Water conservancy element data intelligent acquisition and query method and system

Abstract

The application relates to the technical field of hydrological data management, in particular to a water conservancy element data intelligent acquisition and query method and system, which comprises the following steps: based on target water body position information, analyzing the acquisition requirements of various water conservancy element data, considering the coverage range and monitoring effect, planning the deployment positions of various sensors, and collecting hydrological data to generate hydrological data acquisition results. In the application, the sensor position deployment is optimized, the coverage range of monitoring data is improved, and data blind areas are reduced; the transmission frequency and compression rate are adjusted by monitoring the real-time data transmission load and signal quality, the stability and speed of data transmission are optimized; abnormal data is identified through statistical analysis, the accuracy and reliability of data are optimized; the automation level of data processing is improved through automatic marking and correction of abnormal data; the semantic analysis and conversion processing of the query request are carried out, the user query is quickly responded, and the relevance and accuracy of the query result are improved.

Description

Technical Field

[0001] This invention relates to the field of hydrological data management technology, and in particular to a method and system for intelligent collection and query of water conservancy element data. Background Technology

[0002] The field of hydrological data management technology focuses on the collection, storage, processing, and analysis of hydrological element data. Its aim is to support sustainable water resource management and decision-making. By ensuring the accuracy, accessibility, and timeliness of hydrological data, it predicts changes in water flow and quality, assesses water resource utilization efficiency, and plans the rational allocation of water resources. Utilizing IoT sensors, satellite remote sensing, geographic information systems, and various information technologies, it improves the accuracy of data collection, optimizes data integration and analysis processes, and provides scientific basis and operational guidance for water resource management.

[0003] Among them, the intelligent data collection and query method for water conservancy elements is used to efficiently acquire and process hydrological and water resource data. Through automated and precise data collection and query processes, it provides necessary hydrological, meteorological, and geographic information, improves the understanding of water resource status, supports water resource monitoring, analysis, and management, optimizes water resource use, reduces waste, and enhances the ability to respond to changes in water resources. Combined with database management and a user-friendly query interface, it enables quick access to and analysis of required data, improving the response speed and decision-making quality of water resource management.

[0004] Traditional intelligent data collection and query methods for water resources elements rely on fixed-frequency data acquisition, lacking sensitivity to environmental changes. This results in insufficient timeliness and accuracy of data under rapidly changing environmental conditions. Under extreme weather conditions such as floods or droughts, fixed-frequency data acquisition cannot capture key data in a timely manner, affecting water resources management decisions and emergency responses. Furthermore, the lack of real-time load adjustment capabilities during data transmission leads to data delays and loss under high load conditions, affecting data integrity and real-time performance. In terms of data querying, the lack of understanding of user query intent results in query results that do not fully match user needs, reducing the efficiency and quality of decision-making. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and to propose an intelligent data collection and query method and system for water conservancy elements.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for intelligent collection and query of water conservancy element data, comprising the following steps: S1: Based on the target water body location information, analyze the data collection needs of various water conservancy elements, consider the coverage and monitoring effect, plan the deployment locations of various sensors, collect hydrological data, and generate hydrological data collection results; S2: Based on the hydrological data acquisition results, analyze the changing trends of various hydrological data, and adjust the frequency of data acquisition from multiple location sensors according to the changing trends to generate acquisition parameter configuration; S3: Based on the acquired parameter configuration, evaluate the load and signal quality of sensor data transmission in real time, adjust the data transmission frequency and compression rate, optimize the data transmission path, and generate network configuration adjustment results; S4: Based on the network configuration adjustment results, perform statistical analysis on the hydrological data, identify the anomaly detection thresholds of various hydrological data and record abnormal data points, and combine cross-validation and anomaly correction to obtain data anomaly detection records; S5: Based on the data anomaly detection record, classify the water conservancy element data according to the type of data points, and generate data index tags by matching index tags according to time information and location information; S6: Based on the data index tags, semantic analysis is used to identify the language expressions in the query request, which are then converted into database query commands to find matching data nodes and generate data query results.

[0007] As a further aspect of the present invention, the hydrological data acquisition results include sensor location information, data acquisition time point information, and water level, flow rate, and water quality datasets; the acquisition parameter configuration includes sensor data acquisition frequency parameters, changing trends of various hydrological data, and sensor operating mode information; the network configuration adjustment results include data transmission path, data compression parameters, and real-time network load information; the data anomaly detection record includes anomaly data point type information, anomaly data point timestamps, and data correction records; the data index tags include time index tags, spatial index tags, and hydrological data classification results; and the data query results include query intent analysis results, data node matching results, and data query commands.

[0008] As a further aspect of the present invention, based on the target water body location information, the steps of analyzing the data collection needs of various water conservancy elements, considering coverage and monitoring effects, planning the deployment locations of various sensors, collecting hydrological data, and generating hydrological data collection results are as follows: S101: Based on the location information of the target water body, collect and record the characteristic parameters of the target water body, including water depth, water flow velocity and topographic information, and generate a water body feature dataset; S102: Based on the water body feature dataset, analyze the monitoring needs of various hydraulic elements of the target water body, consider the monitoring range and effect of various sensors, calculate the required sensor types and quantities, and generate sensor requirement calculation results. S103: Based on the sensor requirement calculation results, plan the deployment location of the sensors, collect hydrological data from multiple locations, and generate hydrological data collection results.

[0009] As a further aspect of the present invention, the steps of analyzing the changing trends of various hydrological data based on the hydrological data acquisition results, and adjusting the frequency of data acquisition from multiple location sensors according to the changing trends to generate acquisition parameter configurations are as follows: S201: Based on the hydrological data acquisition results, perform time series statistics on the rainfall, water level and flow data collected by the sensors, calculate the fluctuation of various data indicators, and generate data fluctuation records; S202: Based on the data fluctuation records, analyze the changing trends of various hydrological elements, consider energy consumption and data acquisition requirements, calculate the data acquisition frequency required by multiple sensors, and generate acquisition frequency adjustment results; S203: Based on the acquisition frequency adjustment results, adjust and update the data acquisition frequencies of multiple sensors, record the adjusted setting parameters, and generate the acquisition parameter configuration.

[0010] As a further aspect of the present invention, the specific formula for calculating the fluctuation of various data indicators is as follows: in, The composite volatility index represents a statistical measure of the volatility of various hydrological data points, calculated through a weighted average. It is used to quantify the degree of volatility and stability of the data. This represents the most recent data value, reflecting the most recently collected hydrological data. The data value represents the previous period, indicating that in The hydrological data collected previously in the last period, This represents the data values ​​from the previous two periods, that is, in Another set of hydrological data from the previous period provides information on data changes over a longer timeframe. , , These represent the weighting coefficients for the most recent period, the previous period, and the two periods prior, respectively, and are allocated based on the timeliness and importance of the data.

[0011] As a further aspect of the present invention, the steps of evaluating the load and signal quality of sensor data transmission in real time based on the acquisition parameter configuration, adjusting the data transmission frequency and compression rate, optimizing the data transmission path, and generating network configuration adjustment results are as follows: S301: Based on the configuration of the acquisition parameters, monitor the data transmission volume and signal strength of multiple sensors in real time, record the transmission delay and error rate, and generate a real-time transmission monitoring record; S302: Based on the real-time transmission monitoring records, adjust the transmission frequency and data compression parameters of multiple sensors to generate transmission parameter configuration; S303: Based on the transmission parameter configuration, optimize the data transmission path according to the load of multiple nodes in the data transmission network, and generate network configuration adjustment results.

[0012] As a further aspect of the present invention, based on the network configuration adjustment results, statistical analysis is performed on the hydrological data to identify anomaly detection thresholds for various hydrological data and record abnormal data points. The specific steps for obtaining the data anomaly detection record, combining cross-validation and anomaly point correction, are as follows: S401: Based on the network configuration adjustment results, perform statistical analysis on various hydrological data points, and identify anomaly detection thresholds for various data types by calculating standard deviation and average value, and generate threshold setting records; S402: Using the threshold setting record, by comparing with the anomaly detection threshold, identify and mark data points that exceed the threshold, record the location and time information of the data points, and generate anomaly data point records; S403: Based on the abnormal data point records, cross-validation is used to correct data that deviates from the normal range and generate a data anomaly detection record.

[0013] As a further aspect of the present invention, based on the data anomaly detection record, the water conservancy element data is classified according to the type of data points, and index tags are matched according to time information and location information to generate data index tags. The specific steps are as follows: S501: Based on the data anomaly detection records, classify the data according to the type of hydrological elements, including water level, flow rate and rainfall, and generate classification data identifiers; S502: Based on the classification data identifier, according to the data point collection time information, match time index tags for multiple data points, and according to the data point collection location information, match location information tags for multiple data points to generate tag matching information; S503: Based on the tag matching information, by configuring the mapping rules between multiple index tags and data storage locations, optimize the efficiency of data retrieval and the response speed of queries, and generate data index tags.

[0014] As a further aspect of the present invention, based on the data index tags, the steps of identifying language expressions in the query request through semantic analysis, converting them into database query commands, finding matching data nodes, and generating data query results are as follows: S601: Based on the data index tags, semantic analysis is used to identify keywords in the user's query request by analyzing the semantic relationships of words, including water level and precipitation, and generate keyword recognition results; S602: Based on the keyword recognition results, the query conditions are converted into database query commands, and the corresponding query parameters are configured to generate the converted query commands; S603: Analyze the transformed query command, execute the query command, match data points for the query request, record the response time, and generate data query results.

[0015] A water resources element data intelligent collection and query system, the system being used to execute the above-mentioned water resources element data intelligent collection and query method, the system comprising: The sensor deployment module analyzes the data collection needs of various water conservancy elements based on the target water body location information, calculates the required sensor types and plans the deployment locations according to the monitoring range and effect of various sensors, performs data collection, and generates a water conservancy element dataset. The data monitoring and transmission module uses the water conservancy element dataset to analyze the changing trends of hydrological data, adjust the data acquisition frequency based on the analysis results, monitor the real-time load and signal quality of sensor data transmission, adjust the data transmission frequency and compression rate, optimize the data transmission path, and generate data transmission configuration parameters. Based on the data transmission configuration parameters, the hydrological data analysis module performs statistical analysis on the transmitted hydrological data, sets anomaly detection threshold, identifies abnormal data points, processes the abnormal data through cross-validation and data correction, and generates abnormal data point processing results. Based on the processing results of the abnormal data points, the data classification module classifies the hydrological data according to the type of data points, and generates data classification label information by matching index labels according to time and location information. Based on the data classification label information, the query command analysis module identifies the language expressions in the query request through semantic analysis, converts them into database query commands and executes them, finds matching data nodes, and generates data query results.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by optimizing the deployment of sensor locations, the coverage of monitoring data is improved and data blind spots are reduced. By real-time monitoring of data transmission load and signal quality, the transmission frequency and compression rate are adjusted to optimize the stability and speed of data transmission. By identifying abnormal data through statistical analysis, the accuracy and reliability of data are optimized. By combining automatic marking and correction of abnormal data, the automation level of data processing is improved. By semantic analysis and transformation processing of query requests, user queries are responded to quickly, and the relevance and accuracy of query results are improved. Attached Figure Description

[0017] Figure 1This is a schematic diagram of the workflow of the present invention; Figure 2 This is a detailed flowchart of S1 of the present invention; Figure 3 This is a detailed flowchart of the S2 process of the present invention; Figure 4 This is a detailed flowchart of the S3 process of the present invention; Figure 5 This is a detailed flowchart of the S4 process of the present invention; Figure 6 This is a detailed flowchart of S5 of the present invention; Figure 7 This is a detailed flowchart of S6 of the present invention; Figure 8 This is a system flowchart of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0020] Please see Figure 1 This invention provides a technical solution, a method for intelligent collection and query of water conservancy element data, comprising the following steps: S1: Based on the target water body location information, analyze the data collection needs of various water conservancy elements, consider the coverage and monitoring effect, plan the deployment locations of various sensors, collect hydrological data, and generate hydrological data collection results; S2: Based on the hydrological data acquisition results, analyze the changing trends of various hydrological data, and adjust the frequency of data acquisition from multiple location sensors according to the changing trends to generate acquisition parameter configurations; S3: Based on the configuration of the acquired parameters, evaluate the load and signal quality of sensor data transmission in real time, adjust the frequency and compression rate of data transmission, optimize the data transmission path, and generate network configuration adjustment results; S4: Based on the network configuration adjustment results, perform statistical analysis on hydrological data, identify anomaly detection thresholds for various hydrological data and record abnormal data points, and obtain data anomaly detection records by combining cross-validation and anomaly point correction. S5: Based on the data anomaly detection records, classify the water conservancy element data according to the type of data points, and generate data index labels by matching index labels according to time and location information; S6: Based on data index tags, semantic analysis is used to identify language expressions in query requests, which are then converted into database query commands to find matching data nodes and generate data query results.

[0021] The hydrological data acquisition results include sensor location information, data acquisition time point information, water level, flow rate, and water quality datasets. The acquisition parameter configuration includes sensor data acquisition frequency parameters, changing trends of various hydrological data, and sensor operating mode information. The network configuration adjustment results include data transmission path, data compression parameters, and real-time network load information. The data anomaly detection records include anomaly data point type information, anomaly data point timestamps, and data correction records. The data index labels include time index labels, spatial index labels, and hydrological data classification results. The data query results include query intent analysis results, data node matching results, and data query commands.

[0022] Please see Figure 2 Based on the target water body location information, the specific steps for analyzing the data collection needs of various water conservancy elements, considering coverage and monitoring effectiveness, planning the deployment locations of various sensors, collecting hydrological data, and generating hydrological data collection results are as follows: S101: Based on the location information of the target water body, collect and record the characteristic parameters of the target water body, including water depth, water flow velocity and topographic information, and generate a water body feature dataset; In sub-step S101, based on the target water body location information, precise positioning is achieved using geographic coordinate systems and global positioning systems. Sonar equipment is used to measure water depth, which is calculated by measuring the time difference of sound wave reflection (data type: continuous numerical). A current meter is used to measure water flow velocity, including fixing the current meter at different depths and positions in the water body (data type: continuous numerical). Topographic information is collected using cameras and terrain radar systems carried by underwater drones to create a three-dimensional terrain model. The collected topographic information includes bottom sediment type and topographic relief (data type: categorical and continuous numerical). The water body characteristic data is integrated into a comprehensive dataset using data integration software, providing a foundation for subsequent analysis and monitoring.

[0023] S102: Based on the water body characteristic dataset, analyze the monitoring needs of various hydraulic elements of the target water body, consider the monitoring range and effect of various sensors, calculate the required sensor types and quantities, and generate sensor requirement calculation results. The above content analyzes the monitoring needs of various hydraulic elements of the target water body, calculates the type and number of sensors, and follows the formula. Calculate the number of sensors required; In the formula, This represents the total number of sensors required. The total monitored area represents the target water body. Represents the monitoring area of ​​a single sensor. Represents monitoring efficiency; Detailed explanation of the formula and its calculation derivation: Assuming total monitored area The effective monitoring area of ​​the sensor Monitoring efficiency ,calculate : Result 22.22 indicates that at least approximately 23 sensors are required to meet the monitoring needs. The calculation process was used to ensure comprehensive monitoring coverage and to obtain the precise number of sensors required.

[0024] S103: Based on the sensor requirement calculation results, plan the deployment location of the sensors, collect hydrological data from multiple locations, and generate hydrological data collection results; In sub-step S103, sensors are deployed using automated deployment tools such as underwater drones and robotic arms according to sensor requirements and layout plans. This ensures that each sensor is accurately deployed in its predetermined location. During deployment, actual conditions such as water flow, depth, and terrain are considered, and sensor positions are adjusted in a timely manner to match environmental changes and optimize data acquisition. After data acquisition, the data is transmitted to the central database through a real-time monitoring system. Data cleaning and preprocessing techniques are employed to ensure the accuracy and usability of the collected data, including data filtering, outlier detection, and data interpolation, ensuring the integrity of the dataset. The collected data is analyzed to evaluate sensor performance and the stability of the data acquisition system, providing support for data analysis and decision-making. The generated hydrological data acquisition results are of significant value for understanding water conditions and supporting water resource management.

[0025] Please see Figure 3 Based on the hydrological data acquisition results, the specific steps for analyzing the changing trends of various hydrological data, adjusting the data acquisition frequency of multiple location sensors according to the changing trends, and generating the acquisition parameter configuration are as follows: S201: Based on the hydrological data acquisition results, perform time series statistics on the rainfall, water level and flow data collected by the sensors, calculate the fluctuation of various data indicators, and generate data fluctuation records; The specific formula for calculating the fluctuation of various data indicators is as follows: in, The composite volatility index represents a statistical measure of the volatility of various hydrological data points, calculated through a weighted average. It is used to quantify the degree of volatility and stability of the data. This represents the most recent data value, reflecting the most recently collected hydrological data. The data value represents the previous period, indicating that in The hydrological data collected previously in the last period, This represents the data values ​​from the previous two periods, that is, in Another set of hydrological data from the previous period provides information on data changes over a longer timeframe. , , These represent the weighting coefficients for the most recent period, the previous period, and the two periods prior, respectively, and are allocated based on the timeliness and importance of the data.

[0026] formula: Detailed explanation of the formula and its calculation derivation: The formula is used to calculate the comprehensive fluctuation index, which reflects the trend and stability of hydrological data over time. Parameter meanings and settings: The value is the most recent data point, let's assume it's 100; The value is the data value from the previous period, let's assume it's 95; The data value is from the previous two periods, let's assume it's 90; The weight of the most recent data is assumed to be 0.6; The weight of the previous period's data is assumed to be 0.3; The weight of the data from the first two periods is assumed to be 0.1; Substitute the parameters into the formula to calculate: The result of 97.5 indicates that the fluctuation index of the hydrological data during the current observation period is 97.5, which means that the hydrological data has shown high stability and consistency in the most recent three periods. This indicates that the data fluctuation is small and the current hydrological situation is relatively stable. The result can be used to assess the current state of water resources and guide future monitoring and management strategies, especially providing a reliable reference in trend analysis.

[0027] S202: Based on data fluctuation records, analyze the changing trends of various hydrological elements, consider energy consumption and data acquisition requirements, calculate the data acquisition frequency required by multiple sensors, and generate acquisition frequency adjustment results; In sub-step S202, based on data fluctuation records, the changing trends of hydrological elements are analyzed using the results of time series analysis. Trend analysis techniques, such as linear regression analysis, are applied to determine the long-term trend and short-term fluctuation characteristics of hydrological data. During the analysis, the actual needs of energy consumption and data acquisition are considered. A multiple regression model is used to calculate the optimal data acquisition frequency for each sensor. The input independent variables are the fluctuation rate and trend slope obtained from the analysis, and the dependent variable is the data acquisition frequency. The frequency setting is adjusted using the gradient descent method to optimize the energy efficiency ratio and data collection effect, generating the acquisition frequency adjustment results. The results affect the operating efficiency of the sensors and the data quality.

[0028] S203: Based on the results of the frequency adjustment, adjust and update the data acquisition frequencies of multiple sensors, record the adjusted settings parameters, and generate the acquisition parameter configuration; In sub-step S203, based on the frequency adjustment results, the sensor setting parameters are adjusted using software tools. This includes accessing the sensor management system, selecting the sensors whose frequencies need to be adjusted, and updating the settings based on the calculated optimal frequency. This also includes sending programming instructions to the sensor's control unit, adjusting the data acquisition interval, and after adjustment, the system automatically records the new setting parameters and performs tests to verify the effectiveness of the new frequency. The generated acquisition parameter configuration records the new acquisition frequency and expected performance of each sensor.

[0029] Please see Figure 4 The specific steps for evaluating the load and signal quality of sensor data transmission in real time based on the acquired parameter configuration, adjusting the data transmission frequency and compression rate, optimizing the data transmission path, and generating network configuration adjustment results are as follows: S301: Based on the configuration of the acquisition parameters, it monitors the data transmission volume and signal strength of multiple sensors in real time, records the transmission delay and error rate, and generates real-time transmission monitoring records; The above content involves real-time monitoring of the data transmission volume and signal strength of multiple sensors, according to the formula. Calculate the transmission rate; In the formula, Represents transmission rate. Represents the amount of data transmitted. Represents transmission time; Detailed explanation of the formula and its calculation derivation: Assuming the amount of data transmitted during monitoring... MB, transfer time Calculate the transmission rate in seconds. : result This indicates the current data transmission rate of the sensor. The calculation results are used to evaluate the sensor's performance during real-time data acquisition, help the monitoring system determine whether data transmission settings need to be adjusted, ensure stable transmission under high load conditions, meet real-time data transmission requirements, and provide crucial information for adjusting transmission frequency and optimizing network configuration.

[0030] S302: Based on real-time transmission monitoring records, adjust the transmission frequency and data compression parameters of multiple sensors to generate transmission parameter configuration; In sub-step S302, based on the analysis results of real-time transmission monitoring records, network optimization algorithms are used to adjust the transmission frequency and data compression parameters of the sensors. Problematic sensors identified in the records are analyzed, and the transmission frequency is adjusted for the target devices using an algorithm that minimizes the average latency to ensure timely data upload without overloading the network. Data compression techniques, such as Huffman coding, are applied to reduce the amount of data that needs to be transmitted and optimize network usage efficiency. The new transmission settings of the sensors are simulated and tested to verify the improvement effect, ensuring that the adjusted parameters can reduce latency and error rate and improve signal transmission efficiency. The generated transmission parameter configuration describes the parameter values ​​of each sensor before and after adjustment, including frequency, compression ratio, and expected impact on network performance.

[0031] S303: Based on transmission parameter configuration, optimize the data transmission path according to the load of multiple nodes in the data transmission network, and generate network configuration adjustment results; In sub-step S303, based on the transmission parameter configuration, load balancing technology is used to optimize the data transmission path. The network configuration adjustment is based on the network load and uses dynamic routing algorithms, such as the Open Shortest Path First protocol, to automatically select the optimal path for data transmission. The process involves analyzing the real-time load data of multiple nodes in the network, identifying overloaded or poorly performing nodes, reconfiguring the data flow, and implementing redundant path configuration to improve the network's robustness and fault tolerance. Each network configuration adjustment is recorded in the system log, including the date, time, involved nodes, and modified path. The generated network configuration adjustment results provide a record of network topology changes and the expected performance improvement, ensuring that both the efficiency and reliability of data transmission are optimized.

[0032] Please see Figure 5 Based on the network configuration adjustment results, statistical analysis is performed on hydrological data to identify anomaly detection thresholds for various hydrological data types and record abnormal data points. The specific steps for obtaining data anomaly detection records, combined with cross-validation and anomaly correction, are as follows: S401: Based on the network configuration adjustment results, perform statistical analysis on various hydrological data points, and identify anomaly detection thresholds for various data types by calculating standard deviation and average value, and generate threshold setting records; The above content involves statistical analysis of various hydrological data points to identify anomaly detection thresholds, according to the formula... Calculate the anomaly detection threshold; In the formula, Represents the anomaly detection threshold. Represents the average value. Represents the standard deviation. Represents the confidence coefficient; Detailed explanation of the formula and its calculation derivation: Assuming the calculated average value Unit, standard deviation Units, confidence coefficients (Corresponding to a 99.7% confidence level), calculate : Results of 8 to 32 units indicate that data points outside the range are identified as anomalies. The calculation process is used to determine the normal operating range of the data and effectively identify and handle abnormal data points.

[0033] S402: Use threshold setting records to identify and mark data points that exceed the threshold by comparing them with anomaly detection thresholds, record the location and time information of the data points, and generate anomaly data point records; In sub-step S402, the generated threshold setting record is used to scan the collected hydrological data through the automated monitoring system. Each data point is compared with the predetermined threshold. The system uses anomaly detection algorithms, such as a simple threshold detection method based on statistical thresholds, to automatically identify and mark data points that exceed the threshold. The specific location and time information of the data points that exceed the threshold will be recorded. Abnormal data caused by equipment failure, data transmission errors, or sudden environmental changes are identified. The generated abnormal data point record lists the relevant information of the abnormal points, including data value, timestamp, and geographical location, providing important information for subsequent data correction and system maintenance.

[0034] S403: Based on the abnormal data point records, cross-validation is used to correct data that deviates from the normal range and generate data anomaly detection records; In sub-step S403, based on the abnormal data point records, cross-validation is used to correct data that deviates from the normal range. This includes comparing data from multiple independent data sources to confirm the authenticity of the data anomalies. If data from multiple sources show similar abnormal trends, it indicates data changes caused by environmental changes or other non-equipment failure factors. If only a single data source shows an anomaly, it indicates equipment failure or data transmission errors. The data correction operation is performed through data fusion technology, which includes comparing and analyzing the affected data points with historical data or adjacent data points, recalculating and adjusting the data values. The generated data anomaly detection record contains detailed information on all corrected data points, such as the original data value, the corrected data value, the basis for correction, and the correction method performed, ensuring the accuracy and reliability of the data.

[0035] Please see Figure 6 Based on data anomaly detection records, water conservancy element data are classified according to the type of data points, and index labels are generated by matching index labels based on time and location information. The specific steps are as follows: S501: Based on data anomaly detection records, classify the data according to the type of hydrological elements, including water level, flow rate and rainfall, and generate classification data labels; In sub-step S501, starting from the data anomaly detection records, the data is classified to ensure the accuracy and efficiency of subsequent processing. Data classification algorithms, such as decision tree classifiers, are used to distinguish different types of hydrological element data: water level, flow rate, and rainfall. The data anomaly detection records are imported, including the value, type, and anomaly status of each data point. The algorithm is used to automatically classify the data according to preset classification rules, including classification based on data range and common hydrological characteristics. The classification process ensures that each data point is accurately identified to its hydrological type. The generated classification data identifier lists the classification results of each data point, including data type and category, providing structured information for data processing and analysis.

[0036] S502: Based on the classification data identifier, match time index labels for multiple data points according to the data point collection time information, match location information labels for multiple data points according to the data point collection location information, and generate label matching information; In sub-step S502, based on categorized data identification, data is managed and utilized by adding time and location tags to data points. The collection time and location information of data points is imported from the database. Data tagging technology, including tag encoding, is used to encode the time and location information. The time index tag is encoded based on the data point's collection time, in the format of year, month, day, hour, minute, and second. The location information tag is encoded according to the geographic coordinates of the data point, including latitude and longitude or geographic region code. Through automated scripts, it is ensured that the time and location information of each data point is accurately matched and recorded. The generated tag matching information contains the time index and location tag of each data point, laying the foundation for rapid data retrieval and efficient management.

[0037] S503: Based on tag matching information, by configuring mapping rules between multiple index tags and data storage locations, optimize the efficiency of data retrieval and the response speed of queries, and generate data index tags; In sub-step S503, based on tag matching information, data retrieval efficiency and query response speed are optimized. By configuring the mapping rules between index tags and data storage locations, mapping rules are defined using database indexing technologies, such as B-tree indexes or hash indexes. The index structure is optimized according to the data access frequency and query patterns. Indexes are created and configured using index building tools in the data management system. The creation of indexes takes into account the data storage location and data type, ensuring rapid location of data points. The indexes are regularly maintained and optimized to cope with changes in data updates and query requirements. The generated data index tags record the index configuration for each data point, including the index type, associated data tags, and mapping rules, thereby improving the retrieval performance and operational efficiency of the data system.

[0038] Please see Figure 7 Based on data index tags, the specific steps for identifying language expressions in query requests through semantic analysis, converting them into database query commands, finding matching data nodes, and generating data query results are as follows: S601: Based on data index tags, using semantic analysis, by analyzing the semantic relationships of words, it identifies keywords in user query requests, including water level and precipitation, and generates keyword recognition results; In sub-step S601, natural language processing technology is applied to identify keywords in the user's query request. Text analysis frameworks, such as NLTK or SpaCy, are used to perform lexical and syntactic analysis on the user's query request. Semantic databases such as WordNet are used to identify and parse the semantic relationships of words. During the processing, terms associated with hydrological data, including "water level" and "precipitation," are focused on and automatically marked as the focus of the query, and extracted to form keyword recognition results. This ensures the accuracy of the query analysis and transforms the user's natural language request into a clear query requirement. The generated keyword recognition results record each identified keyword and its semantic attributes, providing a foundation for subsequent query transformation.

[0039] S602: Based on the keyword recognition results, convert the query conditions into database query commands, configure the corresponding query parameters, and generate the converted query commands; In sub-step S602, based on the identified keywords, the user's query request is converted into a database query command. Using a query builder, such as SQLBuilder, keywords and parameters in natural language are mapped to corresponding database fields and operations. This includes converting "water level" into a query for the "water level" field in a specific hydrological database. Query parameters are configured according to the specific conditions requested by the user. This process involves setting logical conditions for the query, including AND / OR conditions, sorting, and grouping operations, to ensure that the query command meets the user's data requirements. The generated converted query command lists the complete SQL command executed by the database, including the fields, conditions, and configured parameters, providing precise instructions for actual data retrieval.

[0040] S603: Analyze the transformed query command, execute the query command, match data points for the query request, record the response time, and generate data query results; In sub-step S603, the database query command generated in the previous step is analyzed and executed. The database management system is used to execute the query command, and the optimized query processor quickly locates and matches data points. During the process, the response time of the query execution is recorded, the query efficiency is evaluated, and the accuracy and completeness of the query results are analyzed to ensure that the matching of data points strictly corresponds to the user request. The generated data query results include information for each matched data point, including data type, value, timestamp, and geographical location. The overall response time of the query execution is used for system performance evaluation to ensure high efficiency and high reliability of data services and meet the user's data query needs.

[0041] Please see Figure 8 A water resources element data intelligent collection and query system, the water resources element data intelligent collection and query system is used to execute the above-mentioned water resources element data intelligent collection and query method, the system includes: The sensor deployment module analyzes the data collection needs of various water conservancy elements based on the target water body location information, calculates the required sensor types and plans the deployment locations according to the monitoring range and effect of various sensors, performs data collection, and generates a water conservancy element dataset. The data monitoring and transmission module uses water conservancy element datasets to analyze the changing trends of hydrological data, adjusts the data acquisition frequency based on the analysis results, monitors the real-time load and signal quality of sensor data transmission, adjusts the data transmission frequency and compression rate, optimizes the data transmission path, and generates data transmission configuration parameters. The hydrological data analysis module, based on the data transmission configuration parameters, performs statistical analysis on the transmitted hydrological data, sets anomaly detection threshold, identifies abnormal data points, processes abnormal data through cross-validation and data correction, and generates abnormal data point processing results. The data classification module classifies hydrological data based on the type of data points, and generates data classification label information by matching index labels according to time and location information. The query command analysis module uses data classification tag information and semantic analysis to identify the language expressions in the query request, convert them into database query commands and execute them, find matching data nodes, and generate data query results.

[0042] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for intelligent collection and query of water conservancy element data, characterized in that, Includes the following steps: Based on the target water body location information, the data collection needs of various water conservancy elements are analyzed. Considering the coverage and monitoring effect, the deployment locations of various sensors are planned, hydrological data are collected, and hydrological data collection results are generated. Based on the hydrological data acquisition results, the changing trends of various hydrological data are analyzed, and the frequency of data acquisition from multiple location sensors is adjusted according to the changing trends to generate acquisition parameter configurations. Based on the aforementioned acquisition parameter configuration, the load and signal quality of sensor data transmission are evaluated in real time, the frequency and compression rate of data transmission are adjusted, the data transmission path is optimized, and network configuration adjustment results are generated. Based on the network configuration adjustment results, statistical analysis is performed on the hydrological data to identify anomaly detection thresholds for various hydrological data and record abnormal data points. Combined with cross-validation and anomaly correction, data anomaly detection records are obtained. Based on the data anomaly detection records, the water conservancy element data are classified according to the type of data points, and index tags are matched according to time and location information to generate data index tags; Based on the data index tags, semantic analysis is used to identify the language expressions in the query request, which are then converted into database query commands to find matching data nodes and generate data query results.

2. The intelligent data collection and query method for water conservancy elements according to claim 1, characterized in that, The hydrological data acquisition results include sensor location information, data acquisition time point information, and water level, flow rate, and water quality datasets. The acquisition parameter configuration includes sensor data acquisition frequency parameters, changing trends of various hydrological data, and sensor operating mode information. The network configuration adjustment results include data transmission path, data compression parameters, and real-time network load information. The data anomaly detection record includes anomaly data point type information, anomaly data point timestamps, and data correction records. The data index tags include time index tags, spatial index tags, and hydrological data classification results. The data query results include query intent analysis results, data node matching results, and data query commands.

3. The intelligent data collection and query method for water conservancy elements according to claim 1, characterized in that, Based on the target water body location information, the specific steps for analyzing the data collection needs of various water conservancy elements, considering coverage and monitoring effectiveness, planning the deployment locations of various sensors, collecting hydrological data, and generating hydrological data collection results are as follows: Based on the location information of the target water body, the characteristic parameters of the target water body, including water depth, water flow velocity and topographic information, are collected and recorded to generate a water body feature dataset; Based on the water body feature dataset, the monitoring needs of various water conservancy elements of the target water body are analyzed, the monitoring range and effect of various sensors are considered, the required sensor types and quantities are calculated, and sensor requirement calculation results are generated. Based on the sensor requirement calculation results, the deployment locations of the sensors are planned, and hydrological data from multiple locations are collected to generate hydrological data collection results.

4. The intelligent data collection and query method for water conservancy elements according to claim 1, characterized in that, Based on the hydrological data acquisition results, the specific steps for analyzing the changing trends of various hydrological data, adjusting the data acquisition frequency of multiple location sensors according to the changing trends, and generating the acquisition parameter configuration are as follows: Based on the hydrological data acquisition results, time series statistics are performed on the rainfall, water level and flow data collected by the sensors to calculate the fluctuation of various data indicators and generate data fluctuation records. Based on the data fluctuation records, analyze the changing trends of various hydrological elements, consider energy consumption and data acquisition requirements, calculate the data acquisition frequency required by multiple sensors, and generate acquisition frequency adjustment results. Based on the acquisition frequency adjustment results, the data acquisition frequencies of multiple sensors are adjusted and updated, and the adjusted setting parameters are recorded to generate the acquisition parameter configuration.

5. The intelligent data collection and query method for water conservancy elements according to claim 4, characterized in that, The specific formula for calculating the fluctuation of various data indicators is as follows: in, The composite volatility index represents a statistical measure of the volatility of various hydrological data points, calculated through a weighted average. It is used to quantify the degree of volatility and stability of the data. This represents the most recent data value, reflecting the most recently collected hydrological data. The data value represents the previous period, indicating that in The hydrological data collected previously in the last period, This represents the data values ​​from the previous two periods, that is, in Another set of hydrological data from the previous period provides information on data changes over a longer timeframe. , , These represent the weighting coefficients for the most recent period, the previous period, and the two periods prior, respectively, and are allocated based on the timeliness and importance of the data.

6. The intelligent data collection and query method for water conservancy elements according to claim 1, characterized in that, Based on the aforementioned acquisition parameter configuration, the steps for real-time evaluation of sensor data transmission load and signal quality, adjustment of data transmission frequency and compression rate, optimization of data transmission path, and generation of network configuration adjustment results are as follows: Based on the aforementioned acquisition parameter configuration, the data transmission volume and signal strength of multiple sensors are monitored in real time, transmission delay and error rate are recorded, and real-time transmission monitoring records are generated. Based on the real-time transmission monitoring records, the transmission frequency and data compression parameters of multiple sensors are adjusted to generate transmission parameter configurations; Based on the transmission parameter configuration, the data transmission path is optimized according to the load of multiple nodes in the data transmission network, and the network configuration adjustment result is generated.

7. The intelligent data collection and query method for water conservancy elements according to claim 1, characterized in that, Based on the network configuration adjustment results, the hydrological data are statistically analyzed to identify anomaly detection thresholds for various hydrological data types and record abnormal data points. The specific steps for obtaining the data anomaly detection record, combining cross-validation and anomaly correction, are as follows: Based on the network configuration adjustment results, statistical analysis is performed on various hydrological data points. By calculating the standard deviation and average value, anomaly detection thresholds for various data types are identified, and threshold setting records are generated. Using the threshold setting record, by comparing with the anomaly detection threshold, data points exceeding the threshold are identified and marked, the location and time information of the data points are recorded, and anomaly data point records are generated; Based on the recorded abnormal data points, cross-validation is used to correct data that deviates from the normal range, generating a data anomaly detection record.

8. The intelligent collection and query method for water conservancy element data according to claim 1, characterized in that, Based on the aforementioned data anomaly detection records, the water conservancy element data is classified according to the type of data points, and index tags are matched according to time and location information to generate data index tags. The specific steps are as follows: Based on the data anomaly detection records, the data is classified according to the type of hydrological elements, including water level, flow rate and rainfall, and a classification data identifier is generated; Based on the classification data identifier, time index tags are matched for multiple data points according to the data point collection time information, and location information tags are matched for multiple data points according to the data point collection location information, generating tag matching information; Based on the tag matching information, by configuring mapping rules between multiple index tags and data storage locations, the efficiency of data retrieval and the response speed of queries are optimized, and data index tags are generated.

9. The intelligent data collection and query method for water conservancy elements according to claim 1, characterized in that, Based on the data index tags, the specific steps for identifying language expressions in the query request through semantic analysis, converting them into database query commands, finding matching data nodes, and generating data query results are as follows: Based on the data index tags, semantic analysis is used to identify keywords in the user's query request by analyzing the semantic relationships of words, including water level and precipitation, and generate keyword recognition results. Based on the keyword recognition results, the query conditions are converted into database query commands, and the corresponding query parameters are configured to generate the converted query commands; The transformed query command is analyzed, the query command is executed, data points are matched for the query request, the response time is recorded, and data query results are generated.

10. A smart data collection and query system for water conservancy elements, characterized in that, The intelligent data collection and query method for water conservancy elements according to any one of claims 1-9, wherein the system comprises: The sensor deployment module analyzes the data collection needs of various water conservancy elements based on the target water body location information, calculates the required sensor types and plans the deployment locations according to the monitoring range and effect of various sensors, performs data collection, and generates a water conservancy element dataset. The data monitoring and transmission module uses the water conservancy element dataset to analyze the changing trends of hydrological data, adjust the data acquisition frequency based on the analysis results, monitor the real-time load and signal quality of sensor data transmission, adjust the data transmission frequency and compression rate, optimize the data transmission path, and generate data transmission configuration parameters. Based on the data transmission configuration parameters, the hydrological data analysis module performs statistical analysis on the transmitted hydrological data, sets anomaly detection threshold, identifies abnormal data points, processes the abnormal data through cross-validation and data correction, and generates abnormal data point processing results. Based on the processing results of the abnormal data points, the data classification module classifies the hydrological data according to the type of data points, and generates data classification label information by matching index labels according to time and location information. Based on the data classification label information, the query command analysis module identifies the language expressions in the query request through semantic analysis, converts them into database query commands and executes them, finds matching data nodes, and generates data query results.

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