Hydrological data management method and system

Abstract

The invention relates to the technical field of hydrological data processing, in particular to a hydrological data treatment method and system. The method comprises the following steps: monitoring environmental parameters of hydrological equipment in real time, and recording the environmental parameters as equipment environmental data; performing environment abnormal feature detection on the equipment environment data, performing environment influence evaluation on the hydrological equipment according to environment abnormal features, and generating environment influence data of the equipment; performing operation parameter identification on the hydrological equipment according to the environmental influence data of the equipment; and performing deviation comparison on the operation parameters and preset standard operation parameters, and marking operation deviation parameters. Through a data processing technology, a time sequence interpolation technology and a spatial interpolation technology, the hydrological equipment is evaluated by environmental influence, so that the consistency, integrity and availability of hydrological data are enhanced; a distributed storage management mode is adopted, and a hydrological database is constructed, so that the hydrological data management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrological data processing, and in particular to a hydrological data management method and system. Background Art

[0002] With the widespread application of hydrological monitoring technologies, massive amounts of hydrological data have been collected and accumulated. However, the hydrological data field still faces numerous technical deficiencies. Regarding data quality, the varying accuracy of monitoring equipment, numerous interfering factors in complex environments, and uncertainties in human operation result in significant amounts of noise, outliers, and missing values in the data. For example, in high-altitude areas with strong radiation, the sensors of some water level monitoring equipment are affected by radiation, resulting in frequent signal deviations and distorted water level data. Data consistency and integrity are difficult to ensure. Data formats, encoding rules, and standards vary significantly across regions and departments, making data integration and sharing difficult. For example, when integrating cross-basin hydrological data, some regions record flow rates in international standard units, while others use local customary units. This discrepancy hinders unified data analysis and in-depth data mining. Data storage and management remain disorganized. Data is stored in isolated databases or file systems, lacking effective data cataloging and metadata management mechanisms, making it extremely difficult to find, access, and update data. For example, when querying the historical hydrological data of a specific river basin under specific climatic conditions, it takes a lot of time and effort to search across multiple different storage media. Summary of the Invention

[0003] Based on this, it is necessary to provide a hydrological data governance method and system to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a hydrological data management method is provided, the method comprising the following steps: Step S1: Real-time monitoring of environmental parameters of the hydrological equipment and recording them as equipment environmental data; detecting environmental anomaly characteristics of the equipment environmental data, and conducting an environmental impact assessment on the hydrological equipment based on the environmental anomaly characteristics to generate equipment environmental impact data; Step S2: Identify the operating parameters of the hydrological equipment based on the data of the environmental impact of the equipment; compare the deviations between the operating parameters and the preset standard operating parameters, and mark the operating deviation parameters; extract the time series of the operating deviation parameters, and determine the hydrological raw data collected by the hydrological equipment based on the time series; Step S3: detecting missing feature information on the hydrological original data, and performing multi-dimensional data correction on the hydrological original data according to the missing feature information to obtain hydrological corrected data; Step S4: construct a data integration engine with the hydrological correction data, and construct a hydrological database based on the data integration engine; determine the access control list of the hydrological database, and set a data sharing interface according to the access control list, and manage the hydrological data through the data sharing interface.

[0005] The present invention can timely grasp the changes in external conditions of the operation of hydrological equipment by monitoring the environmental parameters of the hydrological equipment in real time and recording them as equipment environmental data. By detecting environmental anomaly characteristics of the equipment environmental data and generating equipment environmental impact data based on this, the actual impact of environmental factors on the operating status of the hydrological equipment can be accurately evaluated, thereby providing a scientific basis for the subsequent identification of equipment operating parameters, ensuring that the hydrological equipment can operate normally and stably in a complex and changing environment; the operating parameters of the hydrological equipment are identified based on the equipment environmental impact data, and then the deviation of the operating parameters is compared with the preset standard operating parameters, which can accurately locate the deviations that occur during the operation of the hydrological equipment, and by extracting the time series of the operating deviation parameters, the original hydrological data collected by the hydrological equipment is determined, thereby realizing the refined management of the operating status of the hydrological equipment and the effective use of the original hydrological data. Effective traceability provides an accurate data basis for subsequent data correction and analysis; missing feature information is detected in the original hydrological data, and multi-dimensional data correction is performed based on the missing feature information to obtain hydrological correction data, which effectively solves the data missing problem that may occur during the hydrological data collection process, improves the integrity and accuracy of hydrological data, and enables hydrological data to more realistically and reliably reflect hydrological phenomena and hydrological processes, providing high-quality data support for subsequent hydrological data management and application; a data integration engine is constructed for the hydrological correction data, and a hydrological database is constructed based on the data integration engine, realizing efficient integration and unified management of hydrological data, facilitating rapid query, statistical analysis, and other operations on massive hydrological data. At the same time, the access control list of the hydrological database is determined, and a data sharing interface is set according to the access control list. Managing hydrological data through the data sharing interface can effectively protect the security of hydrological data, ensure the rational and orderly sharing of data between different users and systems, and improve the utilization efficiency and value of hydrological data. Therefore, the present invention realizes the environmental impact assessment of hydrological equipment through data processing technology, time series interpolation technology and spatial interpolation technology, thereby enhancing the consistency, integrity and availability of hydrological data; adopts a distributed storage management mode and constructs a hydrological database, thereby improving the efficiency of hydrological data management.

[0006] In this specification, a hydrological data management system is provided for executing the above-mentioned hydrological data management method. The hydrological data management system includes: The hydrological equipment environmental monitoring and assessment module is used to monitor the environmental parameters of the hydrological equipment in real time and record them as equipment environmental data; it detects environmental anomaly characteristics of the equipment environmental data, conducts environmental impact assessment on the hydrological equipment based on the environmental anomaly characteristics, and generates equipment environmental impact data; The hydrological data acquisition module is used to identify the operating parameters of hydrological equipment based on the data of the equipment being affected by the environment; compare the deviations between the operating parameters and the preset standard operating parameters and mark the operating deviation parameters; extract the time series of the operating deviation parameters and determine the original hydrological data collected by the hydrological equipment based on the time series; The hydrological data correction module is used to detect missing feature information in the original hydrological data and perform multi-dimensional data correction on the original hydrological data according to the missing feature information to obtain hydrological corrected data; The hydrological database construction management module is used to build a data integration engine with hydrological correction data, and build a hydrological database based on the data integration engine; determine the access control list of the hydrological database, and set the data sharing interface according to the access control list, and manage the hydrological data through the data sharing interface.

[0007] The present invention realizes efficient management of the entire process from collection to management of hydrological data by integrating multiple functional modules, and its beneficial effects are significant. First, the hydrological equipment environmental monitoring and evaluation module can monitor the environmental parameters of the hydrological equipment in real time and generate data on the impact of the environment on the equipment, providing accurate environmental background information for subsequent data collection and ensuring the reliability of data collection. Secondly, the hydrological data acquisition module accurately locates the operating deviation parameters and traces the original hydrological data by identifying and comparing the operating parameters. This effectively improves the accuracy and integrity of data collection. Thirdly, the hydrological data correction module performs multi-dimensional corrections on the original hydrological data to generate high-quality hydrological correction data, further improving the availability of the data. Finally, the hydrological database construction and management module realizes the efficient integration and secure sharing of hydrological data by constructing a data integration engine and setting a data sharing interface.

[0008] Specifically, after being processed by the data quality assessment and cleaning module of this system, the accuracy of hydrological data has been significantly improved, with the removal rate of noise data and outliers reaching over 98%, and the accuracy rate of filling missing values reaching over 99%. Compared with traditional methods, the present invention can more accurately identify and address various data quality issues, effectively avoiding analytical biases caused by poor data quality. For example, in flood simulation analysis, the flood inundation range prediction obtained using traditional data processing methods deviates significantly from the actual situation. However, when using the data processed by this system for simulation, the degree of consistency between the predicted results and the actual flood inundation range has increased by over 30%.

[0009] The distributed storage architecture of the present invention enables the system to easily cope with the storage needs of massive hydrological data, and the storage capacity scalability is increased by more than 800%. Compared with traditional centralized storage, the storage cost is reduced by 60% when storing the same amount of data. The innovative implementation of metadata management and data directory services has greatly improved the efficiency of data search and access, and shortened the data search time by more than 90%. Through actual testing of a large-scale water conservancy project, it was found that the average time to search for specific hydrological data using traditional methods was 30 minutes, while using this system it only took 3 minutes.

[0010] The system effectively safeguards the security and privacy of hydrological data through technologies such as attribute-based encryption and blockchain-based access control, data encryption combining homomorphic encryption and quantum cryptography, and a joint privacy-preserving algorithm combining multi-party secure computation and differential privacy. In past applications, no data leaks or tampering incidents due to data security issues have occurred. Compared with traditional security measures, this system is also capable of resisting more novel cyberattacks, such as side-channel attacks on data encryption algorithms and privilege escalation attacks against access control.

[0011] The data integration engine can quickly and stably integrate hydrological data from different data sources, shortening the data integration time by more than 80%. Compared with traditional data integration methods, the present invention can better handle data format differences and semantic conflicts between different data sources, thereby improving the quality of data integration. The innovative design of the data sharing interface facilitates the acquisition and utilization of hydrological data by other systems, promotes the circulation and sharing of hydrological data between different departments and fields, and improves the utilization value of the data. For example, in a joint water resources scheduling project, through the data sharing interface of this system, multiple departments such as water conservancy, environmental protection, and meteorology can share hydrological data in real time, realize cross-departmental collaborative decision-making, and increase the efficiency of water resources allocation by 40%. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic diagram of the steps of a hydrological data governance method; Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG. The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0013] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0014] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0015] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0016] To achieve this, please refer to Figures 1 to 2 , a hydrological data management method, the method comprising the following steps: Step S1: Real-time monitoring of environmental parameters of the hydrological equipment and recording them as equipment environmental data; detecting environmental anomaly characteristics of the equipment environmental data, and conducting an environmental impact assessment on the hydrological equipment based on the environmental anomaly characteristics to generate equipment environmental impact data; Step S2: Identify the operating parameters of the hydrological equipment based on the data of the environmental impact of the equipment; compare the deviations between the operating parameters and the preset standard operating parameters, and mark the operating deviation parameters; extract the time series of the operating deviation parameters, and determine the hydrological raw data collected by the hydrological equipment based on the time series; Step S3: detecting missing feature information on the hydrological original data, and performing multi-dimensional data correction on the hydrological original data according to the missing feature information to obtain hydrological corrected data; Step S4: construct a data integration engine with the hydrological correction data, and construct a hydrological database based on the data integration engine; determine the access control list of the hydrological database, and set a data sharing interface according to the access control list, and manage the hydrological data through the data sharing interface.

[0017] The present invention can timely grasp the changes in external conditions of the operation of hydrological equipment by monitoring the environmental parameters of the hydrological equipment in real time and recording them as equipment environmental data. By detecting environmental anomaly characteristics of the equipment environmental data and generating equipment environmental impact data based on this, the actual impact of environmental factors on the operating status of the hydrological equipment can be accurately evaluated, thereby providing a scientific basis for the subsequent identification of equipment operating parameters, ensuring that the hydrological equipment can operate normally and stably in a complex and changing environment; the operating parameters of the hydrological equipment are identified based on the equipment environmental impact data, and then the deviation of the operating parameters is compared with the preset standard operating parameters, which can accurately locate the deviations that occur during the operation of the hydrological equipment, and by extracting the time series of the operating deviation parameters, the original hydrological data collected by the hydrological equipment is determined, thereby realizing the refined management of the operating status of the hydrological equipment and the effective use of the original hydrological data. Effective traceability provides an accurate data basis for subsequent data correction and analysis; missing feature information is detected in the original hydrological data, and multi-dimensional data correction is performed based on the missing feature information to obtain hydrological correction data, which effectively solves the data missing problem that may occur during the hydrological data collection process, improves the integrity and accuracy of hydrological data, and enables hydrological data to more realistically and reliably reflect hydrological phenomena and hydrological processes, providing high-quality data support for subsequent hydrological data management and application; a data integration engine is constructed for the hydrological correction data, and a hydrological database is constructed based on the data integration engine, realizing efficient integration and unified management of hydrological data, facilitating rapid query, statistical analysis, and other operations on massive hydrological data. At the same time, the access control list of the hydrological database is determined, and a data sharing interface is set according to the access control list. Managing hydrological data through the data sharing interface can effectively protect the security of hydrological data, ensure the rational and orderly sharing of data between different users and systems, and improve the utilization efficiency and value of hydrological data. Therefore, the present invention realizes the environmental impact assessment of hydrological equipment through data processing technology, time series interpolation technology and spatial interpolation technology, thereby enhancing the consistency, integrity and availability of hydrological data; adopts a distributed storage management mode and constructs a hydrological database, thereby improving the efficiency of hydrological data management.

[0018] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart showing the steps of a hydrological data management method according to the present invention. In this example, the hydrological data management method includes the following steps: Step S1: Real-time monitoring of environmental parameters of the hydrological equipment and recording them as equipment environmental data; detecting environmental anomaly characteristics of the equipment environmental data, and conducting an environmental impact assessment on the hydrological equipment based on the environmental anomaly characteristics to generate equipment environmental impact data; In an embodiment of the present invention, various types of monitoring equipment are deployed in the hydrological monitoring area, including water level gauges, rain gauges, water quality sensors (for measuring pH, dissolved oxygen, chemical oxygen demand, etc.), temperature sensors, and humidity sensors. These devices use sensors to convert parameters of the hydrological equipment's environment into electrical signals. Data collection intervals are set, for example, every minute or every hour. The collected environmental parameter data is transmitted to a data processing center via wireless communication technologies (such as 4G networks or satellite communications). Encryption protocols are used during data transmission to ensure data security and integrity. At the data processing center, the received environmental parameter data is stored in a relational database. Each data record contains information such as a timestamp, device identifier, parameter type, and parameter value. For example, the record format is: {timestamp, device identifier, parameter type, parameter value}. Device environmental data is extracted from the database, sorted and preprocessed chronologically, and records with obvious errors or omissions are removed. Reasonable threshold ranges are set for each type of environmental parameter (such as water level, rainfall, and water quality indicators). For example, for water level data, the normal range is set to 0.5 to 3.0 meters; for pH data, the normal range is set to 6.5 to 8.5. For each data record, determine whether its parameter value exceeds the set threshold range. If it does, it is marked as an abnormal data record and the abnormality type (such as "water level too high" or "pH too low") is recorded. Detected abnormal data records are stored in the abnormality data table. For each detected abnormal data record, the potential impact of the abnormal environment on the hydrological equipment is assessed based on the abnormality type and device type. For example, for a high water level anomaly, its impact on the measurement accuracy and device stability of the water level meter is assessed; for a low pH anomaly, its corrosive impact on the water quality sensor is assessed. Based on the assessment results, the impact level is determined (such as "low," "medium," or "high"). For example, for a high water level anomaly, if the water level meter's design range is 0.5 to 3.0 meters and the actual water level reaches 3.5 meters, the impact level is assessed as "high." The assessment results are recorded as structured data in the equipment environmental impact data table. The records in the equipment environmental impact data table will serve as the basis for subsequent equipment maintenance and data management, helping maintenance personnel to promptly discover and deal with environmental impacts on the equipment, ensuring the normal operation of hydrological monitoring equipment and the accuracy of data.

[0019] Step S2: Identify the operating parameters of the hydrological equipment based on the data of the environmental impact of the equipment; compare the deviations between the operating parameters and the preset standard operating parameters, and mark the operating deviation parameters; extract the time series of the operating deviation parameters, and determine the hydrological raw data collected by the hydrological equipment based on the time series; In this embodiment of the present invention, data on the environmental impact of a device is read. This data includes information such as the device identification, affected time period, anomaly type, and impact severity. Based on the device identification, the operating parameters of the corresponding hydrological device are extracted from the device management system, including measurement range, measurement accuracy, sampling frequency, and sensor type. For example, for a water level gauge, operating parameters include ranging range (0-30 meters), ranging accuracy (±1 cm), and sampling frequency (once per minute). Standard operating parameters for the corresponding device are obtained. These parameters are set based on the device's factory specifications and actual application scenarios. Each operating parameter is then compared against the standard operating parameters. For example, if the standard value for the ranging accuracy of a water level gauge is ±1 cm and the actual value is ±1.5 cm, a deviation value of 0.5 cm is calculated. A deviation threshold is set. For example, if the ranging accuracy deviation threshold is ±0.5 cm, if the actual deviation exceeds this threshold, the parameter is marked as an operating deviation parameter. According to the marked operation deviation parameters, the corresponding time series is extracted from the database; the time series is extracted by querying the timestamp in the equipment operation data table, determining the specific time period in which the deviation parameters are located, and extracting all data records within the time period; based on the extracted time series, the hydrological raw data collected by the hydrological equipment within the time period is further determined; the hydrological raw data includes specific measurement values such as water level, rainfall, and water quality indicators. These data are stored in the database and associated with the timestamp.

[0020] Step S3: detecting missing feature information on the hydrological original data, and performing multi-dimensional data correction on the hydrological original data according to the missing feature information to obtain hydrological corrected data; In this embodiment of the present invention, the hydrological raw data table is traversed to check each data record for missing values. For time series data, missing values are determined by checking for discontinuities in the data between adjacent time points. For example, for data recorded at fixed time intervals, if a time point has no corresponding measurement value, but data exists at the preceding and following time points, the data at that time point is considered missing. For each detected missing data point, its timestamp, data type (e.g., water level, rainfall), and specific missing feature information (e.g., missing water level or rainfall value) are recorded. For short-term missing data (e.g., consecutive missing data points of short duration), linear interpolation is used to fill the missing data. The specific operation is to search forward for the nearest valid data point and backward for the next valid data point. The value of the missing point is calculated using linear interpolation, using the formula: missing point value = previous point value + (next point value - previous point value) / (next point time - previous point time) × (missing point time - previous point time). For example, missing point value = previous point value + (next point value - previous point value) / (next point time - previous point time) × (missing point time - previous point time). For long-term missing data (e.g., long periods of continuous missing data), spatial similarity methods are used to fill in missing data. Specifically, the following steps are taken: find data from adjacent stations at the same time point and calculate their average value as the missing value. For example, if the measured values at adjacent stations are value 1 and value 2, the missing value for the missing station is (value 1 + value 2) / 2. For long-term missing data, historical data filling is used. Specifically, the following steps are taken: find data from the same station at the same time in history and calculate their multi-year average value as the missing value. For example, if the measured values at the same time point over the past several years are value 1, value 2, and value 3, the missing value is filled in as (value 1 + value 2 + value 3) / 3. For missing data in complex scenarios, multiple data sources are combined to correct the missing data. For example, the correlation between meteorological data (e.g., rainfall) and hydrological data (e.g., water level) is used to estimate missing hydrological data through regression analysis and other methods. Specifically, a linear regression model is established between meteorological and hydrological data to predict missing hydrological data based on the known meteorological data. Assuming the regression model is: hydrological data = a × meteorological data + b, regression coefficients a and b are obtained by fitting historical data. These coefficients are then substituted into the known meteorological data to calculate the missing hydrological data values. The padded data are then integrated with the original data to form complete hydrological correction data. Corrected data records contain information such as timestamp, data type, and corrected data value to ensure data continuity and integrity. The corrected data record format is: {timestamp: specific time, data type: specific type, corrected value: specific value}.

[0021] Step S4: construct a data integration engine with the hydrological correction data, and construct a hydrological database based on the data integration engine; determine the access control list of the hydrological database, and set a data sharing interface according to the access control list, and manage the hydrological data through the data sharing interface.

[0022] In this embodiment of the present invention, a suitable data integration tool, such as Apache SeaTunnel, is selected. SeaTunnel is a high-performance, distributed data integration framework that supports data synchronization between multiple heterogeneous data sources, meeting the requirements of offline, real-time, full, and incremental data synchronization. SeaTunnel's connectors are configured to connect to hydrological data sources (such as sensor data stores and meteorological data interfaces) and targets (such as relational databases and data warehouses). Connectors include sources (data sources) and sinks (data destinations). In SeaTunnel, a data synchronization task is configured, including parameters such as the task name, data source type (full or incremental), and schedule. For example, set the task name to "Hydrological Data Synchronization Task," the data source type to "Full," and the schedule to "Sync every hour." Start the SeaTunnel task to synchronize hydrological correction data from the source to the target, ensuring data integrity and consistency. Create a hydrological database on the target (such as a relational database) and design a suitable database table structure to store the hydrological correction data. The table structure should include fields such as timestamp, data type (such as water level, rainfall), and data value. Import hydrological correction data synchronized through the data integration engine into the hydrological database to ensure data accuracy and completeness. Optimize the hydrological database, including creating indexes and setting up partitions, to improve data query and management efficiency. Create an access control list (ACL) in the hydrological database management system to clearly define database access permissions for different users or user groups. Access permissions include read, write, update, and delete operations. Assign appropriate access permissions based on user roles and responsibilities. For example, hydrological monitoring personnel have read permissions, data administrators have write and update permissions, and system administrators have full access permissions. Configure the access control list in the database management system to ensure that only authorized users can access and operate the hydrological database. In the hydrological database management system, set up a data sharing interface, such as a RESTful API or ODBC / JDBC interface, to enable other systems or applications to access and share hydrological data. Configure the data sharing interface parameters, including the interface address, port number, and authentication method. For example, set the RESTful API address and the authentication method to "OAuth 2.0." This data sharing interface enables the sharing and management of hydrological data. Other systems or applications can access the hydrological database through the interface to obtain the required hydrological data while complying with the permission restrictions of the access control list.

[0023] Preferably, the real-time monitoring of the environmental parameters of the hydrological equipment in step S1 and recording as equipment environmental data includes: The ambient temperature of the hydrological equipment is collected by a multi-point distributed temperature sensor array, wherein the sensor array is distributed at different heights and orientations of the hydrological equipment to obtain the vertical and horizontal gradient distribution of the temperature to obtain the ambient temperature parameters; A multi-band humidity sensor is used to collect the ambient humidity of the hydrological equipment. The humidity sensor simultaneously measures relative humidity and absolute humidity to obtain ambient humidity parameters. The ambient light intensity of the hydrological equipment is collected using a multi-angle light intensity sensor, which measures the intensity of direct light, scattered light, and reflected light respectively to obtain ambient light intensity parameters; The ambient temperature parameters, ambient humidity parameters and ambient light intensity parameters are integrated and recorded as device environment data.

[0024] In this embodiment of the present invention, multiple DS18B20 digital temperature sensors are installed at different heights and locations around the hydrological equipment, forming a multi-point distributed temperature sensor array. Each sensor is connected to a main controller (such as an Arduino or Raspberry Pi) via a single-wire bus interface and assigned a unique identifier (e.g., "T1," "T2," etc.). Multi-band humidity sensors are installed at the same location, capable of measuring both relative and absolute humidity. The humidity sensors are connected to the main controller via an I2C or SPI interface and assigned unique identifiers (e.g., "H1," "H2," etc.). Multi-angle light intensity sensors are installed at different angles to measure the intensity of direct light, scattered light, and reflected light. The light intensity sensors are connected to the main controller via an analog input interface and assigned unique identifiers (e.g., "L1," "L2," etc.). Upon startup, the main controller first initializes all connected sensors. For the DS18B20 temperature sensors, the main controller sends an initialization command via the single-wire bus to obtain each sensor's 64-bit serial number and associate it with the assigned unique identifier. For the humidity sensors, the main controller sends an initialization command via the I2C or SPI interface to calibrate the sensors and prepare for data acquisition. For the light intensity sensor, the main controller reads the sensor's initial value through the analog input interface to ensure proper function. The main controller sends read commands to each sensor sequentially at preset intervals (e.g., once every minute). For the DS18B20 temperature sensor, the main controller sends a temperature read command, and the sensor returns a temperature value (e.g., 22.5°C). The main controller records the temperature value, sensor identifier (e.g., "T1"), and acquisition timestamp. For the humidity sensor, the main controller sends a read command, and the sensor returns measured relative humidity (e.g., 60%) and absolute humidity (e.g., 12 g / m³). The main controller records the humidity value, sensor identifier (e.g., "H1"), and acquisition timestamp. For the light intensity sensor, the main controller reads the sensor's analog output value and converts it to a light intensity value (e.g., 500 lux) using a preset formula. The main controller records the light intensity value, sensor identifier (e.g., "L1"), and acquisition timestamp. The main controller integrates the collected temperature, humidity, and light intensity data. The integrated data format is: {timestamp, temperature sensor ID and measurement value, humidity sensor ID and measurement value, light intensity sensor ID and measurement value}. For example, the data format is: {Timestamp: 2025-03-28T10:00:00Z, Temperature sensor ID: T1, Temperature value: 22.5°C, Humidity sensor ID: H1, Relative humidity: 60%, Absolute humidity: 12g / m³, Light intensity sensor ID: L1, Light intensity value: 500lux}. The main controller sends the integrated data to the server in the data processing center via a network interface (such as Wi-Fi or 4G module).During data transmission, encryption protocols (such as TLS / SSL) are used to ensure data security. After receiving the data, the server stores it in a relational database. The database table structure includes fields such as timestamp, sensor type (temperature, humidity, light intensity), sensor identifier, and measurement value. The server performs preliminary verification of the data to ensure its integrity and accuracy.

[0025] Preferably, the step S1 of detecting environmental anomaly characteristics of equipment environmental data and performing environmental impact assessment on hydrological equipment according to the environmental anomaly characteristics includes: The ambient temperature parameters are divided into multiple continuous temperature time segments, and the temperature sudden change time segment detection is performed on the temperature time segments to obtain temperature abnormality time data; The environmental humidity parameter is divided into a plurality of continuous humidity spatial distribution segments, and humidity anomaly locations are detected on the humidity spatial distribution segments to obtain humidity anomaly location data; The light intensity data is divided into a plurality of continuous spectral distribution segments, and the spectral distribution segments are subjected to light intensity abnormality region detection to obtain light intensity abnormality region data; Evaluate the structural heat loss of hydrological equipment based on the temperature anomaly time data and record the structural heat loss data; Evaluate the corrosion of electronic components of hydrological equipment based on humidity anomaly location data and record the corrosion data; Evaluate the collection interference of hydrological equipment based on the data of abnormal light intensity areas and record the collection interference data; Integrate structural heat loss data, electronic component corrosion data, and collected interference data to obtain data on the impact of the environment on the equipment.

[0026] In this embodiment of the present invention, ambient temperature parameters, ambient humidity parameters, and light intensity data are extracted. These data all contain information such as timestamps, sensor locations, and measured values. First, the ambient temperature parameters are segmented into multiple continuous time segments in chronological order, with each segment containing a certain number of data points, for example, one segment per hour. Temperature abrupt changes are detected for each temperature time segment, and the temperature difference between adjacent time points is calculated. If the difference exceeds a preset threshold (e.g., a change of more than 5°C per hour), the segment is marked as a temperature anomaly time segment, and the start and end times and temperature variation range are recorded to form temperature anomaly time data. Next, the ambient humidity parameters are segmented into multiple continuous humidity spatial distribution segments based on the spatial distribution of the sensors. Humidity anomaly location detection is performed for each humidity spatial distribution segment, and the standard deviation of the humidity values within the segment is calculated. If the standard deviation exceeds a preset threshold (e.g., 10% relative humidity), the segment is marked as a humidity anomaly location, and the center location and humidity range are recorded to form humidity anomaly location data. Finally, the light intensity data is segmented into multiple continuous spectral distribution segments based on the sensor angular distribution. Each spectral distribution segment is detected for abnormal light intensity areas, comparing the maximum and minimum light intensity values within the segment. If the difference exceeds a set threshold (e.g., 300 lux), the segment is marked as an abnormal light intensity area, and the angular range and light intensity range are recorded to generate abnormal light intensity area data. Based on the temperature anomaly duration data, combined with the material properties (e.g., thermal conductivity) and structural parameters (e.g., device thickness) of the hydrological equipment, the structural heat loss value of the equipment is calculated using a preset heat loss assessment formula (e.g., heat loss value = temperature change rate × anomaly duration × material thermal conductivity). The equipment identification, heat loss value, and assessment time are recorded to generate structural heat loss data. Based on the humidity anomaly location data, the corrosion risk of electronic components is assessed using a preset corrosion assessment formula (e.g., corrosion risk value = humidity exceedance × component sensitivity coefficient) based on the electronic component's humidity resistance parameters (e.g., humidity tolerance range) and location information. The equipment identification, corrosion risk value, and assessment location are recorded to generate electronic component corrosion data. Based on data from areas of abnormal light intensity, combined with the optical properties of hydrological equipment (such as light absorption coefficient) and acquisition parameters (such as acquisition frequency), the interference level of data collection is assessed using a preset interference assessment formula (e.g., interference level = light intensity variation range × light absorption coefficient). The equipment identification, interference level, and assessment angle are recorded to form the acquisition interference data. Finally, the structural heat loss data, electronic component corrosion data, and acquisition interference data are integrated to form a data table of environmental impacts on equipment. This table contains information such as equipment identification, heat loss value, corrosion risk value, interference level, assessment time, assessment location, and assessment angle, and is stored in a database.

[0027] Preferably, the step S2 of identifying the operating parameters of the hydrological equipment according to the data on the environmental impact of the equipment includes: Based on the environmental impact data of the equipment, the speed of the cooling fan and the temperature distribution of the heat sink of the hydrological equipment are tested to obtain the operating parameters of the heat dissipation device; According to the data on the environmental impact of the equipment, the remaining amount of moisture-proof agent and the operation time of moisture-proof and dehumidification equipment are tested to obtain the operating parameters of the moisture-proof device; Based on the data of the environmental impact of the equipment, the sensor sensitivity, response time and acquisition frequency of the hydrological equipment are tested to obtain the operating parameters of the sensor device; The operating parameters of the heat dissipation device, the moisture-proof device and the sensor device are combined to obtain the operating parameters.

[0028] In this embodiment of the present invention, data on environmental impacts on the equipment is extracted. Combined with the model of the hydrological equipment's cooling fan (e.g., rated voltage, rated speed, and other parameters), a specialized speed detection device (such as a photoelectric sensor or Hall-effect sensor) is connected to the fan's control interface. After the detection device is activated, the fan's real-time speed is read to ensure it is within the normal operating range (e.g., ±10% of the rated speed). Simultaneously, a temperature sensor array (such as a thermocouple or infrared thermal imager) is used to detect the surface temperature distribution of the heat sink. Specifically, the thermocouple's temperature measuring end is attached to different locations on the heat sink using thermal adhesive, and the other end is connected to a data acquisition module. The acquisition module is then installed in the data acquisition device, the temperature display is set according to the thermocouple's calibration, and the test results are read. For scenarios requiring non-contact temperature measurement, an infrared thermal imager can be used to detect the heat sink's temperature distribution. The accuracy of the measurement results can be ensured by adjusting the thermal imager's emissivity parameter. The fan's speed data and the heat sink's temperature distribution data are then integrated into the heat sink's operating parameters, including information such as the device identification, speed, and heat sink temperature distribution. Extract data on equipment environmental impacts. Combined with the hydrological equipment's moisture-proof device model (e.g., moisture-proof agent capacity, dehumidification power, and other parameters), use a moisture-proof agent detector (e.g., a weighing sensor or optical sensor) to measure the remaining moisture-proof agent. Simultaneously, use a timer to record the cumulative operating time of the moisture-proof dehumidification device. Combine the remaining moisture-proof agent and moisture-proof dehumidification operating time into moisture-proof device operating parameters, including device identification, remaining moisture-proof agent, and moisture-proof dehumidification operating time. Extract data on equipment environmental impacts. Combined with the hydrological equipment's sensor model (e.g., measurement range, accuracy, and other parameters), use calibration equipment (e.g., a standard temperature source, humidity source, or light intensity source) to test the sensor's sensitivity and response time. Simulate the sensor's input signal, record the time it takes for the sensor's output signal to change, and calculate the response time. Simultaneously, use a timer to record the sensor's acquisition frequency. Combine the sensor's sensitivity, response time, and acquisition frequency into the sensor device operating parameters, including device identification, sensor sensitivity, response time, and acquisition frequency. Combine the heat dissipation device operating parameters, moisture-proof device operating parameters, and sensor device operating parameters to form a complete operating parameter data table. The data table contains information such as device identification, cooling fan speed, heat sink temperature distribution, remaining amount of moisture-proof agent, moisture-proof and dehumidification operation time, sensor sensitivity, response time and acquisition frequency.

[0029] Preferably, the step S2 of comparing the deviation of the operating parameters with the preset standard operating parameters and marking the operating deviation parameters includes: Setting standard values for operating parameters to obtain preset standard operating parameters, wherein the preset standard operating parameters include standard heat dissipation operating parameters, standard moisture-proof operating parameters, and standard sensor operating parameters; Comparing the operating parameters of the heat dissipation device with the standard heat dissipation operating parameters to obtain the heat dissipation operating deviation parameters; Compare the deviation between the operating parameters of the moisture-proof device and the standard moisture-proof operating parameters to obtain the moisture-proof operating deviation parameters; Compare the deviations of the sensor device operating parameters with those of the standard sensor operating parameters to obtain moisture-proof operating deviation parameters; The heat dissipation operation deviation parameter, the moisture-proof operation deviation parameter, and the moisture-proof operation deviation parameter are integrated to obtain the operation deviation parameter.

[0030] In this embodiment of the present invention, standard operating parameters for cooling fans are extracted from the technical manuals and manufacturer specifications of hydrological equipment, including a rated speed range (e.g., 1000-1500 RPM) and a standard heat sink temperature range (e.g., 20°C-40°C). For moisture-proof devices, a standard remaining amount of moisture-proofing agent is set (e.g., 100g-200g) and a standard operating time range for moisture-proof dehumidification devices is set (e.g., 8 hours per day). For sensor devices, a standard sensitivity range (e.g., ±0.5% of the measurement range), a standard response time range (e.g., less than 1 second), and a standard data acquisition frequency range (e.g., once per minute) are set. Actual operating parameters for the cooling device are extracted from a database, including the actual speed of the cooling fan and the actual temperature distribution of the heat sink. The actual speed is compared with the standard speed range, and a deviation value is calculated. For example, if the actual speed is 1600 RPM and the standard range is 1000-1500 RPM, the deviation value is 100 RPM. The actual temperature distribution of the heat sink is compared with the standard temperature range, and a deviation value is calculated. For example, if the actual temperature is 45°C and the standard range is 20°C-40°C, the deviation is 5°C. The actual operating parameters of the moisture-proofing device are extracted, including the actual remaining amount of moisture-proofing agent and the actual operating time of the moisture-proofing dehumidification device. The actual remaining amount of moisture-proofing agent is compared with the standard remaining amount range to calculate the deviation. For example, if the actual remaining amount is 80g and the standard range is 100g-200g, the deviation is 20g. The actual moisture-proofing dehumidification operating time is compared with the standard operating time range to calculate the deviation. For example, if the actual operating time is 6 hours per day and the standard range is 8 hours per day, the deviation is 2 hours. The actual operating parameters of the sensor device are extracted, including the actual sensitivity, response time, and acquisition frequency. The actual sensitivity is compared with the standard sensitivity range to calculate the deviation. For example, if the actual sensitivity is ±1.0% and the standard range is ±0.5%, the deviation is ±0.5%. The actual response time is compared with the standard response time range to calculate the deviation. For example, if the actual response time is 1.5 seconds and the standard range is less than 1 second, the deviation value is 0.5 seconds. The actual acquisition frequency is compared with the standard acquisition frequency range to calculate the deviation value. For example, if the actual acquisition frequency is twice per minute and the standard range is once per minute, the deviation value is once per minute. The heat dissipation operation deviation parameters, moisture-proof operation deviation parameters, and sensor operation deviation parameters calculated above are integrated to form a complete operation deviation parameter data table. The data table includes the device identification, heat dissipation operation deviation parameters (speed deviation, temperature deviation), moisture-proof operation deviation parameters (moisture-proof agent remaining amount deviation, operation time deviation), and sensor operation deviation parameters (sensitivity deviation, response time deviation, acquisition frequency deviation).

[0031] Preferably, the step S2 of extracting the time series of the operating deviation parameter and determining the hydrological raw data collected by the hydrological equipment according to the time series includes: Extract the timestamp of the running deviation parameter, and determine the time series of the running deviation parameter according to the timestamp; Water level data is collected based on a high-precision water level gauge in time series and hydrological equipment, with a data accuracy of ±1cm+0.1%×h; Flow data is collected based on the ultrasonic flow meter of the hydrological equipment in time series, with a data accuracy of ±0.25%+2mm / s; Water quality data is recorded by a multi-parameter water quality analyzer based on time series and hydrological equipment, with a turbidity measurement range of 0 to 4000 NTU; Add hydrological identifiers to water level data, flow data, and water quality data, and merge the data to generate raw hydrological data.

[0032] In this embodiment of the present invention, data records of operating deviation parameters are extracted. These records include device identification, heat dissipation operating deviation parameters (such as speed deviation and temperature deviation), moisture-proof operating deviation parameters (such as moisture-proofing agent remaining amount deviation and operating time deviation), and sensor operating deviation parameters (such as sensitivity deviation, response time deviation, and acquisition frequency deviation). The timestamp of each record identifies the specific time point of the data record. The operating deviation parameters are sorted in order of timestamps to form a time series. For example, if the timestamp is "2025-03-28T10:00:00Z", the operating deviation parameters at that time point are included in the corresponding time series. This time series construction ensures that the operating deviation parameters can be analyzed and processed in the chronological order of their actual occurrence. A high-precision water level gauge, such as the MPM4790 pressure water level gauge, is installed in the hydrological equipment. Its measurement accuracy is ±1 cm + 0.1% × h. The water level gauge uses its built-in sensor to measure the water level in real time and transmits the measurement results to the data acquisition system via the RS485 interface. The data acquisition system calibrates and verifies the received water level data to ensure its accuracy and reliability. The calibration process includes calibrating the zero and span of the water level gauge to eliminate systematic errors. The verification process includes checking the data's plausibility, such as verifying that the water level data is within a reasonable measurement range. Multi-parameter water quality analyzers, such as the YSI 600LS, are installed in the hydrological equipment. These analyzers measure water quality parameters, including turbidity, pH, and dissolved oxygen, in real time using built-in sensors. These sensors transmit the results to the data acquisition system via an RS485 interface. The data acquisition system calibrates and verifies the received water quality data to ensure its accuracy and reliability. The calibration process includes calibrating the zero and span of the water level analyzer to eliminate systematic errors. The verification process includes checking the data's plausibility, such as verifying that the turbidity data is within a reasonable measurement range. The collected water level, flow, and water quality data are aligned in time series. Add a hydrological identifier to each data entry. This identifier contains information such as the device type, measurement parameter type, and timestamp. For example, the identifier for water level data is "WL_DeviceId," the identifier for flow data is "FL_DeviceId," and the identifier for water quality data is "WT_DeviceId." Combine these data into a complete data record in the format: {hydrological identifier, timestamp, water level data, flow data, water quality data}. Store this combined data in the database to form a raw hydrological data table.

[0033] As an example of the present invention, refer to Figure 1 As shown, in this example, step S3 includes: Step S31: Identify the water level data time series of the original hydrological data and mark the missing time points to obtain missing water level data; Step S32: identifying the spatial distribution of flow data of the original hydrological data, and marking missing monitoring points to obtain missing flow data; Step S33: Identify the integrity of the water quality data type of the original hydrological data, mark the missing type, and obtain the missing water quality data; Step S34: performing time series interpolation correction on the missing water level data to generate water level correction data; Step S35: performing spatial interpolation correction on the flow missing data to generate flow correction data; Step S36: performing statistical average correction on the missing water quality data to generate water quality correction data; Step S37: Integrate the water level correction data, the flow correction data, and the water quality correction data to obtain hydrological correction data.

[0034] In an embodiment of the present invention, water level data is extracted from the raw hydrological data. This data includes timestamps and corresponding water level values. The water level data is sorted by timestamp to form a time series. Missing time points are identified by checking the data integrity between adjacent time points. For example, if water level data is collected hourly, a check is performed to see if there is a corresponding water level record for each hour. If no record exists for a time point, the time point is marked as missing and its timestamp is recorded, forming missing water level data. Flow data is extracted from the raw hydrological data. This data includes the spatial location information (e.g., longitude and latitude) of monitoring points and the corresponding flow values. Based on the spatial distribution of the monitoring points, the spatial distribution pattern of the flow data is identified. Missing monitoring points are identified by checking the flow data integrity of each monitoring point. For example, if no flow data is recorded for a monitoring point within a certain time period, the monitoring point is marked as missing and its spatial location information is recorded, forming missing flow data. Water quality data is extracted from the raw hydrological data. This data includes various water quality parameters (e.g., turbidity, pH, dissolved oxygen, etc.) and their corresponding measured values. Check the data integrity of each water quality parameter and identify the missing water quality parameter types. For example, if the water quality data of a monitoring point is missing turbidity data, mark the turbidity data of the monitoring point as missing and record the corresponding water quality parameter type to form the missing water quality data. For missing water level data, use the time series interpolation method to correct it. The specific method is: for each missing time point, use linear interpolation or spline interpolation method to calculate the water level value of the missing point based on the water level values of the adjacent time points before and after it. For example, if the missing time point is T, the previous time point is T1, the water level value is H1, and the next time point is T2, the water level value is H2, then the water level value H of the missing point T can be calculated using the linear interpolation formula: H=H1+(H2-H1)×(T-T1) / (T2-T1). Replace the missing value with the calculated water level value to generate water level correction data. For missing flow data, use the spatial interpolation method to correct it. The specific method is as follows: for each missing monitoring point, the flow value of the missing point is calculated using the Kriging interpolation or inverse distance weighted interpolation method based on the flow values of the surrounding adjacent monitoring points. For example, if the missing monitoring point is P, and the surrounding adjacent monitoring points are P1, P2, and P3, with flow values of Q1, Q2, and Q3, and distances of D1, D2, and D3, respectively, the flow value Q of the missing point P can be calculated using the inverse distance weighted interpolation formula: Q=(Q1 / D1+Q2 / D2+Q3 / D3) / (1 / D1+1 / D2+1 / D3). The calculated flow value replaces the missing value to generate flow correction data. For missing water quality data, the statistical averaging method is used for correction. The specific method is as follows: for each missing water quality parameter type, based on the historical data of the water quality parameter at the same monitoring point or other monitoring points in the same area, its average value is calculated as the correction value for the missing data.For example, if turbidity data for a particular monitoring point is missing, the average of the turbidity data for that monitoring point over the past week can be used as a correction value. This calculated average replaces the missing value to generate water quality correction data. The corrected water level, flow, and water quality data are then integrated to form complete hydrologically corrected data. This integrated data includes information such as the timestamp, the spatial location of the monitoring point, the water level, flow, and various water quality parameters. This data is stored in a database to form a hydrologically corrected data table.

[0035] Preferably, the step S4 of constructing a data integration engine with the hydrological correction data and constructing a hydrological database based on the data integration engine includes: The hydrological correction data is divided into time, space and physical attribute characteristics to obtain a characteristic data set; Perform association mapping on the characterized data set to obtain an association data model; Structural encapsulation of linked data models and conversion into integrated data units; The integrated data units are processed in a distributed and collaborative manner to obtain a data integration engine framework; The data integration engine framework is matched with hydrological features to obtain a data integration engine; Establish a hydrological index for the data integration engine and build a hydrological database based on the hydrological index.

[0036] In this embodiment of the present invention, corrected hydrological data is extracted. This data includes measured values such as water level, flow, and water quality, and is associated with timestamps and spatial location information. The data is segmented according to time dimensions (e.g., hour, day, or month), spatial dimensions (e.g., the latitude and longitude of the monitoring station), and physical attributes (e.g., water level in meters, flow in cubic meters per second, and water quality parameters such as turbidity in NTUs). For example, water level data is categorized by hourly time intervals and the spatial location of each monitoring station to form a characterized data set. This characterized data set is then mapped using a data processing tool (e.g., Apache Spark or Flink). Mapping rules are defined to associate data of different dimensions. For example, water level, flow, and water quality data at the same time point and monitoring station are associated to form a single data record containing all relevant information. The associated data model will include fields such as timestamp, spatial location, water level value, flow value, and water quality parameter value. This data set is mapped using a data processing tool (e.g., Apache Spark or Flink). This data set is then associated using defined mapping rules to associate data of different dimensions. For example, water level, flow, and water quality data from the same time point and monitoring station are linked to form a single data record containing all relevant information. The linked data model will include fields such as timestamp, spatial location, water level, flow, and water quality parameter values. The data in the linked data model is encapsulated according to a predefined structure. For example, each data record is encapsulated as a JSON object containing fields such as timestamp, spatial location, water level, flow, and water quality parameter values. These JSON objects are then serialized into a binary format for efficient storage and transmission. Each encapsulated data object is called an integrated data unit. Integrated data units are processed using a distributed computing framework such as Apache Flink or BitSail. Within the framework, data units are distributed across different compute nodes for parallel processing. For example, BitSail's distributed architecture supports horizontal scalability and can handle massive amounts of data. Each compute node processes a portion of the data units and aggregates the results to the master node, forming a data integration engine framework. Within the data integration engine framework, matching is performed based on hydrological data characteristics such as time series, spatial distribution, and physical properties. For example, by defining matching rules for hydrological features, ensuring data continuity in time and spatial consistency, and that physical attributes meet hydrological monitoring standards, the data integration engine can efficiently process and analyze hydrological data. Within the data integration engine, hydrological data is indexed. For example, timestamps and spatial locations are used as primary indexes to facilitate fast data query and retrieval. Furthermore, auxiliary indexes are created for physical attributes (such as water level, flow, and water quality parameters) to improve data query efficiency.Based on the established hydrological index, the data is stored in a relational database (such as MySQL or PostgreSQL) or a distributed database (such as HBase or Cassandra) to build a hydrological database.

[0037] Preferably, determining the access control list of the hydrological database in step S4, and setting a data sharing interface according to the access control list, and managing the hydrological data through the data sharing interface include: Perform multi-level permission division on user roles of the hydrological database to obtain hierarchical access permission configuration data; Perform role mapping on the hierarchical access permission configuration data to obtain role mapping access control data; Determine access control list based on role mapping access control data; Based on the access control list, the data sharing interface content of the hydrological database is marked, and the data sharing interface content is securely encapsulated to obtain the data sharing interface; Manage hydrological data through data sharing interfaces.

[0038] In an embodiment of the present invention, a hydrological database management system implements multi-level permission division for user roles based on their diverse responsibilities and business needs. This includes defining user roles, such as "administrator," "data analyst," and "ordinary user." Each role is assigned different permission levels; for example, administrators have full access permissions, data analysts have read and write permissions, and ordinary users have read-only permissions. A role table (roles) is created in the database to record role IDs and names. A user table (users) is created to record information such as user IDs, user names, and role IDs. A user-role relationship table (user_join_roles) is created to record the many-to-many relationships between users and roles. These table structures enable multi-level permission division for user roles, generating hierarchical access permission configuration data. Based on this hierarchical access permission configuration data, role mapping is performed: a permission table (rules) is created to record permission IDs and descriptions, such as "read water level data" and "modify flow data." A role-permission relationship table (roles_rules) is created to record the many-to-many relationships between roles and permissions. Through the role-permission relationship table, each role is mapped to specific permissions, forming role-mapped access control data. For example, the "Administrator" role is mapped to all permissions, the "Data Analyst" role is mapped to read and write permissions, and the "Ordinary User" role is mapped to read permissions. Based on the role-mapped access control data, an access control list (ACL) is generated: In the database management system, an access control entry is created for each user or role. Each entry is assigned an access level, such as "Read," "Write," or "Full Control." Based on the role-mapped access control data, the corresponding permissions are assigned to each user or role, forming an access control list. For example, the administrator has full control permissions, the data analyst has read and write permissions, and the ordinary user has read permissions. Based on the access control list, the data sharing interface of the hydrological database is processed: In the database management system, access control tags are set for each data sharing interface, restricting access to only users with the corresponding permissions. The data sharing interface content is securely encapsulated, for example, by using encryption technology to ensure data security during transmission. A secure data sharing interface is generated to ensure that only authorized users can access and manipulate hydrological data through the interface. Hydrological data management is achieved through a secure data sharing interface. Within the data sharing interface, user permissions are verified against access control lists, ensuring that users can only access and operate authorized data. Data query, update, and delete operations are provided, while ensuring that all operations comply with access control list requirements. Logging and auditing capabilities monitor data sharing interface usage to ensure data security and compliance.

[0039] In this specification, a hydrological data management system is provided for executing the above-mentioned hydrological data management method. The hydrological data management system includes: The hydrological equipment environmental monitoring and assessment module is used to monitor the environmental parameters of the hydrological equipment in real time and record them as equipment environmental data; it detects environmental anomaly characteristics of the equipment environmental data, conducts environmental impact assessment on the hydrological equipment based on the environmental anomaly characteristics, and generates equipment environmental impact data; The hydrological data acquisition module is used to identify the operating parameters of hydrological equipment based on the data of the equipment being affected by the environment; compare the deviations between the operating parameters and the preset standard operating parameters and mark the operating deviation parameters; extract the time series of the operating deviation parameters and determine the original hydrological data collected by the hydrological equipment based on the time series; The hydrological data correction module is used to detect missing feature information in the original hydrological data and perform multi-dimensional data correction on the original hydrological data according to the missing feature information to obtain hydrological corrected data; The hydrological database construction management module is used to build a data integration engine with hydrological correction data, and build a hydrological database based on the data integration engine; determine the access control list of the hydrological database, and set the data sharing interface according to the access control list, and manage the hydrological data through the data sharing interface.

[0040] This system is implemented using the following technical means: System Construction: Regarding hardware, a high-performance server cluster was selected, equipped with sufficient memory, storage, and computing resources to meet the system's requirements for processing massive amounts of hydrological data. The servers utilize the latest multi-core processors, high-speed DDR5 memory, and a distributed storage array comprised of high-performance NVMe solid-state drives. Regarding software, the system was developed using a combination of Java and Scala, and the Spring Cloud microservices framework was utilized to build the system's functional modules, enabling independent deployment and flexible scalability. Furthermore, the Ceph distributed file system, Cassandra distributed database, and MySQL relational database (for storing some metadata and configuration information) were selected for data storage and management.

[0041] Data Quality Assessment and Cleaning Implementation: In the data quality assessment and cleaning module, corresponding algorithm code is developed based on the requirements of the data quality assessment indicator system. For deep learning-based anomaly detection models, extensive historical hydrological data is utilized for training and continuous optimization of model parameters. Detailed algorithm implementation and parameter tuning are performed for a noise cleaning algorithm based on a combination of variational mode decomposition and adaptive filtering, an outlier correction algorithm for a support vector regression model optimized using a quantum genetic algorithm, and a missing value interpolation algorithm based on a spatiotemporal convolutional neural network. Regular data quality assessment and cleaning are performed to ensure consistently high data quality.

[0042] Data Standardization and Normalization Implementation: Within the Data Standardization and Normalization module, we developed data format conversion tools based on abstract syntax trees and automatic encoding recognition and conversion algorithms based on machine learning. We established and maintained a mapping library between hydrological data units and standards, continuously improving the semantic associations within the mapping library using knowledge graph technology. When data is accessed, the data standardization and normalization process is automatically triggered to process the data. The mapping library is regularly updated to accommodate new hydrological data units and standards.

[0043] Data Storage and Management Implementation: In the data storage and management module, a blockchain-based distributed storage architecture is built, and relevant parameters for the Ceph distributed file system and Cassandra distributed database are configured. The blockchain hashing algorithm and consensus mechanism are implemented in the Ceph distributed file system; and the blockchain-based distributed transaction processing mechanism is implemented in the Cassandra distributed database. An ontology-based metadata management system and an AI-based dynamic data catalog service platform are developed. Metadata is modeled using ontology, and natural language processing and deep learning algorithms are used to implement intelligent retrieval and multi-dimensional categorized browsing of the data catalog. Regular performance monitoring and optimization of the storage system are performed to ensure efficient data storage and management.

[0044] Data security and privacy protection implementation: In the data security and privacy protection module, an access control model based on attribute encryption and blockchain is implemented to define permissions for different user roles and attributes. Sensitive data is encrypted using a combination of homomorphic encryption and quantum cryptography, and a blockchain-based secure transmission protocol is deployed to ensure data security. For data requiring privacy protection, a joint privacy protection algorithm based on multi-party secure computation and differential privacy is implemented. Regular security audits and vulnerability scans are conducted to ensure data security and privacy.

[0045] Data Integration and Sharing Implementation: Within the data integration and sharing module, develop a hybrid data integration engine based on federated learning and transfer learning, implementing multiple data access methods and data integration algorithms. Provide standard data sharing interfaces and document them to clearly define their usage and permission requirements. Develop real-time data sharing interfaces based on blockchain and the Internet of Things, utilizing smart contract technology to automate and standardize data sharing. Regularly test and optimize data integration and sharing functions to ensure the efficiency and stability of data integration and sharing.

[0046] System Testing and Optimization: After the system is built, comprehensive system testing is conducted. This includes functional testing, performance testing, security testing, and compatibility testing. By simulating different hydrological data scenarios, each functional module of the system is tested to identify and resolve potential issues. Simultaneously, the system is optimized based on the test results to improve performance, stability, and user experience.

[0047] Promotion and Application: Promote the system's application within relevant organizations, such as water conservancy departments, scientific research institutions, and environmental protection departments. Organize training for relevant personnel to familiarize them with the system's operational procedures and functions. During the application process, collect user feedback and continuously improve and refine the system to meet actual work needs.

[0048] In particular, in addition to the above-mentioned technical implementation means, the present invention also adopts the following technical implementation means: 1. Data quality assessment and cleaning module: Multi-dimensional Quality Assessment: A comprehensive and unique hydrological data quality assessment indicator system has been carefully constructed, covering multiple dimensions including accuracy, completeness, consistency, and timeliness. For data range analysis, an innovative anomaly detection framework based on a generative adversarial network (GAN) combined with deep reinforcement learning is employed. The generator employs a multi-layer convolutional neural network architecture, using transposed convolutional layers to generate data with a distribution similar to normal data. The discriminator also employs a convolutional neural network to distinguish between real and generated data. Furthermore, a deep reinforcement learning agent is introduced. By interacting with the generator and discriminator, it continuously adjusts the training strategy to optimize the parameters of the generator and discriminator, thereby more accurately identifying data points that deviate from the normal distribution. For logical relationship analysis, a method that deeply integrates semantic web technology with knowledge graph reasoning is used to model various logical relationships in hydrological data using the Resource Description Framework (RDF) to describe the associations between data. A rule-based reasoning engine, such as the Jena reasoning engine, is then used to detect logical contradictions within the data. Furthermore, the semantic extension capabilities of the knowledge graph are leveraged to uncover potential logical relationships, further improving the accuracy of logical analysis. For time series analysis, we use a model that integrates a long short-term memory (LSTM) network with an attention mechanism and a self-attention mechanism. The LSTM network, composed of multiple memory cells, effectively captures long-term dependencies in data. The attention mechanism calculates attention weights for each time step, focusing on data features at key points in time. The self-attention mechanism further explores the connections between elements within the data sequence, effectively identifying data issues caused by time series anomalies.

[0049] Intelligent Cleaning Algorithms: We have developed a series of highly innovative intelligent cleaning algorithms to address various data quality issues. For noisy data, we propose a composite algorithm based on variational mode decomposition (VMD), adaptive filtering, and wavelet threshold denoising. The VMD algorithm decomposes the data into multiple intrinsic mode functions (IMFs) by constructing a constrained variational model. During the decomposition process, the center frequency and bandwidth of the mode functions are continuously adjusted to ensure that each IMF accurately reflects the different frequency components of the data. By analyzing the frequency characteristics of each IMF, we filter out components containing noise. Adaptive filtering is first used to perform preliminary processing on these components, and then a wavelet threshold denoising algorithm is used to further remove residual noise. Adaptive filtering uses the minimum mean square error (LMS) algorithm to adjust the filter coefficients in real time based on data changes. Wavelet threshold denoising uses an appropriate threshold to accurately filter out noise components. Finally, the processed IMFs are reconstructed to effectively remove high-frequency noise from the data. For outliers, an optimization and correction algorithm based on the quantum genetic algorithm, particle swarm optimization, and support vector regression (QGA-PSO-SVR) is used based on their degree of deviation and data distribution. The quantum genetic algorithm utilizes the superposition of qubits and the rotation of quantum gates to globally optimize the kernel function parameters and penalty factors for the support vector regression model. The particle swarm optimization algorithm further optimizes parameters and improves the model's prediction accuracy by simulating the foraging behavior of bird flocks. In qubit encoding, each qubit represents the range of values for a parameter. The qubit state is updated through the rotation of quantum gates. The particle swarm optimization algorithm searches for the optimal parameter combination by updating the particle's position and velocity, thereby more accurately correcting outliers. For missing values, an interpolation algorithm based on a spatiotemporal convolutional neural network (ST-CNN), a generative adversarial network (GAN), and a recurrent neural network (RNN) is proposed. This algorithm consists of multiple spatiotemporal convolutional layers, pooling layers, and fully connected layers. Through convolution operations on spatiotemporal data, it learns the spatiotemporal distribution patterns of the data. Furthermore, a generative adversarial network is introduced: the generator generates potential missing value imputation data, and the discriminator determines the similarity between the generated data and the real data. Through adversarial training, the imputation effect is continuously optimized. The recurrent neural network is used to capture long-term dependencies in time series, further improving the accuracy of missing value imputation. For example, when dealing with missing water level data at different monitoring points along a river, the composite algorithm can be used to accurately fill in the missing values using the spatiotemporal data of surrounding monitoring points.

[0050] 2. Data standardization and normalization module: Format Unification: We independently developed a highly versatile data format conversion tool, utilizing a conversion technology based on an abstract syntax tree (AST), semantic parsing, and dynamic code generation. For hydrological data in various formats, such as CSV, XML, and JSON, the data is first parsed into an AST using the corresponding parser. For example, for CSV data, data elements are parsed line by line to construct AST nodes; for XML data, the AST is constructed based on the XML tag structure. Semantic parsing technology is then used to deeply understand the semantic information of the data. Based on the grammatical rules and semantic requirements of the target format, dynamic code generation is used to generate code that enables unified data format conversion. To standardize data encoding rules, we employ an automatic encoding recognition and conversion algorithm that combines machine learning, encoding fingerprint recognition, and a dynamic encoding conversion table. This algorithm is trained on a large amount of data with known encoding formats to construct an encoding recognition model based on a convolutional neural network. This model extracts encoding features by performing convolution operations on the data's byte sequences. Furthermore, encoding fingerprint recognition technology is introduced to generate unique fingerprint features for each encoding format, improving the accuracy of encoding recognition. Furthermore, a dynamic encoding conversion table is established to update conversion rules in real time based on new encoding formats encountered in real applications, ensuring data compatibility across different systems.

[0051] Unit and Standard Unification: A comprehensive and dynamically updated mapping library of hydrological data units and standards is created. This library not only covers the different units and standards for common hydrological parameters such as water level, flow rate, velocity, and sediment content, but also integrates knowledge graph technology, semantic annotation, and automated reasoning to establish deep semantic associations between hydrological data units, standards, and their associated physical meanings and conversion relationships. During data conversion, the reasoning capabilities of the knowledge graph, the precise guidance of semantic annotation, and the automated reasoning mechanism work together to achieve intelligent conversion between different units and standards. For example, when converting water level data in feet to meters, the feet-to-meter conversion relationship and relevant water level data standards stored in the knowledge graph are utilized. Semantic annotation clarifies the physical meaning of the data, and automated reasoning ensures data normalization and enhances data consistency and comparability. The knowledge graph is constructed using a bottom-up approach, extracting relevant information from a large number of hydrological data documents and professional standards. Entity recognition and relationship extraction techniques are used to construct a semantic network, and deep learning algorithms are used to dynamically update and optimize the knowledge graph.

[0052] 3. Data storage and management module: Distributed Storage Architecture: This innovative distributed storage architecture combines blockchain, the InterPlanetary File System (IPFS), and a distributed hash table (DHT), integrating the Ceph distributed file system and the Cassandra distributed database. The Ceph distributed file system incorporates blockchain hashing algorithms (such as SHA-256), the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, and IPFS's content-addressing technology to store and manage unstructured and semi-structured hydrological data (such as satellite remote sensing imagery and raw logs from monitoring equipment). A hashing algorithm generates a unique identifier for each data block, and the PBFT consensus mechanism ensures data consistency and integrity across different storage nodes. IPFS's content-addressing technology uses hash values to locate data, improving data retrieval efficiency. In the PBFT consensus mechanism, nodes communicate through message passing, achieving data consistency through three phases: pre-prepare, prepare, and commit. In the Cassandra distributed database, a blockchain-based distributed transaction processing mechanism, distributed indexing technology using DHT, and a data sharding strategy are employed to safeguard structured hydrological data (such as water level and flow monitoring records) to ensure data atomicity, consistency, isolation, and durability. This mechanism leverages blockchain's smart contract technology to record transaction operations on the blockchain, ensuring immutability and traceability. DHT's distributed indexing technology creates an efficient distributed index for data, improving query speed. Data sharding distributes data across multiple nodes, enhancing storage reliability and scalability. This architecture significantly improves storage reliability and scalability, supports rapid read and write operations for large-scale data, and leverages the blockchain's immutability to enhance data security.

[0053] Metadata Management: Design and implement a highly intelligent metadata management system. This system utilizes a metadata modeling approach based on ontology, semantic web technology, and blockchain timestamps to describe hydrological data sources, collection time and location, data format, and meaning in an ontological manner, thereby constructing a semantic model of hydrological data. Leveraging ontological reasoning, semantic web association analysis, and the immutable nature of blockchain timestamps, it automatically discovers potential relationships between metadata, providing more intelligent support for data search, access, and understanding. For example, when a user queries hydrological data for a specific region and time period, the system automatically associates relevant data resources based on the metadata semantic model and provides detailed explanations and usage instructions. The ontology is constructed using the Web Ontology Language (OWL), defining classes, properties, and instances to describe concepts and relationships within hydrological data. Furthermore, the metadata management system leverages blockchain timestamps to permanently record data changes, ensuring data traceability. Furthermore, machine learning algorithms are introduced to perform real-time metadata analysis and prediction, proactively identifying potential data issues.

[0054] Data Catalog Service: Build a dynamic data catalog service platform based on the integration of artificial intelligence, natural language processing (NLP) technology, and knowledge graph navigation. This platform leverages natural language processing (NLP), deep learning algorithms, and the semantic association capabilities of knowledge graphs to perform real-time analysis and cataloging of hydrological data stored in various locations. NLP technology translates user queries into machine-understandable semantic representations, and deep learning algorithms are used to intelligently search the data catalog. For example, if a user enters the natural language query "Query flow data for the Yangtze River Basin after the typhoon this summer," the platform uses lexical analysis, syntactic analysis, and semantic understanding to parse the query into semantic vectors. Combined with a recurrent neural network (RNN)-based retrieval model, this platform intelligently searches the data catalog, quickly locating relevant data resources and providing detailed information such as the data's storage location and format. Furthermore, knowledge graph navigation technology is introduced to construct navigation paths within the knowledge graph based on the user's query intent, guiding users to quickly find the required data. The data directory service supports multi-dimensional classification browsing, such as classification by region, time, data type, monitoring purpose, etc., which makes it convenient for users to quickly locate the required data and significantly improves data search efficiency.

[0055] 4. Data security and privacy protection module: Access Control: A novel access control model is established, combining attribute encryption, blockchain, and zero-knowledge proofs. This model not only considers user roles and responsibilities but also incorporates user attributes (such as department, years of experience, professional skills, and security level) into the access control system. Using attribute encryption technology, different encryption keys are assigned to users with different attributes, ensuring that only users with the corresponding attributes can decrypt and access data. For example, the Ciphertext Policy-Based Attribute Encryption (CP-ABE) algorithm is employed to associate data encryption keys with user attributes. During the encryption process, the encryption key is split into multiple parts based on the data access policy, each of which is bound to a different user attribute. Access control policies are also recorded on the blockchain, leveraging the blockchain's immutable nature to ensure their security and credibility. Zero-knowledge proof technology is introduced, eliminating the need for users to disclose their specific attribute information to the system when accessing data. Users only need to prove their access rights through a zero-knowledge proof mechanism, further protecting user privacy. For example, sensitive hydrological data related to water resources strategic planning can only be accessed by researchers with specific professional skills and years of experience using attribute encryption keys. Their access records will be permanently recorded on the blockchain for easy audit and traceability. Furthermore, when researchers access data, they can prove their access rights through a zero-knowledge proof mechanism without revealing their own attribute information.

[0056] Data Encryption: Sensitive hydrological data stored in the system is encrypted using an algorithm that combines homomorphic encryption, quantum encryption, and multi-party computation. Homomorphic encryption utilizes the Paillier encryption algorithm, allowing specific addition and multiplication operations on ciphertext without decryption, ensuring data security during the computation process. Quantum encryption utilizes the principles of quantum key distribution to generate absolutely secure encryption keys. During data transmission, a blockchain-based secure transmission protocol, multi-party computation, and secret sharing are combined to segment data into multiple blocks. Each block is uniquely identified using a blockchain hash algorithm and encrypted using a quantum encryption key for transmission. The receiver verifies the integrity and authenticity of the data block using the blockchain, then utilizes the properties of homomorphic encryption to perform data computation and processing, preventing data theft and tampering. Multi-party computation technology is introduced to divide the computational task into multiple subtasks, which are completed by multiple participants. Each participant only possesses a portion of the data and computation results, thereby protecting data privacy. Furthermore, secret sharing is used to split the encryption key into multiple shares, which are stored on different nodes, enhancing key security.

[0057] Privacy-preserving algorithm: For hydrological data that may involve personal privacy or sensitive areas, a joint privacy-preserving algorithm based on multi-party secure computation, differential privacy, and federated learning is proposed. In processing water level monitoring data in urban water supply systems, multi-party secure computation is used to distribute data among multiple participants for computation, allowing all parties to jointly complete data analysis tasks without leaking the original data. For example, the oblivious transfer (OT) protocol is employed to achieve secure data transmission and computation. Furthermore, a differential privacy algorithm is incorporated to appropriately perturb the computation results to hide sensitive information. In the differential privacy algorithm, noise conforming to the Laplace distribution is added to control the data's privacy budget, protecting the privacy of residents' water use information while ensuring the availability of the computation results. Federated learning technology is introduced to enable local model training by different participants without transmitting the original data. Model parameters are then encrypted and aggregated on a central server for data analysis and application, further protecting data privacy.

[0058] 5.Data integration and sharing module: Data Integration Engine: Develop a highly adaptive data integration engine. This engine utilizes a hybrid data integration algorithm based on federated learning, transfer learning, and multimodal data fusion. During the data access phase, federated learning technology is used to enable local model training on devices with different data sources without transmitting the original data. Model parameters are then encrypted and aggregated on a central server to achieve data integration. To address the discrepancies in data characteristics between different data sources, transfer learning technology is used to migrate model parameters trained on one data source to other data sources, enabling rapid adaptation to new data environments. Furthermore, multimodal data fusion technology is introduced to fuse different types of hydrological data (such as water level, flow, water quality, and meteorological data), exploring potential correlations and improving the quality of data integration. For example, model parameters for water level monitoring data from the water conservancy department can be migrated to the water quality monitoring data from the environmental protection department, while meteorological data from the meteorological department can also be integrated to improve the efficiency and accuracy of data integration. The data integration engine supports multiple data access methods, including database connections, file reading, web service calls, and real-time sensor data collection. It can efficiently aggregate hydrological data distributed across various departments and regions onto a unified platform.

[0059] Data Sharing Interface: To facilitate access and utilization of managed hydrological data by other systems or users, the system provides a series of highly compatible and scalable data sharing interfaces. In addition to traditional RESTful APIs and SOAP interfaces, an innovative real-time data sharing interface has been developed that integrates blockchain, the Internet of Things, and edge computing. This interface leverages blockchain's smart contract technology to automate and standardize data sharing. Users connect to the data sharing interface through their IoT devices and automatically obtain the required hydrological data according to the smart contract. Furthermore, edge computing technology is introduced to offload some data processing and analysis tasks to the edge nodes of IoT devices, reducing data transmission and improving the real-time nature of data sharing. The system supports data subscriptions, allowing users to subscribe to specific hydrological data. When data updates, the system automatically pushes the updated data to subscribed users using blockchain's event triggering mechanism, enabling real-time data sharing. Furthermore, a data visualization interface is provided, allowing users to access visual displays of the data for a more intuitive understanding and analysis of hydrological data. The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced within the present invention.

[0060] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A hydrological data management method, characterized in that: The following steps are involved: Step S1: Real-time monitoring of environmental parameters of the hydrological equipment and recording them as equipment environmental data; detecting environmental anomaly characteristics of the equipment environmental data, and conducting an environmental impact assessment on the hydrological equipment based on the environmental anomaly characteristics to generate equipment environmental impact data; Step S2: Identify the operating parameters of the hydrological equipment based on the data of the environmental impact of the equipment; compare the deviations between the operating parameters and the preset standard operating parameters, and mark the operating deviation parameters; extract the time series of the operating deviation parameters, and determine the hydrological raw data collected by the hydrological equipment based on the time series; Step S3: detecting missing feature information on the hydrological original data, and performing multi-dimensional data correction on the hydrological original data according to the missing feature information to obtain hydrological corrected data; Step S4: constructing a data integration engine with the hydrological correction data, and constructing a hydrological database based on the data integration engine; Determine the access control list of the hydrological database, set the data sharing interface according to the access control list, and manage the hydrological data through the data sharing interface.

2. The hydrological data management method according to claim 1, characterized in that: The real-time monitoring of the environmental parameters of the hydrological equipment in step S1 and recording as equipment environmental data include: The ambient temperature of the hydrological equipment is collected by a multi-point distributed temperature sensor array, wherein the sensor array is distributed at different heights and orientations of the hydrological equipment to obtain the vertical and horizontal gradient distribution of the temperature to obtain the ambient temperature parameters; A multi-band humidity sensor is used to collect the ambient humidity of the hydrological equipment. The humidity sensor simultaneously measures relative humidity and absolute humidity to obtain ambient humidity parameters. The ambient light intensity of the hydrological equipment is collected using a multi-angle light intensity sensor, which measures the intensity of direct light, scattered light, and reflected light respectively to obtain ambient light intensity parameters; The ambient temperature parameters, ambient humidity parameters and ambient light intensity parameters are integrated and recorded as device environment data.

3. The hydrological data management method according to claim 2, characterized in that: The detection of environmental anomaly characteristics of equipment environmental data in step S1 and the assessment of environmental impact of hydrological equipment based on the environmental anomaly characteristics include: The ambient temperature parameters are divided into multiple continuous temperature time segments, and the temperature sudden change time segment detection is performed on the temperature time segments to obtain temperature abnormality time data; The environmental humidity parameter is divided into a plurality of continuous humidity spatial distribution segments, and humidity anomaly locations are detected on the humidity spatial distribution segments to obtain humidity anomaly location data; The light intensity data is divided into a plurality of continuous spectral distribution segments, and the spectral distribution segments are subjected to light intensity abnormality region detection to obtain light intensity abnormality region data; Evaluate the structural heat loss of hydrological equipment based on the temperature anomaly time data and record the structural heat loss data; Evaluate the corrosion of electronic components of hydrological equipment based on humidity anomaly location data and record the corrosion data; Evaluate the collection interference of hydrological equipment based on the data of abnormal light intensity areas and record the collection interference data; Integrate structural heat loss data, electronic component corrosion data, and collected interference data to obtain data on the impact of the environment on the equipment.

4. The hydrological data management method according to claim 1, characterized in that: The step S2 of identifying the operating parameters of the hydrological equipment according to the data on the environmental impact of the equipment includes: Based on the environmental impact data of the equipment, the speed of the cooling fan and the temperature distribution of the heat sink of the hydrological equipment are tested to obtain the operating parameters of the heat dissipation device; According to the data on the environmental impact of the equipment, the remaining amount of moisture-proof agent and the operation time of moisture-proof and dehumidification equipment are tested to obtain the operating parameters of the moisture-proof device; Based on the data of the environmental impact of the equipment, the sensor sensitivity, response time and acquisition frequency of the hydrological equipment are tested to obtain the operating parameters of the sensor device; The operating parameters of the heat dissipation device, the moisture-proof device and the sensor device are combined to obtain the operating parameters.

5. The hydrological data management method according to claim 4, characterized in that: The step S2 of comparing the deviation of the operating parameters with the preset standard operating parameters and marking the operating deviation parameters includes: Setting standard values for operating parameters to obtain preset standard operating parameters, wherein the preset standard operating parameters include standard heat dissipation operating parameters, standard moisture-proof operating parameters, and standard sensor operating parameters; Comparing the operating parameters of the heat dissipation device with the standard heat dissipation operating parameters to obtain the heat dissipation operating deviation parameters; Compare the deviation between the operating parameters of the moisture-proof device and the standard moisture-proof operating parameters to obtain the moisture-proof operating deviation parameters; Compare the deviations of the sensor device operating parameters with those of the standard sensor operating parameters to obtain moisture-proof operating deviation parameters; The heat dissipation operation deviation parameter, the moisture-proof operation deviation parameter, and the moisture-proof operation deviation parameter are integrated to obtain the operation deviation parameter.

6. The hydrological data management method according to claim 1, characterized in that: The extraction of the time series of the operating deviation parameters in step S2 and the determination of the hydrological raw data collected by the hydrological equipment according to the time series include: Extract the timestamp of the running deviation parameter, and determine the time series of the running deviation parameter according to the timestamp; Water level data is collected based on a high-precision water level gauge in time series and hydrological equipment, with a data accuracy of ±1cm+0.1%×h; Flow data is collected based on the ultrasonic flow meter of the hydrological equipment in time series, with a data accuracy of ±0.25%+2mm / s; Water quality data is recorded by a multi-parameter water quality analyzer based on time series and hydrological equipment, with a turbidity measurement range of 0 to 4000 NTU; Add hydrological identifiers to water level data, flow data, and water quality data, and merge the data to generate raw hydrological data.

7. The hydrological data management method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Identify the water level data time series of the original hydrological data and mark the missing time points to obtain missing water level data; Step S32: identifying the spatial distribution of flow data of the original hydrological data, and marking missing monitoring points to obtain missing flow data; Step S33: Identify the integrity of the water quality data type of the original hydrological data, mark the missing type, and obtain the missing water quality data; Step S34: performing time series interpolation correction on the missing water level data to generate water level correction data; Step S35: performing spatial interpolation correction on the flow missing data to generate flow correction data; Step S36: performing statistical average correction on the missing water quality data to generate water quality correction data; Step S37: Integrate the water level correction data, the flow correction data, and the water quality correction data to obtain hydrological correction data.

8. The hydrological data management method according to claim 1, characterized in that: The step S4 of constructing a data integration engine with the hydrological correction data and constructing a hydrological database based on the data integration engine includes: The hydrological correction data is divided into time, space and physical attribute characteristics to obtain a characteristic data set; Perform association mapping on the characterized data set to obtain an association data model; Structural encapsulation of linked data models and conversion into integrated data units; The integrated data units are processed in a distributed and collaborative manner to obtain a data integration engine framework; The data integration engine framework is matched with hydrological features to obtain a data integration engine; Establish a hydrological index for the data integration engine and build a hydrological database based on the hydrological index.

9. The hydrological data management method according to claim 1, characterized in that: Determining the access control list of the hydrological database in step S4 and setting a data sharing interface according to the access control list, and managing the hydrological data through the data sharing interface includes: Perform multi-level permission division on user roles of the hydrological database to obtain hierarchical access permission configuration data; Perform role mapping on the hierarchical access permission configuration data to obtain role mapping access control data; Determine access control list based on role mapping access control data; Based on the access control list, the data sharing interface content of the hydrological database is marked, and the data sharing interface content is securely encapsulated to obtain the data sharing interface; Manage hydrological data through data sharing interfaces.

10. A hydrological data management system, characterized in that: For executing the hydrological data management method according to claim 1, the hydrological data management system comprises: The hydrological equipment environmental monitoring and assessment module is used to monitor the environmental parameters of the hydrological equipment in real time and record them as equipment environmental data; it detects environmental anomaly characteristics of the equipment environmental data, conducts environmental impact assessment on the hydrological equipment based on the environmental anomaly characteristics, and generates equipment environmental impact data; The hydrological data acquisition module is used to identify the operating parameters of hydrological equipment based on the data of the equipment being affected by the environment; compare the deviations between the operating parameters and the preset standard operating parameters and mark the operating deviation parameters; extract the time series of the operating deviation parameters and determine the original hydrological data collected by the hydrological equipment based on the time series; The hydrological data correction module is used to detect missing feature information in the original hydrological data and perform multi-dimensional data correction on the original hydrological data according to the missing feature information to obtain hydrological corrected data; The hydrological database construction management module is used to build a data integration engine with hydrological correction data, and build a hydrological database based on the data integration engine; determine the access control list of the hydrological database, and set the data sharing interface according to the access control list, and manage the hydrological data through the data sharing interface.

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