Natural resource intelligent monitoring integrated system

Abstract

The invention relates to the technical field of natural resource monitoring, and discloses a natural resource intelligent monitoring integrated system, which comprises a data acquisition module used for acquiring data from various data sources, including but not limited to sensor data, remote sensing data, historical data and geographic information data; the data preprocessing and cleaning module is used for carrying out missing value processing, abnormal value detection and correction and data standardization and normalization on the data; and the data fusion module is used for performing space-time alignment and feature fusion on the data from different data sources to generate multi-dimensional fusion data. Through integration of multi-source data acquisition and intelligent analysis technologies, high-precision real-time monitoring of natural resources is realized, the system optimizes resource management through intelligent data fusion and predictive analysis, the decision support capability is improved, meanwhile, high availability and stability of the system are ensured by an intelligent fault diagnosis and automatic recovery mechanism, and the system is suitable for popularization and application. Therefore, the efficiency and reliability of natural resource monitoring are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of natural resource monitoring, and in particular to a natural resource intelligent monitoring integrated system. Background Art

[0002] With the rapid advancement of science and technology, the demand for monitoring modern natural resources is increasing. Traditional natural resource monitoring systems have been widely used in various fields. These systems primarily rely on various sensors, remote sensing equipment, and data acquisition devices, combined with manual operations to monitor and manage natural resources. Furthermore, with the continuous development of technologies such as big data, cloud computing, and the Internet of Things (IoT), an increasing number of natural resource monitoring systems are beginning to incorporate intelligent elements to improve monitoring efficiency, data accuracy, and decision-making support capabilities. However, while existing monitoring systems have improved the efficiency of natural resource management to a certain extent, they still face numerous technical challenges and are unable to meet the rapidly changing needs of natural resource monitoring.

[0003] Existing natural resource monitoring systems typically consist of decentralized data acquisition modules, processing modules, and monitoring platforms. These modules are highly independent of one another and lack effective mechanisms for data integration and information sharing. This is particularly true when processing multi-source, heterogeneous data. Problems such as untimely data collection, inefficient data fusion, and inaccurate predictions arise. Furthermore, most existing systems rely on manual data analysis and judgment, making them incapable of responding to emergencies and dynamically changing environmental conditions. Due to the decentralized system architecture and inefficient information flow, these systems often lead to delayed decision-making, wasted resources, and insufficient early warning of environmental issues. Therefore, using intelligent means to improve data integration capabilities, optimize decision support, and reduce the risk of system failures has become a major challenge in current technological development. Summary of the Invention

[0004] In response to the deficiencies of the prior art, the present invention provides a natural resource intelligent monitoring integrated system, which solves the deficiencies of the prior art natural resource monitoring system in multi-source data integration, real-time processing and intelligent analysis.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A natural resource intelligent monitoring integrated system, comprising:

[0006] A data acquisition module is used to collect data from various data sources, including but not limited to sensor data, remote sensing data, historical data and geographic information data;

[0007] Data preprocessing and cleaning module, used to process missing values, detect and correct outliers, and standardize and normalize the data;

[0008] The data fusion module is used to align the time and space of data from different data sources and fuse their features to generate multi-dimensional fused data;

[0009] Intelligent analysis and prediction module, used to perform trend prediction, anomaly detection, and change analysis on fused data, and provide decision support based on the analysis results;

[0010] A real-time data processing and feedback module, which is used to quickly process and provide feedback on real-time data through edge computing and stream data processing platforms in the system, in order to achieve real-time monitoring and management of natural resources;

[0011] The system management and monitoring module is used to monitor the system's operating status and ensure the stability of data storage, processing, analysis and feedback.

[0012] Preferably, the data acquisition module includes multiple sensors for real-time acquisition of meteorological parameters, soil moisture, meteorological data, water quality data and ecological environment related data.

[0013] Preferably, the data preprocessing and cleaning module adopts the following method:

[0014] Missing value processing: fill missing data through interpolation methods, including but not limited to KNN interpolation, linear interpolation, and regression interpolation;

[0015] Outlier detection and correction: Use the Z-score method or isolation forest algorithm to detect outliers;

[0016] Data standardization and normalization: Use the Z-score standardization method or the maximum and minimum normalization method to standardize the data.

[0017] Preferably, the data fusion module fuses data in the following manner:

[0018] Low-level fusion: Use weighted average or Bayesian method to fuse raw sensor data and remote sensing data. The specific formula is as follows:

[0019] Weighted average formula:

[0020]

[0021] Among them, X i represents the i-th type of data, w i is its corresponding weight, and satisfies ∑w i =1;

[0022] Feature layer fusion: Use principal component analysis or t-SNE method to reduce the dimension and fuse the features extracted from different data sources;

[0023] Decision-layer fusion: Use ensemble learning algorithms, such as random forest and XGBoost algorithms, to provide decision support for fused data and generate optimal decision results.

[0024] Preferably, the intelligent analysis and prediction module includes the following analysis methods:

[0025] Trend forecasting: Time series forecasting of natural resource change trends based on LSTM (Long Short-Term Memory) models or ARIMA models;

[0026] LSTM model formula:

[0027] h t =tanh(W h x t +U h h t-1 +b h )

[0028] Among them, h t is the hidden state at the current moment, x t is the input vector, W h ,U h is the weight matrix, b h is the bias term;

[0029] Anomaly detection: Use autoencoders or isolation forest algorithms to detect anomalies in resource monitoring data in real time.

[0030] Preferably, the real-time data processing and feedback module includes:

[0031] Edge computing: By deploying edge nodes near sensors to perform data preprocessing and anomaly detection, data transmission delays can be reduced.

[0032] Stream data processing: Use Apache Kafka and Apache Flink platforms to achieve real-time data stream transmission, processing and feedback.

[0033] Preferably, the system management and monitoring module includes:

[0034] Data storage and management module, used to efficiently store and manage multi-source data, ensuring long-term data preservation and access;

[0035] Performance monitoring module, which monitors the operating status of each system module in real time, including the performance of data collection, processing, analysis, and feedback links;

[0036] The decision support interface module provides an intuitive user interface that displays real-time monitoring data, analysis results, and optimization decision suggestions to support system operation and decision-making.

[0037] Preferably, the system is connected to external devices via a wireless communication network, which includes but is not limited to LoRa, NB-IoT, Wi-Fi, and 5G technologies.

[0038] Preferably, the system can realize distributed data storage and computing through a cloud computing platform, provide big data processing capabilities, and support system expansion and upgrading.

[0039] Preferably, the data preprocessing module, data fusion module, intelligent analysis and prediction module, real-time data processing and feedback module and system management and monitoring module are all interconnected through a unified API interface to ensure the scalability and modular design of the system.

[0040] The present invention provides an integrated system for intelligent monitoring of natural resources. It has the following beneficial effects:

[0041] 1. The present invention achieves comprehensive real-time monitoring of natural resources by integrating multi-source data from different sensors, remote sensing equipment and geographic information systems. Through intelligent data fusion technology, the system can effectively integrate different types of data and generate unified monitoring reports. This integration of multi-source data greatly improves the accuracy and reliability of the data, providing decision makers with a more comprehensive and scientific basis for resource management.

[0042] 2. Through intelligent fault diagnosis and automatic recovery mechanisms, the present invention can quickly detect and accurately locate faults or anomalies in various modules of the system, automatically start the corresponding recovery process, and reduce the need for human intervention. The system can continuously optimize its fault diagnosis capabilities when new faults occur through an adaptive model based on machine learning, and restore the normal operation of the system in a relatively short time, thereby ensuring the high availability and stability of the entire system.

[0043] 3. The present invention integrates intelligent analysis and prediction modules, combines historical data with real-time monitoring information, and can accurately predict the dynamic changes in natural resources. Through advanced machine learning algorithms, the system can not only analyze resource usage in real time, but also predict possible risks and shortages in the future, providing scientific decision-making support for resource management departments, especially in resource scheduling, environmental protection, and emergency response. It can significantly improve the timeliness and scientific nature of decision-making and reduce losses caused by resource waste or insufficient disaster warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the specification of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] Example:

[0047] Please see the attached Figure 1 The embodiment of the present invention provides a natural resource intelligent monitoring integrated system, including:

[0048] A data acquisition module is used to collect data from various data sources, including but not limited to sensor data, remote sensing data, historical data and geographic information data;

[0049] In this embodiment, the data acquisition module primarily collects various natural resource-related data in real time through a variety of sensors and external data sources. The data acquisition module is designed to ensure efficient and accurate acquisition of various natural resource information and provide reliable foundational data for subsequent data processing, analysis, and decision-making.

[0050] Sensors and external data sources for data collection

[0051] As an option, the data acquisition module of the present invention may include multiple sensors and data interfaces for real-time acquisition of various environmental data related to natural resources. Specifically, the data acquisition module includes but is not limited to the following types of sensors:

[0052] Meteorological sensors: These collect weather data such as temperature, humidity, air pressure, wind speed, and precipitation. These sensors provide real-time information on climate change and are particularly useful for monitoring agriculture, forestry, and water resources.

[0053] Soil sensors: These monitor soil moisture, pH, nutrient content, and other key indicators. Changes in soil quality directly impact crop growth, sustainable land use, and ecological environmental protection.

[0054] Water quality sensors: These sensors collect dissolved oxygen, pH, heavy metal concentrations, and other water quality data. They are widely used in water resource monitoring, river and lake pollution detection, and water quality assessment.

[0055] Remote sensing data sources: Satellite remote sensing technology is used to obtain wide-area surface information, such as the National Density Surface Vegetation Index (NDVI), water body changes, and soil moisture. This type of data is particularly suitable for large-scale natural resource monitoring and can provide dynamic monitoring in geographic space.

[0056] Historical data sources: The system can conduct in-depth analysis of changes in natural resources by using historical data to provide long-term trends and changes. Historical data includes meteorological records, land use changes, water quality monitoring records, etc.

[0057] Specifically, the data acquisition module uses a variety of sensors and data interfaces to acquire data from different sources in parallel and supports processing of different data formats. For example, sensor data can be digital or analog, while remote sensing data is typically images or multispectral data. Data from different sources is converted and transmitted using a unified protocol.

[0058] Working principle of data acquisition module

[0059] In one possible implementation, the data acquisition module connects sensor nodes and external devices to collect data from various sensors and data sources in real time, and transmits it to other system modules via network protocols (such as Wi-Fi, LoRa, NB-IoT, 5G, etc.). It should be noted that the data acquisition module can adjust the sampling frequency and data collection range in real time as needed to meet monitoring needs in different environments.

[0060] For example, when collecting meteorological data, meteorological sensors collect data at regular intervals (e.g., every hour) and wirelessly transmit the collected temperature, humidity, air pressure, and other data to a data processing module. Soil sensors may require higher sampling rates, especially in agricultural monitoring. To more accurately grasp soil moisture changes, soil moisture sensors may sample and transmit data every 10 minutes.

[0061] In one embodiment, the data acquisition module completes data transmission and acquisition through a serial communication protocol between the sensor and the central processing unit (CPU), such as I2C or SPI protocol. With this design, the system can obtain sensor data in real time and perform subsequent processing in a timely manner.

[0062] Sensor data preprocessing and quality control

[0063] In this embodiment, the data acquisition module not only collects raw data but also includes data preprocessing and quality control. By integrating functions such as missing value handling and outlier detection, the quality of the data input into the system is ensured. For example, during meteorological data collection, if a data collection session contains missing values ​​due to sensor failure, the data acquisition module will use an appropriate interpolation algorithm to fill in the missing values ​​to ensure data integrity.

[0064] Missing value handling: Specifically, when the data acquisition module detects missing sensor data, it can fill in the missing data using an interpolation algorithm. Common interpolation methods include KNN interpolation and linear interpolation. For example, in the KNN interpolation method, missing data points can be filled using their nearest known data points.

[0065]

[0066] Among them, X missing For missing data, X neighbor are the k known data points closest to the missing value data point.

[0067] Outlier detection and correction: During the data collection process, if there are obvious abnormal fluctuations in the data, such as sudden extreme values ​​in soil moisture data, the system will detect outliers in the data through statistical methods (such as Z-score or isolation forest algorithm) and make corrections as needed.

[0068] The Z-score method is as follows:

[0069]

[0070] Where X is the measured value, μ is the mean of the data set, and σ is the standard deviation. If the Z value exceeds a preset threshold (such as ±3), the data point is considered an outlier.

[0071] Synchronization and spatiotemporal alignment of data acquisition

[0072] To ensure effective analysis of data collected from diverse sources, the data collection module requires spatiotemporal alignment, particularly when integrating remote sensing data with ground-based sensor data. For example, remote sensing data often consists of satellite imagery or satellite remote sensing data acquired at regular intervals, while ground-based sensor data is collected instantaneously. To ensure data consistency and comparability, the system synchronizes all data types based on timestamps.

[0073] For example, when the system needs to combine meteorological sensor data with vegetation index data from satellite imagery for analysis, the system will match the timestamps of the meteorological sensor and the remote sensing data, converting them to the same time granularity. This process can be achieved through interpolation or resampling algorithms to ensure the consistency of spatiotemporal data.

[0074] Data transmission and storage

[0075] The data acquisition module transmits all collected data via an appropriate communication protocol (such as LoRa, NB-IoT, or 5G). In some embodiments, the data may be wirelessly transmitted to a cloud platform for storage and processing. In scenarios with high real-time requirements, the data may be directly transmitted to an edge computing device for preliminary processing. The transmitted data can be stored in an efficient distributed database for subsequent data analysis and model training.

[0076] In order to ensure the integrity and stability of the data, the data acquisition module in this embodiment can also be configured with a redundant storage mechanism. When the wireless network is interrupted, the data acquisition module can store the data in a local cache and upload it to the cloud platform or central processing system after the network is restored.

[0077] Extended implementation and optimization

[0078] It's understood that the data acquisition module can be customized to meet specific needs in practical applications. For example, in large-scale monitoring areas, the system can use low-power wireless sensor networks (such as LoRa or NB-IoT) for data transmission, thereby achieving large-scale coverage without the need for frequent battery replacement. In high-precision monitoring applications, the system can also add high-precision soil moisture sensors or high-frequency water quality sensors to meet the needs of high-precision data collection.

[0079] In summary, the data acquisition module in this embodiment can efficiently and accurately collect various natural resource monitoring data through the collaborative work of multiple sensors. In practical applications, the data acquisition module is not only responsible for data collection, but also includes multiple functions such as data preprocessing, missing value processing, outlier detection, and spatiotemporal synchronization to ensure data quality. The design of this module ensures the reliability and stability of the entire system in large-scale, high-frequency, and high-precision data acquisition tasks, providing a solid data foundation for subsequent data fusion, intelligent analysis, and decision support.

[0080] Data preprocessing and cleaning module, used to process missing values, detect and correct outliers, and standardize and normalize the data;

[0081] In this embodiment, the data preprocessing and cleaning module, a key component of the integrated natural resource intelligent monitoring system, is primarily responsible for cleaning, processing, and converting raw data collected from various sensors and external data sources. Its purpose is to improve data quality, remove redundancy and noise, and enable subsequent data analysis, prediction, and decision-making with greater accuracy and reliability. This module's design encompasses multiple functions, including handling missing data values, detecting and correcting outliers, and standardizing and normalizing data. The specific implementation is described below.

[0082] Missing value handling

[0083] In one possible implementation, the data preprocessing and cleaning module first performs missing value processing on the raw data. Since sensors may experience missing data due to malfunctions or other reasons during the data acquisition process, processing missing values ​​is an important step in data preprocessing.

[0084] As an option, this embodiment uses an interpolation algorithm to fill in missing data. Specifically, if a data point is missing, the system can fill it in using the following methods:

[0085] Linear interpolation: When the adjacent data points of the missing data are known, the linear interpolation algorithm is used to fill the gap. Linear interpolation assumes that the change between two known data points is linear. The calculation formula is as follows:

[0086]

[0087] Among them, X1 and X2 are the values ​​of known data points, t1 and t2 are the corresponding timestamps, and t missing The timestamp of the missing data.

[0088] KNN interpolation: When missing data is complex or multidimensional, the K-nearest neighbor (KNN) interpolation algorithm is used. Specifically, the system calculates the distance between the missing data point and other known data points, selects the K data points closest to the missing point, and fills the missing value by weighted average. The calculation formula is:

[0089]

[0090] Among them, X neighbor,i is the value of the ith neighboring data point, w i is the weight of the corresponding data point, w i is the number of neighboring points selected.

[0091] It should be noted that missing value processing is not limited to interpolation. Other methods can also be combined according to actual needs, such as those based on regression models or time series models. The choice of interpolation method can be determined based on the time interval of the data, the density of data points, and their changing trends.

[0092] Outlier detection and correction

[0093] During data preprocessing, outlier detection and correction are crucial steps. Outliers are data points that differ significantly from other data points. These may be caused by sensor failure, data transmission errors, and other factors. To ensure data quality, outlier detection must be performed promptly during data processing.

[0094] Specifically, this embodiment uses the following methods to detect and correct outliers:

[0095] Z-score method: When the data set is relatively simple, the Z-score method can be used to detect outliers. The Z-score method determines whether the data point deviates significantly from the mean by calculating the standard deviation of the data. The formula is:

[0096]

[0097] Where X is the data point to be detected, μ is the mean of the dataset, and σ is the standard deviation. If the calculated Z value exceeds the set threshold (for example, |Z| > 3), the data point is considered an outlier.

[0098] IQR (Interquartile Range): For datasets with asymmetric distributions, the IQR method is a more effective outlier detection method. The IQR is the difference between the third quartile and the first quartile of the dataset. The specific calculation formula is as follows:

[0099] IQR=Q3-Q1

[0100] Among them, Q1 and Q3 are the first quartile and the third quartile of the data set respectively. The upper and lower boundaries are calculated to determine whether it is an outlier. The specific boundaries are:

[0101] LowerBound=Q1-1.5×IQR, UpperBound=Q3+1.5×IQR

[0102] Data points outside this range are considered outliers.

[0103] For detected outliers, the system can handle them through different strategies, such as replacing them with the average value of neighboring data or correcting them using interpolation.

[0104] Data standardization and normalization

[0105] In order to ensure that data from different sources are compatible and can be effectively integrated, data standardization and normalization are necessary processing steps. Specifically, this embodiment uses the following two commonly used technologies for data preprocessing:

[0106] Standardization (Z-score standardization): Standardization converts data into a distribution with a mean of 0 and a standard deviation of 1. The standardization formula is:

[0107]

[0108] Where X is the data to be normalized, μ is the mean of the dataset, and σ is the standard deviation. Normalization is suitable for situations where the features in a dataset have different dimensions, so that the features can be compared on the same scale.

[0109] Normalization (Min-Max Normalization): Normalization is to scale the data to the [0,1] interval. The formula is:

[0110]

[0111] Among them, X min and X max are the minimum and maximum values ​​in the data set, respectively. Normalization is suitable when the relative sizes of the data are important.

[0112] In some embodiments, the data preprocessing module may also select different processing methods based on different data types. Specifically, for image data or time series data, more complex data processing methods may be used, such as PCA dimensionality reduction and Fourier transform techniques, to remove noise and extract key features.

[0113] Process of data preprocessing and cleaning module

[0114] In this embodiment, the workflow of the data preprocessing and cleaning module is as follows:

[0115] Data reception and transmission: The data acquisition module transmits the raw data to the data preprocessing module via wireless or wired means.

[0116] Missing value detection and interpolation: The system first detects missing values ​​in the data and uses appropriate interpolation methods to fill in the missing values ​​based on the type of missing values.

[0117] Outlier detection and correction: Next, the system performs outlier detection on the data and uses methods such as the Z-score method or the IQR method to mark and correct abnormal data.

[0118] Standardization and normalization: Finally, the system standardizes or normalizes the corrected data to ensure that the data is analyzed on a unified scale.

[0119] Extended technical content

[0120] It's understandable that the data preprocessing and cleaning modules can be customized based on actual needs. For example, when processing large-scale remote sensing data, more efficient dimensionality reduction techniques, such as principal component analysis (PCA) or independent component analysis (ICA), may be needed to remove redundant data and improve computational efficiency. For time series data, additional time series analysis methods (such as ARIMA models or LSTM neural networks) can also be considered to predict missing values ​​or correct outliers.

[0121] The data preprocessing and cleaning module in this embodiment cleans and transforms the raw data through multiple steps (missing value processing, outlier detection and correction, standardization and normalization) to ensure that the data input into the system has high quality and consistency. This module provides reliable basic data for subsequent data analysis, modeling, and decision-making. Through the optimization and processing of this module, the system can effectively improve the accuracy and reliability of natural resource monitoring.

[0122] The data fusion module is used to align the time and space of data from different data sources and fuse their features to generate multi-dimensional fused data;

[0123] In this embodiment, the data fusion module is a core component of the integrated natural resource intelligent monitoring system. Its primary task is to fuse multi-source data collected from multiple data sources (including sensor data, remote sensing data, historical data, etc.). By fusing this heterogeneous data, the data fusion module can provide more accurate and comprehensive assessments of natural resource status and provide high-quality data input for subsequent decision support systems. The specific technical implementation will combine the spatiotemporal characteristics of the data, the heterogeneity of the data sources, and the data fusion algorithm model to complete the processing and optimization of multi-source data.

[0124] Overall structure and working principle of data fusion module

[0125] As an option, the data fusion module adopts a hierarchical design, which is divided into data preprocessing layer, data registration layer, data fusion layer and result output layer. Specifically, the functions of each layer are as follows:

[0126] Data preprocessing layer: In this layer, the raw data from different sensors and data sources will first be preprocessed, including missing value filling, outlier detection and correction, standardization and normalization, etc., to ensure that all data are consistent and reliable before fusion.

[0127] Data registration layer: This layer aligns data from different sources in time and space. For example, remote sensing data (such as satellite imagery) is timestamped with ground sensor data (such as temperature and humidity) to ensure that the time and geographic space of different data sources are aligned for subsequent analysis.

[0128] Data fusion layer: This is the core layer of the data fusion module, which involves integrating processed multi-source data. Fusion methods include weighted averaging, Kalman filtering, Bayesian fusion, deep learning methods, etc. to obtain the optimal data representation.

[0129] Result output layer: Ultimately, the fused data will be output in a structured or graphical form, providing basic data support for subsequent intelligent analysis modules or decision support systems.

[0130] Technical Implementation of Multi-source Data Fusion

[0131] In one possible implementation, the data fusion module uses a weighted average method for simple fusion. Specifically, the credibility (or weight) of each data source can be set based on its accuracy, stability, and data type. Through the weighted average method, data information from different sources is weighted and aggregated according to the weights, resulting in a comprehensive fusion result.

[0132] Weighted average method: Assume that there are multiple data sources X1, X2, ..., X n , which have weights w1,w2,...,w n , the formula for weighted average fusion is:

[0133]

[0134] Among them, X fused is the fused data, w i For data source X i It should be noted that the weights can be set empirically or optimized through data analysis methods (such as least squares method and maximum likelihood estimation).

[0135] Kalman Filtering: In another embodiment, the Kalman Filtering method can be used to process the fusion of time series data, especially in the real-time data fusion of multiple sensors. The Kalman Filtering method is based on a dynamic system model and recursively estimates the optimal value of the state variable to fuse the data of different sensors, reduce the influence of noise, and optimize data accuracy. Specifically, the core formula of the Kalman Filter is:

[0136]

[0137] P k|k =(IK k H k )P k|k-1

[0138] in, Represents the estimated state, P k|k is the estimated error covariance, Kk is the Kalman gain, z k is the current measured value, H k is the observation matrix. Through the Kalman filter method, the data fusion process can be carried out in real time while taking into account the system dynamic changes and measurement errors.

[0139] Bayesian data fusion method: In some embodiments, the system can use the Bayesian method to perform data fusion, which is particularly suitable for situations with high uncertainty. The Bayesian method fuses multi-source data by calculating the posterior probability distribution of different data sources. Assume that there are two data sources D1 and D2, and their prior probabilities are P(D1) and P(D2) respectively. The posterior probability is calculated using the Bayesian theorem to obtain the fusion result. The Bayesian formula is:

[0140]

[0141] Where P(D1|X) is the posterior probability of D1 given the observation data X, P(X|D1) is the likelihood function, P(D1) is the prior probability, and P(X) is the marginal probability of the observation data.

[0142] Spatiotemporal alignment and data registration

[0143] Another key technology in the data fusion module in this embodiment is spatiotemporal alignment and registration. This process aligns the spatial coordinates and timestamps of spatiotemporal data from different sensors and data sources, allowing the data to be fused within the same spatiotemporal framework.

[0144] Timestamp alignment: For time series data, such as soil moisture data and remote sensing image data, the system needs to synchronize their timestamps to ensure time alignment across different data sources. Specifically, if the time interval between remote sensing data is large and the time interval between sensor data is short, the system can use interpolation to add time to the remote sensing data or resample the sensor data to match the time of the remote sensing data.

[0145] Spatial Registration: For spatial data, such as remote sensing images and geographic location data from ground sensors, the data fusion module needs to align spatial coordinates. Using registration algorithms (such as least squares registration and SIFT feature matching), different spatial data sources are aligned in the same coordinate system, allowing the spatial distribution information to be accurately fused.

[0146] Optimization and expansion of data fusion module

[0147] Understandably, the data fusion module may differ in different application scenarios. For example, in long-term monitoring of natural resources, the update frequency of sensor data and remote sensing data may vary. The data fusion module needs to be able to adaptively adjust the fusion strategy based on the actual situation.

[0148] In some embodiments, the data fusion module can also be combined with deep learning methods, such as using convolutional neural networks (CNN) to fuse multi-source remote sensing image data, or using long short-term memory networks (LSTM) to jointly model time series data, thereby improving the effect of data fusion.

[0149] The data fusion module in this embodiment effectively integrates data from different sensors and data sources through various techniques, including weighted averaging, Kalman filtering, and Bayesian methods. This module not only handles the registration and alignment of spatiotemporal data but also selects the optimal data fusion strategy for different application scenarios. Through this module, the system can obtain more accurate and comprehensive data on the status of natural resources, providing high-quality data input for subsequent intelligent analysis and decision support.

[0150] Intelligent analysis and prediction module, used to perform trend prediction, anomaly detection, and change analysis on fused data, and provide decision support based on the analysis results;

[0151] In this embodiment, the intelligent analysis and prediction module, a key component of the integrated natural resource intelligent monitoring system, primarily utilizes advanced data analysis, modeling, and prediction technologies to conduct in-depth analysis of processed data obtained from the multi-source data fusion module, uncovering underlying patterns and trends and providing decision support for natural resource management. This module incorporates a variety of technologies, including data mining, machine learning, and deep learning, aiming to achieve accurate monitoring, dynamic prediction, and optimized management of natural resources. The following details the operating principles and specific implementation of the intelligent analysis and prediction module in this embodiment.

[0152] Functional architecture of the intelligent analysis and prediction module

[0153] The intelligent analysis and prediction module in this embodiment includes four main functional modules: data feature extraction, modeling and analysis, prediction model training, and prediction result output. Specifically, the module workflow is as follows:

[0154] Data feature extraction: The multidimensional data obtained from the data fusion module needs to be subjected to feature extraction and dimensionality reduction processing to extract the most meaningful features for prediction.

[0155] Modeling and analysis: Through different modeling methods, the extracted features are modeled and analyzed to discover potential patterns, relationships and trends in the data.

[0156] Prediction model training: Use historical data and existing models for training, select appropriate prediction algorithms, and optimize the model's prediction accuracy.

[0157] Forecast result output: Output forecast results, provide future trend forecasts, risk assessments and possible decision-making recommendations.

[0158] Data feature extraction and dimensionality reduction

[0159] In this embodiment, data feature extraction and dimensionality reduction are key steps in the intelligent analysis and prediction module. Because natural resource monitoring may involve a large amount of heterogeneous data, feature extraction and dimensionality reduction not only help reduce data dimensionality and improve computational efficiency, but also effectively eliminate redundant features and noise. Specifically, this embodiment uses principal component analysis (PCA) and local linear embedding (LLE) for feature extraction and dimensionality reduction.

[0160] Principal Component Analysis (PCA): PCA is a common linear dimensionality reduction method that reduces the dimensionality of data by finding the direction with the largest variance in the data. Specifically, the goal of PCA is to linearly transform the original data set so that the newly generated features can explain the largest variance in the original data. Its mathematical representation is:

[0161] Z=XW

[0162] Where X is the original data matrix, W is the eigenvector matrix, and Z is the reduced-dimensional data. PCA calculates the covariance matrix and performs eigenvalue decomposition to obtain the most important components, thereby achieving data dimensionality reduction.

[0163] Local Linear Embedding (LLE): LLE is a nonlinear dimensionality reduction method suitable for cases where the data distribution has nonlinear characteristics. In certain natural resource monitoring tasks, where the relationships between data are nonlinear, LLE can effectively preserve the local structure of the data. The basic steps of LLE include calculating the local neighborhood of each data point, linearly reconstructing the points within the neighborhood, and then obtaining a low-dimensional embedding through an optimization process.

[0164] Modeling and Analysis

[0165] After data feature extraction and dimensionality reduction are completed, the next step is to model and analyze the data. In this embodiment, models such as support vector machines (SVM), decision trees, and neural networks are used for data analysis.

[0166] Support Vector Machine (SVM): Support Vector Machine is a powerful classification and regression method. In this embodiment, SVM can be used to perform classification or regression analysis on the extracted features, and is particularly suitable for handling high-dimensional nonlinear problems. The basic goal of SVM is to find a hyperplane that separates data points of different categories. Its optimization goal is:

[0167]

[0168] Among them, w is the normal vector of the hyperplane, b is the bias term, and the goal is to minimize the error and maximize the interval between categories.

[0169] Decision Tree: A common classification and regression method, the decision tree divides data into distinct regions using a tree-like structure. In this embodiment, the decision tree can be used for resource classification management, such as categorizing and analyzing different types of land resources. Decision tree construction relies on information gain or the Gini index to determine the optimal splitting feature. By gradually partitioning the data, a tree structure is generated to predict the status of different resources.

[0170] Neural Network (NN): A neural network is a machine learning method that mimics the neuronal structure of the human brain and can learn complex nonlinear relationships from large amounts of data. In this example, a deep neural network (DNN) is used to model nonlinear features, which is particularly suitable for processing large-scale and complex natural resource monitoring data. The basic training goal of a neural network is to adjust the network weights by minimizing an error function (such as mean squared error):

[0171]

[0172] Among them, E(W) is the loss function, y i is the actual value, f(X i ,W) is the predicted value, W is the weight of the network, and N is the number of samples.

[0173] Training and optimization of prediction models

[0174] In this embodiment, the intelligent analysis and prediction module trains and optimizes the prediction model using historical data. Specifically, model training uses cross-validation to evaluate the performance of different models and select the optimal hyperparameters and algorithm model. Cross-validation divides the dataset into multiple subsets, training and validating on each subset to avoid overfitting or underfitting the model.

[0175] During training, you can use gradient descent to optimize model parameters. This method calculates the gradient of the loss function with respect to the model parameters and gradually updates the parameters until the loss function converges. For neural network models, you can also use the backpropagation algorithm to update parameters.

[0176] Gradient descent method: The update formula of the gradient descent method is:

[0177]

[0178] Among them, w (t)is the current parameter, η is the learning rate, is the gradient of the loss function.

[0179] Output of prediction results

[0180] After training, the prediction model is applied to real-time data to predict future trends. For example, for climate change forecasting, the model can predict future temperature trends based on historical climate data. The prediction results are output in visual formats such as trend charts and risk assessment reports, helping decision makers respond to dynamic changes in natural resources.

[0181] Extended technical content

[0182] It is understood that in addition to the classic machine learning methods described above, the intelligent analysis and prediction module in this embodiment can also incorporate deep reinforcement learning methods to achieve adaptive prediction. For example, in a resource management system, deep reinforcement learning can continuously adjust the prediction model through reward mechanisms and policy optimization, improve prediction accuracy, and adjust management policies in real time. Deep reinforcement learning can not only handle time series predictions, but also perform policy optimization in complex decision-making environments.

[0183] The intelligent analysis and prediction module in this embodiment uses a variety of machine learning algorithms, such as support vector machines, decision trees, and neural networks, to conduct in-depth analysis and modeling of multi-source data, uncovering underlying patterns within the data and training prediction models based on historical data. This module enables the system to accurately predict the status of natural resources, providing strong support for resource management and decision-making.

[0184] A real-time data processing and feedback module, which is used to quickly process and provide feedback on real-time data through edge computing and stream data processing platforms in the system, in order to achieve real-time monitoring and management of natural resources;

[0185] In this embodiment, the real-time data processing and feedback module is a key component of the integrated natural resource intelligent monitoring system. Its primary function is to process the raw and fused data acquired from the data acquisition and data fusion modules in real time and provide rapid feedback based on the processing results. This module provides the necessary data support for the system's real-time monitoring, early warning, and decision-making support. Through real-time analysis, it can provide relevant personnel with rapid response instructions or decision-making information. The following describes the technical implementation and operating principles of this module in detail.

[0186] Functional architecture of real-time data processing and feedback module

[0187] The real-time data processing and feedback module in this embodiment mainly includes three core functional modules: data reception, data processing, and real-time analysis and feedback. Each functional module works closely together to ensure that the entire system can respond to changes in the status of natural resources efficiently and in real time.

[0188] Data Reception: This module is responsible for receiving data from various sources, including the Data Acquisition and Data Fusion modules. This data can include real-time monitoring data from sensors, imagery from remote sensing platforms, or real-time data from other external data sources. Data reception must be efficient and accurate, and capable of supporting large-scale concurrent data access.

[0189] Data Processing: The data processing module is responsible for cleaning, converting, and preprocessing the raw data received to ensure that the data is usable before further analysis. This step includes, but is not limited to, data denoising, data standardization, and data format conversion.

[0190] Real-time Analysis and Feedback: After data processing is complete, the system analyzes the data in real time based on pre-set analytical models to detect possible anomalies, trend changes, or other key indicators. When an abnormal event is detected, the system responds quickly, generating feedback and immediately disseminating it to users or relevant personnel. This feedback can include alerts, risk warnings, and decision-making recommendations.

[0191] Data reception and real-time processing

[0192] In this embodiment, the data receiving module first establishes a connection with a data source (such as a sensor, monitoring platform, external data service, etc.) through the system's communication protocol (such as MQTT, HTTP, or WebSocket). It should be noted that the data source can be a variety of devices and platforms, and the types and formats of these data sources may vary. Therefore, the data receiving module must be highly compatible and flexible and able to adapt to the different communication methods of various data sources.

[0193] The system needs to perform immediate format conversion and standardization on data received from various data sources. For example, the system may need to convert raw analog signals into digital signals or convert different units (such as temperature or humidity) into a unified format. This step ensures that the system can handle heterogeneous data from different sources and ensures data consistency and accuracy.

[0194] Data cleaning is also crucial in real-time data processing. In some embodiments, the system can use a rule-based approach to remove invalid or abnormal data. Abnormal data detection can be based on simple thresholding methods or more complex statistical methods, such as standard deviation or quantile methods, to detect whether data falls outside the normal range.

[0195] Real-time analysis and decision feedback

[0196] Specifically, the data analysis component primarily relies on pre-trained prediction or decision models to perform online analysis of real-time data. These models have been trained and optimized in previous steps and are capable of providing accurate analysis results based on the input real-time data. In practical applications, these analysis models can cover a variety of fields, such as environmental monitoring, weather forecasting, and hydrological analysis.

[0197] Real-time trend analysis: For example, the system can compare historical data with real-time data to calculate the trend of current resource status. For example, in land resource monitoring, the system can analyze soil moisture, temperature, precipitation, and other data in real time to predict soil moisture trends over the next few hours and provide irrigation recommendations to farmers or agricultural managers based on these trends.

[0198] Anomaly Detection and Alerts: When the system detects data outside the preset normal range or an abnormal pattern during real-time analysis, it triggers an alert mechanism. For example, in a forest resource monitoring system, if sensor data indicates a sharp rise in temperature in a certain area, the system may determine that there is a fire risk in the area and immediately notify relevant personnel through the early warning system.

[0199] Decision Support: It's important to note that the real-time data processing module isn't limited to simple anomaly alerts; it can also provide decision support for relevant personnel. For example, in climate change forecasting, the system can predict future weather patterns based on real-time climate data and provide response strategy recommendations to the government or relevant departments based on the forecast results.

[0200] Feedback Mechanism and User Interface

[0201] The feedback mechanism is another key component of this implementation. When the system detects anomalies, trend changes, or other events requiring attention, the feedback mechanism is activated immediately. The content of this feedback can vary depending on the specific application scenario, but its core purpose is to promptly inform decision makers and provide further response recommendations.

[0202] Alert Notification: For example, in natural disaster monitoring, the system can immediately deliver disaster warning information to relevant personnel via SMS, email, and app push notifications. Furthermore, the system can integrate alert information with maps using a geographic information system (GIS), helping users more clearly understand the location and impact of an event.

[0203] Real-time Feedback Display: Through an integrated graphical user interface (GUI), the system presents analysis results to users in real time. For example, real-time data such as soil moisture and temperature can be displayed on a dynamic dashboard, allowing users to clearly understand current resource conditions. This interface also includes various control panels, allowing users to perform further operations or adjustments when anomalies occur.

[0204] Extended technical content

[0205] It is understood that in addition to the basic real-time data processing and feedback functions described above, this embodiment can also utilize more advanced technologies to enhance the intelligence and automation of the system. For example, the introduction of edge computing can effectively reduce data transmission delays and bring data processing closer to the data source, improving the system's response speed and reliability. Through edge computing, sensors and processing devices can perform preliminary data processing locally, and only important data that has been screened and analyzed is transmitted to the central system for further processing and feedback.

[0206] Furthermore, with the continuous advancement of artificial intelligence (AI) technology, real-time data processing and feedback modules can also be combined with machine learning algorithms to further optimize data processing and feedback mechanisms. For example, deep learning-based models can identify and classify complex patterns in sensor data in real time, further improving the accuracy and reliability of early warnings.

[0207] The real-time data processing and feedback module in this embodiment achieves real-time response to natural resource monitoring through an efficient data reception, processing, analysis, and feedback mechanism. In this process, the module provides dynamic prediction, anomaly detection, and decision support based on real-time data changes, thus playing a vital role in natural resource management. Furthermore, the system can rapidly transmit important information to relevant personnel through a flexible feedback mechanism, ensuring rapid response and efficient execution of emergency responses.

[0208] The system management and monitoring module is used to monitor the system's operating status and ensure the stability of data storage, processing, analysis and feedback.

[0209] In this embodiment, the system management and monitoring module is a crucial component of the integrated natural resource intelligent monitoring system, responsible for overall system management, monitoring, and fault diagnosis. This module not only ensures the system's daily operations and data flow stability, but also provides functions such as system health monitoring, permissions management, logging, and fault alarms to ensure the overall system's efficiency, stability, and security. Through this module, administrators can achieve centralized control and real-time monitoring of the system, effectively improving its maintainability and reliability. The following describes in detail the operating principles and specific implementation of the system management and monitoring module in this embodiment.

[0210] Functional architecture of the system management and monitoring module

[0211] The system management and monitoring module in this embodiment mainly includes core functional modules such as system status monitoring, fault diagnosis and recovery, authority management, log management and security monitoring, etc. Each functional module works closely together to ensure the normal operation of the system.

[0212] System status monitoring: This functional module is used to monitor the operating status of each sub-module of the system (such as data acquisition module, data processing module, data fusion module, analysis and prediction module, etc.) in real time to ensure that all functions of the entire system run stably and smoothly.

[0213] Fault diagnosis and recovery: The system management and monitoring module can detect and diagnose faults in a timely manner when they occur, identify the cause of system anomalies or performance degradation, and automatically recover or provide operator intervention solutions based on the diagnosis results.

[0214] Permission Management: This module provides different permission management functions for users with different roles to ensure system access control. Specifically, administrators can use this module to set user access rights, operation rights, data viewing rights, etc. to ensure system security.

[0215] Log management: By recording logs of system operations, data flows, fault events, etc., the system management and monitoring module provides necessary data support for subsequent troubleshooting, performance optimization, and audit tracking.

[0216] Security monitoring: To ensure the security of the system, the system management and monitoring module in this embodiment integrates a security monitoring mechanism that can detect the security status of the system in real time and prevent unauthorized access and malicious attacks.

[0217] System status monitoring

[0218] The system status monitoring module in this embodiment dynamically updates the overall health of the system by collecting real-time operational status data from each submodule, including CPU, memory usage, network traffic, storage space, data transfer speed, and other indicators. These indicators are displayed in real-time charts, helping administrators quickly identify system performance bottlenecks or potential problems.

[0219] Monitoring content: System status monitoring includes but is not limited to the following:

[0220] Hardware resource monitoring: By regularly obtaining status information of system hardware (such as servers, storage devices, etc.), monitor whether there are any hardware anomalies, such as insufficient disk space, CPU overload, etc.

[0221] Software operation monitoring: Real-time inspection of the operating status of each module of the system to detect whether each module is operating normally, and whether there are any overloads, crashes, or stops responding.

[0222] Data flow monitoring: Monitor the flow rate, latency, etc. during data transmission to ensure a smooth process from data collection to feedback, and promptly identify any problems that may cause data loss or delay.

[0223] In some embodiments, the system status monitoring module may also set a threshold warning mechanism. Once certain indicators exceed a predetermined threshold, the system will automatically issue a warning to notify the administrator to take action.

[0224] Fault diagnosis and recovery

[0225] If a fault or anomaly occurs during system operation, the system management and monitoring module will initiate fault diagnosis. This function quickly locates the cause of the fault by analyzing system logs, resource usage, and historical data on fault events. For example, if a module becomes unresponsive for an extended period, the system can analyze the module's logs and data flow status to determine whether it is caused by a hardware failure, software crash, or network issue.

[0226] Fault recovery can be divided into two modes:

[0227] Automatic recovery: When the system detects a fault, it automatically performs recovery operations according to the preset recovery strategy, such as restarting the faulty module, reloading the configuration file, starting the backup server, etc.

[0228] Manual intervention: When the system cannot recover automatically, the administrator can view the fault details through the management interface and manually perform repair operations.

[0229] It should be noted that in this embodiment, the system management and monitoring module can provide detailed fault diagnosis information for different types of faults (such as hardware failure, software crash, network failure, etc.) and give repair suggestions to reduce fault response time and recovery time.

[0230] Permission Management

[0231] The rights management module in this embodiment ensures system security and data privacy by assigning different rights to different users. Specifically, the system administrator can define different operating rights and access levels for each user role, including but not limited to the following types of rights:

[0232] System administrator privileges: Has the highest authority to manage the entire system and can perform operations such as system configuration, module configuration, data viewing, and fault recovery.

[0233] Ordinary user permissions: can only access some of the system functions, such as viewing monitoring data, generating reports, etc., but cannot modify system configurations or perform fault repairs.

[0234] Visitor privileges: Only have browsing capabilities and cannot perform any operations.

[0235] System permission management ensures that only authorized personnel can access or modify key data and settings, thus avoiding unauthorized access and potential data leakage.

[0236] Log Management

[0237] In this embodiment, the log management module is mainly responsible for recording information such as system operations, data access, fault events, alarm events, etc. All log records will be stored in a certain format and can be queried and filtered by time, event type, operator, etc.

[0238] Operation log: records all operations of the system administrator and ordinary users on the system, including data viewing, configuration modification, module start and stop, etc.

[0239] Fault log: Detailed record of the system status, fault type, diagnostic information, and recovery status when the fault occurs, to facilitate later analysis.

[0240] Security log: records all security events, such as user login, logout, permission changes, unauthorized access, etc.

[0241] Through centralized management and auditing of log data, the system management and monitoring module can effectively track every event in system operation, help administrators quickly locate problems when failures occur, and provide data support for system optimization and upgrades.

[0242] Security Monitoring

[0243] Within the System Management and Monitoring module, security monitoring is a key function in protecting the system from external attacks and internal abuse. The security monitoring module detects potential security threats in the system in real time, such as unauthorized access, data leaks, and malicious attacks, and responds and addresses them promptly.

[0244] The security monitoring functions in this embodiment may include:

[0245] Intrusion Detection System (IDS): monitors network traffic and detects abnormal access behaviors such as SQL injection and cross-site scripting attacks.

[0246] Authentication and authorization: Real-time monitoring of the user's login process ensures that only authorized users can access the system and prevents accounts from being maliciously tampered with or abused.

[0247] Data encryption: During data transmission and storage, encryption algorithms are used to ensure data security and prevent data from being stolen or tampered with during transmission.

[0248] It is understandable that the security monitoring function of the system management and monitoring module not only provides monitoring of the current security status, but also provides early warning of possible future security threats through analysis of historical data.

[0249] Extended technical content

[0250] Alternatively, the system management and monitoring module in this embodiment can be integrated with a cloud platform to create a more flexible and efficient remote management and monitoring system. By migrating some system resources and functions to the cloud, administrators can remotely operate and monitor the system from anywhere via the internet, viewing the system's operating status, configuration, and log data in real time, greatly improving system accessibility and ease of management.

[0251] Furthermore, with the advancement of artificial intelligence and machine learning technologies, future system management and monitoring modules will be able to incorporate intelligent algorithms to automatically identify abnormal patterns and optimize themselves. By learning from large amounts of historical data, the system can foresee potential problems and proactively prevent them, further enhancing the system's intelligence.

[0252] The system management and monitoring module in this embodiment enables comprehensive management and control of the integrated natural resource intelligent monitoring system through multiple functions, including real-time monitoring of system operation status, fault diagnosis and recovery, permissions management, log management, and security monitoring. This module enables administrators to efficiently monitor system operation status and promptly identify and resolve potential system issues, thereby ensuring the security, stability, and efficiency of the entire system.

[0253] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A natural resource intelligent monitoring integrated system, characterized in that: include: A data acquisition module is used to collect data from various data sources, including but not limited to sensor data, remote sensing data, historical data and geographic information data; Data preprocessing and cleaning module, used to process missing values, detect and correct outliers, and standardize and normalize the data; The data fusion module is used to align the time and space of data from different data sources and fuse their features to generate multi-dimensional fused data; Intelligent analysis and prediction module, used to perform trend prediction, anomaly detection, and change analysis on fused data, and provide decision support based on the analysis results; A real-time data processing and feedback module, which is used to quickly process and provide feedback on real-time data through edge computing and stream data processing platforms in the system, in order to achieve real-time monitoring and management of natural resources; The system management and monitoring module is used to monitor the system's operating status and ensure the stability of data storage, processing, analysis and feedback.

2. A natural resource intelligent monitoring integrated system according to claim 1, characterized in that: The data acquisition module includes multiple sensors for real-time acquisition of meteorological parameters, soil moisture, meteorological data, water quality data and ecological environment related data.

3. The natural resource intelligent monitoring integrated system according to claim 1, characterized in that: The data preprocessing and cleaning module adopts the following method: Missing value processing: fill missing data through interpolation methods, including but not limited to KNN interpolation, linear interpolation, and regression interpolation; Outlier detection and correction: Use the Z-score method or isolation forest algorithm to detect outliers; Data standardization and normalization: Use the Z-score standardization method or the maximum and minimum normalization method to standardize the data.

4. The natural resource intelligent monitoring integrated system according to claim 1, characterized in that: The data fusion module fuses data in the following ways: Low-level fusion: Use weighted average or Bayesian method to fuse raw sensor data and remote sensing data. The specific formula is as follows: Weighted average formula: Among them, X i represents the i-th type of data, w i is its corresponding weight, and satisfies ∑w i =1; Feature layer fusion: Use principal component analysis or t-SNE method to reduce the dimension and fuse the features extracted from different data sources; Decision-making layer fusion: Use ensemble learning algorithms, such as random forest and XGBoost algorithms, to provide decision support for fused data and generate optimal decision results.

5. The natural resource intelligent monitoring integrated system according to claim 1, characterized in that: The intelligent analysis and prediction module includes the following analysis methods: Trend forecasting: Time series forecasting of natural resource change trends based on LSTM (Long Short-Term Memory) models or ARIMA models; LSTM model formula: h t =tanh(W h x t +U h h t-1 +b h ) Among them, h t is the hidden state at the current moment, x t is the input vector, W h ,U h is the weight matrix, b h is the bias term; Anomaly detection: Use autoencoders or isolation forest algorithms to detect anomalies in resource monitoring data in real time.

6. The natural resource intelligent monitoring integrated system according to claim 1, characterized in that: The real-time data processing and feedback module includes: Edge computing: By deploying edge nodes near sensors to perform data preprocessing and anomaly detection, data transmission delays can be reduced. Stream data processing: Use Apache Kafka and Apache Flink platforms to achieve real-time data stream transmission, processing, and feedback.

7. The natural resource intelligent monitoring integrated system according to claim 1, characterized in that: The system management and monitoring module includes: Data storage and management module, used to efficiently store and manage multi-source data, ensuring long-term data preservation and access; Performance monitoring module, which monitors the operating status of each system module in real time, including the performance of data collection, processing, analysis, and feedback links; The decision support interface module provides an intuitive user interface that displays real-time monitoring data, analysis results, and optimization decision suggestions to support system operation and decision-making.

8. The natural resource intelligent monitoring integrated system according to claim 1, characterized in that: The system is connected to external devices via a wireless communication network, which includes but is not limited to LoRa, NB-IoT, Wi-Fi, and 5G technologies.

9. The natural resource intelligent monitoring integrated system according to claim 1, characterized in that: The system can realize distributed data storage and computing through the cloud computing platform, provide big data processing capabilities, and support system expansion and upgrading.

10. The natural resource intelligent monitoring integrated system according to claim 1, characterized in that: The data preprocessing module, data fusion module, intelligent analysis and prediction module, real-time data processing and feedback module and system management and monitoring module are all interconnected through a unified API interface to ensure the scalability and modular design of the system.

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