An air-space-ground-water integrated intelligent monitoring system powered by wind and solar energy

Abstract

This invention discloses an integrated intelligent monitoring system for air, space, and water using a wind-solar hybrid power supply, specifically relating to the field of intelligent monitoring. It includes a wind-solar hybrid power supply module, a sensing and monitoring module, a wireless self-organizing network communication module, and an intelligent management and control module. The wind-solar hybrid power supply module provides clean energy; the sensing and monitoring module collects environmental and operational data; the wireless self-organizing network communication module enables bidirectional data transmission and interaction between monitoring nodes; and the intelligent management and control module performs fusion analysis of multi-source data, water quality level prediction, and early warning triggering. The operation of the wind-solar hybrid power supply module specifically includes the following: classifying power supply differences into different levels based on power supply evaluation differences. This invention achieves long-term unattended, accurate, and efficient monitoring in outdoor scenarios without mains power through stable wind-solar hybrid power supply, comprehensive air, space, and water monitoring, reliable Mesh network transmission, and intelligent analysis, early warning, and dynamic optimization.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring, and more specifically, to an integrated intelligent monitoring system for air, land, and water powered by wind and solar complementary technologies. Background Technology

[0002] Currently, the fields of watershed water environment, water conservancy projects, and ecological environment monitoring are rapidly developing towards full-area coverage, unmanned operation, and intelligentization. The core driving force of this trend stems from the refined needs of modern environmental governance and water conservancy safety management. With the continuous improvement of requirements for watershed ecological protection, optimal allocation of water resources, and flood disaster prevention, traditional point-based monitoring and manual inspection models are no longer sufficient to cover vast watersheds, complex terrains, and remote water areas. There is an urgent need to break through spatial limitations and human dependence, and achieve a transformation from local monitoring to full-area coverage, from manual intervention to autonomous operation, and from data collection to intelligent analysis.

[0003] Most field monitoring sites are far from the municipal power grid and cannot be powered by conventional municipal power. Traditional single solar or wind power supply methods are greatly affected by weather, season, and day and night. When wind and solar resources fluctuate, power supply is easily insufficient or interrupted, causing monitoring equipment to shut down and data acquisition to be interrupted, making it difficult to achieve long-term stable unattended operation.

[0004] Existing monitoring mainly relies on fixed ground stations, lacking coordinated aerial and water surface monitoring methods. This makes it difficult to conduct comprehensive inspections of vast waters, complex shorelines, and remote river sections. Manual inspections are inefficient, costly, and dangerous, and cannot promptly detect abnormalities such as hidden pollution, shoreline damage, and floating garbage.

[0005] Complex terrain, obstructions, and long-distance transmission environments in the field can easily lead to problems such as weak or interrupted communication signals. Different monitoring devices have inconsistent data protocols and heterogeneous formats, which can easily result in data loss and parsing failures during transmission, making it difficult to achieve efficient interconnection and interoperability among multiple nodes in the air, space, and water.

[0006] Existing systems mostly remain at the level of data collection and simple display, lacking in-depth fusion processing of multi-source water quality data. They are unable to finely distinguish and model the correlation of water quality indicators, resulting in low accuracy in predicting water quality change trends. Most alarms are triggered after the fact, making it difficult to predict the risk of water quality deterioration in advance. At the same time, the systems lack adaptive adjustment capabilities, and the sampling and inspection strategies are fixed and rigid, which can easily lead to resource waste or insufficient monitoring of key areas, resulting in low overall operation and maintenance efficiency.

[0007] To address the aforementioned problems, a technical solution is provided. Summary of the Invention

[0008] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an integrated intelligent monitoring system for air, space, and water power supply with wind and solar hybrid power supply to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A wind-solar hybrid power supply integrated air-ground-water intelligent monitoring system includes a wind-solar hybrid power supply module, a sensing and monitoring module, a wireless self-organizing network communication module, and an intelligent control module;

[0011] Wind-solar hybrid power supply modules are used to provide clean energy;

[0012] The sensing and monitoring module is used to collect environmental and operating condition data;

[0013] The wireless self-organizing network communication module is used to realize bidirectional data transmission and interaction between monitoring nodes;

[0014] The intelligent control module is used for fusion analysis of multi-source data, water quality level prediction, and early warning triggering.

[0015] In a preferred embodiment, the operation of the wind-solar hybrid power supply module specifically includes the following:

[0016] Based on the power supply evaluation difference, different levels of power supply differences are divided, and a set of corresponding power supply strategies is matched for each level of difference.

[0017] By combining real-time and predicted environmental data, the feasibility of the wind and solar power supply ratio of candidate strategies is verified, and the final power supply allocation scheme is determined through evaluation of operating time, stability and benefits.

[0018] In a preferred embodiment, the power supply strategy set includes multiple preset schemes corresponding to different power supply difference levels;

[0019] When selecting strategies, first match the candidate schemes that best fit the current difference level, and then verify whether the supply ratio of the schemes is suitable based on real-time and predicted wind and solar resource data to complete the initial screening.

[0020] Finally, based on subsequent benefit evaluations, the optimal power supply allocation decision is output.

[0021] In a preferred embodiment, the operation of the sensing and monitoring module specifically includes the following:

[0022] Meteorological, hydrological, and geological data are collected at fixed locations via ground nodes;

[0023] The aerial equipment is used to conduct inspections along a preset path, collect images and environmental information, and identify anomalies.

[0024] Water quality samples were collected by navigating the waterway using surface equipment.

[0025] In a preferred embodiment, the sensing and monitoring module performs cross-validation on the collected multi-source data to support the initial judgment of anomalies at the field level.

[0026] Each monitoring node has local data storage capabilities, ensuring the retention of basic data even without external control.

[0027] In a preferred embodiment, the operation of the wireless ad hoc network communication module specifically includes the following:

[0028] Construct a multi-node mesh network architecture to achieve multi-hop relay transmission between monitoring nodes on the ground, in the air, and on the water;

[0029] The system performs protocol adaptation, format conversion, and integrity verification on the collected multi-source heterogeneous data.

[0030] In a preferred embodiment, the intelligent control module operates specifically including the following:

[0031] A standardized water quality monitoring dataset was constructed from the received multi-source data, and the comprehensive correlation between various water quality evaluation indicators was calculated.

[0032] Based on the correlation threshold, the indicators are divided into three categories: single-correlation indicators, multi-correlation indicators, and isolated indicators. Time series data of each category are extracted to construct a dedicated data support set.

[0033] For single-correlation indicators, a first-class prediction model is constructed, and for multi-correlation indicators, a second-class prediction model is constructed using mode decomposition and component fusion methods.

[0034] Based on the model's prediction results, the future water quality level of the water body is predicted, and a graded early warning process is automatically triggered when the indicators exceed the preset threshold.

[0035] In a preferred embodiment, the sampling frequency and inspection path of the sensing and monitoring module are dynamically adjusted based on the deviation between the water quality prediction results and the actual monitoring data.

[0036] The technical effects and advantages of the integrated air-space-ground-water intelligent monitoring system with wind-solar hybrid power supply of the present invention are as follows:

[0037] 1. By combining wind and solar hybrid power supply modules with real-time and predicted environmental data to accurately adjust the power distribution scheme, the instability of single energy supply is effectively avoided, and a continuous clean energy supply is achieved in the field without mains power. At the same time, multiple rounds of strategy screening and benefit evaluation ensure the maximization of energy utilization efficiency, extend the battery life of energy storage equipment, and significantly reduce the risk of data loss and equipment downtime caused by power outages.

[0038] 2. The sensing and monitoring module breaks through the spatial limitations of traditional single-monitoring modes, effectively eliminating monitoring blind spots in complex terrain and remote water areas. This is achieved through a multi-source data cross-validation mechanism and the local storage capabilities of each node;

[0039] 3. The wireless self-organizing network communication module adopts a multi-node mesh network architecture, which has multi-hop relay transmission, self-repair, and anti-interference capabilities, ensuring stable data interaction between distributed monitoring nodes, effectively covering communication blind spots in remote areas and complex terrains. At the same time, through data protocol adaptation, format conversion, and integrity verification, it realizes standardized processing of multi-source heterogeneous data.

[0040] 4. The intelligent control module significantly improves the accuracy and foresight of water quality level prediction through differentiated prediction model construction and deep fusion analysis of multi-source data. The hierarchical early warning mechanism enables rapid response to abnormal situations. It can optimize the monitoring sampling frequency and inspection path in a targeted manner, realize the rational allocation of monitoring resources, reduce energy waste and equipment wear caused by over-sampling, and enable the system to have the ability to iterate and optimize autonomously, continuously adapting to complex and ever-changing monitoring needs. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the structure of an integrated intelligent monitoring system for air, space, and water power supply based on wind and solar complementary power generation, according to the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0043] Example 1

[0044] Figure 1 The present invention provides an integrated intelligent monitoring system for air, space, and water powered by wind and solar hybrid power supply, comprising a wind and solar hybrid power supply module, a sensing and monitoring module, a wireless self-organizing network communication module, and an intelligent control module;

[0045] Wind-solar hybrid power supply modules are used to provide clean energy;

[0046] The sensing and monitoring module is used to collect environmental and operating condition data;

[0047] The wireless self-organizing network communication module is used to realize bidirectional data transmission and interaction between monitoring nodes;

[0048] The intelligent control module is used for fusion analysis of multi-source data, water quality level prediction, and early warning triggering.

[0049] The operation of the wind-solar hybrid power supply module includes the following:

[0050] Based on the power supply evaluation difference, different levels of power supply differences are divided, and a set of corresponding power supply strategies is matched for each level of difference.

[0051] By combining real-time and predicted environmental data, the feasibility of the wind and solar power supply ratio of candidate strategies is verified, and the final power supply allocation scheme is determined through evaluation of operating time, stability and benefits.

[0052] The power supply strategy set includes multiple preset schemes corresponding to different power supply levels;

[0053] When selecting strategies, first match the candidate schemes that best fit the current difference level, and then verify whether the supply ratio of the schemes is suitable based on real-time and predicted wind and solar resource data to complete the initial screening.

[0054] Finally, based on subsequent benefit evaluations, the optimal power supply allocation decision is output.

[0055] By classifying power supply difference levels based on power supply evaluation differences and matching them with corresponding preset strategy sets, the system can quickly identify suitable solutions based on different operating conditions. Combined with real-time and predicted environmental data to verify the wind and solar power supply ratio, it can predict the future trends of wind and solar energy resources in advance. This ensures that power supply distribution can be adjusted in advance even in resource fluctuation scenarios such as cloudy and rainy days and weak wind periods, guaranteeing the continuous operation of core monitoring equipment and significantly reducing the risk of data loss and equipment downtime due to power outages.

[0056] The final solution was determined through multiple rounds of screening, ensuring the adaptability of the power supply strategy to the current operating conditions while prioritizing stable operation and high energy utilization. This reduced energy waste, improved the conversion efficiency of wind and solar resources, extended the battery life of energy storage equipment, and lowered subsequent maintenance costs. The pre-designed strategy set incorporates historical power supply data, adapting to the characteristics of wind and solar resources in different seasons and regions. Combined with dynamic verification and evaluation processes, the power supply solution can be continuously iterated and optimized according to environmental changes, meeting the differentiated power supply needs of different monitoring nodes. This achieves coordinated adaptation between energy distribution and system operation, providing reliable energy security for the long-term unattended operation of the entire integrated air-space-ground-water monitoring system.

[0057] The operation of the perception and monitoring module specifically includes the following:

[0058] Meteorological, hydrological, and geological data are collected at fixed locations via ground nodes;

[0059] The aerial equipment is used to conduct inspections along a preset path, collect images and environmental information, and identify anomalies.

[0060] Water quality samples were collected by navigating the waterway using surface equipment.

[0061] The sensing and monitoring module performs cross-validation on the collected multi-source data to support the initial judgment of anomalies at the field level;

[0062] Each monitoring node has local data storage capabilities, ensuring the retention of basic data even without external control.

[0063] By coordinating ground-based fixed nodes, aerial inspection equipment, and surface operation equipment, the spatial limitations of traditional single-monitoring modes are overcome. This approach enables precise collection of meteorological, hydrological, and geological data from fixed locations, while also allowing for large-scale drone patrols to identify shoreline anomalies and potential hazards. Unmanned surface vessels (USVs) facilitate comprehensive water quality sampling across the entire area, achieving full coverage monitoring of complex terrain and remote waterways and effectively eliminating monitoring blind spots. A multi-source data cross-validation mechanism verifies the consistency of information collected from different dimensions, quickly identifying data deviations or equipment malfunctions, providing preliminary judgments of on-site conditions, and avoiding misjudgments that may arise from a single data source. Furthermore, the local storage capabilities of each node ensure that basic data is fully preserved even in scenarios involving communication interruptions or lack of external control.

[0064] Each monitoring node can independently complete data collection, local verification, and storage without relying on external commands, possessing basic on-site anomaly assessment capabilities and reducing reliance on cloud-based management. Even in complex environments with poor communication, it can maintain basic monitoring capabilities. At the same time, the collaborative mode of different devices can flexibly adjust the collection frequency and inspection path according to monitoring needs, balancing the accuracy of fixed-point monitoring with the efficiency of full-area inspection. It can meet the needs of routine daily monitoring and also quickly respond to supplementary sampling tasks for sudden working conditions.

[0065] Based on the topography (such as mountains, plains, and complex shorelines), water area, and monitoring needs of the monitored watershed, a full-scenario monitoring node deployment was completed: fixed ground monitoring nodes (integrating meteorological sensors, hydrological monitoring instruments, and geological subsidence sensors) were deployed at key locations (such as water sources, sewage outlets, and geological hazard points); drones were configured as aerial inspection equipment, with preset patrol routes covering remote waters and broad shorelines; and unmanned surface vessels were deployed as surface monitoring equipment, with planned water patrol paths adapted to the watershed area. Simultaneously, a wireless self-organizing network communication network was established, with ground, aerial, and surface monitoring nodes serving as Mesh networking nodes, automatically completing network topology construction and forming multi-hop relay transmission links to ensure communication coverage between distributed nodes. The wind-solar hybrid power supply module completes the installation and commissioning of solar panels, wind turbines, and energy storage equipment. Based on historical wind and solar resource data of the monitored area (such as sunshine duration, wind speed distribution, and seasonal variation patterns), it presets multiple sets of power supply strategies. Each strategy corresponds to different power supply levels, clarifying the wind and solar energy allocation ratio, energy storage charging and discharging thresholds, and equipment power supply priorities under each level. The intelligent management and control module inputs the water quality evaluation index system, graded early warning thresholds, data correlation judgment standards, and initial prediction model parameters to complete the basic configuration for system operation.

[0066] The wind-solar hybrid power supply module collects real-time data on solar panel power generation, wind turbine output power, remaining energy storage capacity, and energy consumption at each monitoring node. It calculates the power supply evaluation difference (i.e., the difference between the actual power supply capacity and the total energy consumption of the system) and classifies the power supply difference level according to the range of the difference.

[0067] The system matches the most suitable candidate scheme from a preset strategy set based on the current power supply disparity level; it verifies the feasibility of the wind and solar power supply ratio of the candidate scheme by combining real-time meteorological data (such as light intensity and wind speed) and short-term forecast data, completing the initial screening; and it determines the final power allocation scheme through a three-dimensional evaluation of runtime, stability, and efficiency. For example, in the sufficient level, priority is given to maximizing energy storage charging while ensuring that all equipment operates at full load; in the balanced level, energy is allocated according to equipment priority, and the power consumption of non-core equipment is appropriately reduced; in the scarce level, only the core monitoring and communication equipment is powered, and non-essential functions are suspended to ensure that the core operation of the system is not interrupted.

[0068] The operation of the wireless self-organizing network communication module specifically includes the following:

[0069] Construct a multi-node mesh network architecture to achieve multi-hop relay transmission between monitoring nodes on the ground, in the air, and on the water;

[0070] The system performs protocol adaptation, format conversion, and integrity verification on the collected multi-source heterogeneous data.

[0071] The multi-node mesh networking architecture, through a multi-hop relay transmission mode, enables ground, air, and water-based monitoring nodes to form an interconnected communication network. Even if some nodes fail or their signals are blocked, data can still be transmitted through other paths, significantly improving the integrity of network coverage and effectively covering remote areas, complex terrain, and other communication blind spots. Simultaneously, protocol adaptation, format conversion, and integrity verification for multi-source heterogeneous data achieve standardized processing of different devices and data types, avoiding transmission failures or data corruption caused by incompatible data formats, and ensuring the accurate and complete transmission of various monitoring data such as meteorological, hydrological, and imagery data.

[0072] Mesh architecture has flexible scalability, allowing nodes to be dynamically added or removed based on the monitoring area and the number of devices, without the need for large-scale network reconstruction, and adapts to monitoring tasks of different scales and scenarios; its self-organizing and self-healing characteristics can automatically respond to emergencies such as node addition or removal and environmental interference, reducing manual maintenance intervention and lowering the difficulty and cost of operation and maintenance in complex field environments.

[0073] The intelligent management and control module operates by including the following:

[0074] A standardized water quality monitoring dataset was constructed from the received multi-source data, and the comprehensive correlation between various water quality evaluation indicators was calculated.

[0075] Based on the correlation threshold, the indicators are divided into three categories: single-correlation indicators, multi-correlation indicators, and isolated indicators. Time series data of each category are extracted to construct a dedicated data support set.

[0076] For single-correlation indicators, a first-class prediction model is constructed, and for multi-correlation indicators, a second-class prediction model is constructed using mode decomposition and component fusion methods.

[0077] Based on the model's prediction results, the future water quality level of the water body is predicted, and a graded early warning process is automatically triggered when the indicators exceed the preset threshold.

[0078] Based on the deviation between the water quality prediction results and the actual monitoring data, the sampling frequency and inspection path of the sensing and monitoring module are dynamically adjusted.

[0079] By constructing a standardized water quality monitoring dataset, the system achieves unified organization of multi-source heterogeneous data, calculates and classifies the comprehensive correlations among various water quality indicators, and accurately identifies the correlation characteristics of different indicators, avoiding the problem of insufficient adaptation of traditional single models to complex water quality data. Dedicated prediction models are constructed for different types of indicators. Single-correlation indicator models focus on core influencing factors, while multi-correlation indicator models, through modal decomposition and component fusion, take into account both high- and low-frequency variation characteristics, making the prediction results more consistent with the actual changes in water quality and effectively improving the accuracy of predicting future water quality levels.

[0080] The intelligent control module dynamically adjusts the sampling frequency and inspection path of the sensing and monitoring module by comparing the deviation between the water quality prediction results and the actual monitoring data. When the deviation is large, it can selectively increase the sampling frequency in key areas and optimize the inspection route, so that the monitoring resources are more concentrated on key points. When the data tends to be stable, it can appropriately reduce the sampling frequency in non-core areas to achieve reasonable allocation of resources.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0082] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A wind-solar hybrid power supply integrated air-ground-water intelligent monitoring system, characterized in that, It includes a wind-solar hybrid power supply module, a sensing and monitoring module, a wireless self-organizing network communication module, and an intelligent management and control module; Wind-solar hybrid power supply modules are used to provide clean energy; The sensing and monitoring module is used to collect environmental and operating condition data; The wireless self-organizing network communication module is used to realize bidirectional data transmission and interaction between monitoring nodes; The intelligent control module is used for fusion analysis of multi-source data, water quality level prediction, and early warning triggering.

2. The integrated intelligent monitoring system for air-space-ground-water power supply based on wind-solar hybrid power supply according to claim 1, characterized in that: The operation of the wind-solar hybrid power supply module includes the following: Based on the power supply evaluation difference, different levels of power supply differences are divided, and a set of corresponding power supply strategies is matched for each level of difference. By combining real-time and predicted environmental data, the feasibility of the wind and solar power supply ratio of candidate strategies is verified, and the final power supply allocation scheme is determined through evaluation of operating time, stability and benefits.

3. The integrated intelligent monitoring system for air-space-ground-water power supply based on wind-solar hybrid power supply according to claim 2, characterized in that: The power supply strategy set includes multiple preset schemes corresponding to different power supply levels; When selecting strategies, first match the candidate schemes that best fit the current difference level, and then verify whether the supply ratio of the schemes is suitable based on real-time and predicted wind and solar resource data to complete the initial screening. Finally, based on subsequent benefit evaluations, the optimal power supply allocation decision is output.

4. The integrated intelligent monitoring system for air-space-ground-water power supply based on wind-solar hybrid power supply according to claim 3, characterized in that: The operation of the perception and monitoring module specifically includes the following: Meteorological, hydrological, and geological data are collected at fixed locations via ground nodes; The aerial equipment is used to conduct inspections along a preset path, collect images and environmental information, and identify anomalies. Water quality samples were collected by navigating the waterway using surface equipment.

5. The integrated intelligent monitoring system for air-space-ground-water power supply based on wind-solar hybrid power supply according to claim 4, characterized in that: The sensing and monitoring module performs cross-validation on the collected multi-source data to support the initial judgment of anomalies at the field level; Each monitoring node has local data storage capabilities, ensuring the retention of basic data even without external control.

6. The integrated intelligent monitoring system for air-space-ground-water power supply based on wind-solar hybrid power supply according to claim 5, characterized in that: The operation of the wireless self-organizing network communication module specifically includes the following: Construct a multi-node mesh network architecture to achieve multi-hop relay transmission between monitoring nodes on the ground, in the air, and on the water; The system performs protocol adaptation, format conversion, and integrity verification on the collected multi-source heterogeneous data.

7. The integrated intelligent monitoring system for air-space-ground-water power supply based on wind-solar hybrid power supply according to claim 6, characterized in that: The intelligent management and control module operates by including the following: A standardized water quality monitoring dataset was constructed from the received multi-source data, and the comprehensive correlation between various water quality evaluation indicators was calculated. Based on the correlation threshold, the indicators are divided into three categories: single-correlation indicators, multi-correlation indicators, and isolated indicators. Time series data of each category are extracted to construct a dedicated data support set. For single-correlation indicators, a first-class prediction model is constructed, and for multi-correlation indicators, a second-class prediction model is constructed using mode decomposition and component fusion methods. Based on the model's prediction results, the future water quality level of the water body is predicted, and a graded early warning process is automatically triggered when the indicators exceed the preset threshold.

8. The integrated intelligent monitoring system for air-space-ground-water power supply based on wind-solar hybrid power supply according to claim 7, characterized in that: Based on the deviation between the water quality prediction results and the actual monitoring data, the sampling frequency and inspection path of the sensing and monitoring module are dynamically adjusted.

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