1496 results about "Data reliability" patented technology

In embodiments, systems and methods for improving machine-learning systems are disclosed. In embodiments, a system includes a data pool system that is configured to receive data from a plurality of different data sources and maintain a training data set that is used to train a specific machine-learning model based on the data from the plurality of different data sources. In embodiments, the system further includes a data scoring system that determines a data reliability score corresponding to the new data based on a set of intrinsic features of the new data and a data scoring model, wherein the data pool system selectively adds the new data to the training data set based on the reliability score of the new data. The system also includes a machine learning system that trains the specific machine-learning model based on the training data set.

The invention, which relates to the technical field of disaster monitoring and early warning, discloses a landslide and debris flow disaster monitoring and early warning system comprising a multi-source data acquisition module, an intelligent processing module, an analysis decision module and an early warning response module. The space-time reference unified framework is constructed, the high-precision time synchronization and space projection conversion technology is adopted, the space-time matching problem of the satellite-ground heterogeneous data in the prior art is solved, the multi-source parameter fusion proportion is adaptively adjusted according to indexes such as signal quality and environment interference degree through a dynamic weight distribution mechanism based on data reliability, and the space-time matching precision of the satellite-ground heterogeneous data is improved. In addition, a multi-model collaborative decision-making framework is established, the advantages of three models of mechanism driving, probability statistics and intelligent learning are fused, a comprehensive research and judgment result is generated through a confidence quantitative evaluation and conflict resolution algorithm, an early warning response mechanism is matched, a pre-arranged plan library is automatically matched according to a risk level, and an external control system is linked. And a closed-loop management chain of monitoring early warning, analysis decision and emergency disposal is formed.

The invention relates to the technical field of data acquisition, and discloses a multi-protocol extensible data acquisition system architecture and method with a unified interface standard, and the architecture comprises a data source management module which is used for storing data source configuration information and distributing protocol identifiers; the protocol adaptation module comprises an analysis unit and a matching unit and is used for realizing data format conversion and protocol compatibility verification; the data conversion module is used for generating target data through format conversion and semantic mapping; the data verification module and the anomaly detection module are used for comparing and monitoring data integrity based on an integrity algorithm and a threshold value; the data quality monitoring module and the early warning module are used for comparing and evaluating data quality through a consistency model and a threshold value; and the result output module transmits the early warning information to a target system. The method comprises the steps of data source configuration, protocol adaptation, data conversion, integrity and quality monitoring, early warning and the like. The system expansibility and the data reliability are improved, and the method is suitable for multi-scene data acquisition.

The invention relates to the technical field of data analysis, in particular to a road traffic intelligent operation and maintenance method and system based on multi-source data fusion, and the method comprises the steps: constructing a three-dimensional model through a credibility calculation module by integrating three types of factors, namely a road environment, an equipment state and historical verification, generating a dynamically updated credibility index, and correcting a weight in real time based on feedback; the problem that the reliability of the multi-source data fluctuates dynamically due to the influence of environmental interference, equipment aging and historical deviation is solved; the road feature conflict resolution module maps to feature dimensions corresponding to road topology conflict features through dynamic credibility indexes, constructs a road conflict decision matrix, and fuses and generates a road anomaly decision set with credible labels according to the feature dimensions; the resource scheduling module is used for dividing three-level response areas based on credible indexes, executing accurate scheduling of triple constraints in combination with device priorities and credible labels and generating a resource-decision association graph, and self-adaptive optimal configuration of limited maintenance resources according to data reliability is achieved.

The invention discloses an Internet of Things intelligent gas meter leakage detection and early warning system and method, and relates to the technical field of gas leakage detection, and the method comprises the following steps: S1, collecting data through multiple sensors; s2, identifying an equipment operation state based on the data set and outputting a state confidence coefficient; s3, a stable monitoring window period is judged, a corresponding strategy is selected to compensate pressure data, and reliability is marked; s4, dynamically generating a detection threshold in combination with the historical mode, the real-time parameters and the data reliability; s5, dynamically adjusting a risk assessment weight according to the confidence coefficient and the reliability, calculating a risk score and determining an early warning level; s6, safety operation is executed according to grades; and S7, updating the model by using process data to realize self-optimization. According to the invention, by deploying a multi-sensor array and adopting a multi-modal signal fusion algorithm, the system can accurately identify the running state of the gas appliance, provides reliable preposition information for subsequent analysis, and overcomes the defect that data of a traditional single sensor is easily interfered.

The invention discloses a data filtering and noise reduction method based on laser point cloud. According to the method, the multi-scale features are intelligently screened through the random forest algorithm, and the system can automatically generate the optimal filtering rule according to the local geometric characteristics of the point cloud without depending on manual parameter presetting. In a complex urban scene, a fine structure of a high-rise vertical face can be accurately reserved through curvature features, and sparse noise of a vegetation area is rapidly eliminated through a density threshold value. The data-driven adaptive mechanism not only reduces the manual intervention cost, but also greatly improves the scene generalization ability of the filtering strategy, so that the filtering strategy can still keep stable output in a dynamic environment. The dynamic interference is accurately positioned by combining the consistency verification of the motion trails of the multi-frame point clouds and utilizing the time sequence correlation analysis, so that the data reliability in a complex dynamic scene is remarkably improved, and a solid foundation is provided for high-precision three-dimensional reconstruction and real-time perception.

The invention relates to the technical field of polishing, and discloses an intelligent steel rail polishing control method which comprises the following steps: acquiring surface and internal defect data of a steel rail through laser and ultrasonic fusion detection, discretizing the data into multi-dimensional nodes, and constructing a dynamic graph model comprising a microscopic-macroscopic dual-cluster neural network to predict a defect evolution trend; based on fractal dimensions and information entropy, multi-source data are dynamically fused, abnormities are eliminated, chaotic optimization and wear prediction are adopted to generate global optimal polishing parameters, and finally parameter dynamic adjustment is achieved through closed-loop feedback of pulse frequency and control errors. Steel rail defect collaborative perception is achieved through a multi-scale dynamic graph model, fractal-entropy weight dynamic fusion is combined to enhance data reliability, global optimal parameters are generated through chaos optimization and abrasion prediction, accurate grinding is achieved through pulse-error closed-loop control, and the method is simple and convenient to implement. The problems of data fusion failure, defect response lag, parameter local optimization and insufficient control precision in the prior art are solved.

The invention relates to a land utilization type intelligent identification method and system based on remote sensing data. The method comprises the following steps: acquiring multi-source remote sensing data; based on a preset land utilization characteristic model, analyzing and processing the multi-source remote sensing data to obtain corresponding land utilization characteristics in the land utilization characteristic model; based on a land utilization characteristic knowledge base, matching and analyzing the land utilization characteristics to obtain a matching result; and according to a matching result, determining a land utilization type corresponding to the matching result, and outputting the land utilization type as an identification result. By adopting the method, multi-dimensional information complementation can be realized, space-time coverage is enhanced, and data reliability is improved; a high-precision classification result is provided, dynamic monitoring is supported, and an application scene is expanded; the defect of a single data source is overcome, manual dependence is reduced, the operation period is shortened, the cost is reduced, the complex scene recognition capability is improved, and space-time continuous dynamic monitoring and fine management support is provided for the fields of ecological assessment and the like.

The invention relates to the technical field of intelligent control of automobile air conditioners, and discloses an intelligent regulation and control method of an automobile air conditioner controller based on a self-adaptive neural network, which realizes comprehensive perception of environment information inside and outside an automobile through distributed multi-source data intelligent fusion processing. Establishing a data quality evaluation model and an intelligent data interpolation algorithm to ensure data reliability; constructing a lightweight dual-path adaptive neural network model by adopting a knowledge distillation technology; establishing a hierarchical multi-objective optimization control architecture which comprises a three-layer optimization structure of a strategic layer, a tactical layer and an operation layer, and realizing organic combination of long-term planning, medium-term prediction and real-time control; the method further comprises key technologies such as multi-level data missing fault-tolerant processing, actuator self-adaptive compensation control, system-level fault self-healing and degradation control and the like, and stable and reliable intelligent regulation and control can be achieved under the extreme environment condition. According to the invention, the control precision, the energy consumption optimization and the user experience in extreme environments such as polar regions, plateaus and deserts are improved.

The invention provides an underwater target identification method and system based on multi-source sensor data fusion. The method comprises the following steps: firstly, synchronously acquiring an original sound wave reflection signal and original optical image data of a target underwater area, then carrying out acoustic compensation processing on the original sound wave reflection signal to generate a target sound wave reflection signal, and meanwhile, carrying out optical compensation processing on the original optical image data to generate target optical image data; performing dynamic weighted fusion on the reliability measurement of the target sound wave reflection signal and the reliability measurement of the target optical image data through an adaptive fusion module to generate an enhanced multi-modal data set; and finally identifying the specific category of the underwater target through a multi-modal feature matching and decision-making mechanism. According to the technical scheme provided by the invention, the limitation of a single sensing mode is overcome, the quality of collected information is improved, the direct confirmation efficiency of common target recognition is also improved, and high accuracy and data reliability of target category recognition in a complex scene are ensured.

The invention discloses a water conservancy facility monitoring method and system based on digital twinning, and relates to the technical field of water conservancy monitoring. Historical operation data of a water conservancy facility are obtained based on a digital twinning model for analogue simulation, and full life cycle data of the water conservancy facility are obtained; and importing the standard operation data into the digital twin model, carrying out data comparison and data compensation on the standard operation data and the full life cycle data, verifying the accuracy of the standard operation data, and generating output verification data after comparison is completed. The verification data is generated by comparing the operation data with the data simulated by the digital twin model and combining cross verification of multiple operation data in the same area, so that data accuracy confirmation and data compensation can be carried out on the data distortion problem caused by abnormal data acquisition of the water conservancy facility operation data acquisition equipment; the reliability of the collected operation data is ensured, and the accuracy of evaluation and decision-making of the subsequent water conservancy facility monitoring operation state is guaranteed.

The invention belongs to the technical field of product inventory management, and discloses an e-commerce product inventory management method based on machine vision, which realizes a full-automatic process of product information from acquisition, recognition to statistics by means of a machine vision technology, acquires images in real time from multiple angles by a high-definition industrial camera, inputs the images into a CNN model to extract features after preprocessing, and realizes product inventory management. Through accurate identification classification and quantity statistics, manual intervention of inventory and data entry is not needed; through the automatic inventory management process, the manpower cost expenditure of manual checking, data processing and the like is greatly reduced, and the personnel employment and training cost is reduced. And on the other hand, historical data is deeply mined by using algorithms such as time sequence analysis, the sales trend and the inventory demand are accurately predicted, the replenishment strategy is optimized, capital and storage space occupation caused by inventory overstock is avoided, meanwhile, order loss caused by stockout is prevented, and in addition, a distributed database storage technology ensures data reliability, and the efficiency is improved. And extra cost caused by data errors is reduced.

The invention belongs to the technical field of data processing, and particularly relates to a rutile type titanium ore prospecting model construction method. Comprising the following steps: collecting remote sensing time sequence, real-time geophysical prospecting and geochemical data, carrying out geological causal feature analysis after standardization so as to construct a metallogenic causal graph, and meanwhile, collecting anti-fact features to generate virtual mineralization feature vectors; a prospecting model comprising an input layer, a weight optimization layer (comprising a causal attention module and a particle swarm anti-fact comparison module) and a mineralization probability output layer is constructed, and a high-frequency data acquisition mechanism is triggered with assistance of a metallogenic event threshold. According to the method, the data reliability and the model generalization ability are improved, the mineralization probability calculation precision and the potential mineralization target region judgment accuracy are remarkably improved, and a scientific support is provided for efficient prospecting of rutile type titanium ore.

The invention relates to the technical field of automobile automatic transmission, and discloses an AMT bearing operation state detection system and method under an active and passive switching working condition, and the method comprises the steps: collecting real-time data through a vibration acceleration sensor, a current sensor, a temperature sensor and a rotating speed encoder, carrying out the preprocessing of envelope demodulation, Kalman filtering and the like, and carrying out the detection of the operation state of an AMT bearing; and generating a time-frequency characteristic matrix by using variational mode decomposition and Hilbert transform. And inputting the matrix into a probabilistic neural network model adopting a sliding time window mechanism, and outputting a bearing health state probability value. A multi-parameter state evaluation model optimized by a quantum genetic algorithm is constructed, an optimal feature combination is obtained, and early warning levels and maintenance suggestions are output through a belief rule base inference device in combination with a hierarchical diagnosis control model (including an acquisition layer, an analysis layer and an execution layer). The system is further provided with a signal verification module to ensure data reliability. According to the invention, multi-source data fusion and dynamic adaptive diagnosis are realized, and the state detection precision and real-time performance of the AMT bearing under complex working conditions are improved.

Systems and methods for dynamically evaluating metadata consistency and data reliability in a data management system are disclosed herein. The system may retrieve first metadata and second metadata. The system may retrieve a metadata ruleset. Based on the metadata ruleset, the system may generate a first metadata consistency metric indicating a first measure of consistency. The system may determine to process each record of the first metadata as a batch. The system may generate a second metadata consistency metric indicating a second measure of consistency. The system may determine to process each record of the second metadata independently.

The invention discloses a construction monitoring system for a horizontal warehouse arched roof based on multi-modal data fusion, relates to the technical field of construction monitoring, and is used for solving the problem of poor structural construction anomaly recognition. The method comprises the steps of collecting multi-modal data and completing synchronous preprocessing, setting a sampling frequency according to modal characteristics, dividing construction stages, constructing a stage response function, dynamically updating during stage switching, fitting an actual observation value and a prediction value, extracting a modal response deviation degree, constructing a credibility function and evaluating data reliability. Further analyzing a residual trend, adjusting an abnormal score in combination with credibility, identifying a collaborative abnormal behavior between modals, systematically constructing a modal causal graph, identifying an abnormal propagation path, extracting modal conduction characteristic information, and triggering a risk response strategy according to the modal causal graph, so that dynamic tracking of the structural abnormality in the construction process of the horizontal warehouse arched roof is realized; and the accuracy of construction process monitoring identification is improved.

The invention relates to the technical field of soil remediation management, in particular to a method and system for soil remediation and digital management, and the method comprises the steps: data construction: fusing multi-source monitoring soil environment parameters with space-time background data such as a GIS layer and a meteorological time sequence to form a data set; state quantification: outputting a comprehensive state index for quantifying restoration urgency and complexity through the soil health assessment model; scheme generation: matching the restoration target library and the resource library, and generating a digital restoration scheme containing an expected state trajectory through an optimization algorithm; closed-loop control is carried out, feedback data comparison tracks are continuously collected, if the deviation exceeds a threshold value, the index is recalculated, the scheme is dynamically adjusted, and the system comprises a data construction module, a state quantification module, a scheme generation module and a closed-loop control module and executes the method. According to the method, the data reliability and evaluation scientificity are improved, the scheme adaptability is guaranteed, dynamic and controllable remediation is realized, and digital and efficient management of soil remediation is promoted.

The invention discloses a gear performance accurate evaluation method based on voiceprint analysis, and relates to the field of gear performance evaluation. Determining the optimal arrangement position of the acoustic sensor according to different gears, and adjusting the sampling frequency and duration in real time according to the operation state change of the gears to collect voiceprint data; de-noising by using a composite de-noising network based on deep learning, and processing data by using a new normalization method; extracting a plurality of innovative features to construct a feature vector, and combining a deep forest with model fusion to establish an evaluation model; and inputting a to-be-evaluated gear feature vector into the model to evaluate the performance, verifying an evaluation result through a virtual verification mode and the like, and optimizing the model based on feedback. The gear performance is accurately evaluated, voiceprint data are intelligently collected, and the evaluation accuracy is improved through feature extraction and model construction; real-time monitoring and early warning are achieved, and different gears are adapted; the block chain ensures data reliability, the evaluation model evolves continuously, the maintenance cost is reduced, and stable operation of equipment is guaranteed.

The invention provides a method and a device for evaluating and measuring microscopic mechanical properties of TC4 alloy, relates to the technical field of microscopic mechanical properties, and realizes accurate evaluation of material properties through combination of multi-scale characterization and numerical simulation. Firstly, a standard sample is prepared through selective laser melting, and residual stress field distribution is obtained through X-ray diffraction; dividing a stress field into sub-regions, and establishing a quantitative relationship between microscopic structure parameters and residual stress by combining SEM and EBSD analysis; the method comprises the following steps: acquiring local mechanical properties through a nanoindentation test, and constructing a stress-performance correlation model; and finally, establishing a finite element model, transmitting stress and tissue parameters by adopting trilinear interpolation and an inverse distance weighting method, verifying data reliability through a stress difference criterion, and calculating mechanical properties of each unit. According to the method, multi-scale correlation from microstructure characteristics to macroscopic mechanical properties is innovatively realized, so that the mechanical property distribution of the material is accurately evaluated.

The invention discloses a multifunctional safety helmet management system and method based on the Internet of Things, relates to the field of safety production, and solves the problems that a traditional safety helmet is single in safety management function, insufficient in data processing precision and the like. A multi-dimensional state data sequence is continuously collected through a three-axis acceleration sensor, a temperature and humidity sensor, a positioning sensor, a vital sign sensor and the like integrated on the safety helmet terminal; terminal monitoring characteristics including an acceleration module value, a position drift distance and a physical sign fluctuation index are constructed, data reliability is evaluated through weighted fusion of dynamic fluctuation values, and an abnormal terminal is isolated. And carrying out space-time calibration on the reliable data to generate a unified coordinate system trajectory, analyzing and identifying abnormal behaviors through group trajectory consistency, and constructing a regional safety risk map in combination with a signal blind area coverage rate, thereby realizing safety risk level prediction based on historical data and real-time trajectory matching. According to the invention, comprehensive real-time monitoring of the state of the operator is realized, and the intelligent level of safety production management is improved.

The invention discloses a pressure injury risk early warning method and equipment, and relates to the technical field of medical monitoring instruments. The pressure sensor array layout can be self-adaptive to the body shape of a patient, a key osseous process part is covered, no monitoring dead angle is ensured, a temperature compensation mechanism is built in, environmental interference is reduced, the measurement precision is improved, an abnormal pressure threshold value can be dynamically set based on individual medical record data, data verification is performed before early warning is triggered, false alarm is avoided, and the array structure is light, thin and flexible. The mask is comfortable to fit skin, supports long-term wearing and is easy to disinfect and maintain; environment temperature and humidity parameters and pressure data are subjected to fusion analysis, the influence of a high-temperature and high-humidity environment on skin microcirculation can be recognized, the risk of pressure sores can be evaluated in an auxiliary mode, a calibration algorithm is built in a sensor, environment fluctuation interference is reduced, and data reliability is ensured; and the early warning unit comprises sound-light alarm equipment which supports a multi-stage alarm mode and is used for triggering an early warning signal and pushing information when the risk index exceeds a threshold value, so that the early warning timeliness is ensured and the false alarm risk is reduced.

The invention relates to the field of traffic control, in particular to an intelligent network connection inductive control platform for road traffic safety facilities. The traffic data acquisition module is used for acquiring a data flow of a traffic sensor and outputting traffic data with a traffic data source identifier through an identification technology; the traffic data processing module is used for obtaining standardized traffic parameters according to a traffic data source identifier adaptive analysis protocol; evaluating a data reliability index according to the index system; the situation fusion module is used for forming a vehicle driving track through a graph neural network, generating a global traffic situation map by using a data reliability index, and constructing a traffic network digital twinborn model; and the decision and control module is used for analyzing traffic states from the traffic network digital twin model, predicting traffic events and collision risks and generating traffic control instructions. According to the platform, through the equipment fingerprint and protocol reverse technology, an information island is broken, the comprehensiveness and high credibility of a data source are ensured, and the road traffic safety and passing efficiency are remarkably improved.

The invention discloses a laser radar attitude measurement positioning method and device for an aviation electromagnetic detection suspension system, and belongs to the technical field of aviation electromagnetic detection, attitude measurement and coordinate positioning. According to the invention, real-time space positioning information and attitude information of the aviation soft suspension flight pod subsystem are accurately obtained by adopting a non-contact laser remote sensing measurement mode, and attitude and positioning devices do not need to be fixedly installed on the suspension flight pod subsystem. Electromagnetic interference or dynamic stability of the attitude and positioning device and related auxiliary electronic equipment on the soft suspension flight pod subsystem is avoided, the accuracy of electromagnetic signals and data reliability in the detection process are ensured, and therefore the precision of aviation electromagnetic detection is improved.

The invention relates to the technical field of equipment monitoring, in particular to an anomaly monitoring method for water quality detection equipment, which realizes multi-level anomaly detection by deploying a plurality of monitoring nodes in a target water area, constructing a distributed monitoring topological structure and combining an auto-encoder, an isolated forest and an LOF algorithm. Based on historical data, a reference model of normal equipment is established, Apache Kafka is used for collecting data streams in real time, and standardization and windowing processing is carried out through Flink. A reconstruction error is calculated based on an auto-encoder, a feature matrix is constructed, global abnormal points are screened by using an isolated forest, local outlier is verified through an LOF algorithm, and an abnormal state is comprehensively judged. The method supports incremental learning, adapts to environmental changes, has the characteristics of high real-time performance and low delay, can effectively identify abnormal conditions such as sensor aging and measurement errors, improves data reliability and system stability, and remarkably reduces maintenance cost.

The invention provides a geotechnical engineering slope deformation monitoring method and system, belongs to the field of geotechnical engineering monitoring, and is used for solving the problems of low monitoring precision and poor early warning reliability caused by spatial-temporal dislocation of multi-source data, static geological parameters, poor swivel scene adaptation and no closed-loop optimization in related technologies. According to the method, multi-source deformation data, geological parameters and rotation characteristics are collected, the type and progress of a rotation are identified, multi-resolution hierarchical fusion (dynamic weight adaptive data credibility and environmental interference) and dynamic inversion geological parameters are performed after space-time alignment to construct a prediction model, model parameters and an early warning threshold are dynamically scheduled in combination with the rotation characteristics, and the prediction result is obtained. The precision is improved through closed-loop feedback optimization iteration, the system provides hardware support for the method, and precise monitoring and efficient early warning of slope deformation are achieved.

The invention relates to the technical field of power system power distribution network fault detection, in particular to an arc fault diagnosis and analysis method based on an artificial intelligence algorithm, and the method comprises the four steps: synchronous data collection and topological excitation, deployment of terminals at transformer area nodes, injection of characteristic current, and synchronous collection of response waveforms; signal preprocessing: separating a key frequency band through double-digital band-pass filtering, and calculating energy ratio and other enhanced fault features; performing multi-source feature fusion and intelligent diagnosis, constructing a vector containing statistical features, time domain distortion features and topology identification, and inputting a gradient boosting decision tree and deep neural network hybrid model to obtain fault confidence and type; and based on fault positioning and verification of topology, scheduling multi-node cooperative monitoring, and determining a fault point in combination with a topological relation. The method improves the data reliability and diagnosis precision, achieves the precise positioning of a fault, and guarantees the safe operation of a power distribution network.

The invention provides an elevator intelligent operation and maintenance management method and system based on a multi-modal neural network, and relates to the technical field of operation and maintenance management. The method comprises the steps that 1, multi-source time sequence monitoring data are collected, the multi-modal neural network is constructed, the multi-source time sequence monitoring data are input into the multi-modal neural network, and the multi-source time sequence monitoring data are input into the multi-modal neural network; obtaining a vibration feature vector, an acoustic feature vector and a visual feature vector; 2, inputting the vibration feature vector, the acoustic feature vector and the visual feature vector into a cross-modal feature fusion layer in a multi-modal neural network for fusion, and generating a multi-modal joint feature vector; through multi-modal feature fusion of multi-source time sequence monitoring data, multi-dimensional health state representation modeling and fusion weight dynamic adjustment driven by data reliability evaluation, the accuracy of elevator fault diagnosis and the reasonability of maintenance decision are effectively improved, and the improvement of elevator intelligent operation and maintenance precision and efficiency is achieved.

The invention discloses a water-wind-light combined intelligent control system based on multi-energy coordinated control, and belongs to the technical field of multi-energy coordinated control, and the system comprises the steps: firstly collecting environment and operation parameters of wind power, photovoltaic and hydroelectric equipment, and carrying out the preprocessing of the environment and operation parameters to form a structured data set with a space-time label and a dynamic sampling identifier; performing space-time correlation analysis on the data set, constructing a wind power fluctuation and hydropower regulation margin real-time response matrix and a photovoltaic output attenuation and hydropower storage capacity compensation time sequence correlation model, and quantifying the multi-energy dynamic complementation degree and grading the data credibility through a two-dimensional coupling algorithm; then combining with a historical meteorological scene association rule base, constructing a water-wind-light combined power supply capability prediction model containing an energy characteristic mutual compensation coefficient, and generating a hierarchical regulation and control strategy with a space-time priority; and finally, executing a regulation and control strategy to complete parameter adjustment, continuously monitoring through closed-loop feedback, and triggering multi-energy collaborative complementary regulation when the single energy regulation capability is saturated, thereby realizing water-wind-light efficient collaborative control.

The invention discloses a multi-chain collaborative center node switching method for ensuring authentication efficiency and data reliability, which is applied to a system comprising an identity chain, an authentication chain, an edge computing center, a key generation center, a plurality of unmanned aerial vehicle clusters and at least two aerial directors, and is characterized in that the identity chain is used for identity management of all the unmanned aerial vehicle clusters; the authentication chain is jointly maintained by all the unmanned aerial vehicle clusters and all the air directors. According to the multi-chain collaborative central node switching method for ensuring the authentication efficiency and the data reliability provided by the invention, the secure communication between the clusters can be quickly established in the authentication process of the unmanned aerial vehicle clusters, and the switching of the central nodes can be quickly carried out when the central nodes are threatened and attacked in a multi-chain manner; the new central node continues to execute the flight task, and the forward and backward security is ensured in the switching process, so that the problems of low authentication efficiency of the unmanned aerial vehicle cluster, strong dependency of the central node and insufficient security in the switching process of the central node are solved.

The invention discloses a frozen soil roadbed hot melt settlement laser scanning measurement method and system, and relates to the technical field of measurement, the method comprises the following steps: obtaining gridding surface data, and obtaining an elevation data sequence of each grid point; acquiring an elevation change index of each grid point according to the elevation data sequence of each grid point; acquiring a plurality of grid points adjacent to the ith grid point and taking the plurality of grid points as a plurality of reference grid points of the ith grid point, acquiring each relative difference quantity of the ith grid point, and acquiring a correction proportion of the ith grid point according to the plurality of relative difference quantities of the ith grid point; and obtaining target elevation data of each grid point according to the correction proportion of each grid point and the measured elevation data at the current moment, and forming a measurement result including the gridding surface data according to the target elevation data of each grid point. The method has the advantages of identifying abnormal values, dynamically correcting measurement and improving data reliability.