3798 results about "Field data" patented technology

The present invention relates to the technical field of data transmission. Disclosed is an application-driven three-dimensional spatial data transmission method. The method comprises: determining system key performance indicators (KPIs) by means of qualitative and quantitative analysis; constructing an AI-driven adaptive three-dimensional data transmission mechanism, and dynamically adjusting a transmission strategy on the basis of a real-time network state, a device capability, an application scenario and the KPIs; developing an adaptive compression algorithm set oriented to three-dimensional data, so as to meet differentiated compression requirements of different application scenarios; performing loop execution of a test, and adjusting and optimizing the data transmission mechanism and the compression algorithm set on the basis of a test feedback result and the real-time network state; and deploying an optimized transmission method to a production environment, and collecting field data to optimize the system performance and verify the achievement of the KPIs. By constructing a qualitative and quantitative analysis framework based on machine learning, the present invention quantifies differentiated transmission requirements of different application scenarios and formulates transmission strategies meeting the scenario requirements.

The invention relates to the technical field of data processing, and discloses a BIM-based building construction quality information intelligent supervision system, which is used for improving the efficiency and accuracy of BIM-based building construction quality information intelligent supervision. The method comprises the following steps: a modeling module performs BIM modeling processing on a building engineering design drawing and building specification data to obtain a building twinborn body; the deployment module carries out field data acquisition system deployment on the building twinborn body to obtain a real-time data acquisition system; the detection module performs anomaly detection processing on the data acquired by the system to obtain a quality anomaly alarm and a priority processing level; the processing module carries out multi-dimensional visualization processing on the quality abnormity alarm and the priority processing level to obtain a visual interface; the decision-making module performs intelligent decision-making analysis on the quality abnormal information in the visual interface to obtain a target processing scheme; and the analysis module performs time sequence analysis and multivariable correlation analysis on the target processing scheme to obtain a dynamic quality evaluation report.

The invention relates to the technical field of welding processing, and discloses a dissimilar metal welding joint temperature field optimization control method and system. Real-time temperature field scanning is conducted on the dissimilar metal welded joint, image segmentation processing is conducted on a temperature field image obtained through scanning, temperature data of a weld joint area, a heat affected area and a base metal area are extracted, and whether the temperature gradient exceeds a preset threshold value or not is judged; aiming at an area with non-uniform distribution of heat input quantity, analyzing the performance of the welding joint under the combined action of thermal stress and mechanical stress, obtaining the difference between residual stress distribution and plastic deformation capacity, and analyzing a stress concentration area by combining with the yield strength of the material; and a mapping relation between the welding process parameters and temperature field distribution is established, and a welding process parameter adjustment prediction model is obtained through historical welding process parameters and corresponding temperature field data training in combination with heat flux density distribution. The problems of uneven heat input, stress concentration and the like in dissimilar metal welding are effectively solved, and the welding quality and efficiency are improved.

The invention relates to the technical field of fan blade fatigue damage prediction. The invention provides a fan blade fatigue damage prediction method and system. The method comprises the following steps: constructing a coupling finite element model based on blade anisotropy parameters; blade surface three-dimensional strain field data, blade vibration acceleration signals, environment temperature and humidity and wind speed and direction data are obtained in real time, and a multi-dimensional monitoring data set is constructed; based on the multi-dimensional monitoring data set, nonlinear coupling features of all the load components are extracted, a multi-dimensional feature tensor is obtained, and a reference stress field matched with the current working condition is generated; inputting the multi-dimensional feature tensor and the reference stress field into a bidirectional long-short-term memory network, and establishing a data-physics combined driven damage evolution model; and positioning a damage area based on a damage probability distribution diagram output by the damage evolution model. The problems that in the prior art, prediction errors are obvious, sensitivity to early damage is insufficient, the false alarm rate is high, and accurate positioning of the damage position and quantitative prediction of the residual life are difficult to achieve are solved.

The invention relates to the technical field of anti-cracking performance testing, in particular to an ultra-high performance concrete anti-cracking performance testing system and a dynamic monitoring method thereof.The system comprises a dynamic stress field simulation feedback module used for sensing three-dimensional distribution of a micro stress field in concrete in real time by constructing a flexible loading array to obtain dynamic stress field data; the multi-scale damage evolution tracking module is used for deploying a high-resolution acoustic emission network and a distributed optical fiber sensing layer based on dynamic stress field data of the flexible loading array to form a full-scale evolution graph from microdefects to macroscopic cracks; and the intelligent healing efficiency evaluation module obtains stress redistribution data and a full-scale evolution graph, and a repairing medium containing a tracer agent is injected into a preset crack path. According to the method, the whole process of crack resistance of the material from defect initiation to repair and regeneration can be quantitatively evaluated; and finally, outputting a dynamic evolution rule of the crack resistance and enhancing potential evaluation.

The invention relates to a heat dissipation control method and device for a high-order computing power automatic driving FPC sensing module, and the method comprises the following steps: carrying out the temperature data collection of a target FPC sensing module through a preset distributed thermosensitive array, and obtaining the data of a three-dimensional temperature field; performing heat flow path calculation on the target FPC sensing module based on the three-dimensional temperature field data to obtain a heat flow density distribution diagram; performing topological optimization calculation on the target FPC sensing module based on the heat flux density distribution diagram to obtain a heat dissipation structure layout scheme; performing dynamic optimization solution on the heat dissipation structure layout scheme through a branch and bound algorithm to obtain a heat dissipation control parameter set; and inputting the heat dissipation control parameter set into a preset execution unit, and performing real-time heat dissipation control on the target FPC sensing module to obtain an ideal heat dissipation state, thereby solving the problems that a traditional method often cannot accurately and effectively cool local hot spots and lacks flexibility to adapt to dynamic thermal changes under different working loads.

Various automated techniques are disclosed herein for monitoring and assessing real-time and future operational and health statuses of real-world assets (e.g., physical devices, components, equipment, structures, buildings, machines, infrastructure, piping systems, etc.). A monitoring system incorporates the use of intelligent Monitoring Devices for monitoring pipe systems and / or other infrastructure for leaks / issues using IoT devices and machine learning. One or more Monitoring Device(s) are attached to specific physical equipment / structure(s) to be monitored. A Monitoring Device performs comprehensive field data gathering. The collected field data is used to train a customized ML model, which is stored locally in that Monitoring Device. The Monitoring Device monitors current conditions of the pipe using various sensors, and uses its locally stored customized, trained model to perform real-time, edge-based analysis of the monitored data to identify possible issues in real-time and / or to predict future maintenance / service needs without relying on continuous cloud connectivity.

The invention relates to the technical field of crustal stress field data processing, in particular to a crustal stress field three-dimensional dynamic inversion method based on a multi-scale adaptive algorithm. The method comprises the following steps: acquiring a geological data set of a target area; constructing a crustal stress field three-dimensional initial model based on the geological data set, and performing geologic body space division and mesh generation to obtain crustal stress field three-dimensional mesh model data; performing multi-scale region division on the crustal stress field three-dimensional grid model data, and establishing a multi-scale weighting function to obtain multi-scale partition mapping information; and constructing a cross-scale boundary adaptive transmission mechanism, and establishing a stress tensor continuity constraint model at a multi-scale partition boundary to obtain cross-scale stress boundary coupling data. Through a multi-scale adaptive algorithm and dynamic closed-loop optimization, high-precision, dynamic and continuous inversion of a crustal stress field in a complex geologic structure is realized.

The invention relates to an intelligent diagnosis and treatment method and system based on a knowledge graph and a large language model. The method comprises the steps of constructing a basic framework of a medical field knowledge graph based on collected medical field data, constructing multi-level knowledge graph expression, obtaining symptom description of a patient through multiple rounds of dialogues, diagnosing the patient and generating a diagnosis report, rechecking and adjusting the diagnosis report by a professional physician, and finally outputting the diagnosis report. The intelligent diagnosis and treatment system comprises a knowledge graph construction module, an intelligent question and answer system, an entity matching module, an intelligent diagnosis module, a doctor review and diagnosis report adjustment module, a diagnosis report output module and the like. And main diseases are screened, so that the coverage is wider. And three levels of knowledge maps are constructed, so that text matching with finer granularity can be carried out. The non-standardized expression of the patient is firstly converted into standardized medical terms through a large model, and drug allergy and the like are taken into consideration, so that the diagnosis accuracy is improved, and the diagnosis and treatment risk is reduced.

The invention discloses a large-diameter shield tunneling parameter optimization method and system based on digital twinning, and relates to the technical field of shield tunnel numerical simulation, machine learning and digital twinning, and the method comprises the steps: obtaining shield tunnel construction site data, and constructing a virtual digital twinning body; building a training sample set based on virtual numerical twinborn generation data; training an agent model by using the training sample set; during construction, actual stratum response data and tunneling parameter data are obtained in real time, the real-time data are input into the soil mass parameter inversion agent model, and the optimal value of the soil mass parameter is rapidly output; the optimal value is input into the virtual digital twin, and dynamic real-time updating of numerical simulation is carried out; and on the basis of the updated soil parameter data and the tunneling parameter data in the next construction step, stratum response prediction data are obtained through the stratum response prediction agent model, so that the tunneling parameter data are adjusted and optimized. According to the invention, more accurate numerical simulation and safe and efficient tunneling of the shield tunnel can be realized.

The invention discloses an LLM-driven complex report OCR error self-correction method and system, and the method comprises the following steps: S1, obtaining complex report image data, executing OCR processing, and constructing an original field data set; s2, extracting context information, identifying semantic contradiction fields, and generating a to-be-corrected field set; s3, the pointer generation network generates a plurality of field correction candidates to form a candidate field set; s4, constructing a dobby machine model, selecting an optimal field correction result, and forming a correction field output set; s5, executing format analysis, and extracting a chart title field, a legend field and a data region text; s6, generating a corrected field result of a chart title field according to a chart structure semantic consistency mechanism; and S7, performing field restoration and format reconstruction, and outputting structured report data. According to the method, intelligent error correction and structured reconstruction of fields in a complex report are realized by fusing a large language model, a pointer generation network and a multi-arm machine mechanism.

The invention relates to the technical field of material physicochemical property monitoring of a multi-source sensing network, in particular to a multi-source sensing storage environment collaborative monitoring and early warning method, which comprises the following steps of: dividing grid units according to material storage types, binding sensor node coordinates, constructing a grid region reference model, and constructing a grid region reference model; integrating the volatilization characteristic parameters analyzed by the laboratory, historical monitoring data and an environment threshold value to generate a substance and environment relation matrix; the method comprises the following steps of: calling model parameters to invert a theoretical gas concentration value, and triggering finite element physical field simulation through residual analysis to generate a three-dimensional simulation field data set containing a temperature gradient and a diffusion path. And the dynamic correlation analysis precision and the early warning reliability of storage environment monitoring are improved.

The invention discloses a cable insulation life prediction method and system based on LSTM accelerated aging mapping, and relates to the technical field of submarine cable insulation life prediction. The existing method has the defects of insufficient multi-stress nonlinear modeling, laboratory and field data separation, difficulty in small sample modeling and the like, and the insulation life of the submarine cable is difficult to accurately predict. The method comprises the following steps: data acquisition: acquiring parameters of insulation electrical performance, physical and chemical performance and mechanical performance under an accelerated aging condition; carrying out data preprocessing: carrying out homodromous processing on the inverse indexes, improving box plot denoising, filling missing data with a space-time K nearest neighbor algorithm, and carrying out normalization; constructing an LSTM model: embedding a dielectric constant differential equation as a physical constraint, and introducing an index weight; and model training and evaluation: adopting five-fold cross validation, and quantizing prediction precision through mean square errors and decision coefficients. According to the technical scheme, the prediction precision is improved, the fault risk caused by insulation aging is reduced, the maintenance cost is reduced, and the reliability of the ocean energy transmission system is improved.

The invention discloses an industrial data real-time acquisition monitoring system fusing edge computing and 5G, and belongs to the technical field of industrial Internet of Things. The system is composed of a multi-source heterogeneous data acquisition module, an edge computing node cluster, a 5G communication network and a cloud analysis platform, an edge-cloud collaborative architecture is innovatively adopted, a multi-protocol adapter is integrated to realize unified access of heterogeneous data of industrial equipment, a low-delay transmission channel is constructed by using a 5G network slicing technology, and the heterogeneous data of the industrial equipment is transmitted to the cloud analysis platform. And transmitting the preprocessed data to the edge computing node in parallel. And the edge layer realizes dynamic resource scheduling by adopting a containerization technology, and realizes millisecond-level response and local decision feedback. And meanwhile, through an edge-cloud data synchronization mechanism, a distributed time sequence database is constructed, and visual monitoring and deep analysis of multi-dimensional data are supported. The system improves the real-time processing capability of industrial field data and the reliability of the system, and has the technical advantages of low time delay, high concurrency and optimized resource utilization rate.

The invention belongs to the technical field of concrete crack detection, and particularly discloses and provides a high-strength concrete construction crack detection system and method.The system comprises a multi-parameter cooperative sensing module, a construction crack recognition module, a crack risk analysis module and a crack early warning processing module. According to the invention, the temperature, humidity, strain and multispectral image data from a plasticity stage to a hardening stage are collected in real time through the multi-parameter cooperative sensing module, and crack characteristics are extracted by combining staged differential signal processing and a deep convolutional neural network, so that the detection precision of micro cracks and internal defects is remarkably improved. Meanwhile, multi-physics field data are fused, a crack evolution risk rate model is innovatively constructed, dynamic probability prediction before crack initiation is achieved, a grading disposal scheme is automatically triggered through risk grade division and a progressive early warning mechanism, drying shrinkage and temperature crack causes are effectively distinguished, the prevention and control efficiency is remarkably improved, and the method is suitable for large-scale popularization and application. And full-life-cycle accurate monitoring of the high-strength concrete is supported.

The invention relates to a power field Text2SQL (Structured Query Language) data analysis method based on LLM (Logical Language Model) and RAG (Random Access Gateway), and solves the defects that a retrieval result contains noise and redundancy and a large model still has a large number of illusion phenomena due to the absence of a vectorization model in a specific field and the singleness of knowledge base types and dimensions in the RAG technology compared with the prior art. The method comprises the following steps: constructing a data set of a Text2SQL data analysis method in the power field; constructing a multi-dimensional text knowledge base of the Text2SQL data analysis method in the power field; fine adjustment of an LLM base of a Text2SQL data analysis method in the electric power field; constructing an RAG retrieval module of the Text2SQL data analysis method in the power field; and constructing an Agent data analysis module of the Text2SQL data analysis method in the power field, and performing data analysis processing. According to the method, related information required by Text2SQL data analysis in the electric power field can be obtained by a large language model through retrieval on the basis of an RAG technology, meanwhile, the privacy and timely updating of the data are ensured by field data stored in a knowledge base form, and the model training cost is also greatly reduced by the LLM-based Text2SQL data analysis method taking a prompt project as a main part; and by setting the mixed retrieval parameters, the retrieval result has relatively high accuracy, and the effect of Text2SQL data analysis is ensured.

The invention relates to the technical field of gas pipeline safety assessment, in particular to a gas pipeline safety assessment early warning system and method based on data analysis. The method comprises the following steps: collecting gas pressure data of a gas pipeline at multiple points at the same time, carrying out time-space synchronization processing to obtain aligned gas pressure data so as to construct a continuous gas pressure field of the pipeline, based on the gas pressure field, identifying the pressure distribution difference of the pipeline, estimating the transient flow velocity of the gas, continuously recording the transient flow velocity of the gas, and deducing the change of the flow velocity. Judging the deformation condition of the inner wall of the pipeline in combination with air pressure field data, positioning a deformation area, reconstructing a pipeline frame, analyzing deformation response, identifying a deformation type, identifying a corrosion evolution trend, performing safety evaluation, judging the gas circulation degree and performing physical safety evaluation. According to the invention, dynamic monitoring of the gas pipeline is realized, a data-driven safety management mode is promoted, the early warning capability of potential risks is improved, and a more intelligent safety assessment and early warning mechanism is formed.

The invention relates to the technical field of vacuum electric furnace monitoring, and discloses a state monitoring system suitable for a vacuum electric furnace. A multi-source sensor array of the system collects multi-dimensional physical signals such as temperature distribution, pressure change and vacuum degree fluctuation in a furnace in real time; a furnace cavity feature reconstruction module extracts sampling point feature parameters and correlates coordinates to construct a three-dimensional dynamic feature field; the process anomaly analysis module calculates a process deviation degree in combination with a preset reference parameter, and marks an anomaly coordinate area; the state transition evaluation module analyzes an abnormal trend according to historical records and predicts a state transition path and rate; the collaborative regulation and control decision-making module generates a multi-stage vacuum maintenance compensation strategy and a heating power regulation gradient scheme according to the multi-stage vacuum maintenance compensation strategy; and the running log feedback module records a strategy execution process, and associates the three-dimensional feature field data to generate a state tracing log. The system can realize comprehensive monitoring of the state of the vacuum electric furnace, accurate abnormity identification, trend prediction, cooperative regulation and control and state tracing, and helps to improve the operation management level of the vacuum electric furnace.

The invention provides a multi-construction-method collaborative dust fall intelligent control method based on deep learning. The method comprises the following steps: collecting mine field data of a mine field in real time; wherein the mine field data comprises mine field environment data, equipment operation data and topographic data; constructing a digital mine field model based on the mine field data; wherein the digital mine field model comprises a terrain model, an equipment model and a dust generation and diffusion model; based on the mine field data and the digital mine field model, constructing a reinforcement learning agent; based on the reinforcement learning agent, performing dust fall control on the mine field; the dust fall control is monitored on the basis of the pre-created human-computer interaction interface, intelligent and efficient control over the mine field dust fall process is achieved, the dust fall effect and the resource utilization efficiency are improved, and meanwhile the flexibility and reliability of the system are improved through human-computer cooperative control.

The invention relates to the technical field of image recognition, in particular to an image monitoring system for traditional village heritage risk assessment, and the system comprises a heritage image collection module which is used for obtaining an image data stream of a target traditional village building surface, carrying out the geometric correction of original heritage image data, carrying out the image brightness equalization processing, and obtaining an image data stream of the target traditional village building surface; and establishing a calibrated image set. According to the method, image distortion and local overexposure caused by shooting angle difference or uneven illumination on the surface of a traditional village building are eliminated through geometric correction and brightness equalization processing, and the input data quality of subsequent feature analysis is improved. The consistency degree of crack textures in a neighborhood range is quantified based on gray gradient direction field data, the gray contrast of continuous crack edges is enhanced in combination with a dynamic threshold adjustment mechanism, non-structural texture interference is inhibited, and separability of micro cracks and background materials is enhanced.

The invention provides a water conservancy data acquisition supervision method and system based on big data analysis. The method comprises the following steps: firstly, obtaining dam body surface temperature field data, reservoir water level time sequence monitoring data and a dam body three-dimensional structure, and carrying out environmental radiation interference elimination processing on the temperature field data; secondly, extracting temperature fluctuation amplitude time sequence characteristics and water level change rate in the corrected temperature field data, and constructing a matrix reflecting correlation strength of the temperature fluctuation amplitude time sequence characteristics and the water level change rate through dynamic correlation analysis; and identifying temperature fluctuation abnormal points by using the matrix, and generating a seepage correlation map in combination with the water level change rate. And dividing seepage state categories according to the water level change rate, and generating a leakage risk probability distribution diagram by combining periodic trend prediction. And finally, fusing the graph with a dam body three-dimensional structure to generate positioning supervision information with a seepage abnormal identifier. The technical scheme provided by the invention can improve the efficiency and accuracy of water conservancy data acquisition supervision.

The invention provides an intelligent event identification method and system based on a high-speed camera, and the method comprises the steps: setting the collection parameters of the high-speed camera, and triggering the camera to collect a target scene video stream. And hardware acceleration decoding processing is carried out on the collected original video data stream, real-time environment illumination information of the environment illumination sensor is obtained, and dynamic brightness equalization processing is executed. And performing motion adaptive denoising processing on the video sequence. Geometric distortion correction is carried out on the image sequence through camera calibration parameters, sub-pixel-level displacement vectors and dense optical flow field data of a moving object are extracted, and multi-scale morphological features are extracted. And the features are fused to generate motion feature data, the data are processed through a spatio-temporal joint event classification model, an event identification result is output, the result is bound with a high-precision timestamp, and event identification information is output to an industrial control system display device in real time. According to the invention, the accuracy and real-time performance of event identification can be improved.

The invention discloses a strip-shaped compound planting mode water and fertilizer zoning management method and system, and relates to the technical field of modern agriculture, and the method comprises the following steps: installing a sensor network in a field, collecting data through a soil moisture sensor, an intelligent water meter and an unmanned aerial vehicle multispectral camera, threshold values are set according to the water demand and the fertilizer demand in the crop growth cycle to judge fertigation, crop demands are predicted by applying multiple linear regression and a random forest algorithm, a fertigation plan is optimized, the growth trend is analyzed, the yield potential is evaluated, the influence of the irrigation level is researched, and an irrigation strategy adjustment suggestion is provided. The system collects field data through an integrated sensor network, accurately sets an irrigation and fertilization threshold value, predicts crop demands by using an advanced algorithm, optimizes a water and fertilizer management plan, effectively analyzes a growth trend and evaluates yield potential, provides a scientific basis for irrigation strategy adjustment, and significantly improves modern agricultural production efficiency and resource utilization efficiency.

The invention provides a domain data management method and system based on an artificial intelligence platform, and the method comprises the steps: obtaining financial transaction data through a data warehouse, carrying out the preprocessing, and extracting transaction metadata and semantic association thereof through a natural language technology; and classifying the metadata by adopting a hierarchical clustering algorithm, and constructing a transaction data consanguinity map. And extracting time sequence and spatial features from the atlas, calculating a node interaction index by using a pre-configured data interaction prediction model, and marking a high-heat node. And extracting high-dimensional transaction association features based on the graph neural network model, and identifying abnormal transaction data. And marking an abnormal node in the blood relationship map, and tracking and positioning an origin node of abnormal data. According to the method, multiple advanced technologies are comprehensively utilized, and rapid and accurate risk identification can be realized in the face of massive financial transaction data.

The invention belongs to the technical field of automatic detection, and discloses an online laser detection and grading regulation and control system for particle size distribution of crushed raw ore. The invention aims to solve the problems of halo effect interference caused by adhesion of fine dust in a complex industrial environment and insufficient detection precision of a traditional method. The system obtains dynamic scattering light field data through the laser scanning module, identifies a halo effect region and generates a compensation coefficient by using the light field preprocessing module, and realizes accurate reconstruction of particle boundaries in combination with the edge reconstruction module. The particle size distribution analysis module extracts multi-dimensional geometric features based on the accurate boundary data, and generates a real-time particle size distribution curve. The grading strategy generation module formulates a regulation and control strategy according to deviation characteristics of the curve and a preset standard, and the dynamic regulation and control module adjusts parameters of crushing equipment in real time and optimizes particle size distribution. The method effectively overcomes the halo effect interference, improves the detection precision and the system stability, breaks through the single limitation of a traditional method, and provides comprehensive data support for a downstream process.

The invention discloses a metadata real-time self-adaptive standardization system for multi-field data sharing. The metadata real-time self-adaptive standardization system comprises a metadata information layering module, an intelligent information acquisition module, a real-time self-adaptive standardization module and a visual management module, the metadata information layering module identifies sources of various metadata, and sequentially performs cleaning, classification and layering preprocessing on the metadata; the intelligent information acquisition module uniformly acquires metadata information and converts the acquired data into standardized intermediate representation; the real-time adaptive standardization module is used for realizing rule dynamic adaptation and real-time optimization in a metadata standardization process, is internally provided with a dynamic rule library, is used for storing dynamically generated metadata mapping rules and supports automatic loading, updating and semantic extension of the rules; and the visual management module realizes management, analysis and dynamic adjustment of metadata through a visual interface. The method is high in accuracy of processing unknown data, can adapt to different data scenes in real time, and is high in semantic interoperation capability.

The invention provides a pump station flow channel flow field prediction and optimization method based on a transfer learning enhanced physical information neural network. The method comprises the following steps: constructing a training data set; constructing a loss function based on the control equation, the initial runner condition and the inlet and outlet boundary; a physical information neural network is constructed, hydraulic design parameters, space coordinates and time data are input into the physical information neural network, and pump station flow channel flow field data are output into the physical information neural network; intensively training the physical information neural network through time slice transfer learning by using the training data set based on the loss function to obtain a target physical information neural network model; determining a plurality of candidate hydraulic design parameters, and inputting each candidate hydraulic design parameter into the target physical information neural network model to obtain corresponding candidate pump station flow channel flow field data so as to obtain candidate performance parameters; and an objective function is constructed based on the performance parameters, the objective function is solved to obtain optimal performance parameters, and the candidate hydraulic design parameters corresponding to the optimal performance parameters are the optimal candidate hydraulic design parameters.

The invention relates to the technical field of three-dimensional observation, in particular to a geological deformation three-dimensional observation system and method, and the method comprises the steps: multi-dimensional data collection: synchronously obtaining data, and collecting ground temperature gradient field data and underground water flow velocity field data of a monitoring region; data space-time calibration processing: performing space-time reference unification processing on the acquired data to generate a multi-source displacement data set after space-time calibration; cold door parameter extraction: generating an environment coupling parameter matrix based on ground temperature gradient field data and underground water flow velocity field data in multi-dimensional data acquisition; modeling a three-dimensional deformation field: generating an initial three-dimensional deformation field model; dynamic model correction: performing iterative correction on the initial three-dimensional deformation field model to generate an optimized three-dimensional deformation field model; and visual early warning output: generating an early warning signal with a risk level identifier. According to the method, a multi-source heterogeneous data fusion system is constructed, and a cold geological parameter dynamic compensation mechanism is combined, so that high-precision three-dimensional deformation field modeling and early warning are realized.

The invention relates to the field of engineering management, and discloses a wharf steel structure construction progress dynamic management and control and resource allocation system, which comprises a real-time data perception and fusion module for collecting field data and an initial plan and generating a fusion data stream; the dynamic construction digital twinborn construction and evolution module is used for updating the digital twinborn and a dynamic empirical risk layer in real time based on the fusion data flow; the multi-agent self-adaptive decision engine is used for generating a self-adaptive construction scheme based on digital twinborn simulation deduction and feeding back a risk signal; and the man-machine cooperation and closed-loop execution module is used for visualizing the scheme for manual decision making and issuing an instruction to a site. According to the method, the physical field data sensed in real time is fused with the digital model, the dynamic construction digital twin capable of being synchronously updated with the physical construction field state is constructed, and the technical effect that digital information and the physical entity state are kept highly consistent is achieved.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.