Reservoir water conservancy monitoring and intelligent decision-making method based on multi-modal sensing fusion

Abstract

The invention particularly relates to a reservoir water conservancy monitoring and intelligent decision-making method based on multi-modal sensing fusion. According to the reservoir water conservancy monitoring and intelligent decision-making method based on multi-modal sensing fusion, the water level, rainfall, water quality parameters and meteorological data of a reservoir are collected in real time through a multi-modal sensor and transmitted to a data center through a wireless communication technology for data preprocessing; fusing the collected multi-modal data to generate a comprehensive reservoir operation state description; based on a real-time data processing engine, high-throughput and low-delay data processing is realized; and generating intelligent decision suggestions by utilizing a machine learning model and combining historical data and real-time data. According to the reservoir water conservancy monitoring and intelligent decision-making method based on multi-modal sensing fusion, the limitation of traditional single data monitoring is broken through, multi-modal fusion and intelligent upgrading are achieved, the reservoir operation state can be accurately described, mass data can be efficiently processed, the optimal decision can be recommended in real time, and guidance is provided for reservoir management work.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water conservancy monitoring, in particular to a reservoir water conservancy monitoring and intelligent decision-making method based on multi-modal perception fusion. BACKGROUND

[0002] In the traditional field of reservoir water conservancy monitoring, data has been collected mainly relying on a single type of sensor for a long time, such as using only water level gauges to monitor water level changes, or simply relying on rain gauges to record rainfall information. This monitoring mode based on a single data source has significant limitations:

[0003] On the one hand, its data coverage is extremely limited and it is difficult to fully and multi-angulary present the actual operating state of the reservoir. Since only the measurement value of a specific physical quantity can be obtained, a complete understanding of the overall working condition of the reservoir cannot be formed, making it difficult for management personnel to accurately grasp the complex dynamic process of the reservoir system.

[0004] On the other hand, this mode is severely lacking in intelligent decision-making support capabilities. In the face of massive and complex hydrological data, traditional methods cannot perform real-time deep mining and analysis, making it difficult to provide scientific and reasonable management recommendations and optimization schemes for managers in a timely manner, thereby restricting the accuracy and efficiency of reservoir scheduling.

[0005] In addition, as the monitoring scale continues to expand, the traditional system has obvious shortcomings in data processing and analysis efficiency. Limited by technical level and architecture design, it is difficult to effectively handle real-time processing of large-scale data, and there is a significant performance bottleneck, which cannot meet the high requirements of modern water conservancy management for timeliness and response speed.

[0006] In summary, the single modal data collection method of the traditional reservoir water conservancy monitoring system has been difficult to adapt to the intelligent and refined development needs of the current water conservancy industry, and it is urgent to introduce multi-modal data fusion technology to build a more comprehensive, intelligent and efficient monitoring system.

[0007] Based on the above situation, the present application provides a reservoir water conservancy monitoring and intelligent decision-making method based on multi-modal perception fusion. SUMMARY

[0008] The present application provides a simple and efficient reservoir water conservancy monitoring and intelligent decision-making method based on multi-modal perception fusion to overcome the shortcomings of the prior art.

[0009] The present application is achieved by the following technical solutions:

[0010] A reservoir water conservancy monitoring and intelligent decision-making system based on multi-modal perception fusion, characterized by comprising the following steps:

[0011] Step S1, real-time collection of water level, rainfall, water quality parameters and meteorological data of the reservoir by multi-modal sensors, transmission to the data center for data preprocessing through wireless communication technology;

[0012] Step S2, fusion of the collected multi-modal data to generate a comprehensive description of the reservoir operation state;

[0013] Step S3, based on real-time data processing engine, high throughput and low latency data processing is realized;

[0014] Step S4, using machine learning model, combining historical data and real-time data, intelligent decision-making suggestions are generated.

[0015] In step S1, the multi-modal sensors include water level gauge, rain gauge, weather station and water quality sensor;

[0016] Real-time collected data is transmitted to the data center using LoRa or NB-IoT technology, and the collected data is preprocessed, including data cleaning, noise reduction and unified formatting.

[0017] In step S2, through data fusion algorithm, different modal data collected in real time is comprehensively analyzed to generate a comprehensive description of the reservoir operation state;

[0018] Deep learning technology is used to automatically extract data features and improve fusion effect.

[0019] In step S3, the real-time data processing engine realizes real-time data stream processing based on Apache Flink or Apache Kafka, supports high throughput and low latency data processing, and dynamically adjusts data processing parameters to optimize processing efficiency according to system load and data characteristics.

[0020] In step S4, using machine learning model, combining historical data and real-time data, intelligent decision-making suggestions are generated; decision support is provided for flood warning and water quality improvement measures to help managers scientifically manage the reservoir.

[0021] A reservoir water conservancy monitoring and intelligent decision-making system based on multi-modal perception fusion is used to realize the above method, which adopts edge cloud collaborative architecture, the edge side includes data collection layer, the cloud side includes data processing layer, intelligent analysis layer and application layer;

[0022] The data collection layer includes multi-modal sensors and data transmission module;

[0023] Multi-modal sensors are responsible for integrating multiple sensors, including water level gauge, rain gauge, water quality sensor and weather station, real-time collection of water level, rainfall, water quality parameters and meteorological data of the reservoir;

[0024] Data transmission module, responsible for transmitting sensor data to data center through wireless communication technology (such as LoRa, NB-IoT);

[0025] The data processing layer includes a data preprocessing module, a multi-modal data fusion module, and a real-time data processing engine.

[0026] The data preprocessing module is responsible for cleaning, denoising, and formatting the collected multi-modal data.

[0027] The multi-modal data fusion module is responsible for fusing the collected multi-modal data through data fusion algorithms to generate a comprehensive description of the reservoir operation state.

[0028] The real-time data processing engine is responsible for implementing real-time data stream processing based on Apache Flink or Apache Kafka, supporting high throughput and low latency data processing.

[0029] The intelligent analysis layer includes a machine learning model training module and an intelligent decision module.

[0030] The machine learning model training module is responsible for training machine learning models using historical data to implement intelligent analysis of reservoir water level changes, flood prediction, and water quality changes.

[0031] The intelligent decision module is responsible for generating intelligent decision recommendations based on analysis results, including flood warning and water quality improvement measures.

[0032] The application layer is responsible for providing an intuitive graphical interface to display the real-time operation state of the reservoir and intelligent decision recommendations, providing scientific decision support for reservoir management personnel, including flood warning, scheduling recommendations, and water quality management.

[0033] A reservoir water conservancy monitoring and intelligent decision-making construction device based on multi-modal perception fusion, characterized by: including a memory and a processor; the memory is used to store a computer program, and the processor is used to execute the computer program to realize the method steps described above.

[0034] A readable storage medium, characterized by: the readable storage medium stores a computer program, and the computer program is executed by a processor to realize the method steps described above.

[0035] The advantages of the present application are: the reservoir water conservancy monitoring and intelligent decision-making method based on multi-modal perception fusion breaks through the limitations of traditional single data monitoring, realizes multi-modal fusion and intelligent upgrading, can accurately describe the reservoir operation state, efficiently processes massive data, and real-time recommends optimal decisions, providing guidance for reservoir management work. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to make the technical personnel in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described below in conjunction with the drawings needed in the embodiment or prior art description. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor should belong to the scope of protection of the present application.

[0037] Appendix Figure 1 The present application is based on a multi-modal perception fusion reservoir water conservancy monitoring and intelligent decision-making system schematic diagram. DETAILED DESCRIPTION

[0038] In order to make the technical personnel in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described below in conjunction with the drawings needed in the embodiment or prior art description. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor should belong to the scope of protection of the present application.

[0039] The multi-modal perception fusion-based reservoir water conservancy monitoring and intelligent decision-making method comprises the following steps:

[0040] Step S1, real-time acquisition of water level, rainfall, water quality parameters and meteorological data of the reservoir by multi-modal sensors, transmission to the data center for data preprocessing by wireless communication technology;

[0041] Step S2, fusion of the collected multi-modal data to generate a comprehensive reservoir operation state description;

[0042] Step S3, based on real-time data processing engine, realizing high throughput and low delay data processing;

[0043] Step S4, using machine learning model, combining historical data and real-time data to generate intelligent decision-making suggestions.

[0044] In step S1, the multi-modal sensor includes a water level gauge, a rain gauge, a weather station and a water quality sensor;

[0045] Use LoRa or NB-IoT technology to transmit the real-time collected data to the data center, and preprocess the collected data, including data cleaning, noise reduction and unified formatting.

[0046] In step S2, by using data fusion algorithm, the real-time collected data of different modalities are comprehensively analyzed to generate a comprehensive reservoir operation state description;

[0047] And use deep learning technology to automatically extract data features to improve the fusion effect.

[0048] In step S3, the real-time data processing engine implements real-time data stream processing based on Apache Flink or Apache Kafka, supporting high throughput and low latency data processing; at the same time, dynamically adjusting data processing parameters, optimizing processing efficiency according to system load and data characteristics.

[0049] In step S4, use machine learning models to combine historical data and real-time data to generate intelligent decision-making suggestions; provide decision support for flood warning and water quality improvement measures, and help managers scientifically manage the reservoir.

[0050] Example 1: Reservoir water level monitoring and flood warning

[0051] Data characteristics:

[0052] Data sources: water level gauge, rain gauge, weather station.

[0053] Data format: JSON format, containing timestamp, water level, rainfall, air temperature, etc.

[0054] Implementation steps:

[0055] Data collection: Collect data in real time through water level gauges, rain gauges and weather stations.

[0056] Data transmission: Use LoRa or NB-IoT technology to transmit data to the data center.

[0057] Data processing: Clean, denoise and format the collected data.

[0058] Multi-modal data fusion: Combine water level, rainfall and weather data to generate a comprehensive description of the reservoir's operating status.

[0059] Intelligent analysis: Use machine learning models to predict water level changes and flood risks.

[0060] Decision support: Generate flood warning and scheduling suggestions to help managers take timely action.

[0061] Example 2: Reservoir water quality monitoring and management

[0062] Data characteristics:

[0063] Data sources: water quality sensors.

[0064] Data format: JSON format, containing timestamp, pH value, dissolved oxygen, chemical oxygen demand, etc.

[0065] Implementation steps:

[0066] Data collection: Collect data in real time through water quality sensors.

[0067] Data Transmission: Use LoRa or NB-IoT technology to transmit data to the data center.

[0068] Data Processing: Clean, denoise and format the collected data.

[0069] Multimodal Data Fusion: Combine water quality data and weather data to generate a comprehensive description of the reservoir water quality status.

[0070] Intelligent Analysis: Use machine learning models to analyze water quality trends.

[0071] Decision Support: Generate water quality improvement recommendations to help managers scientifically manage reservoir water quality.

[0072] The reservoir water conservancy monitoring and intelligent decision-making system based on multimodal perception fusion is used to implement the above method, adopts an edge cloud collaborative architecture, the edge side includes a data collection layer, the cloud side includes a data processing layer, an intelligent analysis layer and an application layer;

[0073] The data collection layer includes multimodal sensors and a data transmission module;

[0074] Multimodal sensors are responsible for integrating multiple sensors, including water level gauges, rain gauges, water quality sensors and weather stations, to collect real-time water level, rainfall, water quality parameters and weather data of the reservoir;

[0075] The data transmission module is responsible for transmitting sensor data to the data center through wireless communication technology (such as LoRa, NB-IoT);

[0076] The data processing layer includes a data preprocessing module, a multimodal data fusion module and a real-time data processing engine;

[0077] The data preprocessing module is responsible for cleaning, denoising and formatting the collected multimodal data.

[0078] The multimodal data fusion module is responsible for combining multiple data sources and fusing the collected multimodal data through data fusion algorithms to generate a comprehensive description of the reservoir operation status;

[0079] The real-time data processing engine is responsible for implementing real-time data stream processing based on Apache Flink or Apache Kafka, supporting high throughput and low latency data processing;

[0080] The intelligent analysis layer includes a machine learning model training module and an intelligent decision-making module;

[0081] The machine learning model training module is responsible for training the machine learning model using historical data to realize intelligent analysis of reservoir water level change, flood prediction and water quality change.

[0082] The intelligent decision-making module is responsible for generating intelligent decision-making suggestions according to the analysis results, including flood warning and water quality improvement measures.

[0083] The application layer is responsible for providing an intuitive graphical interface to display the real-time operation state of the reservoir and intelligent decision-making suggestions, providing scientific decision-making support for reservoir management personnel, including flood warning, scheduling suggestions and water quality management.

[0084] The reservoir water conservancy monitoring and intelligent decision-making device based on multi-modal perception fusion comprises a memory and a processor; the memory is used to store a computer program, and the processor is used to execute the computer program to realize the method steps described above.

[0085] The computer program is stored on the readable storage medium, and the computer program is executed by the processor to realize the method steps described above.

[0086] Compared with the prior art, the reservoir water conservancy monitoring and intelligent decision-making method based on multi-modal perception fusion has the following characteristics:

[0087] First, it breaks through the limitations of traditional single data monitoring and realizes multi-modal fusion and intelligent upgrading; by integrating multi-sensor data such as water level, rainfall, flow rate, etc., a full-dimensional perception network is constructed, which can accurately depict the operation state of the reservoir.

[0088] Second, relying on AI algorithms and digital twin models, dynamic scheduling schemes can be derived, and optimal decisions can be recommended in real time, changing experience-driven to data-intelligent-driven.

[0089] Third, the edge cloud collaborative architecture can efficiently process massive data and respond to sudden emergencies in milliseconds.

[0090] The above-described embodiments are only one of the specific embodiments of the present application, and the usual changes and replacements made by those skilled in the art within the scope of the technical solutions of the present application should be included in the protection scope of the present application.

Claims

1. A reservoir water conservancy monitoring and intelligent decision-making method based on multi-modal perception fusion, characterized by: Step S1, real-time acquisition of water level, rainfall, water quality parameters and meteorological data of the reservoir by multi-modal sensors, transmission to the data center for data preprocessing through wireless communication technology; Step S2, fusion of the collected multi-modal data to generate a comprehensive description of the reservoir operation state; Step S3, based on real-time data processing engine, realizing high throughput and low delay data processing; Step S4, using machine learning model, combining historical data and real-time data to generate intelligent decision-making suggestions.

2. The multi-modal perception fusion based reservoir water conservancy monitoring and intelligent decision method according to claim 1, characterized in that: In step S1, the multi-modal sensors include water level gauge, rain gauge, weather station and water quality sensor; Use LoRa or NB-IoT technology to transmit the real-time collected data to the data center, and preprocess the collected data, including data cleaning, noise reduction and unified formatting.

3. The multi-modal perception fusion based reservoir water conservancy monitoring and intelligent decision method according to claim 1, characterized in that: In step S2, through data fusion algorithm, the real-time collected data of different modalities are comprehensively analyzed to generate a comprehensive description of the reservoir operation state; And use deep learning technology to automatically extract data features to improve the fusion effect.

4. The multi-modal perception fusion based reservoir water conservancy monitoring and intelligent decision method according to claim 1, characterized in that: In step S3, the real-time data processing engine realizes real-time data stream processing based on Apache Flink or Apache Kafka, supports high throughput and low delay data processing; at the same time, dynamically adjusts the data processing parameters to optimize the processing efficiency according to the system load and data characteristics.

5. The multi-modal perception fusion based reservoir water conservancy monitoring and intelligent decision method according to claim 1, characterized in that: In step S4, use machine learning model to combine historical data and real-time data to generate intelligent decision-making suggestions; provide decision support for flood warning and water quality improvement measures to help managers scientifically manage the reservoir.

6. A reservoir water conservancy monitoring and intelligent decision system based on multi-modal perception fusion, characterized in that: For realizing the method of any one of claims 1-5, edge cloud collaborative architecture is adopted, the edge side includes data acquisition layer, the cloud side includes data processing layer, intelligent analysis layer and application layer; The data acquisition layer includes multi-modal sensors and data transmission module; Multi-modal sensors are responsible for integrating multiple sensors, including water level gauge, rain gauge, water quality sensor and weather station, real-time acquisition of water level, rainfall, water quality parameters and meteorological data of the reservoir; The data transmission module is responsible for transmitting sensor data to the data center through wireless communication technology; The data processing layer includes data preprocessing module, multi-modal data fusion module and real-time data processing engine; The data preprocessing module is responsible for cleaning, denoising and formatting the collected multi-modal data. The multi-modal data fusion module is responsible for fusing the collected multi-modal data through data fusion algorithm to generate a comprehensive description of the reservoir operation state; The real-time data processing engine is responsible for realizing real-time data stream processing based on Apache Flink or Apache Kafka, supporting high throughput and low delay data processing; The intelligent analysis layer includes machine learning model training module and intelligent decision-making module; The machine learning model training module is responsible for training machine learning model with historical data to realize intelligent analysis of reservoir water level change, flood prediction and water quality change; The intelligent decision module is responsible for generating intelligent decision suggestions, including flood warning and water quality improvement measures, according to the analysis results. The application layer is responsible for providing an intuitive graphical interface to display the real-time operation state of the reservoir and the intelligent decision suggestions, thereby providing scientific decision support for the reservoir management personnel, including flood warning, scheduling suggestions and water quality management.

7. A reservoir water conservancy monitoring and intelligent decision-making construction device based on multi-modal perception fusion, characterized by: The computer program is stored in the memory and executed by the processor to implement the method in any one of claims 1-5.

8. A readable storage medium characterized by: The computer program is stored in the readable storage medium and executed by the processor to implement the method in any one of claims 1-5.

Images