1462 results about "Point data" patented technology

The invention relates to a power distribution network distributed energy bearing capacity assessment method and device. The method comprises the following steps: carrying out topology analysis on a network structure of a power distribution network to obtain an initial network topology model; obtaining a node dynamic feature data set based on the initial network topology model and the distributed energy access point data of the power distribution network; wherein the node dynamic characteristic data set comprises operation parameters of each node of the power distribution network in different load scenes; generating a parameter incidence matrix according to the node dynamic characteristic data set, and obtaining a bearing capacity reference model of the power distribution network according to the parameter incidence matrix and real-time data of the power distribution network in an operation state; wherein the parameter incidence matrix is used for quantifying the coupling degree between the operation parameters; and obtaining a risk distribution mapping graph according to the bearing capacity reference model, and identifying a potential overload area of the power distribution network based on the risk distribution mapping graph. According to the invention, power distribution network operation risk assessment can be accurately realized.

The invention discloses an intelligent method and system for multi-source heterogeneous data fusion management, and belongs to the field of data processing, and the intelligent method for multi-source heterogeneous data fusion management comprises the following steps: deploying a sensor network in a historical ponding area based on historical ponding point data and urban hydrological geographic information, acquiring real-time monitoring data, wherein the real-time monitoring data comprises rainfall intensity, road waterlogging depth, community waterlogging condition and drainage pipe network water level / flow; compared with the prior art, the method has the beneficial effects that sensors are arranged in the historical ponding area to obtain real-time monitoring data, and weather forecast, a terrain model and a pipe network model are combined to construct a waterlogging prediction model to predict whether a ponding risk exists in the historical ponding area in future set time (such as 0.5-3 hours in the future); the ponding risk is treated in advance, so that the problems of traffic interruption caused by ponding, citizen travel obstruction and the like are solved.

The invention relates to the technical field of injection molding pressure control, in particular to a multi-stage pressure closed-loop compensation system and method for high-precision double-color injection molding, and the system comprises an interface gradient positioning module, a flow resistance state recognition module, a fitting function reconstruction module, a time sequence deviation triggering module and a gain reverse correction module. According to the method, the high-density measuring point data is collected, the interface radial pressure gradient change is extracted through the finite difference method, the stably-arranged nodes at the material intersection can be accurately locked, and the flow resistance state of the flow channel is judged according to the slope difference value of the screw speed and the pressure intensity change. A linear boosting or index slow increasing strategy is automatically switched to reconstruct a pressure function form, local high-pressure accumulation characteristics of a product sensitive area are accurately matched, the coincidence degree of a time window drift trend and a slope sudden change window is monitored, historical displacement and pressure errors are converted into backstepping factors through an iterative learning model, and the backstepping factors are calculated. And a driving gain curve is initialized and revised, so that forming deviation caused by thermal drift of equipment is eliminated.

The invention discloses a soil heavy metal distribution prediction method and system based on machine learning, and the method comprises the steps: obtaining topographic factor land use industrial activity data, carrying out the fusion remote sensing information processing, and determining a multi-source data set; performing standardization processing according to the multi-source data set, and performing space-time registration if the scale difference after standardization processing exceeds a preset threshold value to obtain data in a unified format; key features are extracted according to the unified format data, and a dimension reduction feature set is obtained through principal component analysis; a random forest model is constructed according to the dimension reduction feature set, parameters are optimized, and a heavy metal content prediction model is determined; inputting the sampling finite point location data into a heavy metal content prediction model, judging an industrial activity influence area, and obtaining a preliminary distribution estimation result; based on the preliminary distribution estimation result, high-resolution grid data are obtained by fusing topographic factors for the space complex region; and generating a pollution distribution diagram according to the high-resolution grid data, judging a low-prediction-precision region, and obtaining a final optimized layer.

The invention relates to the technical field of subway passenger flow prediction, and discloses a space-time perception subway passenger flow prediction method based on large model and multi-source information fusion, which comprises the following steps: constructing a data agent based on a large language model and a model context protocol; generating text description of each subway station and a corresponding spatial context embedding vector based on the peripheral interest point data of each subway station by utilizing a data agent; constructing an enhanced graph neural network according to the spatial context embedded vector, and performing spatio-temporal joint prediction based on the historical passenger flow data and the spatial context embedded vector by using the enhanced graph neural network to obtain a preliminary passenger flow prediction result; and constructing a cue word template, and performing domain knowledge calibration based on the cue word template by using the pre-trained large language model to obtain a final passenger flow prediction result. According to the method, external multi-source information is deeply fused, so that the urban rail transit passenger flow prediction task performance, robustness and interpretability are remarkably improved.

The application provides a pollution site portrait construction method and system based on knowledge graph reasoning, which comprises the following steps: collecting key data in a pollution site investigation report; obtaining a pollution land block structured data table of each pollution land block based on the pollution site investigation report, and obtaining a knowledge graph ontology structure; integrating the pollution land block structured data table to form a pollution site structured data table; obtaining a node table, a relationship table and an attribute table of each node in the knowledge graph ontology structure to obtain a triple table; establishing a graph database based on the triple table, and performing reasoning based on the graph database to obtain known pollution information of the pollution site; performing potential risk reasoning on the triple table based on a graph neural network to obtain potential pollution information of the pollution site; and obtaining a pollution site portrait based on the known pollution information of the pollution site and the potential pollution information of the pollution site. The method of the application evaluates the pollution site based on known facts and potential risk reasoning, and improves the accuracy of the evaluation.

The invention discloses a data leakage early warning method, device and equipment based on buried point collection and a storage medium, and relates to the technical field of system exception monitoring, and the method comprises the steps: obtaining exception log information and historical data information sent by a buried point collection service node; performing aggregation counting on the abnormal log information according to preset time granularity to obtain abnormal quantity statistical data of each time granularity; performing transverse and longitudinal analysis on the abnormal quantity statistical data and the historical data information according to a preset transverse and longitudinal analysis strategy to obtain a comparison deviation value; and performing data leakage early warning according to the comparison deviation value. According to the method, time-sharing processing and multi-dimensional analysis are carried out on the abnormal log data of the system, so that real-time early warning and service subdivision monitoring of buried point data leakage are realized, and the reliability and fault positioning efficiency of the system are improved.

The invention belongs to the technical field of blasting vibration prediction, and particularly discloses a blasting vibration speed attenuation law prediction method based on regression analysis, and the method comprises the steps: laying monitoring points in multiple directions around a blasting source, synchronously collecting the geological parameters and mass point peak vibration speed of each point, and constructing a segmented propagation path; a single dominant propagation section is identified based on adjacent monitoring point data, independent influence of specific geological conditions on vibration attenuation is quantified by combining single-factor control regression analysis, decoupling modeling of geological factors and attenuation behaviors is realized, and meanwhile, a double-factor dominant propagation section is identified on the basis of quantizing the independent influence of the single geological conditions. And deducting a known effect through residual analysis to separate out an independent contribution of another geological parameter, and finally integrating a multi-factor influence function to construct a comprehensive attenuation model. Progressive modeling from single-factor decoupling to multi-factor collaboration is achieved, and the precision and physical interpretability of blasting vibration prediction in a complex heterogeneous stratum are remarkably improved.

The invention provides a large language model training method based on a time-space tensor division strategy, and the method comprises the steps: employing a time-space collaborative tensor division strategy to divide tensor operation to a plurality of calculation devices in a time dimension and a space dimension, and enabling the calculation devices to carry out the parallel processing, each computing device only caches matrix data required by the current computing step, complete tensors or redundant copies do not need to be reserved, the problem of repeated storage of data such as activation values and weights in traditional tensor parallel is fundamentally solved, video memory occupation is greatly reduced, and the problem of redundant storage of the tensors is solved; point-to-point data transmission replaces set communication, efficient folding of communication and calculation delay is achieved, communication overhead and video memory occupation in the training process are remarkably reduced, the parallel efficiency and resource utilization rate of large language model training are remarkably improved on the premise that training precision is guaranteed, and the method is suitable for large language model training. And a brand new solution is provided for efficient training of a large language model.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine tunnel laser radar three-dimensional dynamic monitoring method. Aiming at the problems that in the prior art, data of a laser radar is easy to interrupt or unreliable under a severe working condition, and monitoring continuity is poor due to insufficient multi-source data fusion, the method is characterized in that laser radar surface data and optical strain gauge point data are synchronously acquired; constructing a roadway digital twinborn model; carrying out fusion and assimilation on the multi-source data by utilizing a Transform mechanism to obtain initial roadway deformation data; judging the credibility of the laser radar data based on Bayesian estimation; when the data are missing or uncredible, deducing full-field deformation by utilizing a gating mechanism in combination with optical strain gauge point data and a digital twin model; and finally, generating and outputting a three-dimensional dynamic deformation field. The system is mainly used for real-time, continuous and high-reliability deformation monitoring and safety early warning of the coal mine tunnel.

The invention discloses a multi-modal data adaptive denoising and missing reconstruction method and system, and relates to the technical field of point data denoising and reconstruction, and the method comprises the steps: obtaining to-be-processed multi-modal original data and a modal missing mask; performing unsupervised denoising on image data in the multi-modal original data to obtain a denoised image, and further obtaining multi-modal data; inputting the multi-modal data into a double-flow encoder for processing to obtain a multi-modal embedded vector of cross-modal alignment; the method comprises the following steps of: performing mapping and adding position embedding on a modal embedding vector to obtain each modal coding feature, determining a missing modal based on a modal missing mask, inputting an available modal coding feature into a retrieval enhanced expert model based on prototype memory to perform missing reconstruction to obtain a multi-modal joint representation, and mapping the multi-modal joint representation to a task output space through a full connection layer. Through introduction of unsupervised denoising, double-flow coding alignment and modal knowledge expert hybrid reconstruction, robust representation learning and information complementation under the condition that noise and modal missing exist in multi-modal data are realized.

The invention discloses a multi-mode set heavy rainfall forecasting method fusing space loss function deep learning, which comprises the following steps: acquiring rainfall site observation data, meteorological element data and various physical factor data to form multivariate meteorological factor data; processing the multivariate meteorological factor data into equal-resolution lattice point data and preprocessing the lattice point data; screening out meteorological element and physical factor data of which the importance measurement value is greater than a threshold value, and dividing a data set according to research requirements; constructing a mixed loss function fusing precipitation spatial features; a mixed loss function is developed to train the U-NET deep learning neural network model, and the performance of the model is evaluated; and inputting the multi-element meteorological factor data of the multi-mode output real-time forecast into the model, and generating the real-time heavy rainfall forecast of the multi-mode set. According to the method, precipitation space structure characteristics are integrated into a deep learning model, the problems of deep learning forecast averaging and peak loss caused by a traditional point-to-point strength loss function are solved, and the method aims at improving the precision of heavy precipitation forecast.

The invention discloses a distributed data use control method and system based on a DID and a verifiable credential, a storage medium and an electronic device, and the method comprises the steps: carrying out the identity verification of a data user based on a DID, carrying out the authentication of the identity through a verifiable credential issued by an authority, and carrying out the verification of the identity through the verifiable credential; the privacy information range is controlled by adopting a selective disclosure mechanism; generating data authorization information based on the verifiable certificate, establishing an encrypted point-to-point data transmission channel in combination with the DID identifiers of the authorizer and the authorized person, and executing encrypted transmission and encrypted storage of data; and verifying the identity of a data user based on a DID signature mechanism, carrying out tamper-proof verification on a use strategy in the authorization certificate, and carrying out compliance judgment on an authorization range, strategy content and a decryption condition. According to the method provided by the invention, identity credibility right confirmation in a distributed environment is realized, the security and compliance of data exchange are improved, and security autonomy and verifiable control in a data use stage are realized.

The embodiment of the invention relates to the technical field of geographic space platforms and visualization, and provides a forest fire spreading dynamic visualization simulation method and device, electronic equipment and a medium, and the method comprises the steps: obtaining image data and topographic data of a target region, and constructing a three-dimensional topographic scene; receiving a combustible point coordinate and a real-time meteorological parameter selected by a user, and configuring an initial fire behavior in the three-dimensional terrain scene based on the combustible point coordinate and the real-time meteorological parameter; performing periodic forest fire spreading calculation based on the configured initial fire behavior through a forest fire spreading algorithm to obtain simulated fire point data corresponding to each calculation period; generating flame particles and smoke particles corresponding to each calculation period based on the simulated fire point data corresponding to each calculation period; and sequentially rendering the flame particles and the smoke particles corresponding to each calculation period into a three-dimensional terrain scene, and carrying out real-time dynamic forest fire spreading visual simulation. Therefore, real-time forest fire spreading three-dimensional visualization with high precision and high trueness in the browser is realized.

The invention relates to the technical field of big data storage, and discloses a multi-level data storage and mixed query method and system, a storage medium and equipment, and the method comprises the steps: collecting original buried point data, carrying out real-time stream processing, storing the processed real-time data in a hot storage layer, carrying out timing offline batch processing on historical buried point data, and storing the processed real-time data in a hot storage layer; converting into an optimized storage format and storing in an object storage system; an extended metadata management system is constructed, and structure information, partition information and access frequency information of data in the object storage system are automatically extracted and managed; executing query, and reducing the data scanning amount through a query optimization strategy; automatically migrating data among different storage layers through a dynamic cold and hot data layering mechanism; a unified query interface is provided, a query request is distributed to a corresponding real-time processing system or an offline query system based on an intelligent routing strategy, and a query result is cached and returned, so that high-performance and low-cost storage and second-level query of data are realized.

The invention discloses a six-dimensional judgment intelligent operation and maintenance diagnosis method and system for an oil field gathering and transportation station yard, and belongs to the technical field of oil field gathering and transportation intelligent operation and maintenance. Comprising a data acquisition and single point location model establishment module, a six-dimensional analysis and judgment module, a six-dimensional fusion processing module and a feedback updating module, comprising flow, pressure, temperature, liquid level, water content, pump frequency / current, valve position / state position, furnace gas amount / air door and station control alarm position data in an oil field gathering and transportation station. According to the six-dimensional judgment intelligent operation and maintenance diagnosis method and system for the oil field gathering and transportation station, by integrating multi-link analysis, dynamic threshold adjustment, a closed-loop optimization mechanism and safety guarantee design, the problems that current diagnosis is poor in stability, insufficient in precision, weak in decision support and the like are solved; and reliable technical support is provided for efficient and stable operation of an oil field gathering and transportation station.

The invention discloses a method and equipment for compressing multi-dimensional data based on column storage and self-adaption, and the method comprises the steps: recombining original multi-dimensional data into a column data set structure, and organizing data block storage for the column data set structure according to a hierarchical structure; each data block stores observation grid data of a single variable of multi-dimensional data at a certain moment so as to support column type storage and self-adaptive compression; and carrying out real-time statistical analysis on characteristics or parameters of variables in the data blocks, and dynamically selecting and configuring a filter and parameters thereof to compress the multi-dimensional data. The data of the same variable are stored together through column storage, so that the data access efficiency is greatly improved, especially during variable-level analysis. Meanwhile, the self-adaptive compression technology can dynamically select a compression mode according to the distribution characteristics of the data, so that the compression rate of the data is effectively improved, and the occupied storage space is reduced. The processing speed of the meteorological grid point data can be obviously improved, and the storage cost is reduced.

The invention relates to the technical field of deformation detection, in particular to a method for measuring the deformation of a rotary body part, which comprises the following steps of: arranging measuring points on a target rotary body part, and performing dotting measurement by using a three-coordinate measuring instrument to obtain dotting data which are three-dimensional coordinates; based on the dotting data, calculation is carried out, and deformation information of the target rotary body part is obtained, the calculation comprises the steps of determining a data source, normalizing coordinates, converting a coordinate form, sequencing path measuring points, compensating a measuring head, calculating a position-error function, calculating all-directional axial deformation and radial deformation, speculating all-position deformation, and performing any combination calculation in multi-coordinate system data integration. According to the invention, the deformation of the rotary part can be measured conveniently, quickly and efficiently.

The invention discloses an intelligent redevelopment research method for low-efficiency land use, which comprises the following steps of: acquiring multi-source data including land change survey data, topographic data, urban planning data, remote sensing data, point-of-interest data, mobile signaling data and social economic data in a research area, and inputting the multi-source data into a land use identification model; obtaining a spatial distribution diagram of low-efficiency land in the research area and corresponding low-efficiency factors; inputting the spatial distribution diagram and the low-efficiency factors into a redevelopment potential calculation model to obtain a redevelopment comprehensive potential value of the low-efficiency land; a plurality of redevelopment schemes are obtained based on the redevelopment comprehensive potential value, the rigid constraint of the low-efficiency land during redevelopment and the redevelopment mode library; and obtaining respective corresponding benefit index difference values after the low-efficiency land is redeveloped by using the plurality of redevelopment schemes, and obtaining an optimal redevelopment scheme from the plurality of redevelopment schemes by using a multi-attribute decision algorithm and the benefit index difference values.

The invention discloses a shield construction full-process management and control system and method based on intelligent scheduling, and relates to the technical field of tunnel construction, the system comprises a data acquisition layer, a data fusion and intelligent scheduling layer, an execution control layer and a man-machine interaction and visualization platform. The data acquisition layer acquires multi-source data of shield construction in real time; the data fusion and intelligent scheduling layer performs fusion processing on multi-source data, generates a comprehensive scheduling scheme based on an intelligent algorithm, and dynamically manages task priorities and security constraints; the execution control layer receives and executes the scheme, and cooperatively controls tunneling, transportation, assembly and vertical transportation equipment; the man-machine interaction and visualization platform realizes construction state visualization, scheduling display, abnormity alarm, remote control and multi-work-point data access and authority management. According to the invention, through a multi-layer collaborative architecture, full-process intelligent management and control of shield construction are realized, the data-driven decision-making capability, the equipment collaborative efficiency and the multi-project scheduling level are improved, and the construction efficiency is improved.

The invention discloses an edge sea petroliferous basin paleotopography inversion method based on a machine learning algorithm, and belongs to the technical field of geological exploration and data processing. The method comprises the following steps: constructing a seismic profile data pool and carrying out image preprocessing; establishing an affine transformation relationship between pixel coordinates and physical coordinates by using an image digitization tool, and extracting a two-dimensional data sequence; establishing a mapping function of the measuring line distance and the longitude and latitude, and constructing a three-dimensional scatter data set; carrying out gridding interpolation by adopting a K nearest neighbor regression algorithm, and constructing a three-dimensional grid model; performing motion back-pushing and deformation-removing correction by using a plate reconstruction technology to obtain a paleotopography model; and generating a three-dimensional dynamic visualization result of paleotopography evolution through time interpolation. The method solves the problems that seismic profile images cannot be quantitatively analyzed, sparse data interpolation is difficult, and paleotopography inversion neglects structural deformation, and provides technical support for multiple fields such as oil-gas exploration.

The invention relates to the technical field of intelligent irrigation, in particular to an intelligent watering control method and system for agricultural production, and the method comprises the steps: data collection, global data reconstruction, soil moisture content prediction, watering strategy optimization, sensor layout optimization, control execution and model updating. In the prior art, a pure mathematical method is generally adopted to calculate global humidity from sparse point data, and multi-dimensional environment information cannot be effectively fused, so that the reconstruction precision is limited and the physical credibility is insufficient; a physical information enhanced confrontation generation network is adopted to carry out global perception, sparse measurement values, geographic coordinates and real-time environment data are fused into conditional tensors to be input into a generator, and training is guided by forcing sensor data consistency in a loss function and introducing moisture diffusion physical constraints; the method can generate a high-resolution and physically reasonable global soil humidity distribution diagram, and significantly improves the reconstruction precision and reliability from sparse points to planar information in a complex farmland environment.

The invention discloses a precise point distribution method for intelligent patrol cameras of a transformer substation. Relates to the field of substation intelligent patrol systems. Comprising the following steps: step 1, determining an installable area and limiting conditions of the intelligent patrol camera; 2, an adjustable temporary intelligent patrol camera is accurately arranged in the installable area; 3, identifying and recording an equipment state point position which can be observed by the intelligent patrol camera at the installation point position; 4, recording all temporary intelligent patrol cameras in all installable areas and observable equipment state point location data of the temporary intelligent patrol cameras; 5, establishing an intelligent patrol camera precise point distribution mathematical model, and solving intelligent patrol camera point distribution points by taking the lowest camera installation cost as a target; 6, according to the solving content, intelligent patrol camera accurate point distribution is carried out; according to the method, the mixed integer linear programming model under the comprehensive constraint is established, the lowest cost is taken as a target, meanwhile, reliable coverage of all key point positions is ensured, the number of installed cameras can be effectively reduced, unnecessary waste is avoided, and the total cost of system construction is remarkably reduced.

The invention provides an intelligent inspection management system in a hybrid expert mode under a cloud edge collaborative architecture, and relates to the technical field of data processing, the system is used for cutting basic inspection data into a plurality of section units according to a business time window, and combining record elements and node sequences in each section unit to obtain a combined data segment; constructing a track series sequence by taking the occurrence sequence of each record element as a reference, and converting the track series sequence into track matrix data; constructing node load data and calculating a process aggregation factor; screening out candidate sections in section combination data and constructing a section traffic matrix; calculating a process shunting factor; and determining process node data, and constructing an inspection service link according to the process node data. According to the invention, the problem of multiple circulation of error events in the process can be solved, and the uniqueness of the inspection task is ensured.

The invention relates to an urban turbulent wind field digital twinning method based on a pattern constraint generative adversarial network, and the method comprises the steps: taking randomly distributed wind speed sensor measurement point data and building geometric information as model input based on the data input of a sparse sensor; physical feature embedding based on comparative learning: pre-training and extracting dominant flow mode features of cross-regional urban turbulence through an encoder to form a physical prior knowledge base; generating confrontation training based on submerged space constraint, constructing physical constraint loss by calculating submerged space mode difference, and guiding a generator to learn physically consistent flow field reconstruction mapping; based on high-fidelity reconstruction of real-time data, a trained model processes real-time sparse measurement point data, a complete two-dimensional velocity field is output, and accurate mapping from local to global is achieved. A dominant flow mode of urban turbulence is extracted as a physical prior constraint by a contrast learning framework, and high-fidelity reconstruction from extremely sparse sensor data to a high-resolution wind field is carried out in combination with the strong nonlinear fitting capability of the generative adversarial network.

The invention discloses a deep coal bed gas seam network density prediction method, and relates to the technical field of reservoir development. The method comprises the following steps: extracting an average seam net distance from microseismic event point data as a seam net density representation label; establishing a multi-source data fusion framework taking a fracturing section as a sample unit, and splicing geological and engineering parameters into a feature vector; a machine learning algorithm is adopted to construct an intelligent prediction model, and a complex nonlinear mapping relation between multi-source features and labels is automatically learned. According to the method, intelligent prediction of the fracture network density is realized, and reliable support can be provided for fracturing effect evaluation and construction parameter optimization.

The invention discloses a forest carbon flux statistical method and system under the influence of a wildfire, and the method comprises the steps: obtaining forest multi-dimensional data including fire point data, and constructing a multi-scale collaborative database; constructing a carbon source-carbon sink dynamic coupling model according to the multi-scale collaborative database; obtaining a forest multi-dimensional remote sensing index, and constructing a correlation matrix; correcting the carbon source-carbon sink dynamic coupling model according to the correlation matrix; and generating a forest spatialization decision map based on the multi-scale collaborative database and the carbon source-carbon sink dynamic coupling model. According to the technical scheme provided by the invention, by fusing forest multi-scale data, a nationwide unified grid system is constructed, and large-scale forest fire carbon flux statistics and accurate quantification are realized; a carbon source-carbon sink dynamic coupling model is constructed, a forest multi-dimensional remote sensing index is used for correction, and the monitoring precision of the fire influence is remarkably improved; and the emission factors are dynamically calibrated, so that the inter-region difference is reduced, and the universality of the method is enhanced.

The invention discloses a localization-adaptive track point visual rendering method based on partitioning and hierarchical caching, and the method comprises the steps: S1, inputting a track data stream, carrying out the reading according to the source type of the data stream, and carrying out the data partitioning and LOD processing of the track point data; s2, performing data analysis and rendering on the data; s3, carrying out batch processing on GPU vertexes, according to the state of the circular buffer, if the vertexes are writable, incrementally writing vertex data, and if Wrap is needed, carrying out loopback writing and updating a pointer; and S4, finally, the descriptors and the styles are updated, rendering increment drawing is carried out on the track points, and visual display is carried out. The method supports real-time rendering and playback of ten-million-level track points, has visual and instantiated rendering of window dynamic loading, space-time partitioning, hierarchical caching and WebWorker decoupling calculation, and improves the space-time data rendering capability of the domestic autonomous controllable field by ten thousand times; and the user experience is greatly improved by a non-perceptual interaction and transition smoothing algorithm.

The invention relates to a 10kV switch cabinet intelligent automatic operation monitoring and early warning method and system and a medium, and the method specifically comprises the following steps: 1, collecting the partial discharge data information of each key part, including a sound wave signal, an instantaneous pulse current signal and a high-frequency electromagnetic wave signal; 2, carrying out data processing, and obtaining a partial discharge abnormal point data value of each key part; 3, realizing partial discharge early warning of the switch cabinet by adopting a fault early warning method based on current and sound wave abnormal point data of the switch cabinet; 4, collecting the temperature of each key part and the environment temperature; 5, carrying out data processing, and obtaining a change relation function of the temperature rise of each key part along with the current; step 6, establishing a switch cabinet dynamic early warning model to realize dynamic temperature rise early warning; the method has the advantages that the operation and maintenance cost is reduced, the equipment operation efficiency is improved, potential safety hazards are found in time, and remote monitoring and early warning are achieved.

The invention discloses an internet channel-based aviation information query service method and system, and relates to the technical field of internet aviation information processing. Comprising the following steps: constructing a depth feature extraction model based on a flight attribute graph and a historical query mode feature extraction unit, and analyzing and predicting query popularity and identifying sudden query hotspots through multi-dimensional feature fusion; constructing a three-level cache storage system, and dynamically evaluating the cache priority based on the real-time level and the predicted query popularity; determining a data storage position and an updating strategy according to the priority, and starting distributed preloading and CDN node directional preheating for sudden hotspot data; when a query request is responded, parallel processing of instant return and asynchronous update is implemented, and differential degradation response is implemented according to real-time sensitivity; and dynamically generating a multi-path parallel scheme for complex query, and adaptively adjusting an execution strategy. And the stability and the response speed of the query service are obviously improved.