Natural resource high-frequency database construction method

Abstract

The invention relates to the technical field of database construction, in particular to a natural resource high-frequency database construction method. The method comprises the following steps: acquiring geographic information data; performing multi-missing-value type filling on the geographic information cleaning data to obtain geographic information filling data; performing data standardization on the geographic information filling data to generate standard geographic information data; performing multi-source sensing dynamic data acquisition based on the standard geographic information data to obtain multi-source sensing dynamic filtering data; performing multi-frequency detection on the multi-source sensing dynamic filtering data to generate a dynamic data change frequency; and performing distributed stream response processing on the multi-source sensing dynamic filtering data based on the dynamic data change frequency to generate natural resource high-frequency stream data. According to the method, data integration, dynamic processing, space-time prediction and optimization management are carried out on the geographic resource data, so that the real-time performance and responsiveness of subsequent database design are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of database construction, and particularly relates to a method for constructing a high-frequency database of natural resources. Background Art

[0002] In the initial stage, the technology focused on the development of Geographic Information System (GIS) for data acquisition, geospatial analysis, and data visualization. Over time, the rise of sensor network technology has made data collection more refined and real-time, providing more data sources for the construction of high-frequency databases. At the same time, the maturity of distributed stream processing technology has made it possible to perform real-time processing and analysis of large-scale data, providing a technical foundation for the processing of high-frequency data. After entering the 21st century, the development of artificial intelligence and machine learning technologies has promoted the application of data mining and pattern recognition technologies in natural resource data, including the analysis and prediction of spatio-temporal data. In addition, the progress of mathematical tools such as wavelet transform and frequency domain analysis has provided theoretical support and calculation methods for the in-depth analysis of multi-source sensor dynamic data. In recent years, the popularization of high-performance computing and cloud computing platforms has further accelerated the improvement of data processing and storage capabilities, supporting more complex and real-time high-frequency database construction requirements. However, the current capabilities for collecting, filtering, and multi-frequency detection processing of multi-source sensing dynamic data are limited, and there is a lack of systematic methods for simulating high-frequency data resource allocation and abnormal resource handling strategies, resulting in low real-time performance and responsiveness in database design. Summary of the Invention

[0003] Based on this, it is necessary to provide a method for constructing a high-frequency database of natural resources to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for constructing a high-frequency database of natural resources, the method includes the following steps:

[0005] Step S1: Obtain geographic information data; fill multiple missing value types in the cleaned geographic information data to obtain filled geographic information data; standardize the filled geographic information data to generate standard geographic information data;

[0006] Step S2: Collect multi-source sensing dynamic data based on the standard geographic information data to obtain multi-source sensing dynamic filtered data; perform multi-frequency detection on the multi-source sensing dynamic filtered data to generate the change frequency of dynamic data; perform distributed stream response processing on the multi-source sensing dynamic filtered data based on the change frequency of dynamic data to generate high-frequency stream data of natural resources;

[0007] Step S3: Perform spatio-temporal data sharding on the high-frequency flow data of natural resources to generate sharded spatio-temporal data of natural resources; perform dynamic change prediction on the sharded spatio-temporal data of natural resources to obtain high-frequency dynamic change prediction data of natural resources; optimize the resource allocation of the high-frequency flow data of natural resources based on the high-frequency dynamic change prediction data of natural resources to generate optimized data for high-frequency data resource allocation;

[0008] Step S4: Perform distribution simulation on the high-frequency flow data of natural resources according to the optimized data for high-frequency data resource allocation to generate result data of resource distribution simulation; construct a hierarchical release strategy for abnormal resources for the result data of resource distribution simulation to obtain a hierarchical release strategy for abnormal resources; perform database design on the high-frequency flow data of natural resources through the hierarchical release strategy for abnormal resources and the result data of resource distribution simulation, thereby generating a high-frequency database of natural resources.

[0009] The present invention ensures the integrity of geographical information data by filling various types of missing values, avoiding analysis biases caused by data missing. Standardize the filled data to ensure data format consistency, facilitating subsequent data integration and analysis. Use multi-source sensor data for dynamic filtering and multi-frequency detection to accurately capture the dynamic change characteristics of natural resources. Generate data reflecting the high-frequency changes of natural resources in real time through distributed stream response processing, providing a basis for subsequent dynamic change prediction and optimized resource allocation. Perform spatio-temporal sharding on the high-frequency flow data, extract and manage data within different time periods and spatial ranges, facilitating targeted resource management and analysis. Predict the high-frequency dynamic changes of natural resources based on historical data and trend analysis, providing decision-making support for resource allocation and coping with future changes. Optimize resource allocation using the prediction data to improve resource utilization efficiency and reduce resource waste. Effectively manage and utilize resources in abnormal situations through simulation analysis and a hierarchical release strategy for abnormal resources, enhancing the flexibility and efficiency of resource utilization. Design and construct a high-frequency database of natural resources, integrating and storing the data required for analysis, supporting real-time data query and decision-making analysis. Therefore, the present invention improves the real-time performance and responsiveness of subsequent database design through data integration, dynamic processing, spatio-temporal prediction, and optimized management of geographical resource data.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain geographical information data;

[0012] Step S12: Perform data cleaning on the geographical information data to generate cleaned geographical information data;

[0013] Step S13: Detect missing values in the cleaned geographic information data to obtain the proportion of missing values in the cleaned geographic information data. When the proportion of missing values in the cleaned geographic information data is greater than or equal to the preset missing value proportion threshold, return to Step S11 to re-obtain the data. When the proportion of missing values in the geographic information data is less than the preset missing value proportion threshold, fill in the missing values in the cleaned geographic information data based on the proportion of missing values in the cleaned geographic information data to generate filled geographic information data.

[0014] Step S14: Standardize the filled geographic information data to generate standard geographic information data.

[0015] Through data cleaning, the present invention can remove noise and incorrect data in the geographic information data, thereby improving the accuracy of the data. By performing missing value detection and filling, the integrity of the data is ensured, and the impact of missing values on the analysis results is avoided. Data standardization enables data from different sources to be processed on the same scale and in the same format, ensuring data consistency and facilitating subsequent analysis and processing. The mechanism of detecting the proportion of missing values and re-obtaining data ensures that the data is processed subsequently only when it meets the preset standards, guaranteeing the reliability of the data. Through a systematic process (from acquisition to cleaning, filling, and standardization), the data processing flow becomes more efficient, helping to shorten the data processing time and improve work efficiency. The standardized geographic information data provides a reliable basic data for subsequent analysis, modeling, and decision-making, ensuring the scientificity and accuracy of the analysis results.

[0016] Preferably, when the proportion of missing values in the geographic information data is less than the preset missing value proportion threshold, filling in the missing values in the cleaned geographic information data based on the proportion of missing values in the cleaned geographic information data includes:

[0017] When the proportion of missing values in the cleaned geographic information data is less than the preset missing value proportion threshold, analyze the types of missing data in the cleaned geographic information data to generate types of missing data, where the types of missing data include numerical data and categorical data;

[0018] When it is confirmed that the type of missing data in the cleaned geographic information data is numerical data, fill in the corresponding fields of the cleaned geographic information data with the median based on the proportion of missing values in the cleaned geographic information data to generate filled geographic information data;

[0019] When it is confirmed that the type of missing data in the cleaned geographic information data is categorical data, fill in the corresponding fields of the cleaned geographic information data with the mode based on the proportion of missing values in the geographic information data to also generate filled geographic information data.

[0020] Through reasonable filling of missing values in the present invention (using the field median to fill numerical data and the field mode to fill categorical data), missing data can be effectively supplemented, thereby improving the integrity of the data. Using the median to fill numerical data can avoid the influence of outliers and better reflect the true distribution of the data than using the average. Using the mode to fill categorical data can maintain the original classification characteristics of the data and reduce the deviation caused by filling. Through specific missing value filling, the impact of missing values in the dataset on subsequent analysis and modeling is reduced, thereby improving the stability and accuracy of data analysis and models. Automatically selecting the filling method according to the data type improves the efficiency of data preprocessing and reduces the need for manual intervention. Adopting different filling strategies for different types of data makes the entire data processing process more flexible and can adapt to various types of data situations. Using the median and mode to fill missing values can effectively reduce the data deviation caused by missing value filling and ensure that the filled data is closer to the real situation.

[0021] Preferably, step S2 includes the following steps:

[0022] Step S21: Deploy the Internet of Things sensor network based on standard geographic information data to obtain a geographic sensor network;

[0023] Step S22: Collect multi-source sensing dynamic data according to the geographic sensor network to obtain multi-source sensing dynamic data; perform data filtering on the multi-source sensing dynamic data to generate multi-source sensing dynamic filtered data;

[0024] Step S23: Perform multi-frequency detection on the multi-source sensing dynamic filtered data to generate the change frequency of the dynamic data; sort the multi-source sensing dynamic filtered data based on the change frequency of the dynamic data to generate a multi-source sensing dynamic data priority sorting array;

[0025] Step S24: Use the multi-source sensing dynamic data priority sorting array to perform distributed flow response processing on the multi-source sensing dynamic filtered data to generate natural resource high-frequency flow data.

[0026] By deploying the Internet of Things sensor network based on standard geographic information data, the present invention can collect dynamic data in real time, ensuring the timeliness and accuracy of the data. The multi-source sensing dynamic data collection and filtering ensure the purity of the data, removing noise and improving the data quality. Conducting multi-frequency detection on the multi-source sensing dynamic filtered data can identify data changes at different frequencies, providing more dimensional information for data analysis. Sorting the data priorities based on the change frequency can process important data more effectively, improving the efficiency and accuracy of data processing. Using the data priority sorting array to perform distributed stream response processing on the multi-source sensing dynamic filtered data can quickly respond to high-frequency data changes, enhancing the reaction speed and decision-making quality of natural resource management. Through distributed processing, the data processing load can be effectively shared, improving the overall processing efficiency of the system and ensuring that high-frequency data can be processed and applied in a timely manner. The dynamic data priority sorting and distributed stream response processing can ensure that high-frequency important data is preferentially processed and applied, maximizing the utilization value of the data. The multi-frequency detection and priority sorting can identify and process data changes at different frequencies, reducing the load fluctuation of the system and enhancing the stability and reliability of the system.

[0027] Preferably, step S23 includes the following steps:

[0028] Step S231: Perform wavelet transform on the multi-source sensing dynamic filtered data to obtain the multi-source sensing dynamic frequency components;

[0029] Step S232: Identify the frequency changes of the multi-source sensing dynamic frequency components to obtain the multi-source sensing dynamic frequency change data; analyze the time-varying characteristics of the multi-source sensing dynamic frequency components to generate the multi-source sensing dynamic time-varying data; integrate the multi-source sensing dynamic frequency change data and the multi-source sensing dynamic time-varying data to generate the dynamic data change frequency;

[0030] Step S233: Perform dynamic priority weighting on the data points within the same timestamp of the multi-source sensing dynamic filtered data based on the dynamic data change frequency to obtain the dynamic weight dataset; calculate the mean value of the weights of the dynamic weight dataset to obtain the priority division threshold;

[0031] Step S234: Use the priority division threshold to sort the priorities of the multi-source sensing dynamic filtered data, thereby generating the multi-source sensing dynamic data priority sorting array.

[0032] The present invention can decompose multi-source sensing dynamic filtering data into different frequency components by using wavelet transform, which helps to identify the frequency characteristics of the data and improve the accuracy of data analysis. Through frequency change identification and time-varying characteristic analysis, the dynamic change characteristics of the data can be captured, providing a reliable basis for subsequent priority ranking. Based on the dynamic data change frequency for priority weighting, the weight value can be dynamically adjusted according to the importance of the data, ensuring that high-priority data receives more attention during the processing. The calculation of the weight value mean and the use of the priority division threshold ensure the scientificity and rationality of the priority division. Through priority ranking, high-priority data can be quickly identified and processed, improving the efficiency and effect of data processing. The generation of the priority ranking array facilitates subsequent distributed stream response processing and realizes the efficient management of high-frequency stream data. Precise frequency analysis and priority ranking can ensure that important data is preferentially processed and applied, maximizing the utilization value of the data. The dynamic change identification and priority weighting processing mechanism can handle data with different frequencies and changes, effectively allocate system resources, and enhance the stability and reliability of the system.

[0033] Preferably, step S24 includes the following steps:

[0034] Step S241: Configure distributed stream processing nodes for the multi-source sensing dynamic filtering data to obtain node configuration data;

[0035] Step S242: Construct a priority queue for the multi-source sensing dynamic data priority ranking array based on the first-in-first-out algorithm to obtain priority queue data;

[0036] Step S243: Perform dynamic task allocation on the priority queue data to generate node task allocation data;

[0037] Step S244: Perform real-time data stream processing on the node task allocation data through a stream processing framework to generate real-time processing data; store the stream data of the real-time processing data to generate natural resource high-frequency stream data.

[0038] Through the distributed stream processing node configuration (step S241), the present invention can make full use of distributed computing resources, improve the processing efficiency of multi-source sensing dynamic filtering data, and avoid single-point bottlenecks. A priority queue is constructed based on the first-in-first-out algorithm to ensure that high-priority data is preferentially processed in the queue, thereby improving the processing speed and response time of critical data. Dynamic task allocation is performed on the data in the priority queue. According to the importance and real-time requirements of the data, tasks are reasonably allocated to different processing nodes to improve the processing capacity and load balance of the system. Real-time data stream processing is carried out through the stream processing framework, which can quickly process and generate real-time processed data to ensure the timeliness and accuracy of the data. The stream data storage of the real-time processed data ensures the persistent storage and subsequent analysis and utilization of high-frequency stream data. The real-time data stream processing mechanism improves the response speed of the system to dynamic changes, enabling the system to quickly adapt to and process sudden data. Through the configuration and optimization of the priority queue and distributed processing nodes, the data processing process is ensured to be more orderly and efficient, and the overall management level of the system is improved.

[0039] Preferably, optimizing the resource allocation of high-frequency natural resource stream data based on high-frequency dynamic change prediction data of natural resources includes:

[0040] Evaluating the resource requirements of high-frequency natural resource stream data based on high-frequency dynamic change prediction data of natural resources to generate resource requirement evaluation data; configuring resource strategies according to the resource requirement evaluation data to generate resource allocation strategy data;

[0041] Performing dynamic resource scheduling on high-frequency natural resource stream data according to the resource allocation strategy data to generate dynamic resource scheduling data; monitoring the resource utilization rate of the dynamic resource scheduling data to generate resource utilization rate monitoring data;

[0042] Optimizing the resource allocation strategy data through the resource utilization rate monitoring data to generate optimized high-frequency data resource allocation data.

[0043] Through resource demand assessment (generating resource demand assessment data) based on high-frequency dynamic change prediction data of natural resources, the present invention can accurately predict resource demands, ensuring the scientificity and rationality of resource allocation. Generating resource allocation strategy data according to the resource demand assessment data enables the resource strategy to better match the actual demands and optimize the resource allocation plan. Dynamic resource scheduling (generating dynamic resource scheduling data) can adjust resource allocation in real time according to actual demands, improving resource utilization efficiency and response speed. Through resource utilization rate monitoring data, the usage of resources is monitored in real time to ensure the high efficiency and effectiveness of resource utilization. Optimizing the resource allocation strategy according to the resource utilization rate monitoring data (generating high-frequency data resource allocation optimization data) can continuously adjust and improve the resource allocation plan, enhancing resource utilization efficiency. Through dynamic resource scheduling and resource allocation optimization, it is possible to quickly respond to changes in resource demands, improving decision-making efficiency and response speed. Through precise resource demand assessment and optimized resource allocation strategies, resource waste can be effectively reduced and resource utilization efficiency can be enhanced. Dynamic resource scheduling and resource utilization rate monitoring ensure that the system can operate stably, timely adjust resource allocation, and avoid situations of resource shortage or surplus.

[0044] Preferably, step S4 includes the following steps:

[0045] Step S41: Perform allocation simulation on the high-frequency natural resource flow data according to the high-frequency data resource allocation optimization data to generate resource allocation simulation result data;

[0046] Step S42: Conduct abnormal allocation simulation analysis on the resource allocation simulation result data to generate abnormal resource allocation simulation data; perform hierarchical release of resources based on the abnormal resource allocation simulation data to generate an abnormal resource hierarchical release strategy, where the abnormal resource hierarchical release strategy includes an abnormal resource misallocation strategy and an abnormal resource over-allocation strategy;

[0047] Step S43: Design a database for the high-frequency natural resource flow data through the abnormal resource hierarchical release strategy and the resource allocation simulation result data, thereby generating a high-frequency natural resource database.

[0048] Through resource allocation simulation by optimizing data according to high-frequency data resources, the present invention can simulate the impact of different resource allocation schemes on high-frequency flow data of natural resources, and evaluate the effects and results of various allocation strategies. By performing abnormal allocation simulation analysis on the resource allocation simulation result data, it is possible to identify and analyze abnormal resource allocation situations, including incorrect allocation and over-allocation. Based on the abnormal resource allocation simulation data, an abnormal resource hierarchical release strategy is formulated, including specific strategies for incorrect allocation and over-allocation of abnormal resources, to reduce their impact and improve resource utilization efficiency. Through the abnormal resource hierarchical release strategy and the resource allocation simulation result data, database design can be carried out to establish and manage a high-frequency database of natural resources, ensuring the effective storage and efficient query of data. The high-frequency database of natural resources provides a reliable data basis for subsequent data analysis, supporting various resource management decisions and scientific research.

[0049] Preferably, step S42 includes the following steps:

[0050] Step S421: Perform abnormal allocation simulation analysis on the resource allocation simulation result data to generate abnormal resource allocation simulation data, where the abnormal resource allocation simulation data includes incorrect allocation simulation data and over-allocation simulation data of resources;

[0051] Step S422; Perform pre-interception of resource allocation on the resource allocation simulation result data according to the incorrect allocation simulation data to obtain resource allocation interception data; perform reconfirmation of resource allocation on the resource allocation interception data to obtain an abnormal resource incorrect allocation strategy;

[0052] Step S423: Analyze the degree of over-allocation of the over-allocation simulation data of resources to generate over-allocation degree data of resources; release redundant resources from the resource allocation simulation result data based on the over-allocation degree data of resources, thereby generating an abnormal resource over-allocation strategy.

[0053] The present invention generates abnormal resource allocation simulation data, including incorrect allocation simulation data and over-allocation simulation data, by performing abnormal allocation simulation analysis on the resource allocation simulation result data. These data can help identify errors and over-allocation situations in resource allocation. According to the incorrect allocation simulation data, pre-interception of resource allocation is carried out. By intercepting and reconfirming resource allocation, the occurrence of incorrect allocation is reduced, and thus an abnormal resource incorrect allocation strategy is formulated. The over-allocation degree analysis is performed on the over-allocation simulation data of resources to generate over-allocation degree data of resources. Based on these data, an over-allocation strategy for resources can be formulated. By releasing redundant resources, the resource allocation is optimized, and the resource utilization efficiency is improved. Through the abnormal resource incorrect allocation strategy and the abnormal resource over-allocation strategy, errors and over-allocation in resource allocation can be effectively reduced, resource waste can be decreased, and the resource utilization efficiency can be enhanced. The formulation and implementation of these strategies can optimize the resource management process, improve the accuracy and real-time performance of the system for resource allocation, and thus enhance the overall resource management efficiency.

[0054] The beneficial effects of the present invention are as follows: By filling in missing values, cleaning and standardizing geographical information data, a more complete and consistent geographical information data set can be obtained, improving the quality of subsequent analysis and modeling. By collecting multi-source sensor data and performing filtering processing, the influence of noise and outliers can be reduced, and more accurate and reliable dynamic data can be obtained. By performing frequency detection on the multi-source sensing dynamic filtering data, the change frequency information of the data can be obtained, helping to understand and analyze the dynamic characteristics of natural resources. Through distributed stream response processing, high-frequency stream data of natural resources can be generated. These data have a higher time resolution and can better capture the rapid changes and instantaneous characteristics of resources. Performing spatio-temporal slicing on the high-frequency stream data can better organize and manage the data, making it easier to process and analyze. By predicting the dynamic changes of the spatio-temporal sliced data of natural resources, the future change trends and patterns of resources can be predicted, helping to make corresponding decisions and plans. Based on the dynamic change prediction data, resource allocation optimization can be carried out. Under the condition of limited resources, resources can be reasonably allocated, improving the resource utilization efficiency and meeting the demands. By analyzing and evaluating the resource allocation simulation result data, the effects and impacts of the resource allocation scheme can be understood, providing a basis for decision-making. By constructing an abnormal resource hierarchical release strategy, abnormal situations can be responded to in a timely manner, and resource allocation can be reasonably adjusted to ensure the sustainable utilization and protection of natural resources. Through database design, integrating high-frequency stream data, resource allocation simulation result data, abnormal resource hierarchical release strategy, etc., a database for storing and managing high-frequency data of natural resources can be established, providing convenience for subsequent analysis and applications. Therefore, the present invention improves the real-time performance and responsiveness of subsequent database design by performing data integration, dynamic processing, spatio-temporal prediction, and optimization management on geographical resource data. Brief Description of the Drawings

[0055] Figure 1 It is a schematic diagram of the step flow for constructing a high-frequency database of natural resources;

[0056] Figure 2 It is Figure 1 a detailed schematic diagram of the implementation steps of step S2 in

[0057] Figure 3 It is Figure 1 a detailed schematic diagram of the implementation steps of step S3 in

[0058] Figure 4 It is Figure 1 a detailed schematic diagram of the implementation steps of step S4 in

[0059] The realization, functional features and advantages of the purpose of the present invention will be further described in conjunction with the embodiments with reference to the attached drawings. Specific Embodiments

[0060] The technical method of the present invention for patents will be clearly and completely described below with reference to the attached drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative efforts belong to the scope of protection of the present invention.

[0061] In addition, the attached drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0062] It should be understood that although terms such as "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0063] To achieve the above object, please refer to Figures 1 to 4 , a method for constructing a high-frequency database of natural resources, the method includes the following steps:

[0064] Step S1: Obtain geographical information data; perform multi-missing value type filling on the cleaned geographical information data to obtain filled geographical information data; perform data standardization on the filled geographical information data to generate standard geographical information data;

[0065] Step S2: Based on the standard geographical information data, perform multi-source sensing dynamic data acquisition to obtain multi-source sensing dynamic filtering data; perform multi-frequency detection on the multi-source sensing dynamic filtering data to generate the dynamic data change frequency; based on the dynamic data change frequency, perform distributed flow response processing on the multi-source sensing dynamic filtering data to generate natural resource high-frequency flow data;

[0066] Step S3: Perform spatio-temporal data sharding on the natural resource high-frequency flow data to generate natural resource sharded spatio-temporal data; perform dynamic change prediction on the natural resource sharded spatio-temporal data to obtain natural resource high-frequency dynamic change prediction data; based on the natural resource high-frequency dynamic change prediction data, perform resource allocation optimization on the natural resource high-frequency flow data to generate optimized high-frequency data resource allocation data;

[0067] Step S4: According to the optimized high-frequency data resource allocation data, perform distribution simulation on the natural resource high-frequency flow data to generate resource allocation simulation result data; construct an abnormal resource hierarchical release strategy for the resource allocation simulation result data to obtain an abnormal resource hierarchical release strategy; through the abnormal resource hierarchical release strategy and the resource allocation simulation result data, perform database design on the natural resource high-frequency flow data, thereby generating a natural resource high-frequency database.

[0068] The present invention ensures the integrity of geographical information data by filling multiple missing value types, avoiding analysis biases caused by data missing. Perform standardization processing on the filled data to ensure data format consistency, facilitating subsequent data integration and analysis. Use multi-source sensor data for dynamic filtering and multi-frequency detection to accurately capture the dynamic change characteristics of natural resources. Through distributed flow response processing, generate data reflecting the high-frequency changes of natural resources in real time, providing a basis for subsequent dynamic change prediction and optimized resource allocation. Perform spatio-temporal sharding on the high-frequency flow data to extract and manage data within different time periods and spatial ranges, facilitating targeted resource management and analysis. Based on historical data and trend analysis, predict the high-frequency dynamic changes of natural resources, providing decision-making support for resource allocation and coping with future changes. Use the prediction data for resource allocation optimization to improve resource utilization efficiency and reduce resource waste. Through simulation analysis and abnormal resource hierarchical release strategy, effectively manage and utilize resources in abnormal situations, enhancing the flexibility and efficiency of resource utilization. Design and construct a natural resource high-frequency database to integrate and store the data required for analysis, supporting real-time data query and decision-making analysis. Therefore, the present invention improves the real-time performance and responsiveness of subsequent database design through data integration, dynamic processing, spatio-temporal prediction, and optimized management of geographical resource data.

[0069] In the embodiments of the present invention, with reference to Figure 1 As described, it is a schematic flow chart of the steps of a method for constructing a high-frequency database of natural resources according to the present invention. In this example, the method for constructing a high-frequency database of natural resources includes the following steps:

[0070] Step S1: Obtain geographic information data; fill multiple missing value types in the cleaned geographic information data to obtain filled geographic information data; standardize the filled geographic information data to generate standard geographic information data;

[0071] In the embodiments of the present invention, the original geographic information data is obtained by using a geographic information system (GIS) or other specific data sources. Data cleaning tools and techniques, such as interpolation methods or machine learning models, are used to fill multiple missing value types in the geographic information data to ensure data integrity. The filled data forms a dataset of filled geographic information. Standardization methods (such as mean-variance standardization or min-max standardization) are used to adjust the filled geographic information data to a unified data range and format. Finally, standard geographic information data that meets the standard format and range is obtained, which can be used for subsequent data analysis and applications.

[0072] Step S2: Perform multi-source sensing dynamic data collection based on the standard geographic information data to obtain multi-source sensing dynamic filtered data; perform multi-frequency detection on the multi-source sensing dynamic filtered data to generate the dynamic data change frequency; perform distributed stream response processing on the multi-source sensing dynamic filtered data based on the dynamic data change frequency to generate high-frequency natural resource stream data;

[0073] In the embodiments of the present invention, multiple sensors or data collection nodes are deployed to obtain geographic information data in real time through network or satellite connections. Data collection protocols and techniques are used to ensure data synchronization and integrity. The collected data is filtered to reduce noise and data inconsistencies, resulting in clear and stable dynamic data. Signal processing techniques or frequency domain analysis tools are used to detect multi-frequency changes in the dynamic data and determine the frequency characteristics of the data. Signal processing techniques or frequency domain analysis tools are used to detect multi-frequency changes in the dynamic data and determine the frequency characteristics of the data. A distributed stream processing framework (such as Apache Flink or Spark Streaming) is used to perform real-time processing and analysis on the multi-source sensing dynamic filtered data according to the dynamic data change frequency. Combining the results of the distributed stream response processing, a high-frequency natural resource data stream is generated, including the real-time characteristics and spatio-temporal information of the data changes.

[0074] Step S3: Perform spatio-temporal data sharding on the high-frequency flow data of natural resources to generate sharded spatio-temporal data of natural resources; perform dynamic change prediction on the sharded spatio-temporal data of natural resources to obtain high-frequency dynamic change prediction data of natural resources; optimize the resource allocation of the high-frequency flow data of natural resources based on the high-frequency dynamic change prediction data of natural resources to generate optimized data for high-frequency data resource allocation;

[0075] In the embodiment of the present invention, by using a spatio-temporal data analysis tool, the high-frequency flow data of natural resources is cut and segmented according to time and space dimensions to form a spatio-temporal sharded data set. Temporal analysis, machine learning, or deep learning models are used to predict the dynamic changes of the spatio-temporal sharded data. For example, methods such as time series models and neural networks are used to predict the future change trends of natural resources. Based on the output of the prediction model, prediction data describing the high-frequency dynamic changes of natural resources is obtained, including the change trends at time points and spatial positions. Based on the prediction data, optimization algorithms (such as genetic algorithms, simulated annealing algorithms, etc.) are used to optimize the resource allocation of the high-frequency flow data of natural resources to achieve the best allocation and utilization of resources. Combining the results of the optimization algorithm, data describing the resource allocation optimization process and results is generated, including the recommended resource allocation plan and the evaluation of the optimization effect.

[0076] Step S4: Perform allocation simulation on the high-frequency flow data of natural resources according to the optimized data for high-frequency data resource allocation to generate resource allocation simulation result data; construct an abnormal resource hierarchical release strategy for the resource allocation simulation result data to obtain an abnormal resource hierarchical release strategy; design a database for the high-frequency flow data of natural resources through the abnormal resource hierarchical release strategy and the resource allocation simulation result data, thereby generating a high-frequency database of natural resources.

[0077] In the embodiment of the present invention, by using simulation algorithms (such as Monte Carlo simulation, Agent-based simulation, etc.), based on the optimized data for high-frequency data resource allocation, the effects and results of different resource allocation strategies are simulated. According to the output of the simulation algorithm, resource allocation simulation result data is generated, recording the resource utilization conditions and effects under different strategies. Analyze the resource allocation simulation result data to identify and evaluate abnormal resource distributions and utilization conditions. Design an abnormal resource hierarchical release strategy, including formulating corresponding release priorities and strategies according to the severity and impact degree of abnormal resources. Combine the abnormal resource hierarchical release strategy and the resource allocation simulation result data to design a high-frequency database of natural resources. Determine the structure and fields of the database, including storing information such as high-frequency flow data, simulation results, prediction data, and abnormal handling strategies. Use a database management system (such as MySQL, MongoDB, etc.) to implement the construction of the database and data storage.

[0078] Preferably, step S1 includes the following steps:

[0079] Step S11: Obtain geographical information data;

[0080] Step S12: Clean the geographical information data to generate cleaned geographical information data;

[0081] Step S13: Detect missing values in the cleaned geographical information data to obtain the missing value ratio of the cleaned geographical information data; when the missing value ratio of the cleaned geographical information data is greater than or equal to a preset missing value ratio threshold, return to Step S11 to re-obtain the data; when the missing value ratio of the geographical information data is less than the preset missing value ratio threshold, fill in the missing values in the cleaned geographical information data based on the missing value ratio of the cleaned geographical information data to generate filled geographical information data;

[0082] Step S14: Standardize the filled geographical information data to generate standard geographical information data.

[0083] In the embodiment of the present invention, by obtaining original geographical information data from various data sources (such as sensors, satellites, geographical databases, etc.), such as satellite images, ground sensors or geographical databases, using a data acquisition tool or API to obtain geographical information data from the selected data source. Clean the original geographical information data, remove incorrect, redundant or incomplete data, remove outliers, duplicate values and irrelevant data, unify the data format and unit to ensure data consistency. Analyze the missing value situation in the dataset, calculate the missing value ratio, compare the missing value ratio with the preset threshold, and decide whether to re-obtain the data. Adopt a suitable filling algorithm (such as mean filling, interpolation method, etc.) to process the missing values to generate filled geographical information data. Convert the geographical information data into a unified data format and range, and apply standardization techniques (such as min-max standardization, Z-score standardization, etc.) to make the data distributed within a unified numerical range.

[0084] Preferably, when the missing value ratio of the geographical information data is less than the preset missing value ratio threshold, filling in the missing values in the cleaned geographical information data based on the missing value ratio of the cleaned geographical information data includes:

[0085] When the missing value ratio of the cleaned geographical information data is less than the preset missing value ratio threshold, analyze the missing data type of the cleaned geographical information data to generate a missing data type, where the missing data type includes numerical data and categorical data;

[0086] When it is confirmed that the missing data type of the cleaned geographical information data is numerical data, fill in the median of the corresponding fields in the cleaned geographical information data based on the missing value ratio of the cleaned geographical information data to generate filled geographical information data;

[0087] When it is confirmed that the missing data type of the geospatial information cleaning data is categorical data, the corresponding geospatial information cleaning data is filled with the field mode based on the missing value ratio of the geospatial information data, and geospatial information filled data is also generated.

[0088] In the embodiments of the present invention, by determining a preset missing value ratio threshold. This threshold is set as a certain percentage, such as 5% or 10%, to determine which fields or data have a missing value ratio exceeding the threshold and need to be filled. Detect the missing value ratio in the geospatial information data. If the missing value ratio of a certain field is less than the preset threshold, filling it can be considered. Analyze the type of missing data, including numerical and categorical data. This step is to select a specific filling method according to the data type. For numerical data, the median of the field can be used for filling, which can be achieved by calculating the median of each numerical field and replacing the missing value with the median. For categorical data, the mode of the field can be used for filling. The mode refers to the value that appears most frequently in the dataset and is usually used to fill missing categorical data. After completing the filling operation, a filled geospatial information dataset is generated, and these filled data will contain the values after the missing values in the original data are replaced.

[0089] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0090] Step S21: Deploy an Internet of Things sensor network based on the standard geospatial information data to obtain a geosensor network;

[0091] Step S22: Perform multi-source sensing dynamic data collection according to the geosensor network to obtain multi-source sensing dynamic data; perform data filtering on the multi-source sensing dynamic data to generate multi-source sensing dynamic filtered data;

[0092] Step S23: Perform multi-frequency detection on the multi-source sensing dynamic filtered data to generate the dynamic data change frequency; perform data priority sorting on the multi-source sensing dynamic filtered data based on the dynamic data change frequency, thereby generating a multi-source sensing dynamic data priority sorting array;

[0093] Step S24: Use the multi-source sensing dynamic data priority sorting array to perform distributed flow response processing on the multi-source sensing dynamic filtered data to generate natural resource high-frequency flow data.

[0094] In the embodiments of the present invention, standard geographic information data is obtained and prepared, including relevant data such as terrain, climate, soil type, etc. According to the geographic information data and requirements, appropriate sensor types (such as temperature, humidity, pressure, light, etc.) are selected. A sensor network is deployed to cover the target area to ensure reasonable sensor positions and stable communication capabilities. The data acquisition period and data transmission protocol of the sensors are set to ensure the real-time and accuracy of the data. Ensure unobstructed data communication between sensor nodes and between the central server. Real-time collect the data uploaded by each sensor node and integrate it into a multi-source dynamic data stream. According to needs, perform time synchronization and spatial alignment on the data. Use appropriate filtering algorithms (such as Kalman filters, moving average filters, etc.) to process the noise and outliers in the data to improve data quality and stability. Analyze the frequency changes of the multi-source dynamic data to identify the high-frequency change parts in the data. According to the change frequency of the dynamic data, set priorities for the data, and give priority to processing the high-frequency changed data to ensure the real-time and response capabilities of key data. Utilize a distributed computing architecture to perform real-time response processing on the multi-source sensing dynamic filtered data. A stream processing engine (such as Apache Kafka, Apache Flink, etc.) can be used to process and analyze the data stream to ensure processing efficiency and real-time performance. Combine the processed data to generate high-frequency stream data of natural resources, such as water flow, air quality changes, soil humidity, etc.

[0095] Preferably, step S23 includes the following steps:

[0096] Step S231: Perform wavelet transform on the multi-source sensing dynamic filtered data to obtain multi-source sensing dynamic frequency components;

[0097] Step S232: Perform frequency change identification on the multi-source sensing dynamic frequency components to obtain multi-source sensing dynamic frequency change data; perform time-varying characteristic analysis on the multi-source sensing dynamic frequency components to generate multi-source sensing dynamic time-varying data; integrate the multi-source sensing dynamic frequency change data and the multi-source sensing dynamic time-varying data to generate the dynamic data change frequency;

[0098] Step S233: Perform dynamic priority weighting on the data points within the same timestamp of the multi-source sensing dynamic filtered data based on the dynamic data change frequency to obtain a dynamic weight dataset; calculate the mean value of the weights of the dynamic weight dataset to obtain a priority division threshold;

[0099] Step S234: Use the priority division threshold to perform data priority sorting on the multi-source sensing dynamic filtered data, thereby generating a multi-source sensing dynamic data priority sorting array.

[0100] In the embodiments of the present invention, wavelet transform (Wavelet Transform) is applied to multi-source sensing dynamic filtering data to extract the frequency components and time-varying characteristics of the data. Specific wavelet basis functions (such as Haar wavelet, Daubechies wavelet, etc.) are selected, and the scale and level of wavelet transform are adjusted according to the data characteristics. The multi-source sensing dynamic frequency components obtained after wavelet transform are analyzed to identify the frequency changes in the data. Using spectral analysis or frequency-domain analysis methods based on wavelet transform, the frequency change patterns in the data are determined. The time-varying characteristics of the multi-source sensing dynamic frequency components are analyzed, that is, the change trend and periodicity of the data over time are analyzed. Based on the dynamic data change frequency and time-varying characteristics, dynamic priority weighting is performed on the data points within each timestamp. Weighted average or other appropriate weighting methods can be used to calculate the dynamic weight dataset. Statistical analysis or mathematical modeling is performed on the dynamic weight dataset to calculate the threshold for priority division. The selection of the threshold can be based on the distribution characteristics of the data and the actual application requirements to ensure the effective division of data priorities. According to the calculated priority division threshold, the multi-source sensing dynamic filtering data is sorted by priority. The data is sorted from high to low priority to generate an array of sorted priorities for multi-source sensing dynamic data.

[0101] Preferably, step S24 includes the following steps:

[0102] Step S241: Configure distributed stream processing nodes for the multi-source sensing dynamic filtering data to obtain node configuration data;

[0103] Step S242: Construct a priority queue for the multi-source sensing dynamic data sorted priority array based on the first-in-first-out algorithm to obtain priority queue data;

[0104] Step S243: Perform dynamic task allocation on the priority queue data to generate node task allocation data;

[0105] Step S244: Perform real-time data stream processing on the node task allocation data through a stream processing framework to generate real-time processed data; store the stream data of the real-time processed data to generate natural resource high-frequency stream data.

[0106] In the embodiments of the present invention, by configuring specific numbers and types of distributed stream processing nodes according to system requirements and processing capabilities, the communication and data transmission speeds between nodes are ensured to meet the requirements of real-time processing. An appropriate stream processing framework (such as Apache Flink, Apache Kafka Streams, etc.) is selected to process distributed stream data. Based on the First-In-First-Out (FIFO) algorithm, a priority queue is constructed using the multi-source sensing dynamic data priority sorting array generated in step S233. Ensure that the priority queue can process data in order of priority. According to the data priorities in the priority queue, tasks are dynamically assigned to each stream processing node. Considering the load balancing and processing capabilities of the nodes, tasks are reasonably assigned to ensure the efficient operation of the system. Under the stream processing framework, real-time data stream processing is performed based on the data assigned to the nodes. Real-time data processing includes operations such as data filtering, aggregation, and calculation to generate real-time processing results that meet the requirements. The data after real-time processing is stored in a suitable stream data storage system, such as a time series database or a data lake, etc. Ensure the high reliability and accessibility of the data for subsequent analysis and applications.

[0107] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0108] Step S31: Perform spatio-temporal data sharding on the high-frequency stream data of natural resources to generate sharded spatio-temporal data of natural resources;

[0109] Step S32: Perform dataset partitioning on the sharded spatio-temporal data of natural resources to generate a model training set and a model test set;

[0110] Step S33: Perform model training on the model training set through the spatio-temporal Bayesian network algorithm to generate a natural resource dynamic change training model; use the model test set to perform model test iterations on the natural resource change training model to generate a natural resource dynamic change prediction model;

[0111] Step S34: Import the sharded spatio-temporal data of natural resources into the natural resource dynamic change prediction model for dynamic change prediction to obtain high-frequency dynamic change prediction data of natural resources; based on the high-frequency dynamic change prediction data of natural resources, optimize the resource allocation of the high-frequency stream data of natural resources to generate optimized data for high-frequency data resource allocation.

[0112] In the embodiments of the present invention, by performing spatio-temporal analysis on high-frequency flow data of natural resources, spatio-temporal characteristics and change patterns in the data are identified. According to the data characteristics, the data is divided into specific spatio-temporal segments for subsequent model training and prediction. The spatio-temporal segmented data is divided into a training set and a test set. Ensure that the spatio-temporal distribution and change patterns of the training set and test set data can effectively represent the actual scenario and can verify the generalization ability of the model. Use the spatio-temporal Bayesian network algorithm to train the model on the training set. The spatio-temporal Bayesian network can effectively capture the correlation and dynamic change characteristics in spatio-temporal data. Use the test set to test the trained model and evaluate the prediction accuracy and effect of the model. Adjust and optimize the model parameters according to the test results to improve the prediction ability and stability of the model. Import the spatio-temporal segmented data of natural resources into the trained spatio-temporal Bayesian network model for dynamic change prediction. Generate prediction data of high-frequency dynamic changes of natural resources, including change trends and probability distributions in time and space. Based on the predicted high-frequency dynamic change data, optimize the resource allocation. Adjust the resource allocation and utilization strategies according to the prediction results to maximize the resource benefits and utilization rates.

[0113] Preferably, optimizing the resource allocation for the high-frequency flow data of natural resources based on the prediction data of high-frequency dynamic changes of natural resources includes:

[0114] Evaluating the resource requirements for the high-frequency flow data of natural resources based on the prediction data of high-frequency dynamic changes of natural resources to generate resource requirement evaluation data; configuring resource strategies according to the resource requirement evaluation data to generate resource configuration strategy data;

[0115] Performing dynamic resource scheduling on the high-frequency flow data of natural resources according to the resource configuration strategy data to generate dynamic resource scheduling data; monitoring the resource utilization rate of the dynamic resource scheduling data to generate resource utilization rate monitoring data;

[0116] Optimizing the resource configuration of the resource configuration strategy data through the resource utilization rate monitoring data to generate optimized data for high-frequency data resource configuration.

[0117] In the embodiments of the present invention, by analyzing the prediction data, including the dynamic change trends of resources in terms of time and space, the demand and change patterns of resources within each spatio-temporal segment are determined. According to the prediction data, the demand for resources within each spatio-temporal segment is calculated. The results are integrated into resource demand assessment data, including detailed information such as quantity, time period, and location. According to the resource demand assessment data, a resource allocation strategy is formulated, including determining the balance relationship between resource supply and demand, setting the priorities and strategies for resource allocation. The resource strategy is specified as data, including resource allocation plans, priority orders, and operation processes. According to the resource allocation strategy data, dynamic resource scheduling is performed. The resource allocation is monitored and adjusted in real time to ensure the timely supply and effective utilization of resources. The specific operations and results of each resource scheduling are recorded to generate dynamic resource scheduling data. The resource utilization situation after dynamic resource scheduling is monitored. The resource utilization rate is calculated, including resource usage efficiency and resource waste situation. According to the resource utilization rate monitoring data, the resource allocation strategy is optimized and adjusted. The resource allocation plan and priorities are adjusted to maximize the resource utilization efficiency and economic benefits.

[0118] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0119] Step S41: Allocate and simulate the natural resource high-frequency flow data according to the high-frequency data resource allocation optimization data to generate resource allocation simulation result data;

[0120] Step S42: Conduct abnormal allocation simulation analysis on the resource allocation simulation result data to generate abnormal resource allocation simulation data; based on the abnormal resource allocation simulation data, perform hierarchical release of resources to generate an abnormal resource hierarchical release strategy, where the abnormal resource hierarchical release strategy includes an abnormal resource misallocation strategy and an abnormal resource over-allocation strategy;

[0121] Step S43: Design a database for the natural resource high-frequency flow data through the abnormal resource hierarchical release strategy and the resource allocation simulation result data, thereby generating a natural resource high-frequency database.

[0122] In the embodiments of the present invention, by analyzing the optimized high-frequency data resource configuration optimization data, including information such as resource demand assessment and configuration strategies, the basic data and parameters for resource allocation simulation are determined. According to the resource configuration optimization data, resource allocation simulation is performed. The resource allocation effects in different scenarios are simulated, including the distribution of time, space, and resource types. The detailed results and data of each resource allocation simulation are recorded. Resource allocation simulation result data is generated, including the time series of simulation results and the evaluation of allocation effects. The abnormal allocation situations in the resource allocation simulation result data are analyzed. The types and frequencies of abnormal resource allocations are determined, such as incorrect allocations and over-allocations, etc. According to the abnormal allocation simulation analysis, abnormal resource allocation simulation data is generated, including recording the specific situations and influence ranges of each abnormal allocation. Based on the abnormal resource allocation simulation data, a resource hierarchical release strategy is formulated, including an abnormal resource incorrect allocation strategy and an abnormal resource over-allocation strategy, to determine how to release and re-allocate resources. According to the resource hierarchical release strategy and the resource allocation simulation result data, a natural resource high-frequency database is designed. The structure, fields, and data storage methods of the database are determined to support real-time data storage and query requirements.

[0123] Preferably, step S42 includes the following steps:

[0124] Step S421: Perform abnormal allocation simulation analysis on the resource allocation simulation result data to generate abnormal resource allocation simulation data, where the abnormal resource allocation simulation data includes incorrect allocation simulation data and resource over-allocation simulation data;

[0125] Step S422; Perform pre-interception of resource allocation on the resource allocation simulation result data according to the incorrect allocation simulation data to obtain resource allocation interception data; perform re-confirmation of resource allocation on the resource allocation interception data to obtain an abnormal resource incorrect allocation strategy;

[0126] Step S423: Analyze the over-allocation degree of the resource over-allocation simulation data to generate resource over-allocation degree data; release the redundant resources from the resource allocation simulation result data based on the resource over-allocation degree data, thereby generating an abnormal resource over-allocation strategy.

[0127] In the embodiments of the present invention, by analyzing the resource allocation simulation result data, abnormal allocation situations are identified, including incorrect allocation and over-allocation of resources. According to the analysis results, abnormal resource allocation simulation data is generated. Incorrect allocation simulation data: Records the incorrect resource allocation situations found in the simulation, including the time, location, and type of errors. Over-allocation simulation data of resources: Analyzes the over-allocation situations existing in resource allocation, including the quantity of redundant resources and the scope of influence. Conduct a detailed analysis of the incorrect allocation simulation data to identify the causes and frequencies of errors. Based on the incorrect allocation simulation data, formulate a pre-interception strategy for resource allocation. Set the system to intercept and detect abnormal situations before resource allocation to prevent incorrect resource allocation. Re-confirm and correct the intercepted resource allocation data. Ensure the correct allocation and use of resources, and improve the accuracy and efficiency of resource allocation. Conduct a detailed analysis of the over-allocation simulation data of resources to evaluate the degree and impact of over-allocation. Determine which resources are redundant and how to release and reconfigure them. Based on the over-allocation degree data of resources, formulate a strategy for releasing redundant resources. Release the redundant resources and reconfigure them to where they are needed to improve the effective utilization rate of resources and economic benefits.

[0128] The beneficial effects of the present invention are as follows: By filling in missing values, cleaning, and standardizing geographical information data, a more complete and consistent geographical information dataset can be obtained, improving the quality of subsequent analysis and modeling. By collecting multi-source sensor data and performing filtering processing, the influence of noise and outliers can be reduced, and more accurate and reliable dynamic data can be obtained. By performing frequency detection on the multi-source sensing dynamic filtered data, the change frequency information of the data can be obtained, helping to understand and analyze the dynamic characteristics of natural resources. By means of distributed stream response processing, high-frequency stream data of natural resources can be generated. These data have a higher time resolution and can better capture the rapid changes and instantaneous characteristics of resources. Spatiotemporal slicing of the high-frequency stream data can better organize and manage the data, making it easier to process and analyze. By predicting the dynamic changes of the spatiotemporal sliced data of natural resources, the future change trends and patterns of resources can be predicted, helping to make corresponding decisions and plans. Based on the dynamic change prediction data, optimizing resource allocation can reasonably allocate resources under limited resources, improve resource utilization efficiency, and meet demands. By analyzing and evaluating the resource allocation simulation result data, the effects and impacts of the resource allocation scheme can be understood, providing a basis for decision-making. By constructing an abnormal resource hierarchical release strategy, abnormal situations can be responded to in a timely manner, resource allocation can be reasonably adjusted, and the sustainable utilization and protection of natural resources can be ensured. Through database design, integrating high-frequency stream data, resource allocation simulation result data, abnormal resource hierarchical release strategies, etc., a database for storing and managing high-frequency data of natural resources can be established, facilitating subsequent analysis and applications. Therefore, the present invention improves the real-time performance and responsiveness of subsequent database design through data integration, dynamic processing, spatiotemporal prediction, and optimized management of geographical resource data.

[0129] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to encompass all changes falling within the meaning and scope of the equivalent elements of the application document within the present invention.

[0130] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a high-frequency database of natural resources, characterized in that: The following steps are involved: Step S1: Acquire geographic information data; Perform multiple missing value type filling on geographic information cleansing data to obtain geographic information filling data; Standardize geographic information filling data to generate standard geographic information data; Step S2: performing multi-source sensor dynamic data collection based on standard geographic information data to obtain multi-source sensor dynamic filtering data; performing multi-frequency detection on the multi-source sensor dynamic filtering data to generate dynamic data change frequency; performing distributed flow response processing on the multi-source sensor dynamic filtering data based on the dynamic data change frequency to generate natural resource high-frequency flow data; Step S3: Perform spatiotemporal data slicing on the high-frequency stream data of natural resources to generate spatiotemporal data of natural resource slicing; perform dynamic change prediction on the spatiotemporal data of natural resource slicing to obtain high-frequency dynamic change prediction data of natural resources; Optimize resource allocation of high-frequency flow data of natural resources based on high-frequency dynamic change prediction data of natural resources, and generate high-frequency data resource allocation optimization data; Step S4: Perform allocation simulation on the high-frequency flow data of natural resources according to the high-frequency data resource configuration optimization data to generate resource allocation simulation result data; construct an abnormal resource hierarchical release strategy for the resource allocation simulation result data to obtain the abnormal resource hierarchical release strategy; design a database for the high-frequency flow data of natural resources through the abnormal resource hierarchical release strategy and the resource allocation simulation result data to generate a natural resource high-frequency database.

2. The method for constructing a high-frequency database of natural resources according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire geographic information data; Step S12: performing data cleaning on the geographic information data to generate geographic information cleansing data; Step S13: performing missing value detection on the geographic information cleansing data to obtain the missing value ratio of the geographic information cleansing data; when the missing value ratio of the geographic information cleansing data is greater than or equal to the preset missing value ratio threshold, returning to step S11 to re-acquire the data; when the missing value ratio of the geographic information data is less than the preset missing value ratio threshold, filling the missing values ​​of the geographic information cleansing data based on the missing value ratio of the geographic information cleansing data to generate geographic information filled data; Step S14: standardize the geographic information filling data to generate standard geographic information data.

3. The method for constructing a high-frequency database of natural resources according to claim 1, characterized in that: When the missing value ratio of geographic information data is less than a preset missing value ratio threshold, the missing value filling of geographic information cleansing data based on the missing value ratio of geographic information cleansing data includes: When the missing value ratio of the geographic information cleansing data is less than the preset missing value ratio threshold, the missing data type analysis is performed on the geographic information cleansing data to generate the missing data type, where the missing data type includes numerical data and categorical data; When it is confirmed that the missing data type of the geographic information cleansing data is numerical data, the corresponding geographic information cleansing data is filled with field medians based on the missing value ratio of the geographic information cleansing data to generate geographic information filled data; When it is confirmed that the missing data type of the geographic information cleansing data is categorical data, the field mode of the corresponding geographic information cleansing data is filled based on the missing value ratio of the geographic information data, and the geographic information filling data is also generated.

4. The method for constructing a high-frequency database of natural resources according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: deploying an IoT sensor network based on standard geographic information data to obtain a geographic sensor network; Step S22: collecting multi-source sensor dynamic data according to the geographic sensor network to obtain multi-source sensor dynamic data; filtering the multi-source sensor dynamic data to generate multi-source sensor dynamic filtered data; Step S23: performing multi-frequency detection on the multi-source sensing dynamic filtering data to generate a dynamic data change frequency; performing data priority sorting on the multi-source sensing dynamic filtering data based on the dynamic data change frequency, thereby generating a multi-source sensing dynamic data priority sorting array; Step S24: using the multi-source sensor dynamic data priority sorting array to perform distributed stream response processing on the multi-source sensor dynamic filtering data to generate natural resource high-frequency stream data.

5. The method for constructing a high-frequency database of natural resources according to claim 4, characterized in that: Step S23 includes the following steps: Step S231: performing wavelet transform on the multi-source sensing dynamic filtering data to obtain the multi-source sensing dynamic frequency component; Step S232: performing frequency change identification on the multi-source sensing dynamic frequency component to obtain multi-source sensing dynamic frequency change data; performing time-varying characteristic analysis on the multi-source sensing dynamic frequency component to generate multi-source sensing dynamic time-varying data; integrating the multi-source sensing dynamic frequency change data and the multi-source sensing dynamic time-varying data to generate dynamic data change frequency; Step S233: performing dynamic priority weighting on the data points in the same timestamp of the multi-source sensor dynamic filtering data based on the dynamic data change frequency to obtain a dynamic weighted data set; performing weighted mean calculation on the dynamic weighted data set to obtain a priority division threshold; Step S234: using the priority division threshold to sort the multi-source sensor dynamic filtering data according to data priority, thereby generating a multi-source sensor dynamic data priority sorting array.

6. The method for constructing a high-frequency database of natural resources according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: performing distributed stream processing node configuration on multi-source sensor dynamic filtering data to obtain node configuration data; Step S242: constructing a priority queue for the priority sorting array of multi-source sensor dynamic data based on a first-in-first-out algorithm to obtain priority queue data; Step S243: dynamically assign tasks to the priority queue data to generate node task assignment data; Step S244: Perform real-time data stream processing based on node task allocation data through the stream processing framework to generate real-time processing data; store the real-time processing data as stream data, thereby generating high-frequency stream data of natural resources.

7. The method for constructing a high-frequency database of natural resources according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing spatiotemporal data slicing on the high-frequency stream data of natural resources to generate spatiotemporal data of natural resource slicing; Step S32: Divide the natural resource fragmented spatiotemporal data into data sets to generate a model training set and a model test set; Step S33: Performing model training on the model training set by using the spatiotemporal Bayesian network algorithm to generate a natural resource dynamic change training model; performing model testing iteration on the natural resource change training model by using the model test set to generate a natural resource dynamic change prediction model; Step S34: Import the natural resource segmented spatiotemporal data into the natural resource dynamic change prediction model to perform dynamic change prediction, and obtain natural resource high-frequency dynamic change prediction data; optimize the resource allocation of natural resource high-frequency flow data based on the natural resource high-frequency dynamic change prediction data, and generate high-frequency data resource allocation optimization data.

8. The method for constructing a high-frequency database of natural resources according to claim 7, characterized in that: The resource allocation optimization of high-frequency flow data of natural resources based on the high-frequency dynamic change prediction data of natural resources includes: Based on the natural resource high-frequency dynamic change prediction data, the resource demand assessment is performed on the natural resource high-frequency flow data to generate resource demand assessment data; resource strategy configuration is performed based on the resource demand assessment data to generate resource configuration strategy data; Perform dynamic resource scheduling on high-frequency flow data of natural resources according to resource allocation strategy data to generate dynamic resource scheduling data; perform resource utilization monitoring on dynamic resource scheduling data to generate resource utilization monitoring data; Resource allocation strategy data is optimized through resource utilization monitoring data, thereby generating high-frequency data resource configuration optimization data.

9. The method for constructing a high-frequency database of natural resources according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing allocation simulation on the high-frequency flow data of natural resources according to the high-frequency data resource configuration optimization data to generate resource allocation simulation result data; Step S42: performing abnormal allocation simulation analysis on the resource allocation simulation result data to generate abnormal resource allocation simulation data; performing hierarchical resource release based on the abnormal resource allocation simulation data to generate an abnormal resource hierarchical release strategy, wherein the abnormal resource hierarchical release strategy includes an abnormal resource error allocation strategy and an abnormal resource over-allocation strategy; Step S43: Design a database for the high-frequency flow data of natural resources through the abnormal resource classification release strategy and the resource allocation simulation result data, thereby generating a high-frequency database of natural resources.

10. The method for constructing a high-frequency database of natural resources according to claim 9, characterized in that: Step S42 includes the following steps: Step S421: performing abnormal allocation simulation analysis on the resource allocation simulation result data to generate abnormal resource allocation simulation data, wherein the abnormal resource allocation simulation data includes wrong allocation simulation data and resource over-allocation simulation data; Step S422: Perform resource allocation pre-interception on the resource allocation simulation result data according to the error allocation simulation data to obtain resource allocation interception data; perform resource allocation re-confirmation on the resource allocation interception data to obtain an abnormal resource error allocation strategy; Step S423: Analyze the degree of over-allocation of the resource over-allocation simulation data to generate resource over-allocation degree data; release excess resources from the resource allocation simulation result data based on the resource over-allocation degree data, thereby generating an abnormal resource over-allocation strategy.

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