196 results about "Spatiotemporal Analysis" patented technology

A federated distributed computational system enables secure biological data analysis and genomic medicine with enhanced oncological therapy capabilities. The system implements patient-specific tumor-on-a-chip analysis through microfluidic control systems and cellular heterogeneity preservation, while integrating fluorescence-enhanced diagnostics using CRISPR-LNP targeting and robotic surgical navigation. The architecture coordinates spatiotemporal analysis of gene therapy delivery through molecular imaging and immune response tracking, and implements bridge RNA integration with multi-target synchronization. Treatment selection is optimized through multi-criteria scoring and patient-specific simulation modeling. Each federated node contains a local processing unit for biological data analysis, privacy preservation protocols, and a hierarchical knowledge graph structure. The system implements cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration, enabling research institutions to collaborate on complex, large-scale biological analyses while maintaining strict data privacy controls.

The invention discloses a composite interference-oriented cultivated land system toughness evaluation and optimization method, and relates to the technical field of cultivated land toughness optimization, and the method comprises the steps: collecting and preprocessing multi-source cultivated land data, constructing a toughness evaluation index system, and calculating the weight of each index; and calculating a comprehensive toughness score based on weight distribution, drawing a time sequence change curve based on the score, and performing region division to generate a toughness distribution diagram. According to the method, the single-factor pollution index, the Nemo comprehensive index and the interaction effect index of heavy metal pollution are combined, the soil pollution condition is accurately quantified through a weighted summation method, a scientific and reliable basis is provided for toughness evaluation, the dynamic toughness distribution diagram is generated in combination with the LSTM model and the GIS technology, the spatio-temporal dynamic characteristics of a cultivated land system under composite interference are revealed, and the method has the advantages of being high in reliability and high in reliability. According to the method, the defects of an existing model in space-time analysis are overcome, differentiated optimization strategies are formulated based on region division and weak region labeling, and the resource allocation efficiency and the pertinence of an optimization scheme are remarkably improved.

The invention relates to an optimization method for dynamic monitoring of an underground water level. Comprising the following steps: uniformly arranging a plurality of sensors in an underground water monitoring area, and monitoring multi-dimensional parameters including water pressure, temperature and humidity; the sensors form a gridding monitoring system through a wireless network; inputting multi-dimensional data acquired by the sensor array into a data fusion module, performing cooperative processing on the data by using a data fusion algorithm, and filtering noise and abnormal values; a unified water level change model is constructed, so that data of different parameters complement each other; dynamically adjusting the weight in the fusion algorithm regularly according to the historical data and the new measurement value; in the monitoring area, based on the dimensions of time and space, a space-time analysis model of underground water level changes is constructed, and the periodicity and regularity of water level fluctuation are excavated; and performing trend analysis on the historical water level data by adopting a time sequence analysis algorithm including ARIMA or LSTM to generate short-term and long-term water level prediction.

The invention provides an underground water pollution source tracing and evaluation method for a riverside water source in a karst area, and the method comprises the following steps: collecting and preprocessing multi-source information, and obtaining a multi-source data set; a water quality sample is collected and analyzed, the surface water-underground water interaction process and pollution characteristics are comprehensively analyzed, and a pollution source type is obtained; performing space-time analysis on the multi-source data set to obtain a pollution activity time period and a potential pollution source area; constructing a karst area hydrological model, simulating the hydrological characteristics of the karst area, constructing a pollutant migration model, and simulating the migration process of pollutants; and constructing a traceability algorithm, tracking pollutants, confirming a pollution source, carrying out risk partitioning and risk assessment, and carrying out visual display of a result. According to the invention, interaction between surface water and underground water can be effectively assessed, accurate traceability of pollutants and multi-angle accurate identification of pollution sources can be realized, and management efficiency can be optimized through reliable risk assessment prediction.

The invention provides an edge computing traffic light intelligent decision-making method integrated with AI video analysis, and the method comprises the steps: collecting a real-time traffic video stream of a target intersection through an edge computing device, and extracting a dynamic traffic feature set in the real-time traffic video stream, calling a pre-trained space-time analysis model to carry out multi-modal fusion processing on the dynamic traffic characteristic set, generating a traffic state vector of the target intersection, matching a candidate control strategy in a preset decision rule base based on the traffic state vector, and carrying out parameter adjustment on the candidate control strategy through a strategy optimization model to obtain a target traffic state vector; generating a target signal lamp control parameter; and issuing the target signal lamp control parameters to a traffic signal control terminal of the target intersection, and monitoring traffic state change data of the target intersection in real time to update weight parameters of the space-time analysis model. According to the invention, the traffic efficiency, pedestrian safety and traffic management intelligence degree of urban intersections can be improved.

A federated distributed computational system enables secure, multi-institutional biological data analysis and genomic medicine through interconnected, decentralized nodes in a federated distributed graph architecture. A federation manager coordinates computational resource allocation, control and data flows, establishes privacy and security boundaries, implements multi-scale spatiotemporal analysis and simulation modeling, models cross-species or intrapopulation elements, and maintains cross-institutional knowledge relationships. Each node includes a local processing unit for biological data analysis, including multiomics and gene editing, privacy-preserving protocols for secure multi-party computation, a hierarchical knowledge graph for managing multi-domain biological relationships across spatial and temporal scales, and encrypted network connections. The system implements cross-species genetic analysis via phylogenetic integration, environmental response modeling through spatiotemporal tracking, and multi-scale tensor-based data integration with adaptive dimensionality control. This architecture enables research institutions to collaborate on complex biological analyses and genomic medicine applications while maintaining strict data privacy and security controls.

The invention provides a power transmission and transformation project ecological influence space-time boundary determination method and system based on multi-source remote sensing information deep fusion interpretation, and belongs to the technical field of power transmission and transformation project management. The method comprises the steps of data acquisition, data preprocessing and multi-source data integration, multi-scale space-time analysis, ecological influence index construction, remote sensing ecological index model construction, ecological influence space-time boundary determination, dynamic monitoring and early warning system platform establishment and long-term monitoring and evaluation. By integrating satellite remote sensing, unmanned aerial vehicle aerial photography and ground monitoring data and combining an image fusion algorithm, a principal component analysis method and a random forest algorithm technology, the method is beneficial to promoting accurate evaluation of the influence of the power transmission and transformation project on the ecological environment, visualizes the space-time boundary of the ecological influence, breaks through the limitation of a traditional evaluation method on time and space coverage, and improves the evaluation efficiency of the power transmission and transformation project. And a scientific method system for analyzing and evaluating the ecological influence of the power transmission and transformation project is further improved.

The invention discloses a digital production plan scheduling method and system, and belongs to the technical field of optimal scheduling, and the method comprises the steps: constructing a distributed storage architecture based on edge computing nodes; a central coordinator is adopted to realize cross-node data synchronization through an improved Raft consensus algorithm, multi-version concurrency control is realized based on a vector clock, and a global consistent data view is established; a visual scheduling platform is built based on a Vue3 framework, and man-machine interaction is realized by adopting a Canvas and WebGL collaborative rendering framework; establishing a dynamic coordinate conversion model based on bilinear interpolation, designing a space mapping function containing distortion compensation, establishing a multi-thread coordinate service based on WebWorker, and realizing submillimeter-level bidirectional mapping of pixel coordinates and physical coordinates; constructing a three-dimensional space-time analysis model fused with the multi-dimensional features; and all the units are subjected to feature fusion through residual connection, and finally a scheduling scheme with a confidence coefficient weight is output. The method and the device have the effect of meeting various scheduling requirements.

The invention relates to the technical field of fault positioning, in particular to a power wire fault positioning system based on the Internet of Things, which comprises a data collection module, a fault analysis module and a data visualization module, firstly, a data collection module obtains wire data collected by a sensor on a wire in real time; secondly, the fault analysis module is used for carrying out data processing on the wire data to obtain fault data; according to the conductor data and the fault data, constructing a power conductor digital twinborn model; analyzing the fault features and the environmental parameters through a power conductor digital twinborn model to obtain an output result; analyzing the output result through a topology detection algorithm and signal sensitivity adjustment to obtain an optimized output result; obtaining a space-time propagation process formula of the fault signal through signal propagation simulation and space-time analysis according to the output result; evaluating the accuracy of an output result according to a space-time propagation process formula of the fault signal; and finally, the data visualization module generates a visualization chart and an analysis report.

The invention discloses an urban water supply management data trend analysis method based on space-time analysis, and relates to the field of data processing, and the method comprises the steps: collecting data in real time through an urban water supply pipe network sensor network, building a space-time unified coordinate system, building a space-time Kriging interpolation model based on pipe network topology, and achieving the space-time alignment of multi-source data; dividing an adaptive space-time grid by using a Voronoi diagram and a sliding window mechanism, and calculating multi-dimensional features; constructing a dynamic space-time diagram by taking a grid as a node, performing multi-step prediction in combination with a space-time diagram convolution circulation network, fusing a Kriging interpolation result, and evaluating an abnormal probability and a confidence interval through a Bayesian neural network; a monitoring layer, a prediction layer and a risk layer are overlaid in a three-dimensional GIS, a dynamic thermodynamic diagram is generated, an early warning path is optimized based on a Dijkstra algorithm, and a minimum risk topology path is output. The method has the advantages that through space-time analysis and accurate prediction, the intelligence, stability and emergency response efficiency of urban water supply management are remarkably improved, and powerful support is provided for smart city construction.

The invention provides an intelligent crop monitoring system based on spatio-temporal data, and relates to the technical field of crop monitoring, and the system comprises a data collection module which is used for collecting multi-source spatio-temporal data of a target farmland area; the multi-source spatio-temporal data comprises remote sensing images, meteorological data, soil data and farmland sensor data; the data processing module is used for carrying out preprocessing and data fusion on the multi-source spatio-temporal data to obtain a unified spatio-temporal data set; the intelligent analysis module is used for performing intelligent analysis on the growth state, the pest and disease risk and the yield of the crops according to the spatio-temporal data set based on artificial intelligence and spatio-temporal analysis technologies to obtain an analysis result; and the visual display module is used for carrying out visual display on the analysis result. The agricultural production efficiency and the management level are improved, powerful support is provided for sustainable agricultural development, and remarkable economic, social and environmental benefits are achieved.

The invention relates to the technical field of intelligent agricultural equipment, in particular to a tomato disease diagnosis method based on multi-modal data analysis, which comprises the following steps of: 1, synchronously acquiring and preprocessing multi-modal data, synchronously triggering a hyperspectral imaging device and a microscopic camera, respectively acquiring a plant canopy hyperspectral image and a stem microscopic image, and acquiring a plant canopy hyperspectral image and a stem microscopic image; meanwhile, temperature, conductivity and dissolved oxygen environment parameters are continuously collected in the root zone; 2, self-adaptive feature extraction and fusion in the growth stage are carried out, reflectivity correction and leaf segmentation are carried out on the hyperspectral image, and leaf surface spectrum curve features are extracted; step 3, hybrid model construction and space-time analysis: constructing a hybrid model comprising spectrum, microscopy and environment analysis networks, and dynamically adjusting each network weight through a gating network; and 4, generating a disease decision. The method can realize accurate, efficient and real-time tomato disease diagnosis, has high practical value, and can effectively improve the disease prevention and control capability in agricultural production.

The invention discloses an intelligent traffic flow dynamic regulation and control method and system based on multi-source data fusion, and relates to the technical field of data processing. The method comprises the following steps: executing multi-source data acquisition through a data acquisition interface to obtain a multi-source traffic data set; performing spatial fusion on the multi-source traffic data set to obtain a flow marking map; performing current traffic state analysis based on the flow marking map, identifying local and global congestion, and constructing a traffic situation map; and performing traffic flow state prediction in a preset future time zone based on the traffic situation map, performing regulation and control decision based on a prediction result, and generating a traffic flow regulation and control strategy. The technical problem that traffic flow prediction and regulation are not accurate enough in the prior art is solved, and the technical effects that intelligent traffic flow dynamic regulation is achieved through multi-source data fusion and space-time analysis, and the traffic management precision and the response speed are improved are achieved.

The invention provides a key component fault prediction method and system based on multi-source data fusion, and the method comprises the steps: S1, collecting key component data of a screen scarifier, constructing a multi-source heterogeneous sensor network, and completing the time-space alignment and quality optimization of multi-dimensional monitoring data; s2, a dynamic topological graph structure is constructed based on the physical connection relation of the components, the fault propagation path and strength are quantified, and multi-level feature representation covering the local state and the overall health degree is formed; s3, designing a hybrid prediction model fusing space-time analysis and memory modeling, and completing accurate description of an equipment degradation trend and early warning of potential faults through a self-adaptive feature integration mechanism; and S4, combining real-time prediction errors and historical operation and maintenance knowledge to dynamically optimize the hybrid prediction model, establishing a data-driven and knowledge-guided dual learning framework, and completing self-adaptive continuous learning along with equipment aging. The method breaks through the limitation of static state of a traditional prediction model, and the accuracy and reliability of a prediction result are remarkably improved.

The invention belongs to the technical field of cloud computing and communication resource management, and discloses a communication equipment resource dynamic allocation method and system based on cloud computing, and the method comprises the steps: obtaining biological migration tide data, communication network load historical data and environmental parameter data, constructing a migration trajectory prediction model and a resource demand prediction model, and carrying out the prediction of a migration trajectory; generating an elastic resource allocation scheme; base station group division is carried out on a communication network coverage area, resource demand space-time analysis is carried out based on a migration trajectory prediction result, and a resource utilization rate index is calculated; generating a resource scheduling instruction set based on the resource elastic allocation scheme, performing priority ranking, optimizing resource allocation in combination with a resource utilization rate index, and generating a base station parameter configuration instruction; adjusting the resource configuration of the base station according to the configuration instruction, monitoring the operation state, evaluating the resource efficiency, performing feedback tuning and applying to the resource elastic allocation scheme; and the equipment operation and maintenance complexity and the manual intervention requirement are effectively reduced.

The invention provides a multi-level collaborative urban physical examination knowledge reasoning method and related equipment. The method comprises the following steps: acquiring a knowledge base constructed by an urban physical examination knowledge graph and a question request; the question request is input into an entity-level reasoning module for entity and attribute recognition, an initial evidence triple is obtained, knowledge mining is conducted on the initial evidence triple according to a knowledge base, and an evidence triple is obtained; inputting the question request and the evidence triple into a function level reasoning module for identification to obtain an urban physical examination analysis function required in the question request, and calling a space-time analysis tool for space-time analysis according to the urban physical examination analysis function to obtain an evaluation analysis result; inputting the problem request, the evidence triple, the knowledge base and the evaluation analysis result into a decision-making reasoning module to perform knowledge reasoning on the problem request to obtain a reasoning result; the problems that in the urban physical examination knowledge reasoning process, knowledge mining is not deep enough, tool arrangement efficiency is low, and knowledge reasoning is not fine enough are solved.

The invention discloses an intelligent-based abnormal carbon emission data automatic identification early warning system, which belongs to the technical field of intellectualization and comprises a data acquisition preprocessing module, a high-precision space-time analysis module, an intelligent analysis module, an automatic abnormal identification module, a carbon footprint tracing module, a self-adaptive adjustment module and an intelligent emission prediction module. A carbon emission source and time-space distribution characteristics are accurately positioned through spatial positioning and time sequence analysis of the high-precision time-space analysis module, a spatial distribution diagram is more detailed and accurate through the optimized sensor position and an interpolation algorithm, and time sequence analysis is more accurate through timestamp correction and power spectrum density analysis. The method effectively evaluates the periodic intensity of the signal, analyzes a hidden mode and an association rule in the data through the intelligent analysis module integrating the spatial-temporal characteristics and the related information of the data acquisition and preprocessing module, analyzes the spatial-temporal association between variables through the calculation of a clustering center and the association intensity, and facilitates the discovery of a potential abnormal mode.

The invention discloses a surveying and mapping method based on remote sensing big data analysis, and relates to the technical field of remote sensing big data. During operation of the system, images and spectral data of a target water body area are collected through satellite remote sensing, unmanned aerial vehicle remote sensing or a ground sensor, the collected remote sensing data are preprocessed, and the target water body area is obtained through waveband combination or spectral line analysis; the method comprises the following steps: extracting characteristic parameters of a water body by utilizing spectral information in remote sensing data, carrying out multi-dimensional analysis on field data, calculating to obtain a water body turbidity coefficient Ct, an algal bloom coefficient Ca and an oil pollution coefficient Co, and analyzing the influence of potential pollution sources, agricultural runoff and industrial wastewater discharge on the water body by combining the remote sensing data and geographic information. And performing spatial-temporal change analysis on the water body pollution condition, identifying the trend of pollutant expansion or degradation, and based on the results of pollution assessment and spatial-temporal analysis, comparing the comprehensive water quality index WQI with a preset threshold to generate water quality level early warning information.

The invention relates to the field of remote sensing image processing, in particular to a karst landform remote sensing dynamic evolution analysis method and a karst landform remote sensing dynamic evolution analysis system, which can be applied to the fields of karst region resource management, environmental protection, geological disaster early warning and the like. According to the method, karst landform change information is obtained through multi-temporal remote sensing image data, a time-space analysis model composed of self-attention enhancement, a feature pyramid and a recurrent neural network is constructed, a constraint module is designed in combination with karst landform priori knowledge, and accurate prediction is achieved; multi-source data fusion improves the feature capture capability, and the observation advantage is obvious especially under complex meteorological conditions; the multi-module cascade design is used for accurately modeling space-time dynamic change, and the prediction accuracy is improved; priori knowledge constraint is introduced to enhance feature recognition, and misclassification is reduced; on the whole, the method effectively integrates multidisciplinary technologies, the precision and reliability of karst landform dynamic evolution analysis are improved, and a powerful tool is provided for research in related fields.

The invention discloses a risk monitoring method, device and equipment for a source load storage network, a medium and a program product, and the method comprises the steps: obtaining accident monitoring data of a sky-ground system, carrying out the preprocessing of the accident monitoring data, obtaining target risk data, constructing a risk knowledge graph of the source load storage network based on the target risk data, and obtaining a risk knowledge graph of the source load storage network; obtaining a fault factor of the source load storage network, analyzing association information between the fault factor and the risk knowledge graph, and constructing a defect spreading model based on the association information and the environment feature information and seasonal feature information of each risk entity, real-time monitoring data, collected by the sky-ground system, of the source load storage network are input into the defect spreading model for risk analysis, risk monitoring is carried out on the source load storage network based on a risk analysis result, space-time analysis is carried out on multi-dimensional data of the sky-ground system, the relationship between faults is fully mined in combination with a knowledge graph, and the risk of the source load storage network is monitored. The accuracy of risk disaster prediction is effectively improved, and stable operation of a power system is ensured.

The invention discloses a space-time carbon flux digital map construction method, which comprises the steps of three-dimensional vector information acquisition, space-time carbon flux accounting database establishment, carbon flux space-time analysis, carbon flux influence factor identification and space-time carbon flux digital map display. Real-time and high-precision carbon flux data of a target area are measured through a carbon flux measurement system, a space-time carbon flux accounting database is constructed, carbon flux time analysis and space analysis are carried out to judge the carbon flux space-time distribution trend of the measured area, and a carbon flux space-time geographic weighting model is used for identifying carbon flux influence factors. A carbon flux digital map is displayed on the large digital screen in real time; according to the method, high-time and high-space-precision carbon flux accounting and influence factor identification of the measurement area can be realized, and detection, management and prediction of the space-time carbon flux of the target area are realized in a precise quantification, visualization and artificial intelligence manner.

The invention provides a historical and cultural block spatial evolution simulation method and system based on GIS space-time analysis, and the method comprises the steps: firstly obtaining a target space-time data set of a target block, which comprises geographic space distribution, building form change, humanistic activity tracks and the like, and then carrying out the preprocessing of a space-time topological structure; generating a target spatio-temporal topological data set containing a spatio-temporal association block space unit sequence and a dynamic attribute tag, and then extracting a static structure, a dynamic evolution mode and a space interaction dependency feature of each space unit based on a preset spatio-temporal evolution feature extraction model; and calling a space-time evolution prediction model to obtain evolution probability distribution and evolution association strength, and finally constructing a space-time evolution path map according to the evolution probability distribution and the evolution association strength, thereby generating a spatial form simulation result and planning intervention decision parameters of a target block, and realizing effective simulation and planning decision support for spatial evolution of historical and cultural blocks.

The present invention provides a method for improving the accuracy of event detection based on multi-model fusion, comprising the following steps: performing multimodal data preprocessing on a monitoring video stream to generate a standardized feature matrix; performing feature-level fusion of the outputs of a YOLOv5 improved model, a ResNet Transformer hybrid model, and an LSTM spatiotemporal analysis model through an adaptive weighted fusion module; performing multidimensional event detection based on the fused feature matrix to generate a preliminary detection result set; establishing a three-level logical judgment mechanism to verify the confidence of the detection result; and outputting the verified final event detection result to a monitoring system. The present invention can improve the accuracy of event detection and is suitable for monitoring event detection in complex environments.

The invention provides a multi-dimensional flood disaster analysis method. The method comprises disaster space-time analysis, disaster semantic analysis, response adequacy analysis and population exposure degree analysis. According to the disaster situation space-time analysis, disaster situation related information extracted from a text is associated with a time attribute and a space coordinate attribute; analyzing the disaster information from the angles of time sequence and space distribution; the time sequence analysis adopts a quantitative statistics mode, and key nodes of the flood event are analyzed from the precipitation trend, the flood discharge trend, the microblog number trend, the ponding point number trend, the help seeking information number trend and the taken measure number trend; according to disaster semantic analysis, subject analysis is carried out on the text of each help-seeking point, and semantic information of disaster manifestation in space is mined; according to the response measure sufficiency analysis, the disaster response sufficiency is obtained by analyzing the correlation between the number of response measures and the number of help seeking information.

The invention relates to a tartary buckwheat pest and disease damage dynamic monitoring method, system and device and a medium, and belongs to the technical field of agricultural intelligent monitoring, the dynamic monitoring method comprises the following steps: periodically collecting environmental parameter data through fixed sensor nodes deployed in a farmland, and obtaining leaf vibration signals and multispectral image data at the same time; performing space-time alignment on the blade vibration signal and the multispectral image data, correcting radiation distortion in the multispectral image data, and outputting a registration data set; according to the registration data set, fusing to generate a multi-modal feature vector, inputting the multi-modal feature vector into a pre-trained space-time analysis model, and outputting a risk level distribution diagram with a geographic coordinate mark; generating a control instruction set according to the risk level distribution map, and triggering execution equipment to execute pest control operation; and optimizing weight parameters of the space-time analysis model through the generative adversarial network based on the execution log of the control instruction set and the historical multi-modal feature vector. The scientificity and timeliness of pest control can be improved.

The invention discloses a digital intelligent operation monitoring system for an expressway. The system comprises a data acquisition module, a feature extraction and fusion module, a space-time analysis and prediction module, an anomaly detection module and an intelligent decision module. Wherein the feature extraction and fusion module fuses multi-source heterogeneous data by using a collaborative mechanism of a graph neural network and an auto-encoder; the space-time analysis and prediction module generates a prediction result containing a congestion diffusion probability by using a Fourier neural operator, and provides reference data for anomaly detection; and the intelligent decision-making module inputs the prediction result as an environment model of a reinforcement learning agent, and dynamically adjusts a reward function according to the predicted congestion diffusion probability, so as to realize prospective autonomous decision-making. According to the invention, through deep cooperation and feedback closed loop among the modules, the traffic operation efficiency and the safety emergency capability are significantly improved.

The invention discloses a character and activity traceability analysis method and system based on open source geospatial intelligence and electronic equipment, and the method comprises the steps: constructing an intelligence library and a character target library; obtaining to-be-analyzed target person information; searching according to the target person information to obtain intelligence information; performing event extraction on the intelligence information to obtain event information and construct an event library; according to the target person information, supplementing the information library to obtain supplemented target person information; the intelligence information is screened; performing space-time analysis and event analysis on the clustered intelligence information to obtain space-time information; according to the spatio-temporal information, event venation analysis is carried out, an intelligence result is formed, and character and activity traceability analysis based on open source geospatial intelligence is completed. According to the method, the problem that efficient, accurate and reliable traceability analysis on multi-mode and multi-source open-source geospatial information data is difficult to realize is solved.

The invention relates to the technical field of cadastral data dynamic management, and discloses a cadastral data dynamic updating and storage method, device and equipment and a storage medium. By receiving a land law enforcement task set of a target law enforcement vehicle, a law enforcement driving route map for a plurality of law enforcement tasks is constructed; issuing cadastral data of each law enforcement task to a cadastral data edge library at different driving road sections is taken as a decision variable, and the storage capacity of a target law enforcement vehicle, the maximum communication capability of each driving road section, the cadastral data advanced issuing characteristic and the cadastral data real-time performance are considered; and controlling cadastral data scheduling of the cadastral data edge library and the cadastral data core library, and assisting law enforcement officers in land law enforcement. Therefore, by adopting the task-oriented accurate data issuing and dynamic and the space-time analysis-based bandwidth prediction and optimization strategy, organic unification of the real-time performance, the security and the access efficiency of the cadastral data is realized, and efficient and reliable data support is provided for land law enforcement.

The invention discloses a variable polyadenylation dynamic analysis method and device. The method comprises the following steps: acquiring initial space transcriptome data and an initial space bright field image; performing first preprocessing on the initial space transcriptome data to obtain target space transcriptome data; performing second preprocessing on the initial space bright field image to obtain a target space bright field image; performing data analysis and integration processing to obtain spatial variable polyadenylation data, spatial gene expression data and spatial cell distribution dynamic data; constructing a gene expression regulation network; generating an integrated analysis result; performing space-time analysis to obtain a space-time analysis result; performing trajectory analysis to obtain a trajectory analysis result; performing disease process analysis to obtain a disease process analysis result; and generating a variable poly-adenosine acidification dynamic analysis result. According to the invention, dynamic analysis of variable polyadenylation is realized, the accuracy is improved, and multi-angle biological understanding is provided. The method can be applied to the technical field of gene analysis.

The invention relates to the technical field of building energy consumption monitoring, and discloses an intelligent building energy consumption monitoring system, which comprises a data acquisition module for acquiring indoor environment data and outdoor environment data of M sub-regions; the space-time heterogeneous graph construction module is used for constructing a space-time heterogeneous graph according to the indoor environment data and the outdoor environment data of the M sub-regions; the energy consumption prediction module is used for analyzing the space-time heterogeneous graph through an energy consumption prediction model to obtain the power consumption of the M sub-regions in N time periods in the future; the energy consumption optimization scheme generation module is used for generating an energy consumption optimization scheme through an optimization algorithm according to the power consumption of the M sub-regions in the future N time periods; according to the method, the space-time heterogeneous graph is formed by the correlation influence among the areas in the building, the space-time analysis is performed on the space-time heterogeneous graph through the energy consumption prediction model to realize the multi-period energy consumption prediction of all the sub-areas, so that the prediction precision is improved, the intelligent active energy consumption optimization is realized in combination with the optimization algorithm, and the operation cost is minimized.