194 results about "Resource assessment" patented technology

The invention relates to the technical field of power system dispatching, in particular to an emerging load power supply potential improvement method based on dynamic prediction, which comprises the following steps of: acquiring emerging load power consumption, meteorological environment and power grid schedulable resource data in a target area through an Internet of Things sensing terminal, and performing two-channel modeling to obtain a new load power supply potential improvement model; a deep space-time network is used to predict a load curve, a model is established to quantify resource regulation potential, a scheduling priority list and a capacity allocation strategy are generated by means of a matching rule base according to load fluctuation and resource evaluation results, an actual scheduling effect is fed back to the prediction model, parameters are corrected through error back propagation, a closed-loop optimization link is formed, and the scheduling efficiency is improved. The method improves load prediction accuracy and resource scheduling adaptability, is suitable for emerging load power supply optimization in a novel power system, and guarantees stable and efficient operation of a power grid.

The invention relates to a task scheduling optimization method and device based on reinforcement learning, equipment and a medium. The method comprises the steps that firstly, system resource state data are collected in real time, dynamic environment characteristics are determined through preprocessing and time sequence analysis, task characteristic data are analyzed at the same time, and a task priority sequence and a resource demand vector are generated through a priority ranking algorithm and a resource evaluation model; and then a state space and an action space are constructed by adopting a reinforcement learning algorithm, an optimal task allocation scheme is generated through strategy iteration and reward function optimization, and if the scheme meets a resource balance threshold, scheduling is executed, and performance indexes are collected. And finally, fusing real-time indexes with historical data, and updating parameters of the reinforcement learning model through experience playback and gradient descent to form a closed-loop optimized improved scheduling strategy. By adopting the method, the accurate mapping of the resource state and the task requirement can be realized, and the problem of insufficient adaptability of the traditional static scheduling to a complex scene is solved.

The invention relates to the technical field of wind power generation, and discloses a cross-scale wind power plant wind resource evaluation method based on a numerical mode, comprising the following steps: step 1, acquiring multi-source observation data of a target area; step 2, screening a WRF optimal configuration scheme adaptive to the regional terrain; step 3, through a WRF power downscaling method, generating annual scale wind resource data as initial wind resource data; 4, performing partition correction on the initial wind resource data to obtain wind measurement data; step 5, constructing a WRF-WFP model coupling the WRF mode and the wind power plant parameterized model; step 6, optimizing WRF lower boundary conditions; 7, simulating the atmospheric flow of the target area, and quantifying the wake flow evolution characteristics of the wind power plant; 8, constructing a wake flow analysis model; and step 9, formulating a collaborative productivity plan of the wind power plant group. The power generation capacity of the wind power plant to be developed can be accurately predicted, and a reliable basis is provided for scientific decision-making of resource distribution development and planning of the million-kilowatt wind power plant.

The invention discloses an elastic management and optimal scheduling method and system for cloud computing resources, and belongs to the technical field of cloud computing resource management.The method comprises the steps that a cluster composed of multiple types of intelligent agents is constructed, and the intelligent agents comprise a resource evaluation intelligent agent, an elastic telescopic intelligent agent, a load balancing intelligent agent and a fault recovery intelligent agent; the intelligent agents are distributed and deployed on different nodes of a cloud computing data center, and information interaction is realized through a communication interface based on RESTful API and a distributed message bus constructed by a gRPC protocol; a unified agent management platform is set up and is responsible for registration, monitoring, scheduling and dynamic adjustment of agents, and orderly operation and efficient cooperation of the whole multi-agent system are ensured. According to the method, the functions of efficient elastic allocation, intelligent load balancing, rapid fault recovery, continuous optimization evolution and the like of resources in the cloud computing data center can be realized, and the quality and competitiveness of cloud computing services are effectively improved.

The invention discloses a marine fishery resource survey station adaptive optimization method based on a space-time coupling model. According to the method, a space-time continuous species distribution dynamic reference field is constructed by coupling a Bayesian hierarchical species distribution model and a regional ocean model system; performing ecological layering based on time, space and environment dimensions; and when the environment variables change significantly, a habitat suitability index is dynamically reconstructed, and the station layout is optimized again, so that the coverage of a key ecological hot spot area is ensured, and the in-gradient representativeness is maintained. The method has the advantages of fast environmental response, strong spatial representativeness, high adaptability and the like, and is suitable for scenes of resource assessment, ecological protection, biodiversity monitoring and the like.

The invention relates to the technical field of terrain wind resource assessment, in particular to a complex terrain wind resource assessment method and system based on CFD enhancement, and the method comprises the steps: processing terrain, weather and vegetation data, and generating a complex terrain feature vector; constructing bidirectional interaction between a mesoscale meteorological mode and computational fluid dynamics, and iteratively optimizing a flow field; fluid dynamic parameters are calculated through reinforcement learning automatic optimization; turbulence simulation is enhanced by combining the generative adversarial network with large eddy simulation; dynamically allocating resolution according to the grid priority of the multi-feature calculation; the lightweight model pre-judges errors, and after the errors reach the standard, evaluation indexes are calculated to complete wind resource evaluation; the system comprises a multi-source data fusion module, a dynamic coupling module, a parameter optimization module, a reinforced turbulence simulation module, an adaptive grid module and an error prediction and evaluation module, and all the modules cooperate to achieve full-process evaluation. The problems that in the prior art, one-way coupling precision is low, manual parameter selection is low in efficiency, turbulence simulation precision and efficiency are difficult to balance, and static grids are wasted are solved.

The invention provides an emergency resource intelligent scheduling optimization method and system based on large model analysis, and relates to the technical field of emergency resource scheduling. The method comprises the steps of collecting emergency event data and environment data; extracting emergency event features and environment features, constructing a prediction model based on a long short-term memory network, and obtaining a required emergency resource list; constructing an emergency resource evaluation model based on an analytic hierarchy process, and obtaining priority scores of the required emergency resources; constructing a multi-objective optimization model, and solving the multi-objective optimization model by using a particle swarm algorithm to generate a scheduling scheme; and constructing a decision function, and solving by using a genetic algorithm to obtain a scheduling decision. According to the method, through accurate prediction, scientific evaluation, multi-objective optimization and decision in combination with priority, the accuracy and efficiency of emergency resource scheduling and the resource utilization efficiency can be remarkably improved, powerful support is provided for emergency management work, and the method has important practical significance and application value.

The invention discloses a multi-dimensional fusion medical resource intelligent monitoring and early warning method. According to the method and the device, the integrity and the accuracy of the medical resource data are improved through a multi-dimensional data fusion and dynamic calibration mechanism. Multi-source data of a medical institution system, a regional health platform, wearable equipment and the like are integrated, comprehensive data coverage and timely updating are ensured, and resource monitoring blind areas caused by information islands are reduced. Through the processing flows of noise filtering, missing value filling, abnormal value identification and the like, the influence of data errors on resource evaluation is reduced, quantitative analysis of key indexes such as medical resource supply and patient demands is closer to the actual situation, and the timeliness of medical resource risk early warning and the effectiveness of decision support are enhanced. The hierarchical early warning model constructed based on the multi-modal feature matrix can identify potential risks from multiple dimensions of resource exhaustion, service saturation, regional propagation and the like, and ensures reasonable distribution and efficient utilization of medical resources in different scenes.

The invention discloses a CFD (computational fluid dynamics) analogue simulation-based wind power plant wind resource evaluation method and a CFD analogue simulation-based wind power plant wind resource evaluation device. The evaluation method comprises the following steps: collecting and processing landform and meteorological data of a wind power plant site; establishing a three-dimensional CFD simulation model of the wind power plant according to the processed landform and meteorological data of the wind power plant site, performing grid division on a wind power plant area, and setting simulation boundary conditions; performing numerical simulation on the three-dimensional CFD simulation model of the wind power plant, and analyzing wind speed field distribution information of each position in the wind power plant; and according to the wind speed distribution information, calculating and evaluating the wind resource condition of the wind power plant to obtain a wind resource evaluation result. According to the technical scheme, through the three-dimensional simulation-evaluation-optimization closed loop with the CFD as the core, the wind speed and turbulence can be accurately quantified in the design stage, so that unit model selection, tower height, blade length and machine position layout are matched with a real wind field, key parameters can be determined at a time, the design period can be shortened, and the operation and maintenance and grid connection cost can be reduced.

The invention discloses a soil attribute intelligent prediction method and system based on a multi-dimensional dynamic scale, and relates to the technical field of soil prediction.The method comprises the steps that remote sensing images, terrain factors, meteorological factors and soil actual measurement sample data are collected and stored in a multi-source raster data bin in a unified mode; preprocessing the multi-source data, and constructing raster data in a unified format; combining terrain difference, land utilization and climate fluctuation, dynamically dividing a spatial scale, a time scale, a terrain layering scale and a land utilization pattern scale, and constructing a multi-dimensional dynamic scale feature expression vector; carrying out soil attribute modeling and training based on a multi-task deep neural network model; and applying the model to a non-actually-measured area, and outputting a prediction result and a spatial distribution map layer. The system comprises a multi-source raster data construction and management module, a multi-dimensional dynamic scale feature construction module and a soil attribute modeling and prediction module. The method can be used for forestry management and protection, resource management, ecological monitoring, resource evaluation and other scenes.

The invention provides a K8S-based satellite image recognition resource dynamic elastic scheduling system. The system comprises a task perception module, a resource evaluation module, an elastic scheduling module, a fault tolerance module and a learning optimization module. The task sensing module collects satellite image recognition task metadata and transfers the metadata to a priority label library; the resource evaluation module monitors node resources, quantifies the health degree and screens healthy nodes into a candidate pool; the elastic scheduling module executes Pod dynamic capacity expansion and contraction and other decisions according to tasks and resources; the fault-tolerant module identifies the fault Pod, reconstructs and distributes a new Pod according to an anti-affinity rule; the learning optimization module optimizes the system parameters according to the historical data. The invention also provides a dynamic elastic scheduling method. Therefore, the resource utilization rate and the system stability of the satellite image recognition task are remarkably improved.

The invention discloses a visualization method and system for an oversized in-situ filling space under a composite roof, and belongs to the technical field of information processing. The method comprises the following steps: performing multi-angle data acquisition on a filling space under a composite roof structure to obtain original point cloud data; obtaining complete three-dimensional point cloud data based on the original point cloud data; preprocessing the three-dimensional point cloud data to obtain standard point cloud data; performing boundary contour recognition based on the standard point cloud data to obtain space boundary contour data; obtaining a spatial three-dimensional geometric model based on the spatial boundary contour data; performing section slicing based on the space coordinate range of the space three-dimensional geometric model, and obtaining size distribution data based on the obtained contour size parameters of the section; obtaining a total volume and a segmented volume of the filling space based on the size distribution data; and obtaining a three-dimensional visual model of the filling space based on the total volume and the segmented volume. The method provides reliable technical support for mine filling design and resource evaluation.

The invention relates to the technical field of deep-sea polymetallic nodule resource assessment methods and systems, provides a deep-sea polymetallic nodule resource assessment method and system, and effectively solves the problems that overlapped areas among polymetallic nodules are easy to cover by silt, are located at the edge parts of the polymetallic nodules, are fused with a seabed background, and are not easy to damage. And complete filling cannot be performed through an expansion algorithm, so that the accuracy of the finally calculated coverage rate is not high.

The present application relates to the technical field of resource assessment, and particularly relates to a system and method for accurately assessing water resource quantity in an inland river basin, comprising the following steps: arranging a multi-modal sensor network in the inland river basin, collecting hydrological characteristic data of the inland river and surrounding wetlands, extracting implicit water exchange characteristic indexes between the inland river and the wetlands, including wetland groundwater recharge, inland river boundary seepage rate and seasonal water exchange amplitude; based on the implicit water exchange characteristic indexes between the inland river and the wetlands, establishing an inland river-wetland implicit water exchange dynamic model; embedding the inland river-wetland implicit water exchange dynamic model into the water cycle system of the inland river basin, and comprehensively calculating the surface water resource quantity and the groundwater resource quantity in the basin. Through the implicit water exchange between the wetlands and the inland river, the present application eliminates the wetland consumption part, so that the water resource quantity calculation is more accurate.

The invention relates to the technical field of new energy resource evaluation, in particular to a wind and light resource dynamic evaluation method and system based on multi-source remote sensing data, and the method comprises the following steps: inputting a region remote sensing image, calculating a boundary confidence coefficient, generating an interference classification identifier, reconstructing a resource prediction output value, predicting a trend analysis result, and evaluating a development suitability level. And generating a regional resource evaluation result. According to the invention, effective quantification of boundary stability and data confidence is realized through cooperative judgment of a remote sensing image boundary structure and reflected light intensity distribution, fine identification of wind flow offset and area shielding is carried out based on a terrain gradient and a wind direction included angle, and wind speed radiation data is accurately interpolated according to a space grid connection combination and a neighborhood change trend. And meanwhile, climate factors and resource change trends are accurately coupled, and actual path connectivity is determined in combination with terrain and land attributes, so that the spatial suitability and development accuracy of wind and light resources are effectively improved.

The invention discloses a marine fishery resource assessment method and system based on buoy sensing monitoring. Firstly, fishery resource change characteristics are analyzed by utilizing historical fishery resource monitoring data of a target sea area, and a monitoring period is determined according to the fishery resource change characteristics; hydrological parameter change data are obtained, and a hydrodynamic simulation model is established; an initial buoy sensing monitoring array is constructed by combining the effective monitoring range of sonar monitoring buoys; the array is imported into a hydrodynamic simulation model, and node displacement change information in a monitoring period is obtained; and finally, the buoy monitoring array is optimized according to a displacement change result, an optimized monitoring layout scheme is formed, and dynamic monitoring and scientific evaluation of the fishery resources in the target sea area are realized accordingly. According to the method, the rationality and monitoring precision of buoy layout can be improved, the hydrodynamic environment influence and the resource change rule are considered, and technical support is provided for sustainable development and management of marine fishery resources.

The invention discloses a three-dimensional dynamic prediction method and system for deep brine resource quantity, particularly relates to the technical field of mineral resource exploration, and is used for solving the problem of resource evaluation misalignment caused by a well group interference effect in intensive mining. The method comprises the following steps: constructing a spatial topology network model which takes a well location as a node and a well spacing as an edge weight by obtaining a mining area well group space coordinate and mining dynamic time sequence data; excavating pressure conduction characteristics, resource concentration migration characteristics and shaft temperature time sequence data; calculating a thermal buoyancy gradient based on temperature data, determining a stress deflection angle in combination with a network model azimuth angle, and fusing to construct an interference strength coefficient matrix; tracking an inter-well resource siphon path by using a network edge weight, and generating a time-lag compensation factor by coupling a time-lag rule of pressure and concentration characteristics; finally, the interference matrix, the siphon path and the time delay factor are fused to dynamically correct the single well evaluation value, the total area brine resource amount is output, and the intensive mining area resource evaluation precision is remarkably improved.

The invention relates to the technical field of wind energy resource assessment, and discloses a wind resource assessment method based on a millimeter wave wind finding radar. The method comprises the steps of collecting wind field data of a target area through a millimeter wave wind measurement radar and performing preprocessing to generate a standardized data set; inputting the data into a prediction model to obtain a wind energy distribution characteristic graph; generating a parameter set by adopting threshold segmentation based on the feature map, and dynamically constructing an evaluation function for simulation evaluation; synchronously collecting actual wind energy data, and calculating a deviation matrix between the actual wind energy data and a predicted value to quantify an evaluation error; performing parameter correction on the prediction model by using the error to obtain an optimized model; and finally, regenerating a feature map by adopting the optimization model and completing resource evaluation. According to the method, through online model correction and dynamic evaluation function construction, closed-loop optimization and data driving in the evaluation process are realized, and the accuracy of an evaluation result and the adaptability to a specific site are improved.

The invention provides an intelligent scheduling method and device for a scheduling platform. The method comprises the following steps: evaluating the task value of each task by using a value evaluation model; evaluating resource consumption during task execution through a resource evaluation model on the basis of resource requirements during task execution, evaluating the total amount of all resources, and collecting uncompleted tasks; building a backpack problem model based on the task value, the resource consumption, the total amount of all resources and the uncompleted tasks of each task; and solving the knapsack problem model in combination with the scheduling score card to obtain an optimal task combination, and allocating the tasks to corresponding resources or execution units for execution according to the optimal task combination. The dependency, the weight and the resource consumption sequence among the tasks are optimized by automatically analyzing the task values of the tasks; and calculating an optimal task combination in real time by adopting a knapsack algorithm model according to the scheduling progress and the server resource remaining condition. And dynamic changes of task characteristics and resource consumption are fully considered, so that the efficiency and the stability of the system are improved.

The invention discloses a geothermal resource assessment method and system based on big data analysis, and is applied to the technical field of digital analysis. The method comprises the following steps of obtaining multi-source heterogeneous data of historical drilling engineering, performing data preprocessing and feature engineering, and establishing a drilling success probability intelligent prediction-risk quantification model; in combination with geospatial information and environmental multi-source data, identifying an earth surface thermal anomaly region as a target region to perform geothermal potential assessment, and completing preliminary grading assessment of the target region; integrating seismic active band high-frequency monitoring data, inverting deep structure features by using a seismic wave velocity tomography technology, and performing multi-mode correlation matching with a target region; the coupling model realizes accurate evaluation and grading screening of a target region; and constructing a geothermal target area digital twinborn model fused with geologic and geophysical constraints, and realizing dynamic evaluation and productivity prediction of geothermal resources. Automatic geothermal resource evaluation is achieved, and efficient development and reasonable utilization of geothermal resources are promoted.

The invention discloses a PCB layout evaluation method and system integrating layout optimization and resource evaluation, and a medium. Relates to the technical field of electronic design automation. The scheme is based on PCB layout design, layout resource evaluation and layout quality evaluation; according to the method, the devices on the PCB are arranged and placed in combination with the minimum area constraint of the distance constraint between different devices, meanwhile, the actual effective area on the PCB is analyzed and evaluated, the effective utilization degree of the PCB is reflected, an engineer is assisted in predicting the PCB layout design resource condition in advance, the utilization rate of the PCB is conveniently optimized, and the PCB layout design efficiency is improved. Therefore, the raw material cost and the manufacturing cost of the PCB are reduced; after the layout is completed, the layout is evaluated from the overall layout to the local layout, guidance is provided for optimization of the layout result, and the PCB layout design quality and reliability are improved.

The invention relates to a fish body detection counting method for a channel behind a fish pump of a fishing boat, and provides an NMS-free fish body detection model MGI-RTDETR based on an improved RT-DETR for small targets, rapid movement, strong blur, highlight shielding and limited shipborne computing power. The model integrates multi-scale grouping interaction, dynamic context mixing, detail fidelity fusion, deployment period re-parameterization and back projection up-sampling, and real-time detection of one fish and one frame is achieved. And in combination with lightweight multi-target tracking, outputting paragraph counting by adopting a one-way cross-line combined de-duplication strategy. According to the method, missing detection and time delay can be remarkably reduced, edge deployment is facilitated, and the method is suitable for near-real-time fishing estimation, operation monitoring and resource evaluation.

The invention relates to the technical field of clean room full-process intelligent management, and discloses a clean project full-life-cycle intelligent management digital twin platform, which comprises a full-cycle data center module, a virtual-real bidirectional interaction engine module, a full-cycle management module and a multi-terminal collaborative interaction module which are connected with one another, the whole life cycle of design, construction, operation and maintenance and decommissioning is covered, whole-course tracing of design-construction-operation-decommissioning is achieved, closed-loop interaction of virtual model rehearsal and entity execution feedback is constructed, and an intelligent decision function is constructed to cover the whole process. Comprising performance rehearsal in a design stage, progress management and control in a construction stage, deep optimization in an operation and maintenance stage and resource evaluation in a decommissioning stage. Through deep fusion of the dynamic BIM model and multi-stage multi-source data, virtual-real two-way interaction and full-period intelligent management, the method is suitable for full-process collaborative management of clean environments such as semiconductors, biological medicines and foods, and full-stage precise management and control from early planning to final decommissioning are realized.

An improved non-adjacent bit quantum gate efficient analog calculation method comprises the steps that S1, dimension information of each tensor in a tensor network is obtained, the dimension information comprises the physical dimension and the auxiliary dimension of the current quantum state tensor, and the auxiliary dimension of the quantum state tensor after the quantum gate is acted is judged for subsequent contraction judgment and resource evaluation; s2, selecting a corresponding strategy for execution based on the target bit spacing and the tensor dimension; s3, judging whether the dimension of the contracted intermediate tensor exceeds a preset resource limit or not under the condition that the overall contraction mode is adopted; if the target bit exceeds the limit, inserting an SWAP gate through an overall contraction and decomposition strategy to rearrange the target bit to the maximum distance under the memory limit, and circulating to S3 for judgment; and if the limit is not exceeded, executing normally. Experiments show that in a highly entangled quantum circuit, the method of the invention can realize about 50% of simulation time optimization at most, and can limit the maximum intermediate tensor scale at the same time.

The invention belongs to the technical field of information, and discloses a cloud-English rock type ore body construction method and system based on drilling data, and the method comprises the steps: carrying out the boundary recognition processing according to the gradient change characteristics in an alteration intensity space distribution model, and if the alteration intensity gradient change rate of adjacent space points exceeds a preset boundary threshold value, carrying out the boundary recognition processing; if yes, determining that the position is an ore body boundary point, forming an ore body boundary surface by connecting all boundary points, and judging geometric morphology characteristics of the ore body; a three-dimensional reconstruction algorithm is adopted to carry out internal structure analysis on a space area inside the boundary surface of the ore body, mineral combination difference degrees of different positions inside are calculated through a heterogeneity analysis method, internal structure layers are divided according to difference degree distribution characteristics, and occurrence characteristics and spatial distribution rules inside the ore body are determined. According to the method, full-process automation from data preprocessing to three-dimensional space modeling is achieved, the depicting precision of the ore body space distribution characteristics is remarkably improved, and efficient and accurate technical support is provided for geological exploration and resource evaluation.

This application discloses a comprehensive assessment method and system for multi-source data management and correction of watershed fish resources. The method includes: extracting multi-dimensional features from a first data matrix to obtain a first feature matrix; classifying species data based on anomaly scores, outputting verification results, and correcting the first feature matrix to obtain a second feature matrix; acquiring environmental DNA sequencing data and corresponding environmental data; obtaining a first eDNA dataset from the environmental DNA sequencing data through ecological screening; correcting the species in the first eDNA dataset to obtain a second eDNA dataset; fusing the second eDNA dataset with the second feature matrix; calculating biodiversity indices and analyzing community structure; analyzing long-term trends in population and community structure; and generating a comprehensive assessment report. This application achieves full-process automation and intelligence from data quality control and fusion to assessment, significantly improving the accuracy and efficiency of watershed fish resource assessment.

This invention provides a method and agent-based approach for identifying and scheduling computing resource requirements, relating to the field of cloud computing resource management technology. The method includes: receiving a user's fuzzy description of computing resource requirements; parsing the user's requirements using natural language processing (NLP) technology to identify user intent and key entities; proactively guiding the user to complete missing parameters through multi-turn dialogue based on key entities and a built-in knowledge base; calling a resource evaluation model to quantify the parameters and generate a list of computing resource requirements; obtaining a set of candidate resources that meet the list of requirements; calculating multi-dimensional utility scores for each candidate resource; generating multiple recommendation schemes based on different weighting strategies across multiple dimensions, and generating natural language descriptions of recommendation reasons for each scheme; and executing resource allocation based on the user's selection. This invention improves resource matching accuracy and scheduling efficiency by accurately understanding and quantifying the user's computing resource requirements through an agent, thereby generating a computing resource scheduling scheme.

The invention provides a smart tourism online comprehensive management system, belongs to the technical field of tourism data management, and can make tourism resource assessment more accurate. Static attributes and dynamic indexes are integrated by means of a regional industry knowledge graph, a unified data support network is constructed, a plurality of Pareto optimal routes are generated in combination with a multi-objective optimization algorithm, and personalized requirements of tourists on time, cost, interest matching degree and experience credibility are met; route adjustment can be carried out according to tourist real-time states, resource dynamic states and macroscopic passenger flow early warning automatic or response intention changes in the journey, and journey dynamic optimization is achieved; and meanwhile, passenger flow can be predicted through a graph neural network model, a risk early warning and multi-subject collaborative scheduling scheme can be generated, regional tourism collaborative management is assisted, the problems that a traditional system is large in recommendation deviation, cannot dynamically adapt and is insufficient in collaborative scheduling are effectively solved, and tourism experience of tourists and regional tourism service quality are remarkably improved.

The invention relates to the technical field of resource evaluation, in particular to a three-dimensional visual dynamic evaluation method and system for deep brine resource quantity, and the method comprises the following steps: constructing a grid unit and a Kriging interpolation boundary through borehole logging data and geologic horizon three-dimensional coordinates, outputting a topological relation table, obtaining brine thickness data, and calculating volume change; performing sequence symbol detection and classification, extracting mineralization degree and sodium ion data, performing grey correlation analysis to output a coupling coefficient, performing rendering engine color gradient mapping and interpolation, and constructing a three-dimensional dynamic evaluation model. According to the method, fine quantification of underground brine resource structure and change, generation of volume sequence and trend classification, dynamic tracking of resource evolution, linkage of multiple physical and chemical indexes, enhancement of quantity and quality interpretation and improvement of control of distribution heterogeneity and dynamic process are realized by relying on a three-dimensional grid and interpolation technology; the evaluation scientificity, timeliness and intelligent level are remarkably improved, and resource regulation and control and sustainable utilization in a complex environment are effectively supported.

The invention relates to the technical field of water resource assessment, and discloses a regional water resource assessment system based on hydrological survey data, which collects the hydrological survey data in real time through a sensor, builds a convolutional neural network model based on a TensorFlow framework, and performs feature extraction on the hydrological survey data to obtain hydrological survey feature data; obtaining evaluation indexes including the average precipitation of many years, the surface water resource quantity, the underground water resource quantity, the total water resource quantity, the water quality standard reaching rate, the water resource development and utilization rate and the ecological water demand satisfaction rate; a penalty parameter and a kernel function parameter of a support vector machine are optimized by adopting a particle swarm optimization algorithm based on hydrological survey feature data, and a water resource evaluation model is constructed; inputting the real-time data into a water resource evaluation model, performing comprehensive evaluation on the water resource condition of the region based on evaluation indexes, and outputting each current water resource evaluation index value; the regional water resource is evaluated from multiple dimensions, and the regional water resource condition can be comprehensively reflected.