1494 results about "Multi dimensional data" patented technology

The invention discloses an anomaly detection and intelligent diagnosis method, system and device based on digital power grid multi-source data and a medium, and belongs to the technical field of anomaly detection, and the method comprises the steps: obtaining multi-source operation data from a power grid operation process, carrying out the preprocessing, and generating a standardized data set; time sequence features are extracted based on historical data, a power grid state reference model is constructed, and normal operation states in different load scenes are represented; on the basis of deviation calculation of the standardized data set and the power grid state reference model, abnormal candidate signals are detected, and high-confidence-coefficient abnormal signals are screened and generated; determining an abnormal source based on the high-confidence abnormal signal in combination with a power grid topological structure, and performing analysis to obtain fault type information; and generating a control instruction according to the fault type information and issuing the control instruction to a power grid control system. According to the method, a complete technical scheme of multi-dimensional data fusion, dynamic deviation detection, high-confidence anomaly screening, anomaly source accurate positioning and fault type rapid diagnosis is realized.

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

The invention relates to the technical field of error correction, in particular to a water conservancy facility environment interference error correction method and device based on multi-dimensional data. The method comprises the following steps: collecting historical monitoring logs of water conservancy facilities, carrying out environmental disturbance event identification and multi-scale disturbance relevance mining, and constructing an environmental disturbance space-time relevance map; monitoring surrounding environment water monitoring parameters of the water conservancy facility, performing sequential response trend change analysis and dynamic response characteristic modeling, and constructing a facility environment personalized change response portrait; identifying a plurality of water body position monitoring data according to the surrounding environment water body monitoring parameters, and constructing a hydrodynamic full-field sensing graph; and performing nonlinear correlation analysis based on the hydrodynamic full-field perception graph and the personalized change response portrait of the facility environment, and constructing a digital twinborn model of the water conservancy facility under the water flowing condition. According to the invention, through dynamic error trend analysis and correction, the monitoring precision and monitoring stability of the water conservancy facility are improved.

The invention discloses an automatic scheduling method and system for ship unloading equipment, and relates to the technical field of port automation. According to the method, a high-precision digital twinborn model for ship unloading operation is constructed, physical equipment is abstracted into a digital intelligent agent with an autonomous decision-making capability, real-time multi-dimensional data and historical data are utilized to perform deep fusion to drive system synchronization, and a future multi-step scheduling strategy is deduced in a parallel simulation manner in a virtual space based on rolling time domain control, so that the real-time multi-dimensional data and historical data are subjected to real-time multi-dimensional data synchronization driving system synchronization is realized. Dynamic evaluation and optimization are carried out by adopting a multi-objective evolutionary algorithm combined with a cooperative game mechanism, and conflicts and cooperation among equipment are effectively coordinated by defining an individual utility function and introducing cooperative game negotiation and a meta-controller to dynamically adjust target weights, so that system-level global optimal scheduling is realized under multiple objectives of efficiency, energy consumption, safety and the like, and the scheduling efficiency is improved. The intellectualization, the self-adaptability and the comprehensive operation benefit of port ship unloading operation are comprehensively improved.

The invention relates to the field of power data management, and discloses a power big data adaptive management method and system fused with spatial-temporal feature mapping, and the method comprises the steps: obtaining a real-time operation data flow from a multi-source power terminal, and constructing an original power data set with a time sequence label and a device identifier; the method comprises the following steps: dividing an original power data set into parallel processing units based on a storage-while-computing architecture, performing dynamic index updating by adopting an event-driven index mapping rule, and constructing a multi-dimensional data index cache system with real-time responsiveness; identifying a key abnormal trajectory through a time-varying feature nesting mechanism, and performing hierarchical measurement and entropy disturbance analysis on a data fluctuation degree in the key abnormal trajectory by using a streaming feature aggregation network; identifying potential security risk nodes in combination with the structure matching degree between the historical abnormal event evolution graph and the key abnormal trajectory; and generating a multi-level response instruction chain based on the risk assessment result. The method has the advantage of improving the operation safety of the power grid.

The invention discloses an earth and rockfill dam seepage-deformation early warning method and system based on time-space combined anomaly, and belongs to the field of dam body safety data research. The method comprises the following steps: constructing a spatio-temporal topological graph based on an engineering coordinate system, integrating multi-dimensional data by nodes, and constructing a dynamic adjacency matrix according to spatial distance and seepage relevance; extracting features by using a space-time diagram convolutional network, a self-loop mechanism and cross-layer attention; and executing dual-drive early warning through standard threshold preliminary screening, multi-scale LSTM prediction and a time decay evidence theory. The system comprises a sensor network and an intelligent computing module, and the intelligent computing module has adaptive modeling and visualization functions. According to the scheme, seepage-deformation space-time correlation quantitative analysis is achieved, the hysteresis effect is captured, the threshold value is dynamically corrected, multi-source evidences are fused, the early warning timeliness and accuracy are improved, and the risk of false alarm and missing alarm is reduced.

The invention discloses a multi-disaster disaster risk grade coupling evaluation method. The method comprises the following steps: establishing a multi-dimensional database covering multiple disaster types; key features related to the disaster risk are extracted from the multi-dimensional database; establishing a risk assessment model according to the key features, and determining a plurality of single-disaster risk levels; identifying association rules, causal relationships or space-time coupling modes among different disasters by using a big data mining algorithm; coupling weights among different disasters are determined, and a coupling model is constructed to convert a coupling relation into a quantitative risk superposition effect; based on the coupling model, a single-disaster risk and a coupling effect are integrated, a multi-disaster comprehensive risk level is generated, through multi-dimensional data integration, quantitative coupling modeling and a dynamic adjustment mechanism, advantages are formed in evaluation precision, social and economic adaptability and model adaptability, and the method is more suitable for scenes needing accurate risk grading and decision support.

The invention provides a gas ultrasonic transducer fast matching method, device and equipment and a storage medium, original measurement data such as flight time are obtained by deploying an ultrasonic transducer in a gas ultrasonic flowmeter in a gas conveying pipeline, and multi-dimensional data acquisition is carried out in combination with other sensor parameters. Wavelet noise reduction and dynamic time warping processing are carried out on collected signals, signal quality and time sequence consistency are improved, time domain, frequency domain, environment and statistical features are extracted, and multi-dimensional feature vectors are formed. Modeling is carried out through a time sequence feature branch and an environment feature branch, weighted fusion is carried out by adopting an attention mechanism, and the recognition capability of the model on key features is enhanced. A machine learning model is trained based on fusion features, a multi-objective loss function and a data enhancement strategy are adopted, the prediction precision and generalization ability of the model are improved, and a compensation value is output to calibrate the original traffic in real time. According to the method, the accuracy and stability of gas flow measurement in a complex environment are effectively improved, and the method has a good engineering application prospect.

The invention relates to the technical field of life cycle management, in particular to a catalyst industry chain-oriented full life cycle traceability method and system, and the method comprises the steps: obtaining the state attribute data of the same batch of recovered products, and carrying out the analysis to obtain an efficiency feature vector; inputting the efficiency feature vector into a preset quality evaluation model for matching, and identifying quality deviation data of the recycled product; the batch identity codes are analyzed, and resume data of the production full life cycle are obtained; a multi-dimensional data association model is constructed, quality deviation data is taken as a target variable, resume data is taken as an input feature, an influence weight coefficient and an attribution confidence index of each production control variable on the quality deviation are analyzed, and key control elements are screened out; and performing production regulation and control optimization operation based on the key control elements, calculating target parameter offset for correcting quality deviation, dynamically calculating a dynamic tolerance constraint range of a production control variable in combination with an attribution confidence index, and packaging into an execution management and control protocol.

The invention relates to the technical field of smart city traffic, and discloses a smart city traffic dynamic optimization system and method based on digital twinning. The system obtains urban traffic network multi-dimensional data from a plurality of heterogeneous data sources through a traffic multi-dimensional data acquisition module and integrates the urban traffic network multi-dimensional data into a traffic related data warehouse; a traffic digital twinning model construction module extracts features from the data warehouse to generate a traffic related feature matrix, and a digital twinning traffic dynamic model is constructed according to the traffic related feature matrix to output a theoretical traffic state value; the traffic flow map construction module determines a dimension link map of each dimension and constructs a traffic flow link map; the traffic core feature screening module screens a traffic core feature sequence based on the map; the traffic multi-dimensional optimization analysis module performs multi-dimensional difference analysis on the theoretical traffic state value and real-time actually measured traffic data, and generates a region-level difference coefficient matrix in combination with the core feature sequence; and the traffic event association positioning module can realize accurate management and dynamic optimization of urban traffic.

The invention provides a compressor energy-saving operation control method and system based on reinforcement learning, and belongs to the technical field of compressor control. The method comprises the steps that multi-dimensional data in the operation process of a compressor are collected through a multi-parameter sensor network; preprocessing the multi-dimensional data to obtain target feature data; inputting the target characteristic data into a state prediction model, and predicting an operation parameter prediction value in a future control period; splicing and fusing the target feature data and the operation parameter predicted value, and constructing state representation of the reinforcement learning model; inputting the state representation into a target reinforcement learning model based on a near-end strategy optimization framework to obtain an optimal control action; safety verification is conducted on the optimal control action based on preset compressor safety operation constraints, and an execution instruction is determined; and adjusting operation parameters of the compressor based on the execution instruction. According to the compressor energy-saving operation control method and system based on reinforcement learning, the energy-saving performance and the operation stability of the compressor are improved.

The invention relates to the field of constructional engineering, and discloses an intelligent construction management and control method for constructional engineering, which comprises the steps of collecting multi-dimensional data of a building construction site, including construction progress data, personnel positioning information, an equipment operation state and site environment parameters, and constructing a construction behavior feature data set in combination with a multi-source data fusion algorithm; performing automatic structured analysis on the construction behavior feature data set, constructing a construction process modeling framework based on a hierarchical feature clustering method, and introducing a dynamic feedback learning mechanism to perform adaptive optimization on a modeling result; judging whether the optimization process is stable or not according to the evolution trend of the process model, and if so, recording the stage state of the current construction process feature; and based on the corrected construction plan path, performing disturbance factor correction on the prediction progress by applying a multi-scale construction simulation algorithm, extracting corresponding control parameters, and performing intelligent adjustment on prediction nodes. The method has the advantage of improving intelligent construction management.

The invention belongs to the technical field of railway inspection, and particularly relates to a space-air-ground integrated railway power transformation and distribution intelligent inspection method and system which comprises a sensing layer, a platform layer, a data and intelligent center, an application layer and a user and presentation layer. The sensing layer is composed of an aerial inspection layer, a ground inspection layer and a fixed monitoring layer; the platform layer is based on a cloud computing center and edge computing node collaborative architecture, combines data and an intelligent center, integrates multi-source heterogeneous data through a big data platform, carries an AI algorithm engine to realize automatic identification of equipment defects, analyzes images, synchronously predicts the images, and realizes automatic identification of the equipment defects. And the application layer is combined to complete the functions of automatic generation of an inspection report, automatic distribution of a maintenance work order, equipment health state evaluation and the like. According to the method, functions of railway power place inspection, power supply line inspection, power supply and distribution facility operation and maintenance emergency and the like can be realized, deep fusion analysis of multi-dimensional data is realized, and meanwhile, the problems of insufficient training samples, sparse defect types and the like of an existing AI model are solved.

The invention discloses a furnace body temperature control system, which belongs to the technical field of industrial automation control, and comprises the steps of monitoring and collecting multi-dimensional data of a physical furnace body in real time, performing preprocessing, generating a state vector, setting a working condition judgment method, classifying various working conditions, outputting working condition labels and characteristic parameters, and establishing a health degree evaluation model; the state of the actuator is monitored in real time, and frequent maintenance and short service life caused by overuse of equipment are prevented; constructing a furnace body virtual model, setting a real-time prediction method, and simulating furnace body temperature distribution so as to predict a future temperature trend, judge whether to start a rehearsal mode, output an optimal scheme, and realize advanced intervention of prediction and decision making; a working condition identification and temperature prediction result is read, a decision engine method is set, and a dynamic control instruction is generated and executed; and comparing data of the physical furnace body with data of the digital twinborn body, setting a self-evolution method, and carrying out online calibration and correction on the virtual model.

Systems and methods disclosed herein automatically detect, analyze, and mitigate anomalous resource distribution among artificial intelligence (AI)-based agents within a distributed computational network. The system receives a resource allocation request specifying computational resources, agent parameters, and performance objectives for a network of agents. A first AI model set monitors agent activity by tracking resource consumption and behavioral deviations from baseline profiles. The system compares resource usage of agents with historical norms and / or predetermined thresholds to generate an anomaly score for each agent. A second AI model set aggregates scores to construct a multi-dimensional data structure that indicates the comparison and anomaly score. The system ranks agents by anomaly severity to isolate agents with high scores (e.g., misaligned agents), and reallocates resources and updates access privileges for the misaligned agents.

The invention discloses a rock damage assessment method fusing wave velocity and microscopic fracture, and belongs to the technical field of rock damage assessment, and the rock damage assessment method comprises the following steps: arranging a wave velocity testing device on a rock sample, and respectively making an unloaded rock sample and a damaged snapshot rock sample under different stress loading; microcosmic fracture information of the damaged snapshot rock sample is identified, a three-dimensional wave velocity model of the damaged rock sample under different stress loads based on a wave velocity test is constructed, and then a macro-micro scale fusion multi-dimensional data set is constructed; based on the macro-micro scale fusion multi-dimensional data set, constructing and training a rock damage identification model; and inputting the characteristic parameters of the rock sample to be evaluated into the rock damage identification model, identifying the damage distribution characteristics of the rock sample on the spatial scale, and evaluating the rock damage degree. According to the method, accurate prediction and evolution process evaluation of the rock damage degree and spatial distribution can be realized, and a reliable technical means is provided for disaster early warning, stability evaluation and risk prevention and control in complex rock mass projects such as deep mines and tunnels.

The invention discloses a digital twin intelligent test run system based on a marine medium-speed diesel engine and a monitoring method, relates to the technical field of monitoring, and is used for solving the problems of degeneration identification lag and low emission early warning precision of an oil injector. The system comprises a multi-source heterogeneous data preprocessing module and a running state intelligent diagnosis module. The multi-dimensional data acquisition module is used for acquiring in-cylinder pressure, crankshaft torsional vibration and transient air-fuel ratio signals to construct multi-dimensional data vectors, and the multi-dimensional data acquisition module is used for analyzing combustion fluctuation characteristics based on an attention mechanism LSTM (Long Short Term Memory) network, identifying early deterioration of an oil injector and outputting an oil injection consistency coefficient and a fault type identifier. The method comprises the steps that according to an oil injection consistency coefficient, electromagnetic valve response delay and combustion parameter correlation are tracked, a degradation trend is predicted, and emission early warning is generated; and according to prediction and early warning results, fuel injection compensation parameters are dynamically adjusted in the digital twinborn model, a recursive least square method is adopted to identify combustion parameters and feed the combustion parameters back to an electric control unit, and self-adaptive optimization and closed-loop control in the test run stage are achieved.

The invention relates to the technical field of tunnel construction safety monitoring, and discloses a tunnel construction safety evaluation method and system based on multi-dimensional data fusion analysis, and the method comprises the steps: constructing a digital twinborn model, and building an initial twinborn body comprising a BIM model and a three-dimensional geologic model; performing mechanical parameter inversion and dynamic updating on the three-dimensional geologic model through real-time multi-dimensional monitoring data to obtain a dynamic twinborn body; performing fusion analysis on the monitoring data by adopting a fusion algorithm, and outputting a safety risk coupling evaluation result; based on an evaluation result, combining with surrounding rock mechanical response of dynamic twinborn simulation, and adopting fuzzy comprehensive evaluation to output a safety level; and performing risk traceability and positioning main risk factors through a Bayesian network based on the security level, generating a customized support decision through case reasoning, and performing visual display and early warning in a digital twinborn model. The system corresponds to the method. According to the invention, dynamic accurate evaluation and intelligent decision support of tunnel construction safety are realized.

The invention relates to the technical field of water conservancy informatization and intelligent early warning, in particular to a river network water regimen big data intelligent analysis early warning system which comprises a dynamic data fusion module, a topology sensing module, an overload propagation analysis module, an intelligent early warning decision module and an early warning execution module. Wherein the dynamic data fusion module is used for collecting multi-dimensional data of a river network area in real time; the topology sensing module is used for dynamically constructing a real-time topological relation graph of the river network connectivity; the overload propagation analysis module is used for extracting an overload coefficient of each node and outputting a risk diffusion path set; and the intelligent early warning decision module is used for generating a three-level early warning instruction by combining the positions of the engineering vulnerabilities in the real-time topological relation graph. According to the method, through fusion of the multi-source water regimen data and the dynamic topological structure, linkage of accurate identification and hierarchical response of the risk path is realized, and the intelligent early warning and rapid disposal capability of the sudden water regimen of the river network is remarkably improved.

The invention relates to the technical field of electric digital data processing, and discloses a multi-dimensional data logic processing method based on an artificial intelligence algorithm, which comprises the following steps: receiving a target discrete data packet, extracting metadata, and calculating to generate a dimension entropy feature vector representing data logic complexity; inputting the vector into a preset topological mapping model, and outputting an initial adjacent matrix; calling a feedback suppression mask matrix generated based on a historical operator utility state, and executing bitwise logic AND operation with the initial adjacent matrix to generate a corrected effective topological matrix; the matrix is analyzed, a logic operator function pointer is dynamically indexed in an instruction cache, and a directed acyclic execution linked list is constructed; according to the method, redundant logic nodes in AI prediction are definitely eliminated through a bit operation mask mechanism based on historical feedback, and deterministic convergence of processing delay and optimal matching of computing power resources are achieved.

The invention relates to a protective clothing loss prediction method based on big data analysis, and the method comprises the steps: collecting multi-dimensional data, such as temperature and humidity, pollutants, wearing duration, motion amplitude and cleaning, through a high-precision sensor, and outputting a high-consistency loss feature set in combination with normalization, denoising and feature screening algorithms; multi-stage causal chain dynamic construction and node adaptive capacity expansion are realized through working condition clustering and causal inference, a deep neural network model is accessed, and the deep neural network model is used to accurately predict the loss of the protective clothing under complex and abnormal working conditions. According to the scheme, the real-time performance, the accuracy and the environmental adaptability of loss prediction of the protective clothing are remarkably improved, and a scientific basis is provided for management optimization and risk prevention and control.

The invention discloses an intelligent control system for a high-precision emulsifying pot, relates to the field of intelligent control, and is used for collecting multi-dimensional data such as temperature field distribution, material rheological characteristics, liquid drop behaviors and mechanical states in real time through distributed sensors and monitoring the emulsifying process in real time. The system evaluates the phase stability risk level by adopting a multi-source data fusion technology, and dynamically adjusts the process parameters such as the stirring rotating speed, the emulsifier flow and the heating power according to the risk level. For a high-risk state, by analyzing the correlation between a temperature field and torque fluctuation, an abnormal reason is accurately identified, and a targeted regulation and control measure is triggered. A liquid drop coalescence monitoring mechanism is introduced into the system, and when liquid drop coalescence exceeds the standard, temperature compensation is automatically enhanced. The system has a self-learning capability, dynamically optimizes a risk judgment threshold according to historical data, and improves the control precision.

The invention provides a medicine bottle array detection system based on vision and sensing fusion, and relates to the technical field of data processing, and the system comprises the steps: obtaining the spatial distribution data of a vision unit and the posture state data of a sensing unit, fusing the position parameters and state parameters of each bottle body, building a multi-dimensional data node under a unified time index, calculating the spatial aggregation degree of the medicine bottle array to obtain an array quantity, combining the aggregation change of each time point into an evolution chain table, carrying out joint operation on the difference result and the state parameter of adjacent nodes in the evolution chain table, calculating the dynamic coupling degree of the medicine bottle array to obtain a sequence potential energy value, and dividing the sequence potential energy values of the different numerical value intervals into a plurality of level nodes, and performing consistency analysis and anomaly detection on features of the different level nodes to obtain detection result data. By detecting the state of the medicine bottles and the arraying direction, bottle toppling early warning is achieved.

The invention belongs to the technical field of wind power generation equipment state monitoring and intelligent fault diagnosis, and discloses a wind turbine generator blade acoustic fault detection method based on transfer learning, and the method comprises the steps: building a standardized sample through acoustic signal simulation and multi-dimensional data enhancement, and extracting weak fault features through an STFT-Mel frequency spectrum; a dual-scale time-frequency attention module is introduced into the lightweight MobileNetV3, so that the focusing and recognition capability on early crack features is improved; by combining transfer learning with MMD domain difference and entropy minimization regularization, efficient alignment of a simulation domain and actually-measured wind field data is achieved, and the generalization performance of the model under the small sample condition is enhanced; a transverse difference spectrogram and longitudinal historical baseline self-evolution double-flow feature fusion mechanism is adopted, common-mode noise is suppressed, and single-blade positioning, progressive degradation early warning and synchronous aging recognition are achieved. The method is small in parameter quantity, low in calculation overhead and suitable for high-precision and low-cost intelligent detection of early damage of the wind turbine generator blades.

The invention relates to the technical field of forest fire management, in particular to an integrated forest fire intelligent analysis method and system. The system comprises a background service system, a central intelligent analysis system and a mobile intelligent analysis system. The background service system is deployed in a cloud computing server cluster and is used for uniformly storing personnel information, fire scene information and geographic information data and realizing multi-source data fusion and authority management through a standardized API (Application Program Interface); the central intelligent analysis system is deployed in a high-performance computer, integrates a forest fire danger auxiliary decision-making model, a forest fire spreading trend prediction model, a force distribution dynamic display module and a collaborative plotting module, and supports fire spreading prediction and real-time decision-making instruction issuing based on multi-dimensional data such as weather, terrain and vegetation; the mobile intelligent analysis system realizes fire spreading trend analysis, mobile plotting and instruction receiving in an offline environment, supports offline data caching and calling in an emergency scene, and effectively improves forest fire emergency response efficiency and resource scheduling accuracy.

The invention relates to the field of data storage, in particular to a storage and calculation separation multi-dimensional data block indexing method based on adaptive learning. Comprising the steps of receiving original multi-dimensional data and a query load, and outputting a low-dimensional key set through a first model; the low-dimensional key set and the historical query load generate a partitioning scheme through a second model, and the partitioning scheme performs physical recombination on the original multi-dimensional data to output a plurality of physical data blocks; constructing a learning type index through the physical data blocks and the corresponding partitioning schemes; identifying hot spot and cold spot areas through the query frequency of each node of the learning type index and the real-time query load continuous monitoring index; when the access mode changes, adjusting the index structure, and outputting a self-adaptive index; and quickly positioning and reading the target data block through the self-adaptive index to complete access. According to the method, the index of adaptive learning is provided, and the index structure and the physical data layout are adjusted, so that the query accuracy and efficiency in a complex scene are improved, and the data storage overhead is reduced.

The invention discloses a sailing ship safety situation awareness system, and belongs to the technical field of data analysis. The method is used for solving the technical problem that in an existing scheme, multi-dimensional data analysis and timely and reliable early warning cannot be carried out on complex sea conditions, and consequently the coping capacity of the complex sea conditions is weak. Through the whole process of biological anchoring anomaly suppression, data fusion and dynamic feature extraction, the limitation that traditional ship perception depends on a physical sensor can be broken through, high-precision perception of environmental risks and ship states under complex sea conditions is achieved, and compared with a traditional method, the abnormal sea conditions can be recognized in advance, and the method has the advantages of being high in practicability. The robustness of the data and the detection rate of small probability and high influence events can be effectively improved; cross-scale modeling is realized through deep coupling of a microscopic physical mechanism and macroscopic data features; the constructed multi-dimensional risk assessment matrix not only can output a risk value, but also can provide a risk propagation path, and can effectively improve the risk assessment precision of the ship under a complex sea condition.

The invention discloses a hypertensive heart disease early screening system fusing ultrasonic multiple parameters, which comprises the following steps: acquiring ultrasonic image data and physiological index monitoring parameters of a patient at different time points, performing difference comparison on heart structure parameters to generate time sequence change data, and establishing a multi-dimensional data fusion scheme based on the time sequence change data and physiological indexes; according to the fusion scheme, acute risk trigger factors are identified, blood flow abnormal parameters are generated, and emergency threshold comparison is carried out to generate acute risk early warning signals; extracting a myocardial strain degradation rate to determine a chronic degradation acceleration time point, carrying out disease progress stage association to generate a chronic risk layering curve, and establishing a clinical index comparison table; carrying out double-layer correlation on the acute risk early warning signal and the chronic risk layering curve, and carrying out calibration judgment to generate a comprehensive risk judgment sequence; the sequence is decomposed into an acute judgment layer and a chronic judgment layer, a risk threshold value is configured to generate an instant early warning signal, a layered screening report is formed, and technical support is provided for early discovery of the hypertensive heart disease.

The invention discloses a tower grounding grid multi-frequency composite wave electric field imaging and soil environment medium diagnosis method, which comprises the following steps of: generating a composite excitation signal through superposition of a low-frequency sine wave and a high-frequency CHIRP square wave, and collecting multi-dimensional data such as voltage / current response, soil temperature and humidity, electric field intensity and the like in combination with a distributed sensor array; separating signal features by using wavelet transform and extracting impedance information of a target frequency point; a multi-grid homotopy algorithm is adopted to realize grounding grid three-dimensional resistivity distribution reconstruction, corrosion spots and abnormal areas are marked, and high-precision positioning of underground conductors is completed through UWB radar or a capacitance detector; meanwhile, based on the information entropy maximization optimization frequency band combination, the grounding grid state is comprehensively evaluated in combination with a multi-parameter fusion model. The method has the non-contact patrol capability, the unmanned aerial vehicle carries equipment to scan the grounding grid, the detection efficiency and convenience are remarkably improved, and an efficient and accurate solution is provided for guaranteeing safe operation of the power grid.

The invention discloses a low-carbon transformation method, system and device for a building air conditioning system. The objective of the invention is to solve the problems of static model, single target, one-sided evaluation and non-standardized process in the existing reconstruction technology. According to the method, building multi-dimensional data are collected; constructing a building energy consumption prediction physical simulation model coupled with a user behavior prediction model based on machine learning to dynamically reflect a real operation condition; a multi-dimensional objective function is defined, and a Pareto optimal transformation scheme set is generated; constructing an evaluation model based on an analytic hierarchy process and a fuzzy comprehensive evaluation method, and calculating a comprehensive evaluation value of each scheme; and selecting an optimal scheme according to the comprehensive evaluation value, and generating a detailed implementation report. The method can scientifically and efficiently determine the global optimal transformation strategy considering energy conservation, carbon reduction, comfort and economy, and significantly improves the scientificity and comprehensive benefits of building transformation decision.