Hydrology and water resource surveying and mapping data acquisition method and system based on geospatial information

Abstract

The invention relates to the technical field of hydrology and water resource surveying and mapping data acquisition based on geospatial information, and discloses a hydrology and water resource surveying and mapping data acquisition method and system based on geospatial information. By combining satellite remote sensing, water level, flow velocity, temperature, precipitation, air temperature and humidity sensors, hydrological information is obtained from different dimensions, and the accuracy of data is improved. Remote sensing, a ground sensor and meteorological data are fused, and the deviation problem of single data source is solved. And a quality evaluation value is set for each data point and a weight is given to the data point, so that the proportion of high-quality data in fusion is larger, and the overall data quality is improved. Intelligent data quality control automatically evaluates and screens out invalid data, and the processing efficiency is improved. Acquisition coordinates, timestamps and measurement values of data are recorded, time-space consistency is guaranteed, and support is provided for subsequent analysis and prediction. And the data processing efficiency is improved through multi-source data fusion and intelligent control.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrological and water resources mapping data collection based on geospatial information, and specifically to a method and system for collecting hydrological and water resources mapping data based on geospatial information. Background Technique

[0002] The mapping and management of hydrological and water resources are important foundations for the sustainable utilization of water resources, environmental protection, and disaster warning. With the intensification of global climate change and water resource shortage problems, the accurate measurement and real-time monitoring of hydrological data have become particularly important. Most traditional hydrological and water resources mapping methods rely on a single data source from ground observation stations. Although they can provide certain hydrological information, there are many limitations in terms of spatial coverage, data timeliness, and comprehensiveness.

[0003] Traditional hydrological mapping methods usually rely on ground sensors for data collection, such as water level sensors, meteorological sensors, etc. This single data source method is easily restricted by geographical environment, equipment accuracy, and collection frequency. For example, a water level sensor can only provide real-time data for a local area and cannot comprehensively reflect the hydrological conditions of a large area. Although remote sensing technology can obtain data such as the distribution of large-scale water bodies and humidity through satellites, its accuracy and timeliness are somewhat different from ground data. In the process of collecting hydrological data, data from different sources are often involved, including remote sensing data, ground sensor data, and meteorological data, etc. However, how to effectively fuse these different types of data to ensure data accuracy and consistency has always been a difficult point in the existing technology. Traditional methods often rely on manual intervention and cannot achieve automated and intelligent data quality control. Especially when the amount and variety of data collected are large, it is often difficult to ensure data quality.

[0004] Therefore, the purpose of this case is to propose a method and system for collecting hydrological and water resources mapping data based on geospatial information, and to improve the deficiencies of the existing technology through the combination of remote sensing technology and ground sensors, intelligent data quality control, blockchain technology for data verification and security guarantee, and spatio-temporal data compression and transmission optimization. Summary of the Invention

[0005] The present invention provides a method for collecting hydrological and water resources mapping data based on geospatial information, which promotes the solution of the problems mentioned in the above background technique.

[0006] The present invention provides the following technical solution: A method for collecting hydrological and water resources mapping data based on geospatial information, including:

[0007] Collect data from different sources through different types of sensors;

[0008] The different types of sensors include satellite remote sensing sensors, water level sensors, water flow velocity sensors, temperature sensors, precipitation sensors, air temperature sensors, and humidity sensors;

[0009] The data from different sources include remote sensing data, ground sensor data, and meteorological data;

[0010] The remote sensing data includes water body distribution data and wetness data obtained through satellite remote sensing sensors;

[0011] The ground sensor data includes river water level data obtained through water level sensors, river water flow velocity data obtained through water flow velocity sensors, and river water temperature data obtained through temperature sensors;

[0012] The meteorological data includes precipitation data collected through precipitation sensors, air temperature data obtained through air temperature sensors, and humidity data obtained through humidity sensors;

[0013] The data from different sources also includes the collection coordinates, collection timestamps, and measurement values of each data;

[0014] Set up a hydrological and water resources data set, denoted as ;

[0015] Add all the collected data to the hydrological and water resources data set in sequence;

[0016] Obtain the quality evaluation values of each data in the hydrological and water resources data set in sequence;

[0017] Set a weight for each data in the hydrological and water resources data set:

[0018] ;

[0019] where, is the weight of the th data in the hydrological and water resources data set; is the quality evaluation value of the th data in the hydrological and water resources data set; is the total number of all data in the hydrological and water resources data set;

[0020] Fuse all the data in the hydrological and water resources data set:

[0021] ;

[0022] where, is the fused data; is the th data in the hydrological and water resources data set;

[0023] Perform intelligent data quality control on all data in the hydrological and water resources data set.

[0024] Optionally, the intelligent data quality control on all data in the hydrological and water resources data set specifically includes:

[0025] S21. Preliminary data quality detection:

[0026] Set the monitoring area as , and determine the spatial validity of the th data in the hydrological and water resources data set:

[0027] ;

[0028] Among them, is the result of the spatial validity determination of the th data in the hydrological and water resources data set; 1 means valid; 0 means invalid; is the coordinate of the th data in the hydrological and water resources data set;

[0029] Set the time threshold as , and determine the time validity of the th data in the hydrological and water resources data set:

[0030] ;

[0031] Among them, is the result of the time validity determination of the th data in the hydrological and water resources data set; is the collection timestamp of the th data in the hydrological and water resources data set; is the current timestamp;

[0032] Set the data value range for each data in the hydrological and water resources data set;

[0033] Determine the value range validity of the th data in the hydrological and water resources data set:

[0034] ;

[0035] Among them, is the result of the value range validity determination of the th data in the hydrological and water resources data set; is the measured value of the th data in the hydrological and water resources data set; is the The minimum value of the value range of a data measurement; is the maximum value of the value range of the

[0036] th data measurement in the hydrological and water resources data set; Comprehensively judge the validity of the

[0037] th data in the hydrological and water resources data set;

[0038] Optionally, the comprehensive judgment of the validity of the th data in the hydrological and water resources data set specifically includes:

[0039] ;

[0040] Among them, is the comprehensive judgment result of the validity of the th data in the hydrological and water resources data set;

[0041] If , then the th data in the hydrological and water resources data set is valid;

[0042] If , then the th data in the hydrological and water resources data set is invalid.

[0043] Optionally, the data verification based on the blockchain specifically includes:

[0044] Obtain the private key at the time of each data collection, denoted as ;

[0045] Generate a unique digital signature for each data in the hydrological and water resources data set according to the private key at the time of each data collection:

[0046] ;

[0047] Among them, () is the signature function, which uses the private key at the time of data collection for encryption and signature; is the data signature of the th data in the hydrological and water resources data set; is the value after is hashed; is a large prime number;

[0048] After the data is submitted to the blockchain, the smart contract verifies the data:

[0049] ;

[0050] Among them, is the verification result of the th data smart contract in the hydrological and water resources data set;

[0051] If , the smart contract will store it on the blockchain;

[0052] If , the data submission will be rejected and the failure reason will be recorded;

[0053] The data that passes the verification is stored on the blockchain in the form of an encrypted hash:

[0054] ;

[0055] Among them, is the encrypted hash value of the data, ensuring that even if the data is modified in the future, the modified data will be inconsistent with the stored hash value;

[0056] Perform spatio-temporal data compression and intelligent transmission on the collected data.

[0057] Optionally, the spatio-temporal data compression and intelligent transmission of the collected data specifically includes:

[0058] S31. Data compression and preprocessing:

[0059] Set the precision step of spatial compression, denoted as ;

[0060] Compress the spatial data, specifically:

[0061] ;

[0062] Among them, is the result of spatial compression of the data; () is the rounding operation on the data;

[0063] Set the compression time interval, denoted as ;

[0064] Obtain the number of data within the compression time interval , denoted as ;

[0065] Compress the measured values within the compression time interval:

[0066] ;

[0067] Among them, is the compression time interval The th time point within; is the compressed time interval within the value of the th data point;

[0068] Set the compression step size of the numerical data, denoted as ;

[0069] Perform numerical compression on the th data in the hydrological and water resources data set, specifically: ;

[0070] ;

[0071] S32. Data transmission path selection and optimization;

[0072] S33. Ensure the real-time and reliable data transmission.

[0073] Optionally, the data transmission path selection and optimization specifically include:

[0074] By deploying a network status awareness module, monitor the network latency, bandwidth, and packet loss rate in real time, and record the obtained real-time network status information as:

[0075] ;

[0076] Among them, is the latency from node to node ; is the bandwidth from node to node ; is the packet loss rate from node to node ;

[0077] According to the network status information, construct a transmission path selection model, use the weighted summation method for multi-objective optimization, and select the transmission path, specifically:

[0078] ;

[0079] Among them, is the score of path ; , , are weight coefficients used to balance different optimization objectives;

[0080] Obtain all path scores and compare them, and select the path with the maximum path score as the best transmission path;

[0081] When the network status changes, the system monitors in real time and re - selects the best transmission path. The path adjustment is specifically as follows:

[0082] ;

[0083] Among them, is the new best transmission path; is the score of the new transmission path.

[0084] Optionally, the real - time and reliability guarantee of the data transmission specifically includes:

[0085] Divide the data into multiple small packets and transmit them in parallel. The data packet - splitting formula is:

[0086] ;

[0087] Among them, is the th data packet; is the total number of data packets;

[0088] Adopt a redundant backup mechanism during the transmission process. When a data packet is lost, the system automatically re - transmits the data packet. The redundant backup formula is:

[0089] ;

[0090] Among them, means copying the data packet times and performing redundant transmission.

[0091] A system for implementing the above - mentioned method for collecting hydro - meteorological resource mapping data based on geospatial information includes:

[0092] Data collection module: Collect data from different sources through different types of sensors; different types of sensors include satellite remote - sensing sensors, water - level sensors, water - flow velocity sensors, temperature sensors, precipitation sensors, air - temperature sensors, and humidity sensors; different sources of data include remote - sensing data, ground - sensor data, and meteorological data;

[0093] Network status perception module: Monitor the network latency, bandwidth, and packet loss rate in real time;

[0094] Calculation module: Used to perform data calculations.

[0095] The present invention has the following beneficial effects:

[0096] ​1. By combining various types of sensors such as satellite remote sensing sensors, water level sensors, water flow velocity sensors, temperature sensors, precipitation sensors, air temperature sensors, and humidity sensors, hydrological information can be obtained from different dimensions, including water body distribution, wetness, river water level, flow velocity, temperature, air temperature, humidity, etc., greatly enhancing the comprehensiveness and accuracy of the data. In addition, the fusion of remote sensing data, ground sensor data, and meteorological data further solves the deviation problem that may be caused by a single data source. By setting a quality assessment value for each data point and assigning weights to the data according to the assessment value, the quality of the data can be effectively distinguished, enabling high-quality data to account for a larger proportion in the fusion process. This method can ensure that the final data set contains more accurate and reliable information, avoiding biases introduced by low-quality data, thus improving the overall quality of the data and the credibility of the analysis. By fusing and processing sensor data from different sources and of different types, the advantages of multiple data sources can be integrated, fully demonstrating the dynamic changes of hydrological water resources. The implementation of data fusion can provide decision-makers with a more comprehensive and accurate hydrological water resources situation, enhancing the application value of the data. Through intelligent data quality control, not only can the effectiveness and quality of the data be automatically evaluated, but also invalid data can be quickly screened out, avoiding human biases. Intelligent processing can automatically identify and process potential abnormal data according to the set rules and algorithms, improving the efficiency and accuracy of data processing. By including the collection coordinates, timestamps, and measurement values of each data in the data set, the spatio-temporal consistency of the data can be ensured, avoiding data comparison distortion caused by differences in collection time and location. In addition, accurate spatio-temporal marking provides strong support for subsequent data analysis, prediction, and trend monitoring, making the monitoring and management of water resources more precise and effective. By introducing intelligent data quality control and multi-source data fusion into the hydrological water resources data collection method, not only the accuracy and efficiency of data processing are improved, but also the scientificity of decision-making is enhanced. Real-time processing of high-quality data can provide timely and accurate decision support for water resources managers, facilitating the scientific management and sustainable utilization of water resources.

[0097] 2. By setting the monitoring area and determining the spatial validity of each data, it can be ensured that the positions of the data collection points are within the specified monitoring area, avoiding analysis errors caused by data coordinate deviations. Only the data that pass the spatial validity test can be included in the subsequent processing, thereby improving the geographical positioning accuracy of the data and providing reliable spatial data support for further water resources analysis and management. By setting time thresholds and determining the time validity of each data, it can be ensured that all data meet the predetermined time standards. If the data collection time exceeds the preset valid time range, these data will be regarded as invalid and automatically excluded from the data set. This effectively avoids the interference of invalid or outdated data on the analysis results and ensures the timeliness of hydrological and water resources monitoring data. By determining the value range validity of the measured values of each data, abnormal measured values can be detected and excluded in a timely manner. For example, when temperature, humidity or water level data exceed the normal value range, they will be regarded as invalid data, avoiding the impact of abnormal data on the overall analysis results. This can ensure that only reasonable and reliable data are in the final data set, improving the quality of the data and the accuracy of the analysis. By comprehensively judging the data, combining spatial validity, time validity and value range validity, the validity of each data can be judged more comprehensively. Only when all judgment conditions are met will the data be regarded as valid and included in the subsequent processing. This comprehensive judgment method improves the accuracy and comprehensiveness of data screening, ensures that the subsequent analysis is based on high-quality data, and thus improves the reliability of hydrological and water resources data and the decision-making support effect. By using blockchain technology to verify the data, it is ensured that each data has a unique digital signature, and the data is verified through smart contracts to ensure that the data cannot be tampered with during storage. The immutability of blockchain provides strong protection for hydrological and water resources data, ensures the credibility of the data throughout its life cycle, greatly enhances the security and transparency of the data, and prevents data tampering and forgery behavior.

[0098] 3. By comprehensively judging the validity of each data in the hydrological and water resources data set, the problem of incomplete judgment of data validity caused by a single judgment criterion in the data screening process is solved, thereby improving the data screening accuracy and comprehensive judgment ability. In the process of hydrological and water resources data collection and analysis, relying solely on a single judgment criterion such as spatial validity, temporal validity, or value range validity often fails to comprehensively evaluate the validity of data. For example, some data may be spatially valid, but due to the collection time exceeding the predetermined range or the measured value exceeding the normal value range, its reliability is still problematic. Therefore, judging data validity only through a single criterion may result in some problematic data being wrongly included in the analysis, affecting the accuracy of the final analysis result. Through the comprehensive judgment step, first, the spatial validity, temporal validity, and value range validity of the data are independently evaluated, and then the evaluation results of these three are combined to form a final validity judgment. This comprehensive judgment method can comprehensively consider various factors, thus avoiding the limitations of a single judgment criterion and improving the comprehensiveness and accuracy of data screening. For example, when the space and time of the data are both valid, but the measured value exceeds the preset range, the system will determine that the data is invalid, thus avoiding the influence of invalid data.

[0099] 4. By means of the data verification step based on blockchain, the problems of data tampering and forgery in hydrological and water resources data are solved, ensuring the authenticity, immutability, and integrity of the data, thereby greatly enhancing the credibility and security of the data. By using blockchain technology to verify and store data, first, a unique digital signature is generated for each piece of data collected, and the private key at the time of data collection is used to encrypt the signature, ensuring that the data will not be tampered with or forged during the collection process. Even if the data is intercepted during transmission, the unauthorized party cannot modify the data content or forge the data because the digital signature and encrypted hash value of the data will be inconsistent. Secondly, after the data is submitted to the blockchain, the smart contract automatically verifies the data and ensures that only the verified data can be stored on the blockchain, further ensuring the integrity and authenticity of the data. If the data fails to pass the verification, the smart contract will reject its submission and record the reason for failure, preventing untrusted data from entering the system. The data stored on the blockchain is stored in the form of an encrypted hash, ensuring the immutability of the data. Even if the data is modified in the future, the modified data cannot be made consistent through the comparison of hash values, ensuring the security of data storage.

[0100] 5. By performing spatio-temporal data compression and intelligent transmission on the collected data, the problems of data redundancy, transmission delay, and bandwidth pressure in the storage and transmission of hydrological and water resources data are solved, thereby improving the data processing efficiency, transmission rate, and overall reliability of the system. First, data compression and preprocessing reduce data redundancy in the spatial dimension by setting the precision step size of spatial compression. Rounding operations are performed on spatial data to compress unnecessary high-precision data, significantly reducing the storage space requirements and the data transmission load. At the same time, by setting the compression time interval, the measured values within this time interval are compressed, making the collected data more compact in the time dimension, avoiding the repeated transmission of time data, and further improving the data processing efficiency and saving storage space. In addition, the compression of numerical data further reduces unnecessary data fluctuations and redundancy, optimizing the compactness of the entire dataset. Second, the selection and optimization of the data transmission path help to select the optimal network path for data transmission, avoiding network congestion and transmission delay caused by unreasonable transmission paths. By dynamically adjusting the data transmission path, it is ensured that the data can avoid network bottlenecks during transmission, improving the data transmission efficiency and real-time performance. By ensuring the real-time and reliable data transmission, the system can ensure that the data is not lost or delayed during the collection and transmission process, guaranteeing the real-time and accuracy of hydrological and water resources data, so that subsequent data analysis and decision-making can be based on the latest and complete data.

[0101] 6. By selecting and optimizing the data transmission path, the impact of network state fluctuations on data transmission efficiency and reliability is resolved, thereby improving the transmission quality and system response speed, and ensuring the efficient transmission and real-time nature of data. First, by deploying a network state awareness module to monitor network latency, bandwidth, and packet loss rate in real time, the system can obtain real-time network state information. This information includes the latency, bandwidth, and packet loss rate between nodes, which can accurately reflect the real-time performance of the network and provide the necessary basis for path selection. By collecting this data in real time, the system can promptly identify the bottlenecks and unstable factors in the current network, ensuring that the most suitable path is selected for data transmission. Next, based on the network state information, a transmission path selection model is constructed, and the weighted summation method is used for multi-objective optimization. The optimization objectives of this model include minimizing transmission latency, minimizing data packet loss rate, and maximizing bandwidth utilization. The weighted summation method is used to balance different optimization objectives. This weighted summation method adjusts the priority between different optimization objectives through weight coefficients, ensuring that the transmission path selection can take into account the requirements of all aspects. Through this optimization method, the system can select the path with the best performance and dynamically adjust it under different network conditions to achieve the optimization of transmission performance. When the network state changes, the system re-selects the best transmission path by real-time monitoring. The path adjustment mechanism ensures that even if the network environment fluctuates, the system can quickly respond and re-select the optimal path, thus ensuring the real-time transmission of data. This mechanism effectively avoids long transmission delays or data loss caused by network instability.

[0102] 7. By ensuring the real-time and reliability of data transmission, problems such as data loss, delay, and network instability that may occur during data transmission are solved, thereby improving the efficiency and stability of data transmission and ensuring the reliable operation of the system. First, by dividing the data into multiple small packets and transmitting them in parallel, the size of each packet is reduced, and the load of data transmission is lowered. The formula for data packet division can effectively break down a large data set into multiple small packets, making the transmission process of each data packet more independent and efficient. This method solves the problems of network congestion and delay that may be caused by large data packets during network transmission. Since the size of each small packet is small, the network bandwidth can be better utilized during the transmission process, and at the same time, the risk of overall data loss caused by the loss or error of a single packet is reduced. Then, the reliability of data transmission is further improved through a redundant backup mechanism. During the data packet transmission process, a redundant backup mechanism is adopted, that is, each data packet is copied multiple times and transmitted redundantly at the same time. Through formula setting, each data packet can be copied multiple times. During the transmission process, even if a certain packet is lost, the system can automatically re-transmit the data packet. This mechanism ensures the solution of the packet loss problem during data transmission, improves the transmission reliability, and ensures that when network fluctuations or packet losses occur, the system can recover in time to ensure the integrity and consistency of the data. Through these steps, the system effectively solves the instability and packet loss problems during data transmission, enhances the real-time and reliability of data transmission. Data packet division and parallel transmission make the transmission process more efficient and reduce the impact of the loss of a single data packet on the overall data; the redundant backup mechanism ensures that when packet loss occurs, the data can be automatically supplemented, minimizing the risk of data loss, and further improving the system's adaptability to uncertainties and network instabilities during data transmission. Description of the Drawings

[0103] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Embodiments

[0104] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0105] Example, referring to Figure 1 , a method for collecting hydrological and water resources surveying and mapping data based on geospatial information, including:

[0106] Collect data from different sources through different types of sensors;

[0107] The different types of sensors include satellite remote sensing sensors, water level sensors, water flow velocity sensors, temperature sensors, precipitation sensors, air temperature sensors, and humidity sensors;

[0108] The data from different sources include remote sensing data, ground sensor data, and meteorological data;

[0109] The remote sensing data includes water body distribution data and wetness data obtained through satellite remote sensing sensors;

[0110] The ground sensor data includes river water level data obtained through water level sensors, river water flow velocity data obtained through water flow velocity sensors, and river water temperature data obtained through temperature sensors;

[0111] The meteorological data includes precipitation data collected through precipitation sensors, air temperature data obtained through air temperature sensors, and humidity data obtained through humidity sensors;

[0112] The data from different sources also includes the collection coordinates, collection timestamps, and measurement values of each data;

[0113] Set up a hydrological and water resources data set, denoted as ;

[0114] Add all the collected data to the hydrological and water resources data set in sequence;

[0115] Obtain the quality evaluation values of each data in the hydrological and water resources data set in sequence;

[0116] Set a weight for each data in the hydrological and water resources data set:

[0117] ;

[0118] where, is the weight of the th data in the hydrological and water resources data set; is the quality evaluation value of the th data in the hydrological and water resources data set; is the total number of all data in the hydrological and water resources data set;

[0119] Fuse all the data in the hydrological and water resources data set:

[0120] ;

[0121] where, is the fused data; is the th data in the hydrological and water resources data set;

[0122] Intelligently control the data quality of all data in the hydrological and water resources data set.

[0123] By combining various types of sensors such as satellite remote sensing sensors, water level sensors, water flow velocity sensors, temperature sensors, precipitation sensors, air temperature sensors, and humidity sensors, hydrological information can be obtained from different dimensions, including water body distribution, wetness, river water level, flow velocity, temperature, air temperature, humidity, etc., greatly improving the comprehensiveness and accuracy of the data. In addition, the fusion of remote sensing data, ground sensor data, and meteorological data further solves the deviation problem that may be caused by a single data source. By setting a quality evaluation value for each data point and assigning weights to the data according to the evaluation value, the quality of the data can be effectively distinguished, making high-quality data account for a larger proportion in the fusion process. This method can ensure that the final data set contains more accurate and reliable information, avoiding biases introduced by low-quality data, thereby improving the overall quality of the data and the credibility of the analysis. By fusing and processing sensor data from different sources and of different types, the advantages of multiple data sources can be integrated, fully demonstrating the dynamic changes of hydrological and water resources. The implementation of data fusion can provide decision-makers with a more comprehensive and accurate hydrological and water resources situation, enhancing the application value of the data. Through intelligent data quality control, not only can the effectiveness and quality of the data be automatically evaluated, but also invalid data can be quickly screened out, avoiding human biases. Intelligent processing can automatically identify and process potential abnormal data according to the set rules and algorithms, improving the efficiency and accuracy of data processing. By including the collection coordinates, timestamps, and measurement values of each data in the data set, the spatio-temporal consistency of the data can be ensured, avoiding data comparison distortion caused by differences in collection time and location. In addition, accurate spatio-temporal marking provides strong support for subsequent data analysis, prediction, and trend monitoring, making the monitoring and management of water resources more accurate and effective. By introducing intelligent data quality control and multi-source data fusion into the hydrological and water resources data collection method, not only the accuracy and efficiency of data processing are improved, but also the scientificity of decision-making is enhanced. Real-time processing of high-quality data can provide timely and accurate decision-making support for water resources managers, facilitating the scientific management and sustainable utilization of water resources.

[0124] The intelligent data quality control of all data in the hydrological and water resources data set specifically includes:

[0125] S21. Preliminary data quality detection:

[0126] Set the monitoring area as , and conduct a spatial validity determination on the th data in the hydrological and water resources data set:

[0127] ;

[0128] Among them, is the result of the spatial validity determination for the th data in the hydrological and water resources data set; 1 indicates valid; 0 indicates invalid; is the coordinate of the th data in the hydrological and water resources data set;

[0129] Set the time threshold as , and perform the time validity determination on the th data in the hydrological and water resources data set:

[0130] ;

[0131] Among them, is the result of the time validity determination for the th data in the hydrological and water resources data set; is the acquisition timestamp of the th data in the hydrological and water resources data set; is the current timestamp;

[0132] Set the data value range for each data in the hydrological and water resources data set;

[0133] Perform the value range validity determination on the th data in the hydrological and water resources data set:

[0134] ;

[0135] Among them, is the result of the value range validity determination for the th data in the hydrological and water resources data set; is the measured value of the th data in the hydrological and water resources data set; is the minimum value of the value range of the measured value of the th data in the hydrological and water resources data set; is the maximum value of the value range of the measured value of the th data in the hydrological and water resources data set;

[0136] Comprehensively judge the validity of the th data in the hydrological and water resources data set;

[0137] S22. Data verification based on blockchain.

[0138] By setting the monitoring area and determining the spatial validity of each data, it can be ensured that the positions of the data collection points are within the specified monitoring area, avoiding analysis errors caused by data coordinate deviations. Only the data that pass the spatial validity test can be included in the subsequent processing, thereby improving the geographical positioning accuracy of the data and providing reliable spatial data support for further water resources analysis and management. By setting time thresholds and determining the time validity of each data, it can be ensured that all data meet the predetermined time standards. If the data collection time exceeds the preset effective time range, these data will be regarded as invalid and automatically excluded from the data set. This effectively avoids the interference of invalid or outdated data on the analysis results and ensures the timeliness of hydrological and water resources monitoring data. By determining the value range validity of the measured values of each data, abnormal measurement values can be detected and excluded in a timely manner. For example, when temperature, humidity or water level data exceed the normal value range, they will be regarded as invalid data, avoiding the impact of abnormal data on the overall analysis results. This can ensure that only reasonable and reliable data are included in the final data set, improving the quality of the data and the accuracy of the analysis. By comprehensively judging the data, combining spatial validity, time validity and value range validity, the validity of each data can be judged more comprehensively. Only when all judgment conditions are met, the data will be regarded as valid and included in the subsequent processing. This comprehensive judgment method improves the accuracy and comprehensiveness of data screening, ensures that the subsequent analysis is based on high-quality data, and thus improves the reliability of hydrological and water resources data and the decision-making support effect. By using blockchain technology to verify the data, it is ensured that each data has a unique digital signature, and the data is verified through smart contracts to ensure that the data cannot be tampered with during the storage process. The immutability of blockchain provides strong protection for hydrological and water resources data, ensures the credibility of the data throughout its life cycle, greatly enhances the security and transparency of the data, and prevents data tampering and forgery.

[0139] The comprehensive judgment of the validity of the th data in the hydrological and water resources data set specifically includes:

[0140] ;

[0141] Among them, is the comprehensive judgment result of the validity of the th data in the hydrological and water resources data set;

[0142] If , then the th data in the hydrological and water resources data set is valid;

[0143] If , then the The data is invalid.

[0144] By comprehensively judging the validity of each data in the hydrological and water resources data set, the problem of incomplete judgment of data validity caused by a single judgment criterion in the data screening process is solved, thereby improving the data screening accuracy and comprehensive judgment ability. In the process of hydrological and water resources data collection and analysis, solely relying on a single judgment criterion such as spatial validity, temporal validity, or value range validity often fails to comprehensively evaluate the data validity. For example, some data may be spatially valid, but due to the collection time exceeding the predetermined range or the measured value exceeding the normal value range, its reliability is still problematic. Therefore, judging data validity solely by a single criterion may lead to some problematic data being wrongly included in the analysis, affecting the accuracy of the final analysis result. Through the comprehensive judgment step, first, the spatial validity, temporal validity, and value range validity of the data are independently evaluated, and then the evaluation results of these three are combined to form the final validity judgment. This comprehensive judgment method can comprehensively consider various factors, thereby avoiding the limitations of a single judgment criterion and improving the comprehensiveness and accuracy of data screening. For example, when the data is both spatially and temporally valid, but the measured value exceeds the preset range, the system will determine that the data is invalid, thus avoiding the impact of invalid data.

[0145] The data verification based on blockchain specifically includes:

[0146] Obtain the private key at the time of each data collection, denoted as ;

[0147] According to the private key at the time of each data collection, generate a unique digital signature for each data in the hydrological and water resources data set:

[0148] ;

[0149] Among them, () is the signature function, which performs encrypted signature using the private key at the time of data collection; is the data signature of the th data in the hydrological and water resources data set; is for the value after hashing; is a large prime number;

[0150] After the data is submitted to the blockchain, the smart contract verifies the data:

[0151] ;

[0152] Among them, is the verification result of the smart contract of the th data in the hydrological and water resources data set;

[0153] If , the smart contract will store it on the blockchain to ensure its immutability;

[0154] If , the data submission will be rejected and the reason for failure will be recorded;

[0155] The data that passes the verification is stored on the blockchain in the form of an encrypted hash to ensure the immutability of the data:

[0156] ;

[0157] Among them, is the encrypted hash value of the data , ensuring that even if the data is modified in the future, the modified data will be inconsistent with the stored hash value;

[0158] Perform spatio-temporal data compression and intelligent transmission on the collected data.

[0159] Through the data verification step based on the blockchain, the problems of tampering and forgery of hydrological and water resource data are solved, ensuring the authenticity, immutability and integrity of the data, thus greatly improving the credibility and security of the data. By using blockchain technology to verify and store data, first, by generating a unique digital signature for each piece of collected data and encrypting the signature with the private key when collecting the data, it is ensured that the data will not be tampered with or forged during the collection process. Even if the data is intercepted during transmission, the unauthorized party cannot modify the data content or forge the data, because the digital signature and encrypted hash value of the data will be inconsistent. Second, after the data is submitted to the blockchain, the smart contract automatically verifies the data and ensures that only the data that passes the verification can be stored on the blockchain, further ensuring the integrity and authenticity of the data. If the data fails to pass the verification, the smart contract will reject its submission and record the reason for failure, preventing untrusted data from entering the system. The data stored on the blockchain is stored in the form of an encrypted hash, ensuring the immutability of the data. Even if the data is modified in the future, the modified data cannot be made consistent by comparing the hash values, ensuring the security of data storage.

[0160] The spatio-temporal data compression and intelligent transmission of the collected data specifically include:

[0161] S31. Data compression and preprocessing:

[0162] Set the precision step of spatial compression, denoted as ;

[0163] Starting from the data distribution, if the spatial variation of hydrological data is relatively gentle, a larger precision step size can be selected to reduce the amount of data; conversely, if the data variation is large, a smaller step size needs to be chosen to ensure that important spatial variation information is retained;

[0164] Starting from the precision requirements, the step size needs to be selected according to the precision requirements of spatial data for the application scenario. For some applications with high requirements for spatial precision, such as the monitoring of key water source areas, a smaller step size should be chosen; for the monitoring of general areas, a larger step size can be selected;

[0165] Based on the selection of data distribution, perform spatial clustering on historical data, analyze the variation range of the data, and determine the size of the step size according to the statistical characteristics of the variation range, such as standard deviation or change rate; for example, if the change in water level is less than a certain threshold in a certain area , then multiple data points in this area can be merged, and the step size is set to ;

[0166] Based on the selection of precision requirements, determine the step size by comparing with the precision of existing measurement devices or models; generally, the step size can be set as a multiple of the device or model error, such as choosing 2 times the standard deviation;

[0167] Compress the spatial data to reduce the redundancy of data in the spatial dimension, specifically:

[0168] ;

[0169] Among them, is the result of data spatial compression; () is the rounding operation on the data;

[0170] Set the compression time interval, denoted as ;

[0171] Starting from the time variation characteristics, the variation of hydrological data over time may be stable or may change drastically; for periods with relatively small changes, a larger interval can be selected for compression; while for periods with large changes, a smaller interval is required to ensure accuracy;

[0172] Starting from the data update frequency, the data update frequency is closely related to the compression time interval; if the data collection frequency is high, the compression time interval can be appropriately increased; conversely, the time interval should be shortened;

[0173] Based on the selection of data volatility, the appropriate time interval can be determined by calculating the rate of change or volatility of the data, such as standard deviation, range of change. For example, if the water level data has a small range of change within one hour, a larger time interval can be selected, such as 30 minutes to 1 hour.

[0174] Based on the selection of application requirements, if the goal is real-time monitoring and response, such as a flood warning system, a shorter time interval can be selected, such as 5 minutes to 30 minutes. If it is for long-term trend analysis, the time interval can be appropriately increased, such as 1 hour to 24 hours.

[0175] Obtain the number of data within the compressed time interval, denoted as ; ;

[0176] Compress the measured values within the compressed time interval:

[0177] ;

[0178] where is the th time point within the compressed time interval ; is the value of the th data point within the compressed time interval;

[0179] Set the compression step size of the numerical data, denoted as ;

[0180] Starting from the data volatility, if the numerical data has small fluctuations, a larger compression step size can be selected to reduce the data volume. If the data has large fluctuations, especially for important hydrological parameters such as water level and flow rate, a smaller step size is required to ensure the accuracy of the compressed data.

[0181] Starting from the accuracy requirements, for applications with high accuracy requirements, such as precise flow calculation, a smaller step size needs to be selected. For non-critical data, a larger step size can effectively reduce the data volume.

[0182] Based on the standard deviation or error range of the data, select the compression step size by calculating the standard deviation or measurement error of the data. For example, if the measurement error is less than 0.1, a step size of 0.1 or smaller can be selected. If the error is large, a larger step size, such as 1 or 2, can be selected.

[0183] Based on the selection of application requirements, for applications that require high-precision calculations, such as basin water resources scheduling, the step size can be selected to be smaller, such as 0.1 to 0.5. For auxiliary data that does not affect decision-making, a larger step size, such as 1 or 2, can be selected.

[0184] Perform numerical compression on the th data in the hydrological and water resources data set, specifically:

[0185] ;

[0186] S32. Data transmission path selection and optimization;

[0187] S33. Guarantee of real-time and reliability of data transmission.

[0188] By performing spatio-temporal data compression and intelligent transmission on the collected data, the problems of data redundancy, transmission delay, and bandwidth pressure in the storage and transmission of hydrological and water resources data are solved, thereby improving the data processing efficiency, transmission rate, and overall reliability of the system. First, data compression and preprocessing reduce the redundancy of data in the spatial dimension by setting the precision step size of spatial compression. For spatial data, rounding operations are performed to compress unnecessary high-precision data, thereby significantly reducing the storage space requirements and reducing the load of data transmission. At the same time, by setting the compression time interval, the measured values within this time interval are compressed, making the collected data more compact in the time dimension, avoiding the repeated transmission of time data, and further improving the data processing efficiency and saving storage space. In addition, the compression of numerical data further reduces unnecessary data fluctuations and redundancy, optimizing the compactness of the entire data set. Second, data transmission path selection and optimization helps to select the optimal network path for data transmission, avoiding network congestion and transmission delay caused by unreasonable transmission paths. By dynamically adjusting the data transmission path, it is ensured that the data can avoid network bottlenecks during the transmission process, improving the data transmission efficiency and real-time performance. By guaranteeing the real-time and reliability of data transmission, the system can ensure that data is not lost or delayed during the collection and transmission process, guaranteeing the real-time and accuracy of hydrological and water resources data, so that subsequent data analysis and decision-making can be based on the latest and complete data.

[0189] The data transmission path selection and optimization specifically include:

[0190] By deploying a network status awareness module, the delay, bandwidth, and packet loss rate of the network are monitored in real time, and the obtained real-time status information of the network is recorded as:

[0191] ;

[0192] Among them, is the delay from node to node ; is the bandwidth from node to node ; is the node Packet loss rate to the node ;

[0193] According to the network status information, construct a transmission path selection model. This model selects paths based on the following optimization objectives: minimizing transmission delay, minimizing data packet loss rate, maximizing bandwidth utilization, and using the weighted summation method for multi-objective optimization to select the transmission path. Specifically:

[0194] ;

[0195] Among them, is the score of path ; , , are weight coefficients used to balance different optimization objectives;

[0196] Among them, , , determine the values through the following method:

[0197] Collect historical network status data including delay, packet loss rate, and bandwidth information, use regression analysis to train the model, and predict the best path based on historical network status and actual transmission effects. During the training process, adaptively adjust the weight coefficients through the model;

[0198] For example, use a linear regression model to fit , , values:

[0199] ;

[0200] Among them, , , are the weight coefficients calculated by the linear regression model. Adjust the weight coefficients according to the output of the model and the predicted transmission effects to ensure that the transmission path selection results meet the actual application requirements;

[0201] Obtain all path scores and compare them, and select the path with the largest score as the best transmission path;

[0202] When the network status changes, the system monitors in real time and reselects the best transmission path. The path adjustment is specifically:

[0203] ;

[0204] Among them, is the new best transmission path; is the new transmission path score.

[0205] By means of data transmission path selection and optimization, the impact of network state fluctuations on data transmission efficiency and reliability is addressed, thereby improving transmission quality and system response speed, and ensuring efficient data transmission and real-time performance. First, by deploying a network state awareness module to monitor network latency, bandwidth, and packet loss rate in real time, the system can obtain real-time network state information. This information includes the latency, bandwidth, and packet loss rate between nodes, which can accurately reflect the real-time performance of the network and provide a necessary basis for path selection. By collecting this data in real time, the system can promptly identify the bottlenecks and unstable factors in the current network, ensuring that the most suitable path is selected for data transmission. Next, based on the network state information, a transmission path selection model is constructed, and the weighted summation method is used for multi-objective optimization. The optimization objectives of this model include minimizing transmission latency, minimizing data packet loss rate, and maximizing bandwidth utilization. The weighted summation method is used to balance different optimization objectives. This weighted summation method adjusts the priority between different optimization objectives through weight coefficients, ensuring that the transmission path selection can take into account the requirements of all aspects. Through this optimization method, the system can select the path with the best performance and dynamically adjust it under different network conditions to achieve the optimization of transmission performance. When the network state changes, the system reselects the best transmission path through real-time monitoring. The path adjustment mechanism ensures that even if the network environment fluctuates, the system can quickly respond and reselect the optimal path, thereby ensuring the real-time transmission of data. This mechanism effectively avoids long transmission delays or data loss caused by network instability.

[0206] The guarantee of the real-time performance and reliability of data transmission specifically includes:

[0207] The data is divided into multiple small packets and transmitted in parallel. The data packet splitting formula is:

[0208] ;

[0209] where is the th data packet; is the total number of data packets;

[0210] During the transmission process, a redundant backup mechanism is adopted. When a data packet is lost, the system automatically retransmits the data packet. The redundant backup formula is:

[0211] ;

[0212] where means copying the data packet times and performing redundant transmission.

[0213] ​By ensuring the real-time and reliability of data transmission, problems such as data loss, delay, and network instability that may occur during data transmission are solved, thereby improving the efficiency and stability of data transmission and ensuring the reliable operation of the system. First, by dividing the data into multiple small packets and transmitting them in parallel, the size of each packet is reduced, and the load of data transmission is lowered. The formula for data packet division can effectively disassemble a large data set into multiple small packets, making the transmission process of each data packet more independent and efficient. This method solves the problems of network congestion and delay that may be caused by large data packets during network transmission. Since the size of each small packet is small, the network bandwidth can be better utilized during the transmission process, and at the same time, the risk of overall data loss caused by the loss or error of a single packet is reduced. Next, the reliability of data transmission is further improved through a redundant backup mechanism. During the data packet transmission process, a redundant backup mechanism is adopted, that is, each data packet is replicated multiple times and transmitted redundantly at the same time. Through formula setting, each data packet can be replicated multiple times. During the transmission process, even if a certain packet is lost, the system can automatically re-transmit the data packet. This mechanism ensures the solution of the packet loss problem during data transmission, improves the transmission reliability, and ensures that when network fluctuations or packet losses occur, the system can recover in time to ensure the integrity and consistency of the data. Through these steps, the system effectively solves the instability and packet loss problems during data transmission, enhances the real-time and reliability of data transmission. Data packet division and parallel transmission make the transmission process more efficient and reduce the impact of the loss of a single data packet on the overall data; the redundant backup mechanism ensures that when packet loss occurs, the data can be automatically supplemented, minimizing the risk of data loss, and further improving the system's adaptability to the uncertainty and network instability during data transmission.

[0214] This embodiment also provides a system for a method of collecting hydrological and water resources mapping data based on geospatial information, including:

[0215] Data collection module: Collect data from different sources through different types of sensors; different types of sensors include satellite remote sensing sensors, water level sensors, water flow velocity sensors, temperature sensors, precipitation sensors, air temperature sensors, and humidity sensors; different sources of data include remote sensing data, ground sensor data, and meteorological data;

[0216] Network status perception module: Monitor the delay, bandwidth, and packet loss rate of the network in real time;

[0217] Calculation module: Used to perform data calculations.

[0218] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0219] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for collecting hydrological and water resources surveying and mapping data based on geographic spatial information, characterized in that: include: Collect data from different sources through different types of sensors; The different types of sensors include satellite remote sensing sensors, water level sensors, water flow rate sensors, temperature sensors, precipitation sensors, air temperature sensors, and humidity sensors; The data from different sources include remote sensing data, ground sensor data and meteorological data; The remote sensing data includes water body distribution data and wetness data obtained through satellite remote sensing sensors; The ground sensor data includes river water level data obtained through a water level sensor, river water flow rate data obtained through a water flow rate sensor, and river water temperature data obtained through a temperature sensor; The meteorological data includes collecting precipitation data through a precipitation sensor, obtaining temperature data through a temperature sensor, and obtaining humidity data through a humidity sensor; The data from different sources also include the acquisition coordinates, acquisition timestamp and measurement value of each data; Set the hydrological and water resources data set, denoted as ; All collected data are added to the hydrological and water resources data set in sequence; Obtain the quality assessment value of each data in the hydrological and water resources data set in turn; Set a weight for each data in the hydrological and water resources dataset: ； in, The hydrological and water resources data set The weight of the data; The first The quality assessment value of each data; is the total number of all data in the hydrological and water resources data set; Fusion of all data in the hydrological and water resources data set: ； in, is the fused data; The first individual data; Perform intelligent data quality control on all data in the hydrological and water resources data set.

2. The method for collecting hydrological and water resources surveying and mapping data based on geographic spatial information according to claim 1 is characterized in that: The intelligent data quality control of all data in the hydrological and water resources data set specifically includes: S21. Preliminary data quality check: Set the monitoring area to , for the hydrological and water resources data set The data is used to determine the spatial validity: ； in, The first The result of spatial validity determination for each data; 1 is valid; 0 is invalid; The first The coordinates of the data; Set the time threshold to , for the hydrological and water resources data set Time validity determination of each data: ； in, The first The time validity determination result of each data; The first The collection timestamp of each data; is the current timestamp; Set the data value range for each data in the hydrological and water resources data set; The hydrological and water resources data set The validity of the value range is determined by the following data: ； in, For the first The result of determining the validity of the value range of each data; The first The measured value of the data; The first The minimum value of the range of data measurement values; The first The maximum value of the range of data measurement values; The hydrological and water resources data set Comprehensively judge the validity of individual data; S22. Data verification based on blockchain.

3. The method for collecting hydrological and water resources surveying and mapping data based on geographic spatial information according to claim 2 is characterized in that: The hydrological and water resources data set The validity of each data is comprehensively judged, including: ； in, The first Comprehensive judgment result of the validity of individual data; like , then the first The data is valid; like , then the first The data is invalid.

4. The method for collecting hydrological and water resources surveying and mapping data based on geographic spatial information according to claim 3 is characterized in that: The blockchain-based data verification specifically includes: The blockchain is a prior art; Get the private key for each data collection, recorded as ; Based on the private key used each time data is collected, a unique digital signature is generated for each data in the hydrological and water resources data set: ； in, () is the signature function, which uses the private key when collecting data to encrypt and sign; The first The digital signature of the data; For The hashed value; is a large prime number; After the data is submitted to the blockchain, the smart contract verifies the data: ； in, The first Verification results of individual data smart contracts; like , smart contracts will Stored on the blockchain; like , the data submission is rejected and the reason for the failure is recorded; The verified data is stored on the blockchain in the form of a cryptographic hash: ； in, For data The encrypted hash value ensures that even if the data is modified in the future, the modified data will be inconsistent with the stored hash value; Perform spatiotemporal data compression and intelligent transmission on the collected data.

5. The method for collecting hydrological and water resources surveying and mapping data based on geographic spatial information according to claim 4 is characterized in that: The spatiotemporal data compression and intelligent transmission of the collected data specifically include: S31. Data compression and preprocessing: Set the precision step of spatial compression, denoted as ; Compress spatial data, specifically: ； in, For data The result of space compression; () is to round the data; Set the compression time interval, denoted as ; Get the compression time interval The number of data in is denoted as ; Compress the measured values ​​within the compressed time interval: ； in, To compress the time interval The first time point; To compress the time interval Neidi The value of the data point; Set the compression step size of numerical data, denoted as ; The hydrological and water resources data set The data is numerically compressed, specifically: ； S32, data transmission path selection and optimization; S33. Guarantee the real-time and reliability of data transmission.

6. The method for collecting hydrological and water resources surveying and mapping data based on geographic spatial information according to claim 5 is characterized in that: The data transmission path selection and optimization specifically includes: By deploying the network status perception module, the network delay, bandwidth and packet loss rate are monitored in real time, and the real-time status information of the network is recorded as: ； in, For Node To Node Delays; For Node To Node bandwidth; For Node To Node Packet loss rate; According to the network status information, a transmission path selection model is constructed, and the weighted sum method is used to perform multi-objective optimization and select the transmission path. Specifically: ； in, For path Ratings; , , is the weight coefficient, which is used to balance different optimization objectives; Obtain all path scores and compare them, and select the path with the largest score as the best transmission path; When the network status changes, the system monitors in real time and reselects the best transmission path. The path adjustment is as follows: ； in, is the new best transmission path; Score the new transfer path.

7. The method for collecting hydrological and water resources surveying and mapping data based on geographic spatial information according to claim 6 is characterized in that: The real-time and reliability guarantee of the data transmission specifically includes: Divide the data into multiple small packets and transmit them in parallel. The data packetization formula is: ； in, For the Data packets; is the total number of data packets; A redundant backup mechanism is used during the transmission process. When a data packet is lost, the system automatically retransmits the data packet. The redundant backup formula is: ； in, To transfer data packets copy times and perform redundant transmission.

8. A system using the hydrological and water resources surveying and mapping data collection method based on geographic spatial information as claimed in claim 7, characterized in that: include: Data collection module: collects data from different sources through different types of sensors; different types of sensors include satellite remote sensing sensors, water level sensors, water flow rate sensors, temperature sensors, precipitation sensors, air temperature sensors, and humidity sensors; data from different sources include remote sensing data, ground sensor data, and meteorological data; Network status perception module: real-time monitoring of network delay, bandwidth and packet loss rate; Computing module: used to perform data calculations.

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