Water transfer engineering data system based on netcdf structure

Abstract

The invention discloses a water transfer engineering data system based on a netcdf structure. The water transfer engineering data system comprises a multi-dimensional data storage structure, a data increment updating mechanism, a real-time data monitoring and early warning module and a data association analysis module. Complex spatio-temporal data processing, especially meteorological and hydrological data, is supported through a NetCDF format, and transparency, integrity and traceability in a data updating process are ensured in combination with a block chain technology and an intelligent contract. Meanwhile, data can be dynamically monitored, a self-adaptive early warning algorithm is integrated, the method has the characteristics of modularization, cross-platform compatibility and the like, the data processing efficiency and safety are effectively improved, powerful technical support is provided for safe operation and decision support of a water transfer project, and the method has wide application prospects and practical value.

Description

Technical Field

[0001] The invention relates to the technical field of water resource data management, and is particularly applicable to a water diversion engineering data system based on a netcdf structure. Background Art

[0002] As a large-scale cross-regional water resource dispatching project, water diversion projects involve extensive meteorological, hydrological, geographical and economic data management. Such projects usually span multiple regions and face complex spatiotemporal multidimensional data processing and management requirements. Existing database management systems, especially relational databases (such as MySQL, PostgreSQL, etc.), although they perform well in processing structured data and small-scale data sets, have significant limitations when facing complex, multi-source spatiotemporal data such as water diversion projects.

[0003] First, the table structure of relational databases does not perform well in processing multidimensional data (such as meteorological and hydrological data). The performance and flexibility of traditional relational databases are limited when processing high-dimensional, multi-source data fusion, especially in application scenarios such as real-time data updates, spatiotemporal data integration, and dynamic analysis. The efficiency is low and the data query speed is slow. In addition, traditional databases also have significant deficiencies in processing incremental updates, dynamic data analysis, and real-time monitoring, especially when dealing with data management of large-scale cross-regional water transfer projects, which often cannot meet the needs.

[0004] NetCDF (Network Common Data Format) is a scientific data storage and management standard specially designed for processing multidimensional data sets and is widely used in meteorology, hydrology, oceanography and other fields. It has flexible dimension definition, self-descriptive and incremental update characteristics, which can well meet the complex data requirements of water diversion projects. However, existing NetCDF applications are mainly focused on static data management, and the deep integration of dynamic data, real-time monitoring and automated management of cross-source data have not been widely used. Therefore, it is an urgent need in the current water resources management field to develop a water diversion project data system based on the NetCDF structure for efficient management of incremental updates, real-time monitoring and data analysis of multi-source data. Summary of the invention

[0005] The present invention aims to provide a water diversion project data system based on the NetCDF structure, aiming to solve the shortcomings of existing relational data in processing multi-dimensional spatiotemporal data, multi-source data fusion and real-time data update in water diversion project data.

[0006] To achieve the above object, the present invention adopts the following technical solutions: The water diversion project data system based on the netcdf structure of the present invention includes a multidimensional data storage structure, a data incremental update mechanism, a real-time data monitoring and early warning module, and a data association analysis module; The multidimensional data storage structure includes dividing the time dimension and space dimension for each dimensional data of each data source, determining the standard unit and description of the data of each data source; and integrating the data of each data source by using a dynamic spatiotemporal fusion algorithm combined with a netcdf structure; The data incremental update mechanism is based on the netcdf structure to expand the data of each data source according to the time dimension. At the same time, blockchain technology is used to generate a new block for each data update operation and add it to the blockchain; smart contracts are used to automatically manage data update strategies; The real-time data monitoring and early warning directly connects the sensor data to the data system through wireless communication, and automatically stores it according to the time dimension based on the netcdf structure; it uses adaptive early warning algorithms and anomaly detection algorithms to dynamically adjust the early warning threshold, identify abnormal situations, and trigger early warning signals; The data association analysis module identifies the correlation between data from various data sources through a variety of data analysis methods, establishes a data prediction analysis model based on a machine learning algorithm, and presents it through the Java panoply visualization platform.

[0007] Furthermore, the multi-source data includes meteorological, hydrological, geographical, and economic data; and through time interpolation and space interpolation, it is ensured that the data from each data source are integrated according to the same time dimension and space dimension.

[0008] Furthermore, the dynamic spatiotemporal fusion algorithm includes data weight fusion, which assigns different weights to the data from each data source according to credibility and accuracy. When the data from each data source conflicts, the data is fused according to the data weight.

[0009] Furthermore, the smart contract presets trigger conditions and rules for data updates, can dynamically adjust the frequency of data updates, and automatically adjust the priority of data updates.

[0010] Furthermore, a modular architecture of microservice architecture or service-oriented architecture is adopted, and each module communicates with each other through API or message middleware.

[0011] Furthermore, a standardized open API interface is provided.

[0012] The advantage of the present invention is that it supports complex spatiotemporal data processing, especially meteorological and hydrological data, through the NetCDF format, and combines blockchain technology with smart contracts to ensure transparency, integrity and traceability during data update. At the same time, the present invention can dynamically monitor data and integrates an adaptive early warning algorithm, with the characteristics of modularization and cross-platform compatibility, effectively improving data processing efficiency and security, providing strong technical support for the safe operation and decision support of water diversion projects, and has broad application prospects and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic diagram of a water diversion project data system based on a NetCDF structure described in the present invention.

[0014] Figure 2 Schematic diagram of the multi-source data storage structure of the present invention.

[0015] Figure 3 Schematic diagram of the data incremental update mechanism described in the present invention.

[0016] Figure 4 Schematic diagram of the real-time data monitoring and early warning module of the present invention.

[0017] Figure 5 Schematic diagram of the data association analysis module described in the present invention.

[0018] Figure 6 Schematic diagram of the modular architecture of the system of the present invention. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention are described clearly and completely below. 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.

[0020] like Figure 1 As shown, the water diversion project data system based on the netcdf structure described in the present invention is suitable for the multidimensional data system of the water diversion project database, specifically including the storage and management of multidimensional data from different sources such as meteorology, hydrology, geography, and economy, specifically including a multidimensional data storage structure, a data incremental update mechanism, a real-time data monitoring and early warning module, and a data correlation analysis module.

[0021] The multidimensional data storage structure includes dividing the time dimension and space dimension for each dimension of data from each data source, determining the standard unit and description of the data from each data source; and integrating the data from each data source using a dynamic spatiotemporal fusion algorithm combined with a netcdf structure. Figure 2As shown, it is a schematic diagram of the multidimensional data storage structure of the present invention.

[0022] The precipitation data used in water diversion projects involve precipitation data at different times, different spaces, and different sites. At the same time, the precipitation data may also come from multiple different data sources such as meteorological data and hydrological data. Elevation data also involves elevation values ​​at different times, different spaces, and different sites. The elevation values ​​come from multiple different data sources such as hydrology and geography.

[0023] The multidimensional data storage structure designed for the water diversion project data system of the present invention in the netcdf structure divides the various dimensional data of each data source into time dimension and space dimension. Through the scientific division of time and space dimensions, the system can effectively organize and integrate these data and realize dynamic management and query analysis.

[0024] According to the long-term operation characteristics of water diversion projects, this paper adopts the infinite time dimension of NetCDF and divides the time dimension according to the needs of different types of data, such as years, months, days, hours and other time units, to meet the data storage needs of different time scales. For example, precipitation data at a meteorological station may be recorded hourly, while economic data may be recorded annually.

[0025] Similarly, the spatial dimension is based on the geographical location of the water diversion project and is defined using a longitude and latitude coordinate system to ensure the precise location of each data point in space.

[0026] In order to integrate data from different sources and with different resolutions, the present invention ensures that the data from each data source are integrated according to the same time dimension and space dimension through time interpolation and space interpolation.

[0027] Time interpolation algorithms include linear interpolation and nonlinear interpolation. Linear interpolation is suitable for simple time series data. For example, the annual summary of economic data can be converted into a higher time resolution of monthly or daily through linear interpolation. Nonlinear interpolation, such as cubic spline interpolation and Lagrange interpolation, is suitable for processing complex meteorological and hydrological data. This type of interpolation method can ensure the smoothness and accuracy of the data during the time change process. Especially when the data has periodic fluctuations, nonlinear interpolation can generate more accurate time series and ensure the consistency of the data. For example, for precipitation data, cubic spline interpolation can be used to smoothly expand the hourly recorded data into minute-level data.

[0028] Commonly used spatial interpolation methods include the nearest neighbor method, bilinear interpolation, and inverse distance weighted (IDW). For example, low-resolution data of land use data can be simply interpolated using the nearest neighbor method, while meteorological data (such as temperature and precipitation) can be improved in spatial resolution using bilinear interpolation to ensure a smooth transition of the data. For discrete precipitation observation point data, the inverse distance weighted method can be applied to generate a high-precision rainfall grid covering the entire area.

[0029] At the same time, since meteorological data stores precipitation, temperature, humidity, etc., hydrological data covers variables such as river flow and water level, geographic data includes land use type, terrain elevation, etc., and economic data records GDP, agricultural irrigation costs, etc. These data need to be linked through time and space dimensions, so standard units and descriptions are defined for each variable to ensure data consistency, accuracy, and self-descriptiveness.

[0030] The dynamic spatiotemporal fusion algorithm is used to solve the problem of integrating multi-source data in time and space. Through this algorithm, the system can process data from different sources, different time intervals and different spatial resolutions to ensure data alignment and consistency.

[0031] The present invention also includes data weight fusion in the dynamic spatiotemporal fusion algorithm. This mechanism assigns different weights to data based on factors such as the credibility and accuracy of the data source. For example, when there is a conflict in multi-source data, data with a higher weight (such as data from an authoritative weather station) will occupy a higher proportion in the fusion result, thereby ensuring the accuracy and reliability of the data after integration.

[0032] The data incremental update mechanism in the water diversion project data system based on the netcdf structure of the present invention is based on the netcdf structure to expand the data of each data source according to the time dimension. At the same time, blockchain technology is used to generate a new block for each data update operation and add it to the blockchain; smart contracts are used to automatically manage data update strategies. Figure 3 As shown, In this module, the infinite dimension feature of NetCDF is used to achieve seamless expansion of time series data, ensuring that new meteorological, hydrological or economic data can be efficiently added to the existing data without destroying the integrity of the existing data. The design of the time dimension enables each data update to be properly archived and organized, supporting long-term data accumulation and management.

[0033] like Figure 3As shown in the figure, blockchain technology is introduced into the data incremental update mechanism. Each data update operation will generate a new block and add it to the blockchain to ensure that the data update process cannot be tampered with and is completely traceable. Each block contains detailed information about the data update, such as update content, timestamp, data source, modified fields, etc. These blocks generate unique hash values ​​through encryption algorithms and are linked together to form an unalterable chain structure. If the data is changed or there are multiple operations, the system ensures the validity and legitimacy of the update through the consensus mechanism of the blockchain, further ensuring the integrity and transparency of the data.

[0034] Smart contracts are used to dynamically manage data update strategies. The trigger conditions and rules for data updates are set in smart contracts. When certain preset conditions are met, the smart contract will automatically execute the corresponding data update operations to ensure the efficient operation of the system. For example, the smart contract can set the update frequency of meteorological data to once an hour, while the update frequency of economic data is once a month. Different types of data can be updated with different frequencies and priorities through the scheduling mechanism of smart contracts.

[0035] Smart contracts also have the function of automatically adjusting the priority of data updates. Depending on the importance and timeliness of the data, the contract can assign higher priority to certain types of data. For example, the update of real-time weather data has a higher urgency, so the smart contract will prioritize data from the weather station. For economic data or geographic data, the smart contract will dynamically adjust the update frequency according to demand to ensure the timeliness and consistency of high-priority data.

[0036] When the system load is high or the network status is unstable, the smart contract can also dynamically adjust the data update strategy. Smart contracts will reduce the update frequency of non-urgent data to ensure the overall efficiency and stability of the system. In addition, smart contracts can also manage the fault recovery mechanism during the data update process. Assuming that a data update fails, the contract will automatically trigger the recovery process, retry the update and ensure data consistency. If an update conflicts due to inconsistent data sources or timestamps, the smart contract will suspend the processing of the data, perform data verification, and decide whether to continue processing or reject the update based on the verification results.

[0037] By combining blockchain technology and smart contract mechanism, this system can realize incremental data update, which not only ensures the integrity and traceability of data, but also improves the security and reliability of the system when processing large-scale data. At the same time, when facing high-frequency data updates, the system can also dynamically adjust the data update strategy through smart contracts to ensure the real-time and consistency of water diversion project data.

[0038] The real-time data monitoring and early warning in the water diversion project data system based on the netcdf structure of the present invention is as follows: Figure 4 As shown, the sensor data is directly connected to the data system through wireless communication, and automatically stored in the time dimension based on the NetCDF structure; the adaptive early warning algorithm and anomaly detection algorithm are used to dynamically adjust the warning threshold, identify abnormal situations, and trigger warning signals.

[0039] By directly connecting the field sensor data to the data system, the dynamic monitoring and rapid response capabilities of the water diversion project are realized. Specifically, the system of the present invention is connected to various sensors installed in the water diversion project, including water level sensors, flow meters, meteorological stations, etc., to collect key data such as river water level, flow, precipitation, etc. in real time. The sensor transmits the data to the data center through a wireless communication protocol (such as LoRa, NB-IoT or 4G network), and automatically formats the data into a NetCDF structure through a standardized API interface and stores it in the database. This process ensures the spatiotemporal tagging, data cleaning and validity verification of the sensor data, and realizes the efficient storage and management of real-time data on the basis of ensuring data integrity and consistency.

[0040] During the data storage process, the system automatically archives sensor data by time dimension. Every time new data arrives, it can not only be seamlessly attached to the existing time series, but also continuously expand the data scale through the infinite dimension function of NetCDF. Through real-time stored data, managers can directly retrieve historical data and the latest data of any time period through the system and conduct comprehensive analysis.

[0041] The early warning mechanism uses an adaptive algorithm. According to the correlation and change trend between sensor data and historical data, the system can dynamically adjust the early warning threshold. For example, for water level monitoring, the system can automatically calculate the current reasonable threshold by combining historical water level data through a linear regression model or a statistically based adaptive threshold method. In addition, the system also introduces anomaly detection algorithms based on machine learning (such as k-means clustering or Isolation Forest). By learning from a large amount of historical data, it can automatically identify abnormal situations and optimize the early warning threshold. This adaptive mechanism ensures that the early warning can accurately reflect the current operating status and avoid false alarms or missed alarms caused by static threshold settings.

[0042] When the monitoring data exceeds the preset safety threshold, the system will automatically trigger an early warning signal and notify relevant managers in a variety of ways. Notification methods include: SMS alarm, email notification, control platform push, etc. The system also supports a graded early warning mechanism. According to the severity of the abnormal situation, the system will generate different levels of early warning signals. For example, the system will send a yellow early warning signal in a low-risk situation, and a red early warning signal in an emergency situation, ensuring that managers can make decisions and respond quickly. In addition, the system can send early warning information in real time through the management platform or mobile application to ensure that relevant personnel can receive alarm information in a timely manner no matter where they are.

[0043] The analysis of real-time monitoring data supports multi-dimensional association, which means that the system can automatically associate and analyze real-time data with historical data, sensor locations, and other dimensions. For example, the system can predict future water level trends based on the correlation between rainfall and water level changes. Through the GIS visualization platform, managers can intuitively view the spatial distribution of different sensors, monitoring values, and their comparison with historical data, and promptly discover potential anomalies. For example, in a certain area, if the water level at a certain monitoring point is significantly abnormal compared to other points, the system will automatically prompt managers that there may be problems in the area. By integrating real-time data monitoring and early warning mechanisms, strong support is provided for the safe operation of water diversion projects.

[0044] The data association analysis module in the water diversion project data system based on the netcdf structure of the present invention is as follows: Figure 5 As shown in the figure, a variety of data analysis methods are used to identify the correlation between data from various data sources, providing a basis for efficient water diversion project management; a data prediction and analysis model is established based on a machine learning algorithm, and presented through the Java panoply visualization platform.

[0045] The data association analysis module can perform cross-analysis and visualization of data of different dimensions. The module integrates data analysis methods and tools such as multivariate regression analysis, time series analysis, and principal component analysis, and supports efficient analysis of multi-source data. These methods enable the system to extract key relationships and rules from massive data and reveal the mutual influence between different variables.

[0046] For example, multiple regression analysis can help managers analyze the correlation between precipitation and river flow and identify the hydrological changes in the basin under different precipitation conditions. Time series analysis is used to detect the changing trends of temporal data such as meteorology and hydrology in the basin and predict future flow changes or abnormal meteorological events. Principal component analysis is used for dimensionality reduction and important factor extraction of multidimensional data, helping managers extract the most representative indicators from complex multi-source data and simplify the analysis process.

[0047] In addition, the data association analysis module integrates machine learning algorithms (such as random forests, support vector machines, etc.) to perform predictive analysis on historical data. For example, when analyzing precipitation data in a certain area, the system predicts the impact of future precipitation on river flow through training models, thereby helping managers to respond to possible flood risks in advance. This data-driven predictive analysis has greatly improved the decision-making efficiency and accuracy of water diversion projects.

[0048] In order to make data association analysis more intuitive, the data association analysis module integrates the Java-based panoply visualization platform. Through this platform, the system can present complex data relationships in a graphical way, which is convenient for managers to intuitively understand the changes in water diversion projects. For example, through the panoply visualization platform, managers can view the spatial distribution of hydrological and meteorological data (such as real-time temperature, specific humidity, water level, and flow) in different regions in real time, further analyze the relationship between precipitation and river water level, and the impact of water diversion projects on regional economy and environment, and improve the management and scheduling efficiency of water diversion projects. Through the real-time nature of data, the adaptability of early warnings, and the correlation analysis of multi-dimensional data, managers can more effectively grasp the operating status of the project, discover and deal with potential problems as early as possible, and greatly improve the safety and operation efficiency of water diversion projects.

[0049] Finally, the water diversion project data system based on the netcdf structure described in the present invention adopts a modular architecture of microservice architecture or service-oriented architecture, such as Figure 6 As shown, each module communicates with each other through API or message middleware and provides a standardized open API interface.

[0050] The modular system architecture enables each functional module to operate independently and independently of each other. Functional modules such as data storage, incremental update, monitoring and early warning, and data analysis can be flexibly combined according to actual needs. The modular design also ensures the scalability of the system, allowing the water diversion project management system to adapt to the needs of different scenarios and stages. The functional upgrade or expansion of the system only requires modifying or adding a single module without affecting the operation of other modules, thereby improving the maintenance and expansion efficiency of the system.

[0051] The water diversion project data system based on the netcdf structure described in the present invention adopts a microservice architecture or a modular architecture of a service-oriented architecture to split the system functions into independent services, and each service is responsible for a single functional module, such as a multi-source data dynamic spatiotemporal fusion interface, a NETCDF storage interface, a blockchain interface for data updates, a sensor data real-time inflow interface, an early warning trigger interface, and a correlation analysis and visualization interface, to ensure that each module can be independently upgraded or modified, while supporting horizontal expansion to cope with the increasing amount of data and functional requirements.

[0052] In order to ensure that external systems can be seamlessly integrated with this system, the system provides standardized open API interfaces. Through these API interfaces, external applications, database systems or analysis platforms can interact with this system efficiently to achieve data query, write and management operations. The API interface supports the standard HTTP protocol, follows the RESTful style, and provides common operations such as GET, POST, PUT, DELETE, etc. The API also supports data exchange in JSON and XML formats, ensuring that external systems can easily use data in different formats.

[0053] The specific design of the API interface also includes identity authentication and authorization mechanisms. The system uses the OAuth2.0 protocol to ensure the security of API calls, ensuring that only authorized users and applications can access the core data of the system. At the same time, the API interface also supports encrypted transmission based on SSL / TLS to prevent data from being stolen or tampered with during network transmission.

[0054] The cross-platform compatibility of the system is achieved by using containerization technology (such as Docker). Containerization technology can package the various functional modules of the system into independent containers, so that they can run efficiently in different operating systems and hardware environments. By using container orchestration tools such as Kubernetes, the system can be easily expanded to ensure stability in large-scale data and high-concurrency environments. In addition, the system uses platform-independent programming languages ​​(such as Java and Python) and cross-platform frameworks (such as Spring and Django), which further ensures that the system can be deployed and run on different cloud platforms and local servers.

[0055] Whether the system is deployed on a local server, a public cloud, or a private cloud platform, the system's modular design and open API interface can ensure its efficient and stable operation, while achieving seamless switching between different deployment environments through cross-platform technology. In this way, the water diversion project management system can flexibly respond to a variety of data management and analysis tasks according to different deployment requirements and usage scenarios, ensuring the system's efficiency, reliability, and scalability.

[0056] Example 2: The water diversion project data system based on the netcdf structure described in the present invention is directed to the specific application process of multi-source data in the Yellow River Basin.

[0057] S1: Collect multi-source data in the Yellow River Basin and design data storage structure.

[0058] In the Yellow River Basin Water Diversion Project, it is first necessary to collect and store data from multiple dimensions such as meteorology, hydrology, geography and economy. It is assumed that meteorological data includes annual precipitation data from 1990 to 2023, in billion cubic meters, ranging from 20 billion to 60 billion cubic meters; temperature data ranges from -10°C to 35°C. Hydrological data includes the average annual flow of the river, which is assumed to range from 1,000 cubic meters per second to 5,000 cubic meters per second, and water level data varies from 50 meters to 100 meters. Geographic data covers land use type and terrain elevation, assuming that terrain elevation ranges from 50 meters to 1,500 meters. Economic data records the GDP of provinces in the Yellow River Basin, assuming that it ranges from 100 billion yuan to 500 billion yuan.

[0059] These data are stored in multiple dimensions in the NetCDF file format. The time dimension is divided into years and months, and the space dimension covers the main areas of the Yellow River Basin in the longitude and latitude coordinate system. Through this design of time and space dimensions, the system can manage and query multi-source data in the Yellow River Basin and realize long-term storage and management of data.

[0060] S2: Define an incremental update mechanism to ensure real-time update and integrity of data.

[0061] In order to meet the dynamic data update needs of the Yellow River Basin, the infinite time dimension feature of NetCDF is used to realize incremental data updates. For example, on January 1, 2024, the system automatically appends the newly added precipitation and river flow data to the database without affecting the existing historical data.

[0062] Each time the data is updated, the system generates a new block to record the details of the update. Assuming that the size of each block is 2MB, 1 block will be added every day. Through the tamper-proof nature of the blockchain, the system can ensure the transparency and integrity of the data update process and avoid the risk of data loss or tampering.

[0063] Smart contract technology is also used in data management strategies for water diversion projects. During the flood season in the Yellow River Basin (for example, June to September), smart contracts automatically increase the frequency of data updates from monthly to weekly, giving priority to updating meteorological and hydrological data to ensure real-time and data accuracy. In the dry season, the frequency of data updates can be automatically reduced to save system resources.

[0064] S3: Real-time monitoring and early warning mechanism.

[0065] The system monitors key hydrological data in real time through 500 sensor nodes distributed in the Yellow River Basin. Assume that these sensors collect water level, flow and precipitation data once an hour. For example, during the flood in July 2023, sensors located upstream of the river detected a sharp rise in water levels from 80 meters to 95 meters, exceeding the preset safety threshold. The system immediately triggered an early warning mechanism and notified managers to take emergency measures through SMS, email and phone calls.

[0066] The adaptive warning algorithm dynamically adjusts the warning threshold based on historical data. For example, if historical data shows that floods are frequent from July to September, the system will automatically lower the threshold for water level warnings, making the warning system more sensitive to extreme situations.

[0067] S4: Multidimensional data association analysis and visualization.

[0068] Through multi-dimensional data association analysis, the system can deeply explore the interrelationships between different data in the Yellow River Basin. For example, by analyzing the precipitation and river flow data from 1990 to 2023, the system found that when the precipitation in a year exceeds 40 billion cubic meters, there is an 80% probability that the river flow will reach 5,000 cubic meters per second, increasing the risk of floods.

[0069] Based on machine learning algorithms (such as random forest and time series analysis), the system can also predict flood trends in the next five years. Through the GIS visualization platform, managers can view hydrological, meteorological and economic data of different regions in the Yellow River Basin in real time. Assuming the precipitation distribution shown on the map, during July 2023, the precipitation in the upper reaches of the Yellow River reached 45 billion cubic meters, the color was displayed as dark blue, and the flow was displayed as 5,000 cubic meters per second. Real-time visualization helps managers make decisions.

[0070] S5: Modular design and cross-platform compatibility In order to ensure the scalability of the system, a modular design is adopted. Assuming that at a certain stage, users only need to monitor the water level and flow data of the upper Yellow River, they can choose to load only the monitoring module and the early warning module without loading the data analysis module. The modular design allows flexible configuration according to specific needs and reduces the waste of system resources.

[0071] The API interface allows users to directly access the hydrological, meteorological and economic data of the Yellow River Basin from external systems. Through the API interface, the external meteorological analysis platform can obtain the latest precipitation data from the database every hour and use this data for other model analysis. In addition, the system supports local server and cloud deployment, and can adapt to different IT infrastructure environments, whether it is running locally at the monitoring site in the Yellow River Basin or centrally managed in the cloud.

Claims

1. A water diversion project data system based on netcdf structure, characterized in that: include: Multi-dimensional data storage structure, data incremental update mechanism, real-time data monitoring and early warning module, data correlation analysis module; The multidimensional data storage structure includes dividing the time dimension and space dimension for each latitude data of each data source, determining the standard unit and description of each data source data; and integrating the data of each data source by using a dynamic spatiotemporal fusion algorithm combined with a netcdf structure; The data incremental update mechanism is based on the netcdf structure to expand the data of each data source according to the time dimension. At the same time, blockchain technology is used to generate a new block for each data update operation and add it to the blockchain; smart contracts are used to automatically manage data update strategies; The real-time data monitoring and early warning directly connects the sensor data to the data system through wireless communication, and automatically stores it according to the time dimension based on the netcdf structure; it uses adaptive early warning algorithms and anomaly detection algorithms to dynamically adjust the early warning threshold, identify abnormal situations, and trigger early warning signals; The data association analysis module identifies the correlation between data from various data sources through a variety of data analysis methods, establishes a data prediction analysis model based on a machine learning algorithm, and presents it through the Java panoply visualization platform.

2. A water diversion project data system based on a netcdf structure according to claim 1, characterized in that: The multi-source data include meteorological, hydrological, geographical, and economic data; through time interpolation and space interpolation, it is ensured that the data from each data source are integrated according to the same time dimension and space dimension.

3. The water diversion project data system based on the netcdf structure according to claim 1 is characterized by: The dynamic spatiotemporal fusion algorithm includes data weight fusion, which assigns different weights to the data from each data source according to credibility and accuracy. When the data from each data source conflicts, the data is fused according to the data weight.

4. The water diversion project data system based on the netcdf structure according to claim 1 is characterized by: The smart contract presets the triggering conditions and rules for data updates, can dynamically adjust the frequency of data updates, and automatically adjust the priority of data updates.

5. The water diversion project data system based on the netcdf structure according to claim 1 is characterized by: A modular architecture of microservice architecture or service-oriented architecture is adopted, and each module communicates through API or message middleware.

6. The water diversion project data system based on the netcdf structure according to claim 1 is characterized by: Provide a standardized open API interface.

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