computer, or other devices. As shown, the electronic device 400 includes a processor 401 and a memory 402. The processor 401 is electrically connected to the memory 402. The processor 401 is the control center of the terminal 400, and uses various interfaces and lines to connect various parts of the entire terminal. By running or calling the computer program stored in the memory 402, and calling the data stored in the memory 402, the various functions of the terminal are executed and the data is processed, thereby monitoring the terminal as a whole.

[0073] In this embodiment, the processor 401 in the electronic device 400 will load instructions corresponding to the processes of one or more computer programs into the memory 402 according to the following steps, and the processor 401 will run the computer programs stored in the memory 402.

[0074] The memory 402 may be used to store computer programs and data. The computer programs stored in the memory 402 include instructions that can be executed in the processor. The computer programs may constitute various functional modules. The processor 401 executes various functional applications and data processing by calling the computer programs stored in the memory 402.

[0075] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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 A step that specifies a function in one or more boxes.

[0080] 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 cloud platform for monitoring farmland waterlogging disasters, characterized in that: The system includes: The data base is used to provide basic data for farmland waterlogging disaster analysis and monitoring. The basic data in the data base specifically integrates satellite remote sensing data, drone aerial data, ground sensor data and meteorological data; Agricultural knowledge base, used to provide information and data support related to agricultural production, including crop growth database, meteorological database, soil database, hydrological database, historical disaster database, agricultural management database, pest and disease database, fertilization management database, irrigation management database and agricultural product market database; The model platform is used for real-time monitoring, early warning, assessment and prevention of farmland waterlogging disasters. Based on machine learning and deep learning, it builds agricultural disaster prevention and control platforms, agricultural disaster monitoring platforms, agricultural disaster assessment platforms and agricultural disaster early warning platforms.

2. The farmland waterlogging disaster monitoring cloud platform according to claim 1, characterized in that: The data backplane includes an air-space-ground intelligent sensing network for real-time collection and transmission of soil moisture, precipitation, temperature and humidity of farmland.

3. The farmland waterlogging disaster monitoring cloud platform according to claim 1, characterized in that: The agricultural knowledge base supports dynamic updating and management of data and is used to store and call data generated in the agricultural production process.

4. The farmland waterlogging disaster monitoring cloud platform according to claim 1, characterized in that: The model platform further comprises: Agricultural disaster monitoring platform, used for real-time monitoring and visualization of field waterlogging conditions, remote sensing image processing and spatiotemporal data analysis based on the data base; Agricultural disaster early warning platform, which is used to generate early warning information of waterlogging disasters through meteorological prediction models and waterlogging disaster models combined with big data analysis methods; Agricultural disaster assessment platform, which uses monitoring data and historical disaster data to assess the impact, loss extent and risk level of waterlogging disasters; The agricultural disaster prevention and control platform is used to formulate prevention and control strategies and emergency plans based on early warning and assessment results, including engineering measures, agronomic measures and ecological measures.

5. The farmland waterlogging disaster monitoring cloud platform according to claim 1, characterized in that: The cloud platform is integrated through cloud computing technology to achieve multi-user online access and provide data query, monitoring and early warning, risk assessment and prevention and control strategy recommendations.

6. The farmland waterlogging disaster monitoring cloud platform according to claim 1, characterized in that: The monitoring, early warning, assessment and prevention and control functions of the farmland waterlogging disaster monitoring cloud platform are configured with the function of modular configuration and expansion according to actual needs.

7. The farmland waterlogging disaster monitoring cloud platform according to claim 1, characterized in that: The platform has self-learning capabilities and automatically optimizes the accuracy of disaster monitoring and early warning models through continuous monitoring and accumulation of historical data.

8. The farmland waterlogging disaster monitoring cloud platform according to claim 1, characterized in that: The platform can interact with external data sources and systems for data interconnection and comprehensive analysis.

9. The farmland waterlogging disaster monitoring cloud platform according to claim 1, characterized in that: The farmland waterlogging disaster monitoring cloud platform provides a user interface through mobile terminals and PC terminals, supporting users to view real-time monitoring data, receive early warning information and obtain prevention and control strategies.

10. The farmland waterlogging disaster monitoring cloud platform according to claim 1, characterized in that: The farmland waterlogging disaster monitoring cloud platform also includes an integrated disaster data analysis tool for analyzing historical data and current data to provide future trend forecasts and decision support.

Images