7172 results about "Data collecting" patented technology

The invention discloses an Internet of Things multichannel earthquake early warning emergency linkage system, belongs to the technical field of earthquake early warning, and aims to solve the problems of insufficient real-time performance, reliability, scene coverage and safety in the prior art. The sensing layer collects data cooperatively through various sensors, eliminates environmental interference and ensures high-precision seismic wave detection, the edge calculation layer filters noise in real time by adopting an advanced algorithm, and confirms seismic events through multi-dimensional comparison, so that the local processing efficiency is remarkably improved, the cloud load is greatly reduced, a cloud platform fuses multi-source data, and the seismic detection efficiency is improved. A machine learning model is combined to dynamically optimize an early warning threshold value, the accuracy of earthquake type classification and intensity prediction is improved, full-link optimization from data acquisition to intelligent decision making is realized, a multi-channel distribution module is designed, instructions are transmitted according to priority levels, and urban and rural full-scene coverage is ensured through multiple communication technologies.

The invention relates to the technical field of the Internet of Things, and discloses a gas leakage monitoring method, device and equipment based on the Internet of Things and a storage medium, and the method comprises the steps: carrying out the data collection of a gas storage device, and obtaining an initial multi-dimensional data set; performing filtering and edge detection processing to obtain potential abnormal point data; inputting the potential abnormal point data into a mixed deep learning model for gas leakage detection to obtain initial leakage source position data and a leakage intensity value; executing multi-sensor cooperative accurate leakage positioning to obtain target leakage source position data; performing gas diffusion simulation to obtain gas concentration distribution data and risk indexes; based on the gas concentration distribution data and the risk index, dynamic reconstruction of the sensor network is carried out, an emergency response strategy and sensor network configuration information are output, and the method realizes whole-process management from leakage detection and source positioning to risk assessment and emergency response.

The invention discloses a tidal river reach storm surge rapid forecasting method based on deep learning and an AI large model. The method comprises a data acquisition and preprocessing step, a meteorological element forecasting step, a typhoon path and flow forecasting step and a storm surge forecasting step. Multi-source data fusion is realized through a four-dimensional variational assimilation method and an adaptive weight fusion algorithm; a multi-model integrated system such as FourCatNet and the like is used for forecasting meteorological elements; an improved XLSTM-Transformer model is adopted to predict the flow and the typhoon path; and carrying out storm surge forecasting based on a three-layer nested model system and a multi-process coupling technology. According to the method, full-process automation from data acquisition to forecasting is realized, the forecasting timeliness is improved by 60%, the average absolute error is reduced by 45%, and a reliable basis is provided for disaster prevention and reduction decisions.

The invention relates to the technical field of vulnerability management, and discloses a vulnerability management method and system based on an adaptive security platform. The method comprises the following steps: constructing an asset information database based on all IT assets in an organization network environment, and calculating a time sensitivity parameter set; based on the asset information database and the time sensitivity parameter set, executing aperiodic time stratification vulnerability data collection and dynamic self-shaping processing to obtain a standardized structure vulnerability data set; performing vulnerability utilization chain topology analysis through the bidirectional adversarial neural network model to generate a vulnerability risk score and a vulnerability association relationship graph; and generating vulnerability risk decision information according to the vulnerability risk score and the vulnerability association relationship graph, and performing constraint perception adaptive repair arrangement based on the vulnerability risk decision information to generate an optimal vulnerability repair scheme. The vulnerability discovery process is more efficient and accurate, the repair success rate is improved, the service interruption time is shortened, and continuous optimization of the repair process is achieved.

The invention discloses an informatization project management system based on big data analysis, which belongs to the field of big data and comprises a data acquisition module, a time sequence modeling module, a task coupling analysis module, a risk clustering identification module, a resource allocation prediction module and the like. The data acquisition module asynchronously and parallelly acquires structured and unstructured data and uniformly encodes the structured and unstructured data; the time sequence modeling module constructs a multi-dimensional time sequence based on an autoregressive residual network; the task coupling analysis module fuses the task trajectory and the dependency relationship to generate a task influence directed graph; the risk clustering identification module identifies risks through variational graph auto-encoder mapping; the resource allocation prediction module constructs a dynamic resource priority based on a graph attention mechanism; a progress deviation traceability module identifies a deviation causal chain; the knowledge graph decision-making module corrects resource priorities and path strategies in a cross-graph manner; and the project global control module dynamically adjusts key paths and resource configuration and performs closed-loop self-correction. The beneficial effect is that the intelligent level and the risk response capability of project management are improved.

The invention relates to the technical field of intelligent building management, in particular to an intelligent building full-life-cycle monitoring management method and system, and the system comprises a data collection module, a multi-source data fusion and analysis module, a full-life-cycle management model construction module, an intelligent early warning module, a self-adaptive feedback regulation and control module, a data storage module and a remote visual monitoring module. According to the system, environment parameters and equipment operation state data of a building are acquired in real time through the data acquisition module, key operation indexes are extracted by using the multi-source data fusion and analysis module, and accurate risk assessment and early warning are realized under the support of the full-life-cycle management model. The self-adaptive feedback regulation and control module can dynamically adjust key operation parameters according to real-time data to ensure stable operation of equipment under different environment conditions, the data storage module supports long-term performance analysis, and the remote visual monitoring module realizes remote monitoring and multi-party collaborative management. According to the invention, the operation safety, stability and management efficiency of hydraulic facilities are effectively improved.

This application relates to the field of geological disaster early warning technology, and discloses a method for monitoring, predicting and warning of geological disasters based on artificial intelligence, including the following steps: data collection: collecting geological disaster monitoring data; data preprocessing: time alignment and missing completion of the collected data; spatiotemporal correlation matrix construction: analyzing the preprocessed monitoring data; feature extraction: extracting the nonlinear spatiotemporal characteristics of geological disaster monitoring data; time series prediction: outputting the geological disaster risk level in the future period; feedback optimization: based on the error between the prediction result and the actual observation data; warning release: generating geological disaster warning information based on the predicted risk level. Through data collection, preprocessing, feature extraction, prediction optimization and warning release, a complete geological disaster monitoring and prediction method is constructed, which integrates multi-source data, extracts key features and dynamically predicts risks, so as to ensure the accuracy of the model and provide a scientific basis for disaster prevention and control.

The invention discloses a data quality monitoring method and system based on a knowledge graph. According to the method, data tables and fields in multi-source heterogeneous data are constructed into graph nodes, graph edges are constructed through main and foreign key constraint relationships and field value overlapping analysis, and a data blood relationship knowledge graph is formed; business rules are coded into the atlas to execute data constraint check, and an abnormal data influence range is traversed and tracked through a directed graph; and dynamic monitoring is realized based on the time change of the abnormal score sequence. The invention further provides system implementation including data acquisition and preprocessing, knowledge graph construction, quality monitoring, problem diagnosis and quality evaluation. The method solves the problems that a traditional method is difficult to track the influence range of abnormal data and lacks systematic evaluation, and improves the automation level and accuracy of data quality management.

The invention relates to the technical field of ecological environment protection, in particular to an ecological environment damage intelligent identification management method based on multi-source data fusion, which comprises the steps of data acquisition and preprocessing, data fusion and feature extraction, ecological environment damage evaluation model construction, damage dynamic analysis and trend prediction, restoration suggestion generation and visual display. The method comprises the following steps: collecting water quality, atmosphere, biological and social economic data in real time through a sensor network deployed in an ecological sensitive area, preprocessing the data, extracting key features by adopting a multi-modal data fusion technology and a deep learning model, and constructing an ecological damage assessment model; the change rule of ecological damage and the potential high-risk area are predicted based on a time sequence analysis method, and an optimal ecological restoration scheme is generated in combination with a particle swarm optimization algorithm. According to the method, the evaluation result and the restoration suggestion are displayed through the visual interface, a scientific basis is provided for decision makers, and the efficiency and the effect of ecological environment restoration work can be improved.

The invention discloses a robot real-time potential safety hazard recognition system based on multi-modal sensor fusion, and particularly relates to the technical field of intelligent inspection and safety monitoring, the system comprises five parts of data acquisition, information fusion, behavior response, trajectory analysis and risk output, and the potential safety hazard recognition system is used for recognizing potential safety hazards through image acquisition, thermal imaging, gas concentration and temperature and humidity information. Carrying out numerical value normalization and feature extraction, identifying potential abnormity and generating early warning; triggering data enhanced acquisition and track recording in the target area, and constructing a space-time path model to analyze an abnormal evolution trend; and finally, outputting a potential safety hazard assessment result according to a risk level classification rule by combining the enhanced information and the trajectory features. According to the method, high-precision early warning is realized through multi-source data acquisition and normalization fusion, the local recognition capability is improved based on dynamic enhanced acquisition of a behavior response mechanism, an abnormal development trend is tracked by combining track evolution modeling, risk level assessment is output according to the abnormal development trend, and accurate recognition and dynamic management and control of hidden dangers are realized.

The invention discloses a disaster prevention emergency communication command management system based on a remote communication technology, and relates to the technical field of emergency communication and disaster management, and the system comprises a data collection and fusion module which is used for obtaining multi-source data from a communication network state, navigation positioning information and remote sensing monitoring data, and processing the multi-source data by adopting a dynamic weighted fusion algorithm. According to the disaster prevention emergency communication command management system based on the remote communication technology, by using a data acquisition and fusion algorithm, an accurate disaster evolution prediction model and an optimized resource scheduling scheme, the lagging of disaster emergency decision can be significantly reduced, and the emergency response efficiency is improved; through intelligent decision support and resource scheduling, it is ensured that emergency commanders can make correct decisions quickly and efficiently, loss caused by disasters is reduced to the maximum extent, emergency resources can be allocated in time under extreme conditions, and it is ensured that emergency response is not interrupted.

The invention belongs to the technical field of intelligent health monitoring, and discloses a retired old people home safety monitoring system based on Internet of Things, which comprises the following steps: deploying multiple types of sensors for the home environment of old people to carry out all-weather data acquisition and analysis to obtain behavior data with activity feature tags; performing correlation analysis to obtain a personalized security risk assessment model; performing real-time monitoring and mode recognition to obtain a behavior track analysis report; performing security level division and intelligent configuration to obtain a virtual security guard area and a self-adaptive reminding rule; performing grade classification processing and early warning signal generation to obtain graded early warning information and a response authority strategy; performing integration analysis to obtain a dynamic intervention strategy, and updating the personalized security risk assessment model according to the dynamic intervention strategy to obtain a target security monitoring model; blind spots and defects of traditional manual monitoring are effectively overcome, and a safe home environment is provided for the old.

The invention relates to the technical field of defect detection, in particular to a curtain wall defect detection system based on computer vision, which comprises an image acquisition module, a texture analysis module, a dynamic defect tracking module, a defect positioning module and a defect identification module. According to the invention, a multi-angle curtain wall data set is collected through the arranged fixed camera and the angle-adjustable camera, the coverage range of the whole and details of the building curtain wall is increased, the accurate adjustment of synchronous shooting parameters and the deep analysis of each frame of image enable the data collection to be more complete, and the high-quality image data is ensured. The system is allowed to perform local texture feature analysis and dynamic trajectory tracking, so that the automation degree of defect detection is improved, the manual intervention requirement is reduced, the detection efficiency and accuracy are improved, an abnormal motion area is automatically marked and analyzed, the system can find potential structural safety problems in time, and the safety of the system is improved. Accurate data support is provided for maintenance and repair, and maintenance cost and building safety risks are reduced.

The invention relates to the technical field of network security, in particular to a network security analysis method and system based on big data. Comprising the following steps: collecting related multi-source heterogeneous data of a network, and carrying out standardized processing such as cleaning and de-noising; network analysis is carried out based on the preprocessed data, network traffic is analyzed in real time by using machine learning and deep learning algorithms, and abnormal conditions are detected; constructing a risk prediction model according to a network analysis result and related information, and predicting a future network security risk level; if the risk level exceeds the threshold value, determining a security event source and a responsibility subject through data tracing; and finally, generating a safety response strategy according to risk prediction and data traceability results, and performing disposal. The corresponding system covers the modules of data acquisition, preprocessing, network analysis, risk prediction, data tracing, security response and disposal and the like, and all the modules work cooperatively to form a complete network security analysis and guarantee system, so that the stable operation of the network system is guaranteed.

Disclosed is a multi-channel early warning system for real-time monitoring of meteorological data, relates to the field of data monitoring technology, and comprises a meteorological data acquisition module, a meteorological data processing module, a terrain analysis module, a UAV deployment module, a ground situation acquisition module, an emergency degree analysis module, an early warning terminal, and a database. Data acquisition of a disaster-affected site is performed using a UAV. The UAV has the characteristics of being flexible and fast, and can be quickly deployed in a disaster-affected region to monitor the ground situation in real time, comprehensively assessing the impact of meteorological disasters. Then an emergency rescue level corresponding to the meteorological disaster site is analyzed, and different warning manners are provided for different emergency rescue levels, so that people's ability to monitor and respond to meteorological disasters can be greatly improved, and the relevant departments can take corresponding rescue and protection measures, thereby effectively reducing the impact and loss of disasters.

The invention discloses a pig breeding intelligent management and control system based on multi-modal data monitoring, and belongs to the technical field of pig breeding management, and the system comprises a data collection and individual recognition module which comprises an RFID ear tag, an inspection robot, an environment sensor and an intelligent feeder, and is used for determining the identity information of a pig individual based on the data collected by the RFID ear tag and the inspection robot, associating the collected multi-modal data of the live pigs in the pig house, and uploading the multi-modal data to edge computing equipment through an MQTT protocol for preprocessing; the feature extraction module extracts physiological and behavior features of live pigs by using a multi-modal deep learning fusion model; the key breeding index analysis module constructs a live pig health index and an environment quality index; the abnormity early warning module adopts a CUSUM control chart to accumulate index deviation and respond to abnormity; the management regulation and control model constructs a Markov decision process by minimizing energy consumption and maximizing health indexes, intelligently adjusts the rotating speed of a fan and a feeding strategy, and realizes precise environmental control and feeding optimization.

The invention discloses an intelligent water quality monitoring system and method based on a turbidity sensor, and relates to the technical field of water quality monitoring. Data acquisition nodes are arranged in a water area to be monitored, data are acquired by using the turbidity sensor and an intelligent water quality monitoring equipment group, an environment data set S is constructed, and preprocessing is performed; acquiring a turbidity change coefficient # imgabs0 # according to a turbidity data set in the preprocessed environment data set S, performing primary evaluation, marking abnormal data acquisition nodes, acquiring a particulate matter dynamic index KL and a pollution index WR according to a particulate matter data set and an organic pollutant data set in the environment data set S, and performing secondary evaluation according to the turbidity change coefficient # imgabs1 # and the turbidity change coefficient # imgabs0 # in combination with the particulate matter dynamic index KL and the pollution index WR. Acquiring a comprehensive water quality score Q, performing secondary evaluation, and constructing a water quality prediction model to predict the water quality change and regulate and control the water quality for the data acquisition nodes which are normally evaluated for the second time; the intelligent level of water quality monitoring is improved, the accuracy of water quality monitoring is enhanced, and the safety of water quality is ensured.

The invention relates to a high and cold wetland dynamic monitoring and health evaluation method and system based on multi-source remote sensing. The method comprises the following steps: step 1, data acquisition and preprocessing; step 2, multi-modal data feature extraction; step 3, performing multi-modal data fusion; step 4, dynamic monitoring; step 5, evaluating wetland health; according to the invention, the calculation method of the SAR polarization decomposition model and the optical wetland index is optimized due to the unique seasonal change and earth surface complexity of the high and cold wetland, a scientific basis is provided for the management of the wetland protection area, and the ecological restoration efficiency is improved; alpine region water resource management and ecological protection policy making are supported; and data support is provided for researching the wetland carbon sink function and the climate change influence.

The invention relates to the technical field of sewage treatment, and particularly discloses a sewage and wastewater treatment control system and method based on an intelligent optimization algorithm. Water quality data are collected through a data acquisition module, and a water quality characteristic matrix is generated through preprocessing. And the prediction module analyzes the feature matrix by using the trained water quality dynamic prediction model to obtain a water quality prediction result. And processing the prediction result by using a multi-objective optimization algorithm to obtain an initial control parameter. And the parameter optimization module calculates a water load fluctuation ratio, a model confidence coefficient and an equipment state according to the sewage and wastewater treatment data, inputs the water load fluctuation ratio, the model confidence coefficient and the equipment state into the adaptive fuzzy network and generates a multi-target parameter optimization suggestion. And the dynamic optimization module adjusts the multi-objective optimization algorithm parameters according to the parameters, and processes the prediction result again to obtain optimization control parameters. And the control module regulates and controls sewage and wastewater treatment according to the optimized parameters. The system realizes closed-loop management from data acquisition, prediction and optimization to control, can dynamically adapt to water quality change, and operates stably and efficiently.

The invention discloses a link reliability tracking and optimizing method combined with real-time monitoring, and relates to the technical field of data transmission. Comprising the following steps: constructing a topological graph of a full link based on component information and a data flow relationship of a data medium station, and deploying a data acquisition probe to acquire link data in real time; a potential fault or an abnormal link is automatically identified; based on the data map, sorting the data dependency relationship of the upstream and downstream of the link, recording the relevance and dependency path of each component, and forming an accurate link blood relationship map; link abnormal conditions are captured and backtracked, log information is summarized in a unified mode, and centralized analysis and problem positioning are carried out; and on the basis of operation historical data of the link, according to the time sequence prediction model, identifying a bottleneck and a high-risk area in the link, dynamically adjusting a transmission strategy of the link, and optimizing load balance and a link priority of a station in the data in real time. According to the invention, the influence on the service when the fault occurs is reduced, and the abnormal problem positioning efficiency is improved.

The invention provides a wind power plant topographic surveying and mapping method and system based on an unmanned aerial vehicle, and relates to the technical field of topographic surveying and mapping. Comprising the following steps: according to topographic characteristics of a wind power plant, adopting a multi-objective optimization model to respectively generate preliminary surveying and mapping routes for a plurality of unmanned aerial vehicles; the multi-objective optimization model takes coverage rate maximization as an optimization objective, and takes cross minimization and path length optimal solution among a plurality of unmanned aerial vehicle routes as constraint conditions; according to the real-time meteorological data, performing dynamic optimization on the preliminary surveying and mapping routes, and generating dynamically optimized surveying and mapping routes for the multiple unmanned aerial vehicles; the multiple unmanned aerial vehicles fly along the dynamically optimized surveying and mapping route, and surveying and mapping data are acquired through the data acquisition equipment. According to the wind power plant terrain complexity and the real-time meteorological data, the surveying and mapping route with the maximum coverage rate is generated for each unmanned aerial vehicle, and blind areas are eliminated through real-time supplementary measurement, so that the completeness of terrain data in the surveying and mapping area is ensured, and the missing of key terrain information is reduced.

The invention discloses a construction progress dynamic optimization method and system based on BIM and computer vision, and particularly relates to the technical field of building construction management, and the method comprises the steps: carrying out the automatic registration of a BIM model and a construction site image; processing the construction site image by adopting a visual identification algorithm to generate a visual identification result; constructing a four-dimensional dynamic BIM model, and mapping a visual identification result to a corresponding component in real time through multi-feature similarity calculation; the progress deviation is monitored by using key path dynamic identification and a deviation propagation matrix, and the risk is predicted by combining a Bayesian network and Monte Carlo simulation. The BIM and computer vision technologies are fused, a construction progress optimization system integrating automatic registration, dynamic monitoring, risk prediction and intelligent decision making is constructed, and the problems that traditional manual inspection data collection is low in efficiency, progress monitoring is lagged, risk prejudgment is fuzzy and resource allocation is extensive are solved; accurate monitoring, risk early warning and resource optimization configuration of the construction progress are realized.

The invention relates to the technical field of data verification, and discloses an integrity verification and correction method and system for flight data and a storage medium. The method comprises the following steps: performing spatial-temporal feature extraction processing on a multi-modal feature data stream, and constructing an incidence matrix according to a multi-level data mapping relationship to obtain a spatial-temporal feature association network; performing hierarchical inspection processing on the spatial-temporal feature association network to obtain abnormal mark data; performing regional recursion positioning processing on the abnormal mark data to obtain an abnormal propagation link diagram; performing correction scheme design processing on the abnormal propagation link diagram, and obtaining a data correction execution sequence based on a data reconstruction and influence evaluation optimization scheme; and performing progressive processing on the data correction execution sequence to obtain corrected flight data. According to the method, a data integrity checking and correcting mechanism is established in a distributed data acquisition environment, so that data exception can be accurately identified and positioned, and the corrected data can be ensured to meet physical constraint and space-time consistency requirements.

The invention discloses a network security analysis early warning system based on artificial intelligence, and the system comprises a data collection layer which captures full flow based on DPI, aggregates firewall logs, terminal behaviors and threat intelligence, and constructs a structured data pool; through TLS fingerprint identification of AI driving, the encrypted traffic is penetrated, and a sampling strategy is dynamically adjusted in combination with reinforcement learning. The intelligent analysis layer is used for carrying out cross validation on known threats and abnormal behaviors; the time sequence CNN extracts encrypted traffic features, and a novel threat detector is rapidly generated by using historical attack fragments in combination with a meta-learning framework; sHAP value driving dynamic feature selection and optimization feature vector input; the decision-making early warning layer is used for fusing multi-source features through a Bayesian network and generating 0-100 score risk scores; a self-adaptive threshold module is combined to adjust a score threshold in real time, and a high-risk event is pushed; the collaborative response layer is used for triggering a preset decision tree, deploying a GAN dynamic honeypot to trap an attacker and reversely tracing; the Neo4j visually restores the attack path, and blocking is executed after the threat is confirmed by a progressive response mechanism.

The invention relates to the technical field of house safety monitoring, in particular to a house safety detection method and system. The method comprises the following steps: acquiring house structure data, and performing house structure detail acquisition according to the house structure data to obtain house structure detail data; performing image data acquisition on a to-be-detected house by using an unmanned aerial vehicle aerial photography technology to obtain house image data, and performing digital twin model construction according to the house image data and the house structure detail data to obtain a house digital twin model; performing house structure anomaly identification according to the house digital twin model to obtain house structure anomaly data; and performing house safety risk processing on the house structure abnormal data to obtain house safety risk data. By means of dangerous building management, potential safety hazards can be found and handled in time, accidents such as collapse and collapse of dangerous buildings are prevented, and life and property safety of residents is effectively guaranteed.

The invention provides a forest fire early-stage intelligent early warning method based on multi-source remote sensing data, and the method comprises the steps: carrying out the data collection of a target forest through employing an airborne laser radar system and a plurality of sensors, carrying out the data preprocessing, obtaining a three-dimensional forest landscape model and related parameters of the target forest, carrying out the feature extraction, and carrying out the early warning of a forest fire. Obtaining a forest environment key feature library of the target forest, quantitatively evaluating the probability of fire occurrence of the target forest in a specific spatial-temporal scale in the three-dimensional forest landscape model, obtaining a fire risk index, constructing a fire early warning model to solve an optimal early warning strategy, and performing early warning on a target forest region of which the early warning level exceeds a preset threshold value. And automatically forming visual early warning information of the early warning area. Through advanced technologies such as multi-source data fusion, numerical simulation and deep reinforcement learning, a set of complete intelligent forest fire early warning scheme is formed, and reliable data support is provided for forest fire prevention.

The invention discloses an intelligent design method for a tunnel blasting scheme, which comprises the following steps: S1, data acquisition and preprocessing: acquiring geological data of an area where a tunnel is located through geological survey and field survey, and preprocessing the data to generate a geological model; s2, intelligent analysis and parameter matching: intelligently matching optimal blasting parameters by using a big data analysis technology according to the geological model and historical blasting data; s3, blasting scheme generation: generating a specific blasting design scheme based on the matched blasting parameters; s4, simulation and optimization: performing simulation analysis on the blasting scheme by using a numerical simulation technology, evaluating the blasting effect and safety, and optimizing the blasting scheme according to a simulation result; and S5, scheme verification and implementation: carrying out field test and verification on the optimized blasting scheme. Through an intelligent algorithm and a software tool, automatic and rapid blasting scheme design is realized, the design period is greatly shortened, and the efficiency is improved.

A method for controlling motion parameters of a pepper harvester based on combination of point clouds and images, including: (1) performing space-time calibration on a radar and a camera, constructing a data collection platform, and pre-processing image and point cloud data; (2) acquiring a real-time number of pepper in front of a harvester and an average height from canopies of pepper plants to soil through a point cloud, acquiring a cutting width and a pepper fruit proportion in front of the harvester through an image, and acquiring a comprehensive lowermost location of pepper fruits by combination of the point cloud and the image; (3) acquiring real-time motion parameters of the pepper harvester; and (4) controlling a real-time rotation speed, operation speed, and height of a drum using a fuzzy PID control method based on the comprehensive lowermost location of the pepper fruits and a predicted value of a feeding quantity.

The invention discloses a hospital information intelligent analysis and decision-making system based on a multi-modal large model, and relates to the technical field of hospital information analysis and decision-making. The system comprises a data acquisition module, a preprocessing and fusion module, a large model construction and training module, an intelligent analysis module, a decision support module, a knowledge graph construction and application module, a data security and privacy protection module, a system evaluation and optimization module, a multi-hospital cooperation and data sharing module, a mobile application module and the like, and all the modules cooperate to realize hospital information intelligent processing and decision making. The system integrates multi-modal data, assists in precise diagnosis, recommends a treatment scheme, predicts resource demands, monitors medical quality, assists medical research, manages patient health and the like, comprehensively improves the intelligent level of hospitals, optimizes medical services and benefits doctors and patients.

The invention discloses a road disease detection system and method based on multi-source data fusion. The system comprises a data acquisition module, a data preprocessing module, a disease detection module, a three-dimensional modeling module, a data analysis module and a maintenance module. The data acquisition module is used for acquiring road surface data and sending the acquired data to the data preprocessing module; the road surface data comprises road data, road disease data, road vehicle data, non-motor vehicle data and pedestrian data, compared with the prior art, more comprehensive and more accurate detection of road diseases is realized by fusing multi-source data; by means of multi-dimensional data acquisition of radar data, video data, three-dimensional point cloud data, geographical location information and the like, the limitation of a single data source is overcome; the data preprocessing module can effectively remove noise and abnormal values, accurately classify and mark diseases, and fuse data collected by different sensors, so that the quality and integrity of the data are improved.