328 results about "Natural resource" patented technology

The invention provides an intelligent management method for natural resource engineering data, and relates to the technical field of data processing, and the method comprises the steps: inputting a time-space coupling model into real-time weather forecast data, simulating water distribution schemes under different rainfall scenes, automatically recognizing a river reach position of which the ecological flow is lower than a design threshold value, and carrying out the calculation of the river reach position. In combination with a historical water replenishing strategy case library, generating a multi-objective optimization scheduling scheme including a water replenishing time sequence, a water replenishing path and a water replenishing amount; three monitoring points are arranged at a downstream section of an ecological protection area, a flood control section and a canal head of an irrigation area in a drainage basin, and ecological flow, flood control section water level and canal water level data are monitored in real time respectively; and dynamically correcting the water supplementing time sequence, the water supplementing path and the water supplementing amount in the multi-objective optimization scheduling scheme to obtain a corrected scheduling scheme. According to the method, refinement and toughness of drainage basin water resource management can be improved.

The invention relates to the technical field of natural resource monitoring and spatial information processing, in particular to a method and a system for feeding back land utilization change based on a land spatio-temporal model. The method comprises the following steps: deploying multi-source land monitoring equipment, carrying out collaborative data acquisition and standardization processing, and constructing a land space-time reference data set; performing triple mapping on the land space-time reference data set to obtain a land semantic association graph; constructing a land utilization knowledge graph based on the land semantic association graph; constructing a land change detection initial model by using the land utilization knowledge graph; meanwhile, in a high-frequency change scene, such as an urban and rural ecologic zone or an ecological sensitive area, a traditional model is slow in response to short-term land utilization disturbance, automatic adjustment cannot be carried out through deviation feedback between historical errors and model output, and the reliability of the model in actual application scenes such as resource regulation and control is limited.

The invention discloses a natural resource dynamic monitoring system and method based on a multi-source remote sensing image. The system comprises a multi-source data acquisition module, a preprocessing module, a feature extraction module, a dynamic monitoring module, a visual output module and a distributed storage module. According to the method, the precision and timeliness of natural resource change detection are remarkably improved by constructing a multi-source remote sensing image intelligent fusion framework. The system adopts a parallelization preprocessing assembly line to effectively eliminate radiation and geometric differences among multi-source data; a feature fusion module based on deep learning automatically extracts multi-scale change features, and overcomes the environmental adaptability defect of a traditional threshold method; and the dynamic monitoring module realizes automatic extraction and classification of sub-meter change pattern spots, and the monitoring efficiency is improved. Meanwhile, the system supports collaborative analysis of multi-platform data of satellites, aviation, unmanned aerial vehicles and the like, the cloud shielding influence is greatly reduced, and total-factor and high-frequency decision support is provided for natural resource supervision.

The invention provides a natural resource analysis method and system combined with multi-source data, and the method comprises the steps: firstly obtaining a multi-source data set of remote sensing images, geographical monitoring, environment monitoring and the like of a target region, and then carrying out the spatial-temporal feature extraction processing of the multi-source data set, thereby obtaining a resource spatial distribution feature set and a time change feature set; according to the method, features of resources under different time-space dimensions are accurately described, then, based on a preset dynamic association rule set, dynamic association processing is performed on a resource space distribution feature set and a resource time change feature set, a resource dynamic association relationship set is generated, and interaction and evolution rules among the resources are revealed. According to the resource dynamic association relationship set, a resource state evaluation strategy is generated, a resource management optimization direction is determined, finally, the resource management optimization direction is fed back to a resource management system, resource scheduling operation is triggered, scientific, accurate and dynamic management of natural resources is achieved, and resource management efficiency and sustainability are improved.

The invention discloses a multi-dimensional natural resource intelligent monitoring method and system based on big data analysis, and relates to the technical field of resource monitoring, and the method comprises the steps: calculating the information sharing degree between different data streams, constructing an abnormal feature credibility distribution map, and carrying out the calculation of the abnormal feature credibility distribution map; and inputting the abnormal feature credibility distribution graph and the multi-source auxiliary information into an integrated classifier, carrying out joint analysis and comprehensive research and judgment on multi-dimensional information to output a target list, constructing an event response relation graph reflecting a monitoring task priority and an execution logic relation according to the attribute features of each element in the target list, and carrying out event response analysis on the monitoring task priority and the execution logic relation. Wherein each node represents a to-be-monitored target, an edge represents a dependency relationship between tasks, and an optimal monitoring execution path is planned for various intelligent monitoring carriers by using an improved A algorithm integrated with a multi-factor cost function. Through natural resource monitoring of multi-modal data interference correction, intelligent classification and path optimization and dynamic response, the monitoring quality and the execution efficiency are effectively improved.

The invention relates to the field of data traceability, and particularly discloses a natural resource data dynamic monitoring traceability method and system, and the method comprises the steps: building a multi-tense database, automatically writing real-time data into a corresponding partition according to a timestamp, and carrying out the incremental updating of a workflow; data confidence coefficient weights are introduced into SQL screening statements according to attribute tracing logic, and data legality is judged through weighted logic; constructing a land class evolution state transition matrix, determining a legal change path and an illegal path, and carrying out path deduction on attribute data missing to judge whether the attribute data missing is illegal change or not; and detecting land type mutation according to NDVI index change of a target remote sensing image, forming a three-dimensional tracing database of attribute data, space evidence and business files according to the attribute data and the spatial relationship, and dynamically monitoring and tracing natural resource data. The problems that the legal source of the construction land cannot be accurately traced due to land judgment errors and real-time data analysis and traceability are difficult in the prior art are solved.

The invention discloses a real-time multi-dimensional sensitivity evaluation and self-adaptive safety prevention and control method and system for natural resource geographic information. The method comprises the following steps: acquiring real-time natural resource geographic information data; obtaining a preset behavior graph model; updating a preset behavior graph model according to the real-time natural resource geographic information data so as to obtain an updated behavior graph model; acquiring an updated node risk vector according to the updated behavior graph model; obtaining a trained Bayesian risk prediction model; inputting the updated node risk vector into a trained Bayesian risk prediction model so as to obtain a real-time abnormal behavior identification result; and generating a personalized prevention and control strategy scheme according to the real-time abnormal behavior recognition result. According to the method, intelligent identification, dynamic evaluation and active protection of natural resource geographic information in a full life cycle are realized by constructing a multi-dimensional sensitivity quantitative model, a dynamic risk perception mechanism based on a graph structure and a safety prevention and control strategy capable of being updated in real time.

The invention discloses a detection system for natural land resources, and relates to the technical field of resource detection, the detection system comprises a data preprocessing module, a fusion space-time diagram construction module, a multi-target enhancement module and a feedback module, an entropy law is combined with a semantic space-time diagram index, quantitative modeling of an ecological law is realized, and the real-time performance of the system is improved. Natural-human collaborative intervention simulation under multiple time scales, time-space semantic query response time improvement, prediction precision improvement and interdisciplinary fusion are supported, the entropy law of ecological economics is combined with time-space diagram index, the limitation that a traditional model only pays attention to physical indexes is broken through, ecological-economic balance is achieved through an entropy weight method and Monte Carlo simulation, and the prediction precision is improved. According to the method, self-adaptive decision making is supported, a perception-decision-execution integrated platform is constructed based on the real-time updating capability of space-time diagram indexes, a full-chain solution from data collection to dynamic optimization can be provided for natural resource intelligent management, a prediction model is combined with entropy increase constraint, and the ecological economic cost is quantified.

The invention provides a natural resource multi-source heterogeneous data analysis method and system, and the method comprises the steps: carrying out the extraction and recognition of obtained natural resource multi-source heterogeneous data, and obtaining structural features and semantic features; constructing a multi-dimensional metadata mapping relationship based on the structural features and the semantic features, and performing association mapping on the multi-source heterogeneous data based on the metadata mapping relationship to obtain a metadata knowledge graph; performing semantic alignment on the multi-source heterogeneous data based on the metadata knowledge graph to obtain semantic unified data; performing semantic information and structure information analysis on the semantic unified data, determining a data structure conversion strategy, and converting the multi-source heterogeneous data into target space-time grid data based on the data structure conversion strategy; and analyzing the type characteristics of the target space-time grid data to obtain an analysis result, and determining an optimal processing mode based on the analysis result. According to the invention, the calculation speed and the accuracy of an analysis result are improved.

The invention discloses a natural resource investigation monitoring system based on artificial intelligence, and the system comprises a data sensing layer which carries out the whole space-time coverage monitoring of mountain field water resources through a multi-source remote sensing device and an edge calculation node; automatically adjusting the weight of each data source by adopting a dynamic weight multi-modal fusion algorithm; according to the data fusion layer, original data, a processing flow and a model result are encrypted and stored through block chain nodes, data conflicts are detected through a graph neural network, and conflict marks are generated in combination with policy rules; verifying data consistency by a dynamic conflict detection algorithm; according to the intelligent analysis layer, natural resource, forest grass and water conservancy departments locally train exclusive models, and a knowledge-driven reasoning module generates analysis results in combination with geographic knowledge maps and policy rules. According to the application service layer, a three-dimensional space-time model integrates remote sensing data, an Internet of Things sensor and GIS information, the consistency of analysis results and policies is automatically verified through a rule engine, and law enforcement suggestions are generated.

The invention discloses an intelligent fusion processing method and system for forest and grass wet resource pattern spots and annual land change survey data, and belongs to the field of space-time big data in the natural resource industry, and the method comprises the steps: completing the pattern layer fusion based on the annual land change survey data in combination with forest and grass wet pattern spot survey results; superposing county and rural boundaries to form an administrative fusion layer; performing subcompartment division based on the remote sensing image; merging broken classes according to a principle of adjacent positions and similar types; eliminating a pattern spot sharp angle; land classes are refined, and attribute information is perfected; the public welfare forest range and the protection level are optimized; automatically checking and summarizing to form a result layer; and finally performing statistics to generate various thematic reports. According to the method, technologies such as database storage, spatial index construction and a fusion algorithm are adopted, the data processing efficiency is improved by more than 50%, and the sharp angle processing time is shortened; and the labor cost and the time investment are obviously reduced.

The invention discloses a map database-based land type map spot storage and analysis method and system. The method comprises the following steps of: acquiring and processing data; storing data; monitoring pattern spot type distribution analysis; performing annual change investigation pattern spot and land type change analysis; and reading and displaying data. According to the method, spatial analysis is carried out on land type change monitoring pattern spots and annual change investigation pattern spots, and a method based on a pattern database is designed to store analysis results; the incidence relation and important attributes of the change monitoring pattern spots and the annual change pattern spots can be expressed on a visual platform, and the monitoring type distribution condition and the annual change survey of the change conditions of different land types in a certain space range are analyzed; according to the storage and analysis method, the internal relation between the change monitoring pattern spots and the annual change pattern spots is visually presented, the effectiveness of change monitoring pattern spot analysis is improved, the data value of the monitoring pattern spots is deeply mined, natural resource data analysis and application are facilitated, and planning and decision making of related departments of the natural resource industry are supported.

The invention belongs to the technical field of hydraulic systems, and discloses a drive-by-wire full-hydraulic system and method and a steering method. Wherein the drive-by-wire full-hydraulic system comprises an integrated control valve module, the integrated control valve module is connected with a hydraulic oil tank, an oil filter and an electric control full-hydraulic steering gear module, the electric control full-hydraulic steering gear module is connected with a control module and a wheel angle sensor, and the hydraulic oil tank is sequentially connected with an electronic oil pump and the oil filter; according to the system, hydraulic oil leakage can be effectively prevented, the safety risk is remarkably reduced, natural resources and the ecological environment are protected, consistency and synchronization of actual steering and steering required by control are achieved, steering controllability is improved, and system safety is further improved.

The invention provides a natural resource element monitoring method and system based on a scene event camera, and the method comprises the steps: collecting a target video image through the scene event camera, and obtaining the camera parameters of the scene event camera; obtaining video spatialization model construction parameters based on a preset observer viewpoint model and a geographic registration model; based on the trained engineering vehicle target identification model, obtaining a first coordinate system coordinate of the engineering vehicle in the target video image, and converting the first coordinate system coordinate of the engineering vehicle into a fourth coordinate system coordinate by using the video spatialization model construction parameter; fourth coordinate system data of the natural resource typical elements are obtained, and state changes of the natural resource typical elements are monitored in combination with the event judgment model. According to the invention, natural resource typical element monitoring based on the scene event camera is realized, rapid and accurate perception of typical element changes of cultivated land and the like is realized, and time and economic costs generated by manual visual monitoring are reduced.

The invention provides a natural resource archive retrieval and utilization method combining a large model and a knowledge graph technology, and relates to the technical field of data processing, the method comprises the following steps: identifying business entities in the field of natural resources and semantic association thereof, and constructing an RDF triple with attribute constraint; performing geometric feature preserving type coordinate simplification on the plot entity vector boundary coordinate point sequence through a Douglas-Peucker compression algorithm to generate a key coordinate point set after noise reduction; on the basis of the key coordinate point set, vector geometric superposition operation is carried out on polygon boundaries of the two plot entities, the area proportion of overlapped regions is calculated according to the intersection-to-union ratio, and when the intersection-to-union ratio exceeds a preset threshold value, entity space alignment is carried out, so that a triple set of space alignment is obtained; and fusing the triple set with the aligned space, and constructing the natural resource domain knowledge graph. According to the invention, redundant information interference is effectively reduced.

The invention provides a blue carbon market adaptive accounting method based on multi-source data and Bayesian updating, and belongs to the technical field of natural resource planning and ecological management. The method comprises the following steps: firstly, constructing a standardized multi-source fusion data set through a systematic process, and then preprocessing collected data to generate a standardized blue carbon multi-source fusion data set; then, through a Bayesian-based carbon reserve estimation model, outputting a monitoring point carbon reserve prior value, fusing a monitoring point carbon reserve mean value with a spatial prior value, and updating monitoring point carbon reserve distribution; and finally, constructing a carbon credit risk index system, calculating risk indexes of the plot layer and the region layer, updating a plot layer carbon reserve mean value by using the updated monitoring point carbon reserve distribution, and generating a blue carbon project reference transaction price according to the carbon reserve mean value and the risk indexes. According to the invention, a full-chain blue carbon intelligent accounting system is constructed, and interdisciplinary fusion of an ecological model, deep learning and a carbon market mechanism is realized.

The invention discloses a natural resource geographic information collection method and system, and belongs to the technical field of geographic information collection, and the method comprises the steps: obtaining natural resource geographic information data, and carrying out the data preprocessing of the multispectral image data in the data; optical abnormal data in the preprocessed multispectral image data are marked, meanwhile, ground abnormal data in ground survey fixed-point measurement data are marked, and geospatial alignment is carried out; identifying a highly overlapped region in the geographic space, and automatically attributing and judging an abnormal mode in the highly overlapped region as being caused by a specific unrecorded environment event; and adding an environmental event influence mark to the attributed natural resource geographic information data, wherein the environmental event influence mark is used for indicating the physical cause of the natural resource geographic information data exception. According to the method and the device, numerical value change relevance caused by the same physical cause in different natural resource geographic data types can be identified, and wrong geographic information interpretation results caused by data inconsistency in subsequent analysis are avoided.

The invention relates to the technical field of land resource management, in particular to a land stock index management method based on data analysis. The method comprises the following steps: establishing a cross-data-source data sharing mechanism through a block chain technology, and obtaining multi-source land data; performing natural resource, planning and building information fusion on the multi-source land data based on a knowledge graph to obtain a geographic space semantic network; performing multi-scale space-time clustering on the geographic space semantic network through a self-organizing mapping algorithm, and performing key feature capture of land utilization change based on an attention mechanism to obtain land utilization change feature data; and performing land utilization mode identification according to the land utilization change characteristic data to obtain a land utilization dynamic characteristic spectrum. Through a multi-agent system and a hierarchical reinforcement learning algorithm, a complex land utilization decision process can be simulated, and an optimal decision scheme is realized.

The invention relates to the technical field of natural resource supervision, and particularly discloses a cultivated land non-agrochemical high-risk area identification method and device based on a three-dimensional feature space, and the method comprises the steps: obtaining basic data, and carrying out the preprocessing and time-space alignment processing; based on the processed data and an integrated learning model, determining target features corresponding to the cultivated land non-agrochemical degree and contribution values of the target features to the cultivated land non-agrochemical degree so as to determine weight values of the target features; calculating a natural score and a social economic score of the target area; the natural score of the target area is used as an X coordinate value of the target area, the social economic score is used as a Y coordinate value, and the cultivated land non-agrochemical degree value is used as a Z coordinate value; constructing a three-dimensional feature space model based on the coordinate information of all the target areas; and based on a tip mutation theory, determining a mutation threshold value of the three-dimensional feature space model, and based on the mutation threshold value, determining a cultivated land non-agrochemical high-risk area. Therefore, the identification accuracy of the non-agrochemical high-risk area of the cultivated land can be greatly improved.

The invention relates to the technical field of natural resource saving, in particular to a construction project land-saving optimization method and system oriented to resource saving and efficient utilization, and provides a three-level index case library construction and dynamic weight matching technology. The method comprises the following steps: firstly, performing multi-level classification on historical cases based on industry types, regional conditions and construction scales, and extracting standardized land use data (L), unit capacity (E) and land saving effects (S) to construct a case library; secondly, dynamically allocating weight coefficients of industries, regions and scales through an analytic hierarchy process (AHP), calculating case similarity in combination with a weighted Euclidean distance, and accurately screening matched cases; according to the method, the land use nuclear reduction rate reaches 26.48% in projects such as a Minjiang old wood hole avionics hub project, and the intensive utilization level of land resources is remarkably improved.

The invention discloses a natural resource data security management method, and relates to the field of natural resource data security management, and the method comprises the steps: obtaining a target field set requested to be accessed by a user and knowledge graph data, traversing fields, and querying graph association nodes to generate an extended field set; then calculating a conduction risk value of a field pair formed by any two fields in the extension set, if a risk mean value of all the field pairs reaches an evolution threshold value, screening out a high-risk field pair, extracting common associated nodes connected with the high-risk field pair in the atlas, injecting the common associated nodes into the extension set, and carrying out loop iteration on the process until a termination condition is met; and finally, calculating a global risk entropy based on the risk values and sensitivity interference factors of all fields in the extension set, performing desensitization operation on the target field set, and synchronously updating the knowledge graph database. According to the method, the implicit sensitive information conduction risk in the natural resource data multi-field combination query can be dynamically identified and quantified.

The invention discloses a natural resource survey result analysis method and system based on image recognition, and relates to the technical field of natural resource survey monitoring, and the method comprises the steps: obtaining historical remote sensing information and geographic information of a target section, and determining at least one divided land parcel of the target section; analyzing each divided plot according to historical remote sensing information of the target section, and determining gradient feature information of each divided plot; remote sensing data information of the target plot in different time sequence segments is collected, and the real-time gradual change feature of each divided plot is obtained; the remote sensing technology is adopted to divide the plot contour of the target plot in advance, and then the historical remote sensing feature distribution condition of each divided plot in different periods is collected, so that an effective reference is provided for the real-time remote sensing feature distribution expression of crops in each divided plot, and whether the abandoned phenomenon exists or not is effectively judged. Therefore, the purpose of accurately monitoring the abandoned phenomenon of the agricultural cultivated land is effectively achieved.

The invention discloses a natural resource environment quality comprehensive evaluation method and system based on spatial distribution, and relates to the technical field of environment evaluation. The evaluation method comprises the steps of S1, determining an evaluation target and an evaluation range, S2, collecting and processing data, S3, constructing an evaluation index system, S4, implementing comprehensive evaluation, S5, establishing a dynamic weight adjustment mechanism, and S6, analyzing a result and formulating a strategy. The method is technically characterized in that a related mechanism of weight adjustment triggered by abnormal indexes is established, on one hand, the recognition and feedback capability of key problems is enhanced, and on the other hand, conversion of an evaluation system from static evaluation to dynamic response is promoted, so that the problems that traditional evaluation results are lagged and ecological environment changes cannot be reflected in time are solved, and the evaluation efficiency is improved. According to the method, the closed-loop regulation and control capability of dynamic adjustment and optimization is embodied, after a primary weight correction action is carried out, when a certain index deviates from a normal threshold value, a secondary weight correction program is automatically started, and the system weight of a criterion layer where the index is located is improved.

The invention discloses an intelligent land use monitoring system and method based on natural resources, and relates to the technical field of intelligent land use monitoring. The system comprises a multi-source data integration module, a risk source identification module, a risk quantification and early warning module and a dynamic monitoring and slow release module. The multi-source data integration module constructs a space-time reference grid, collects four types of data and establishes a four-dimensional incidence matrix through grid ID anchoring; the risk source identification module generates a violation inducement characteristic spectrum based on the matrix and determines a risk source type; the risk quantification and early warning module calculates an initial risk value and a diffusion probability, and generates a dynamic early warning map and a monitoring priority sequence; and the dynamic monitoring and slow-release module updates data and risk parameters in real time and outputs a slow-release prompt, so that accurate pre-judgment and dynamic supervision of land use risks are realized.

The invention provides a surveying and mapping device for survey and acquisition of natural resources, and relates to the technical field of surveying and mapping equipment, the surveying and mapping device comprises an unmanned aerial vehicle body, four groups of propeller arms are mounted at the top of the unmanned aerial vehicle body, four groups of landing assemblies are mounted on the side edge of the unmanned aerial vehicle body at equal intervals, and each landing assembly comprises a socket; the unmanned aerial vehicle comprises an unmanned aerial vehicle body, a socket is fixed to the unmanned aerial vehicle body, a first electric push rod is inserted into the socket in a sliding mode, a foot pad is fixed to the extension end of the first electric push rod, an enclosure assembly is arranged at the bottom of the circle center of the unmanned aerial vehicle body, and the enclosure assembly comprises four sets of inclined plates. The three-dimensional laser scanner is wrapped through the enclosure assembly, the three-dimensional laser scanner is protected in the rising and falling state and the non-use state, through cooperation of the rising and falling assembly and the winding base, the three-dimensional laser scanner can be independently put under leaves to conduct distance measurement and surveying and mapping on the terrain, and inaccurate surveying and mapping caused by leaf shielding is avoided.

The invention discloses a sample selection device for natural resource engineering surveying and mapping, and relates to the technical field of engineering surveying and mapping. The bottom plate is arranged at the lower end of the sampling pipe in a sleeving manner and is used for ensuring that the sampling pipe is always in a vertical state during sampling, and the depth-keeping sampling mechanism is arranged at the bottom of the movable groove and is used for sampling soil with the required depth. The tip end of the bottom of the sampling pipe is inserted into a soil sampling area firstly, the sampling pipe is rotated to vertically drill into soil, after the through groove is drilled into the soil needing to be sampled by a depth, a soil sample can be taken out more conveniently by utilizing the depth-keeping sampling mechanism, and meanwhile, depth-keeping sampling can be carried out on the soil needing to be sampled.

The invention discloses a natural resource on-site proof task generation method and device, and relates to the technical field of task generation, and the natural resource on-site proof task generation method is applied to a proof scene visualization building platform. A preset proof database and a preset template warehouse are stored on the proof scene visualization building platform. The method comprises the following steps: responding to a building request of a user for a natural resource field proof task; analyzing the building request, and determining to-be-generated proof task information; querying corresponding proof data from a preset proof database according to the to-be-generated proof task information; selecting a target task template from a preset template warehouse according to the proof data; and performing proof task configuration on the proof data on the target task template to generate a natural resource field proof task. Through the automatic process, the generation efficiency of the natural resource on-site proof task is improved, and the complexity and error rate of manual operation are reduced.

The invention provides a natural resource dynamic monitoring method and system based on multi-source data fusion, and the method comprises the steps: obtaining multi-source monitoring data of natural resources of a to-be-monitored region, extracting multi-source spatial-temporal features from the multi-source monitoring data, and carrying out the splicing of the multi-source spatial-temporal features to obtain preliminary fusion features; constructing a feature graph structure based on the preliminary fusion features, performing aggregation transformation on nodes in the feature graph structure by using a graph convolutional network to obtain high-order features of the preliminary fusion features, and performing principal component analysis on the high-order features to obtain target fusion features; inputting the target fusion feature into a dynamic monitoring network for analysis to obtain a resource state analysis result and a change trend prediction result; a graph structure can capture a complex dependency relationship between feature dimensions, and a high-order relationship between the feature dimensions is extracted through a graph convolutional network, so that the discrimination of features is enhanced, and the accuracy of state classification and prediction tasks is improved; dimensionality reduction is carried out on high-order features through principal component analysis, feature redundancy is reduced, and the calculation efficiency is improved.

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.