Multi-modal network communication interface mechanism method of intelligent dynamic heterogeneous array

Abstract

The invention relates to the technical field of modal network communication, and particularly discloses a multi-modal network communication interface mechanism method for an intelligent dynamic heterogeneous array, and the method comprises a field fixed-point observation module which is used for obtaining the rainfall intensity, the rainfall duration and the rainfall type; through cooperation of the field fixed-point observation module, the entity model test module, the remote sensing interpretation module, the theoretical analysis module and the mathematical derivation module, workers can forecast flood in the middle and lower reaches of the Yellow River, effective computing power support can be provided, flood control and drought resistance pressure can be reduced, fine management is realized by utilizing scientific and technological means, and flood control and drought resistance are improved. The system effectively supports flood control and drought control decisions, achieves scientific water control and fine water control, reduces loss caused by natural disasters, correspondingly increases diversity of functions, and increases application universality and sustainable development strategy, thereby achieving comprehensive improvement of water safety, water ecology, water management and water service digitization level, and improving water quality. And high-quality development of the Yellow River and precise decision-making requirements are promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of modal network communication, and in particular relates to a multi-modal network communication interface mechanism method for an intelligent dynamic heterogeneous array. Background Art

[0002] The accuracy and foreseeable period of rainfall and flood forecasting in the middle and lower reaches of the Yellow River are restricted by the progress of mechanisms and model methods, and cannot fully meet the requirements of refined flood control forecasting and scheduling; the sediment production and transport process and the downstream river regime evolution mechanism are complex, the production and transport model has many calibration parameters, and the river regime model lacks dynamic description. It is urgent to establish an intelligent forecasting technology for flood, sediment and river regime that integrates mechanisms and data-driven, build a solid "three lines of defense" (meteorological satellites and rainfall radars, rain gauges, and hydrological stations), and improve the "four predictions" (forecast, warning, rehearsal, and plan) capabilities. However, the existing stand-alone hydraulic models or software in the technology have single functions, closed applications, and unsustainable development. Accordingly, they cannot effectively support flood control and drought relief decisions, refined water management, and reduce losses caused by natural disasters. To this end, a multi-modal network communication interface mechanism method for intelligent dynamic heterogeneous arrays is proposed, which has achieved an all-round improvement in the digital level of water security, water ecology, water management, and water services, and promoted the high-quality development of the Yellow River and the demand for precise decision-making. Summary of the invention

[0003] The purpose of the present invention is to provide a multi-modal network communication interface mechanism method for an intelligent dynamic heterogeneous array to solve the problem of being unable to effectively support flood control and drought relief decision-making, precise water management and reduce losses caused by natural disasters.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A multi-modal network communication interface mechanism method for an intelligent dynamic heterogeneous array includes:

[0006] Field fixed-point observation module, used to obtain rainfall intensity, rainfall duration and rainfall type;

[0007] The operation steps of the field fixed-point observation module are as follows:

[0008] S1: According to the climate, topography and rainfall characteristics of the middle and lower reaches of the Yellow River, select representative observation points for fixed-point observation;

[0009] S2: Install rainfall observation equipment at the observation point;

[0010] S3: Collect and record rainfall and meteorological data in detail;

[0011] S4: Analyze and organize the collected data to extract key information on rainfall characteristics, rainfall intensity, and rainfall duration;

[0012] S5: Apply the analysis results to practical work such as rainfall forecasting and flood warning.

[0013] Wherein, the rainfall observation equipment includes a rain gauge, an anemometer and a thermometer.

[0014] From the above, we can see that through the field fixed-point observation module, real-time data of natural phenomena such as rainfall, floods, and sediment can be directly obtained. These data are the basis for revealing the occurrence and development of rainfall, flood and sediment production and transportation, and the evolution of river regime; the data from field fixed-point observations can be used to verify the accuracy of physical model experiments, remote sensing interpretation, and theoretical analysis results, thereby improving the reliability of research; in the process of field fixed-point observations, some new natural phenomena or problems may be discovered, which can guide researchers to further explore and promote the progress of scientific research.

[0015] Physical model test module, used to reproduce natural phenomena such as rainfall, flooding, and sediment transport;

[0016] The operation steps of the physical model test module are as follows:

[0017] A1: Design a physical model based on the actual situation of the middle and lower reaches of the Yellow River;

[0018] A2: Build a physical model according to the design requirements and perform debugging and verification;

[0019] A3: Simulate the rainfall process on the model and simulate the rainfall process under different rainfall conditions;

[0020] A4: Collect and record the rainfall process, flood evolution, and sediment transport in the observation model in detail;

[0021] A5: Analyze and interpret observed data;

[0022] A6: Apply the test results to actual flood prevention and disaster reduction work.

[0023] The designed entity model includes model size, material selection and boundary conditions, and the entity model simulates the rainfall process by adjusting rainfall intensity and rainfall duration.

[0024] From the above, it can be seen that the physical model test module can reproduce natural phenomena such as rainfall, floods, and sediment transport in the middle and lower reaches of the Yellow River based on similar concepts and theories, thereby intuitively displaying the occurrence and development process of these phenomena; by adjusting the parameters and conditions in the physical model, the influence of different factors on rainfall, floods, sediment transport and river evolution can be explored, thereby revealing its mechanism; by conducting experiments on the physical model, possible future rainfall, floods, sediment transport and river evolution trends can be predicted, providing a scientific basis for flood prevention, disaster reduction and river management.

[0025] Remote sensing interpretation module, used to obtain the spatial distribution and temporal variation characteristics of rainfall, floods and river regime information;

[0026] The operation steps of the remote sensing interpretation module are as follows:

[0027] B1: Use remote sensing satellites or drones to obtain remote sensing image data of the middle and lower reaches of the Yellow River;

[0028] B2: Preprocess remote sensing image data;

[0029] B3: Extract rainfall information, flood information and topography information using remote sensing interpretation technology;

[0030] B4: Perform spatial analysis on the extracted information.

[0031] B5: Apply the analysis results to practical work such as rainfall forecasting, flood warning, and river management.

[0032] The preprocessing of the remote sensing image data includes image enhancement, denoising and correction.

[0033] From the above, we can see that remote sensing technology can realize large-scale and continuous monitoring of the middle and lower reaches of the Yellow River, and obtain the spatial distribution and temporal change characteristics of information such as rainfall, floods, and river flow; remote sensing technology has the ability to respond quickly and can obtain a large amount of information in a short period of time to provide support for emergency response and decision-making; compared with traditional field observation methods, remote sensing interpretation can greatly reduce labor costs and time costs and improve research efficiency.

[0034] Theoretical analysis modules and mathematical derivation modules are used to reveal the inherent laws and mechanisms of rainfall, floods, sediment transport and river evolution.

[0035] Among them, the operation steps of the theoretical analysis module are:

[0036] C1: Clearly define the goal of studying the occurrence and development of rainfall in the middle and lower reaches of the Yellow River;

[0037] C2: Based on relevant theories such as meteorology and hydrology, establish a theoretical model to describe the occurrence and development of rainfall;

[0038] C3: Use mathematical methods to solve the theoretical model and obtain the parameters of rainfall characteristics and rainfall intensity.

[0039] C4: Compare and verify the theoretical analysis results with the results of the field fixed-point observation module and the physical model test module;

[0040] C5: Apply theoretical analysis results to practical work such as rainfall forecasting and flood warning.

[0041] Among them, the goals of rainfall occurrence and development in the middle and lower reaches of the Yellow River include revealing the rainfall mechanism and predicting rainfall trends.

[0042] Wherein, the operation steps of the mathematical derivation module are:

[0043] D1: Based on the laws of physics and mathematical principles, establish a mathematical model to describe the occurrence and development of rainfall in the middle and lower reaches of the Yellow River;

[0044] D2: Use mathematical methods to derive the established mathematical model and obtain mathematical expressions of key parameters of rainfall characteristics and rainfall intensity;

[0045] D3: Optimize the model by adjusting the parameters in the mathematical model;

[0046] D4: Compare and verify the mathematical derivation results with the results of the field fixed-point observation module and the physical model test module;

[0047] D5: Apply mathematical derivation results to practical work such as rainfall forecasting and flood warning.

[0048] From the above, we can see that through theoretical analysis and mathematical deduction, we can reveal the inherent laws and mechanisms of rainfall, floods, sediment transport and river evolution, and provide theoretical support for scientific research; theoretical analysis and mathematical deduction can optimize the parameter settings in physical model experiments and improve the accuracy and reliability of the model; the results of theoretical analysis and mathematical deduction can provide scientific basis and guidance for practical applications such as flood control and disaster reduction, and river management.

[0049] In summary, the coordination between the field fixed-point observation module, the physical model test module, the remote sensing interpretation module, the theoretical analysis module and the mathematical derivation module can reveal the occurrence and development of rainfall, flood and sediment production and transport, and the evolution of river flow in the middle and lower reaches of the Yellow River. The rainfall, flood, sediment, river flow and engineering hazard prediction models are developed by using numerical simulation, transfer learning, fusion of mechanism model and machine learning, and parameter adaptive calibration. The coupling integration of the digital twin water conservancy model platform and the Yellow River flood control consultation and rehearsal system is realized by using component-based packaging, parallel transformation and supercomputing interconnection technologies.

[0050] Based on the above, the staff can provide effective computing power support for flood forecasts in the middle and lower reaches of the Yellow River, reduce the pressure of flood control and drought relief, use scientific and technological means to carry out refined management, effectively support flood control and drought relief decision-making, achieve scientific and refined water management, reduce losses caused by natural disasters, and comprehensively improve the digital level of water security, water ecology, water management, and water services, promote the high-quality development of the Yellow River and the demand for precise decision-making, increase the diversity of functions, increase the breadth of application and sustainable development strategies, thereby achieving an all-round improvement of the digital level of water security, water ecology, water management, and water services, and promote the high-quality development of the Yellow River and the demand for precise decision-making.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] Through the coordinated effects of the field fixed-point observation module, the physical model test module, the remote sensing interpretation module, the theoretical analysis module and the mathematical derivation module, the present invention enables the staff to provide effective computing power support for flood forecasting in the middle and lower reaches of the Yellow River, reduce the pressure of flood control and drought relief, use scientific and technological means to carry out refined management, effectively support flood control and drought relief decision-making, achieve scientific and refined water management, reduce losses caused by natural disasters, and correspondingly increase the diversity of functions, the breadth of application and the sustainable development strategy, thereby achieving an all-round improvement in the digitalization level of water safety, water ecology, water management and water services, and promoting the high-quality development of the Yellow River and the demand for precise decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a module flow chart of the present invention. DETAILED DESCRIPTION

[0054] 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] like Figure 1 As shown, the multi-modal network communication interface mechanism method of the intelligent dynamic heterogeneous array includes:

[0056] Field fixed-point observation module, used to obtain rainfall intensity, rainfall duration and rainfall type;

[0057] The operation steps of the field fixed-point observation module are as follows:

[0058] S1: According to the climate, topography and rainfall characteristics of the middle and lower reaches of the Yellow River, select representative observation points for fixed-point observation;

[0059] S2: Install rainfall observation equipment at the observation point;

[0060] S3: Collect and record rainfall and meteorological data in detail;

[0061] S4: Analyze and organize the collected data to extract key information on rainfall characteristics, rainfall intensity, and rainfall duration;

[0062] S5: Apply the analysis results to practical work such as rainfall forecasting and flood warning.

[0063] Wherein, the rainfall observation equipment includes a rain gauge, an anemometer and a thermometer.

[0064] From the above, we can see that through the field fixed-point observation module, real-time data of natural phenomena such as rainfall, floods, and sediment can be directly obtained. These data are the basis for revealing the occurrence and development of rainfall, flood and sediment production and transportation, and the evolution of river regime; the data from field fixed-point observations can be used to verify the accuracy of physical model experiments, remote sensing interpretation, and theoretical analysis results, thereby improving the reliability of research; in the process of field fixed-point observations, some new natural phenomena or problems may be discovered, which can guide researchers to further explore and promote the progress of scientific research.

[0065] Physical model test module, used to reproduce natural phenomena such as rainfall, flooding, and sediment transport;

[0066] The operation steps of the physical model test module are as follows:

[0067] A1: Design a physical model based on the actual situation of the middle and lower reaches of the Yellow River;

[0068] A2: Build a physical model according to the design requirements and perform debugging and verification;

[0069] A3: Simulate the rainfall process on the model and simulate the rainfall process under different rainfall conditions;

[0070] A4: Collect and record the rainfall process, flood evolution, and sediment transport in the observation model in detail;

[0071] A5: Analyze and interpret observed data;

[0072] A6: Apply the test results to actual flood prevention and disaster reduction work.

[0073] The designed entity model includes model size, material selection and boundary conditions, and the entity model simulates the rainfall process by adjusting rainfall intensity and rainfall duration.

[0074] From the above, it can be seen that the physical model test module can reproduce natural phenomena such as rainfall, floods, and sediment transport in the middle and lower reaches of the Yellow River based on similar concepts and theories, thereby intuitively displaying the occurrence and development process of these phenomena; by adjusting the parameters and conditions in the physical model, the influence of different factors on rainfall, floods, sediment transport and river evolution can be explored, thereby revealing its mechanism; by conducting experiments on the physical model, possible future rainfall, floods, sediment transport and river evolution trends can be predicted, providing a scientific basis for flood prevention, disaster reduction and river management.

[0075] Remote sensing interpretation module, used to obtain the spatial distribution and temporal variation characteristics of rainfall, floods and river regime information;

[0076] The operation steps of the remote sensing interpretation module are as follows:

[0077] B1: Use remote sensing satellites or drones to obtain remote sensing image data of the middle and lower reaches of the Yellow River;

[0078] B2: Preprocess remote sensing image data;

[0079] B3: Extract rainfall information, flood information and topography information using remote sensing interpretation technology;

[0080] B4: Perform spatial analysis on the extracted information.

[0081] B5: Apply the analysis results to practical work such as rainfall forecasting, flood warning, and river management.

[0082] The preprocessing of the remote sensing image data includes image enhancement, denoising and correction.

[0083] From the above, we can see that remote sensing technology can realize large-scale and continuous monitoring of the middle and lower reaches of the Yellow River, and obtain the spatial distribution and temporal change characteristics of information such as rainfall, floods, and river flow; remote sensing technology has the ability to respond quickly and can obtain a large amount of information in a short period of time to provide support for emergency response and decision-making; compared with traditional field observation methods, remote sensing interpretation can greatly reduce labor costs and time costs and improve research efficiency.

[0084] Theoretical analysis modules and mathematical derivation modules are used to reveal the inherent laws and mechanisms of rainfall, floods, sediment transport and river evolution.

[0085] Among them, the operation steps of the theoretical analysis module are:

[0086] C1: Clearly define the goal of studying the occurrence and development of rainfall in the middle and lower reaches of the Yellow River;

[0087] C2: Based on relevant theories such as meteorology and hydrology, establish a theoretical model to describe the occurrence and development of rainfall;

[0088] C3: Use mathematical methods to solve the theoretical model and obtain the parameters of rainfall characteristics and rainfall intensity.

[0089] C4: Compare and verify the theoretical analysis results with the results of the field fixed-point observation module and the physical model test module;

[0090] C5: Apply theoretical analysis results to practical work such as rainfall forecasting and flood warning.

[0091] Among them, the goals of rainfall occurrence and development in the middle and lower reaches of the Yellow River include revealing the rainfall mechanism and predicting rainfall trends.

[0092] Wherein, the operation steps of the mathematical derivation module are:

[0093] D1: Based on the laws of physics and mathematical principles, establish a mathematical model to describe the occurrence and development of rainfall in the middle and lower reaches of the Yellow River;

[0094] D2: Use mathematical methods to derive the established mathematical model and obtain mathematical expressions of key parameters of rainfall characteristics and rainfall intensity;

[0095] D3: Optimize the model by adjusting the parameters in the mathematical model;

[0096] D4: Compare and verify the mathematical derivation results with the results of the field fixed-point observation module and the physical model test module;

[0097] D5: Apply mathematical derivation results to practical work such as rainfall forecasting and flood warning.

[0098] From the above, we can see that through theoretical analysis and mathematical deduction, we can reveal the inherent laws and mechanisms of rainfall, floods, sediment transport and river evolution, and provide theoretical support for scientific research; theoretical analysis and mathematical deduction can optimize the parameter settings in physical model experiments and improve the accuracy and reliability of the model; the results of theoretical analysis and mathematical deduction can provide scientific basis and guidance for practical applications such as flood control and disaster reduction, and river management.

[0099] In summary, the coordination between the field fixed-point observation module, the physical model test module, the remote sensing interpretation module, the theoretical analysis module and the mathematical derivation module can reveal the occurrence and development of rainfall, flood and sediment production and transport, and the evolution of river flow in the middle and lower reaches of the Yellow River. The rainfall, flood, sediment, river flow and engineering hazard prediction models are developed by using numerical simulation, transfer learning, fusion of mechanism model and machine learning, and parameter adaptive calibration. The coupling integration of the digital twin water conservancy model platform and the Yellow River flood control consultation and rehearsal system is realized by using component-based packaging, parallel transformation and supercomputing interconnection technologies.

[0100] Based on the above, the staff can provide effective computing support for flood forecasts in the middle and lower reaches of the Yellow River, reduce the pressure of flood control and drought relief, use scientific and technological means to carry out refined management, effectively support flood control and drought relief decision-making, achieve scientific and refined water management, and reduce losses caused by natural disasters. Correspondingly, it increases the diversity of functions, the breadth of application and the sustainable development strategy, thereby achieving an all-round improvement in the digital level of water security, water ecology, water management, and water services, and promoting the high-quality development of the Yellow River and precise decision-making needs.

[0101] Furthermore, this design application is used to obtain the rainfall in the Yellow River area through multi-modal network communication of intelligent dynamic heterogeneous arrays, so that the staff can provide effective computing power support for flood forecasting in the middle and lower reaches of the Yellow River, reduce the pressure of flood control and drought relief, use scientific and technological means to carry out refined management, effectively support flood control and drought relief decision-making, achieve scientific and refined water management, reduce losses caused by natural disasters, and correspondingly increase the diversity of functions, the breadth of application and the sustainable development strategy, thereby achieving an all-round improvement in the digital level of water security, water ecology, water management, and water services, and promoting the high-quality development of the Yellow River and precise decision-making needs.

[0102] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-modal network communication interface mechanism method for intelligent dynamic heterogeneous arrays, characterized in that: include: Field fixed-point observation module, used to obtain rainfall intensity, rainfall duration and rainfall type; Physical model test module, used to reproduce natural phenomena such as rainfall, flooding, and sediment transport; Remote sensing interpretation module, used to obtain the spatial distribution and temporal variation characteristics of rainfall, floods and river regime information; Theoretical analysis modules and mathematical derivation modules are used to reveal the inherent laws and mechanisms of rainfall, floods, sediment transport and river evolution.

2. The multi-modal network communication interface mechanism method of the intelligent dynamic heterogeneous array according to claim 1 is characterized by: The operation steps of the field fixed-point observation module are as follows: S1: According to the climate, topography and rainfall characteristics of the middle and lower reaches of the Yellow River, select representative observation points for fixed-point observation; S2: Install rainfall observation equipment at the observation point; S3: Collect and record rainfall and meteorological data in detail; S4: Analyze and organize the collected data to extract key information on rainfall characteristics, rainfall intensity, and rainfall duration; S5: Apply the analysis results to practical work such as rainfall forecasting and flood warning.

3. The multi-modal network communication interface mechanism method of the intelligent dynamic heterogeneous array according to claim 2 is characterized by: The rainfall observation equipment includes a rain gauge, an anemometer and a thermometer.

4. The multi-modal network communication interface mechanism method of the intelligent dynamic heterogeneous array according to claim 1 is characterized by: The operation steps of the physical model test module are: A1: Design a physical model based on the actual situation of the middle and lower reaches of the Yellow River; A2: Build a physical model according to the design requirements and perform debugging and verification; A3: Simulate the rainfall process on the model and simulate the rainfall process under different rainfall conditions; A4: Collect and record the rainfall process, flood evolution, and sediment transport in the observation model in detail; A5: Analyze and interpret observed data; A6: Apply the test results to actual flood prevention and disaster reduction work.

5. The multi-modal network communication interface mechanism method of the intelligent dynamic heterogeneous array according to claim 4 is characterized by: The designed entity model includes model size, material selection and boundary conditions. The entity model simulates the rainfall process by adjusting rainfall intensity and rainfall duration.

6. The multi-modal network communication interface mechanism method of the intelligent dynamic heterogeneous array according to claim 1 is characterized by: The operation steps of the remote sensing interpretation module are: B1: Use remote sensing satellites or drones to obtain remote sensing image data of the middle and lower reaches of the Yellow River; B2: Preprocess remote sensing image data; B3: Extract rainfall information, flood information and topography information using remote sensing interpretation technology; B4: Perform spatial analysis on the extracted information. B5: Apply the analysis results to practical work such as rainfall forecasting, flood warning, and river management.

7. The multi-modal network communication interface mechanism method of the intelligent dynamic heterogeneous array according to claim 6 is characterized by: The remote sensing image data preprocessing includes image enhancement, denoising and correction.

8. The multi-modal network communication interface mechanism method of the intelligent dynamic heterogeneous array according to claim 1 is characterized by: The operation steps of the theoretical analysis module are: C1: Clearly define the goal of studying the occurrence and development of rainfall in the middle and lower reaches of the Yellow River; C2: Based on relevant theories such as meteorology and hydrology, establish a theoretical model to describe the occurrence and development of rainfall; C3: Use mathematical methods to solve the theoretical model and obtain the parameters of rainfall characteristics and rainfall intensity. C4: Compare and verify the theoretical analysis results with the results of the field fixed-point observation module and the physical model test module; C5: Apply theoretical analysis results to practical work such as rainfall forecasting and flood warning.

9. The multi-modal network communication interface mechanism method of the intelligent dynamic heterogeneous array according to claim 9, characterized in that: The objectives of the rainfall occurrence and development in the middle and lower reaches of the Yellow River include revealing the rainfall mechanism and predicting rainfall trends.

10. The multi-modal network communication interface mechanism method of the intelligent dynamic heterogeneous array according to claim 1, characterized in that: The operation steps of the mathematical derivation module are: D1: Based on the laws of physics and mathematical principles, establish a mathematical model to describe the occurrence and development of rainfall in the middle and lower reaches of the Yellow River; D2: Use mathematical methods to derive the established mathematical model and obtain mathematical expressions of key parameters of rainfall characteristics and rainfall intensity; D3: Optimize the model by adjusting the parameters in the mathematical model; D4: Compare and verify the mathematical derivation results with the results of the field fixed-point observation module and the physical model test module; D5: Apply mathematical derivation results to practical work such as rainfall forecasting and flood warning.

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