16458results about "Measurement devices" patented technology

The invention discloses a box-type substation state monitoring and early warning method based on artificial intelligence, relates to the technical field of intelligent power grids, and aims to solve the problems of missing report, false report and response lag caused by the fact that an existing static threshold ignores multi-physical coupling and a depth model highly depends on scarce fault samples. According to the scheme, sliding window kernel density estimation is carried out on a multi-channel time sequence signal, a dynamic coupling matrix is constructed through recursion Copula decomposition, a three-level threshold surface is generated through time-varying quantile regression, abnormal samples and graph attention network extraction state representation are generated in combination with a conditional variation auto-encoder, lightweight recursion pruning is carried out, and the dynamic coupling matrix is obtained. An abnormal score is generated through a multilayer Bayesian network and particle filtering, a multi-step risk trend is discriminated through a Gaussian kernel derivative slope, and finally unscented Kalman filtering is used for smoothing and online threshold correction; according to the method, the detection sensitivity and the early warning recall rate of the box-type substation to the transient coupling fault are remarkably improved, the response speed is improved, and the false alarm frequency is effectively reduced.

The invention discloses a real-time surrounding rock deformation monitoring and data acquisition method and system, which is applied to long-distance weak surrounding rock tunnel construction, and comprises the following steps: determining the dynamic change trend of underground water seepage rate and ground stress distribution gradient by adopting a time sequence analysis method; based on the trend, carrying out risk partitioning on the tunnel construction section by adopting a K-means clustering algorithm, determining a deformation sensitive area, and optimizing the spatial distribution of the monitoring points according to the deformation sensitive area; monitoring data are acquired in real time, and when the data fluctuation period exceeds a threshold value, the data acquisition frequency of the corresponding monitoring point is automatically improved; processing high-frequency acquired data by adopting a long-short-term memory network to obtain a real-time surrounding rock deformation prediction result; the prediction result and the multi-source real-time geological parameters are fused, a Bayesian updating method is adopted for processing, a quantitative surrounding rock stability evaluation result is obtained, closed-loop self-adaptive optimization of a monitoring scheme and accurate risk prediction are achieved, and the safety early warning capacity of tunnel construction and the utilization efficiency of monitoring resources are remarkably improved.

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

The invention belongs to the field of equipment coupling monitoring, and particularly relates to a dynamic coupling compensation method for thermal expansion and axial displacement, which comprises the following steps of: calculating a first characteristic value for representing reference dynamic offset of a sensor based on a three-dimensional thermal-structure coupling model by acquiring a thermal expansion parameter and axial displacement parameter sequence of a casing; calculating a second characteristic value representing the real position change of the rotor in combination with a rotor-casing axial thermodynamic model, establishing a piecewise coupling function considering a nonlinear effect, working condition self-adaption and cross interference, and eliminating the mechanical thermal inertia and measurement system lag influence through a dynamic delay compensation mechanism; an accurate axial displacement measurement total error compensation index is generated, and grading compensation actions are intelligently triggered according to error grades; the problem of measurement distortion caused by dynamic coupling of thermal expansion and axial displacement is effectively solved, and the accuracy and reliability of state monitoring of the rotating machine are remarkably improved.

The invention discloses a digital intelligent switch cabinet state comprehensive sensing system based on AI, and the system comprises a multi-source data collection module, a data preprocessing and synchronization module, an edge calculation feature extraction module, an AI intelligent fusion recognition module, an expert rule diagnosis module, and a cloud comprehensive evaluation and decision module. Various types of sensors are deployed to respectively acquire environmental parameters, electrical parameters and partial discharge signals generated in the operation process of the switch cabinet to form an original multi-modal data stream. The method has the advantages that the recognition precision and response speed of the complex operation state of the switch cabinet are improved, hidden faults under multi-modal data mismatch can be effectively found, and the misjudgment and missed judgment risks are reduced. Meanwhile, a closed-loop diagnosis system is constructed, intelligent evaluation and interpretable feedback of fault types, positions and trends are achieved, scientificity and reliability of operation and maintenance decisions are enhanced, and the method is suitable for intelligent upgrading of an electric power system.

The invention relates to the technical field of inspection monitoring, and discloses an unmanned aerial vehicle intelligent inspection monitoring method and system based on a sensor, and the method comprises the steps: obtaining an initial inspection data set; obtaining a feature data set; generating a unified target feature data set; performing anomaly detection on the target feature data set to obtain an anomaly inspection area data set; performing security level division on the abnormal inspection area to obtain a division result; performing risk degree screening on the safety risk area in the division result to obtain a plurality of high-risk point position types, and when the change threshold value of one high-risk point position reaches a preset threshold value, preliminarily determining a high-risk occurrence zone; the unmanned aerial vehicle is controlled to carry out spot hovering to carry out key monitoring and refined inspection on the high-risk occurrence zone, and a secondary inspection data set is obtained; obtaining an analysis result; and secondarily confirming that the current inspection area is in the high-risk zone, and generating a corresponding emergency response early warning strategy and a corresponding regulation and control strategy, thereby accurately monitoring the inspection area in real time.

The invention relates to a groundwater safety assessment method under an extreme climate event, which comprises the following steps: collecting multi-source heterogeneous data such as meteorological data, geological data, hydrological data and remote sensing data, and constructing a unified groundwater safety knowledge graph through standardized cleaning, semantic alignment and deletion completion; monitoring an extreme climate event in real time, and updating a node relation weight and sparsifying a transmission path based on knowledge graph dynamic evolution and a time sequence attention mechanism; performing risk propagation path reasoning on the dynamic knowledge graph in combination with an improved graph neural network, identifying key pollution nodes, and outputting a structured risk level and a coping suggestion; the system continuously optimizes atlas and model parameters based on evolution feedback, and high adaptability and reasoning precision of emergency response are achieved. According to the method, the intelligence, the real-time performance and the accuracy of underground water risk assessment are improved. The problems that the underground water pollution propagation path is difficult to dynamically identify and the decision adaptability is insufficient under extreme climate events are solved.

The invention relates to the technical field of grinding wheel monitoring, in particular to a centerless grinding machine full-life-cycle grinding wheel state monitoring system and method.According to the centerless grinding machine full-life-cycle grinding wheel state monitoring system and method, through combined difference extraction of a main shaft current mean value fluctuation ratio and a power gradient continuous variation value and assisted by transverse comparison of periodic response intervals, effective recognition of time period boundaries of working condition states is achieved; according to the method, operation stage division does not depend on static threshold judgment any more, a track trend is established through linkage judgment of an amplitude kick point and a slope extreme value, a dynamic evolution path of signal response is clearer, the identification resolution of abnormal changes and local deviation trends is improved, response synchronous identification of parameter state changes is achieved, and the identification accuracy is improved. In multi-signal collaborative analysis, directivity comparison between a temperature rise mutation fragment and a current residual error response is carried out, so that the consistency analysis capability of response logic between different monitoring paths is enhanced, and the real-time performance, stability and anti-interference capability of state identification are improved.

The invention discloses a multi-dimensional information integrated transformer health state monitoring method, which relates to the technical field of power systems, and comprises the following steps: deploying a multi-source sensor, collecting various signals of a transformer, and carrying out signal conditioning and analog-to-digital conversion on analog signals collected by the sensor; performing timestamp alignment on the received multi-source heterogeneous data, and eliminating data noise by adopting a filtering algorithm; key features which are sensitive to the health state of the transformer and complement each other are selected from the extracted feature values to form a health state evaluation index set; setting a sliding time window, obtaining historical data in the window, performing normalization processing on the data, calculating the information entropy and the variation coefficient of each index, and fusing the information entropy and the variation coefficient to obtain the dynamic weight of each index; obtaining a health state index of the transformer according to the normalized value and the dynamic weight; and displaying the health state index and the change trend thereof in real time, and setting an early warning threshold value and an alarm threshold value of the health state index.

The invention provides a water conservancy project safety monitoring method and system based on data processing, and relates to the technical field of monitoring, and the method comprises the steps: obtaining and carrying out the multi-dimensional preprocessing of water conservancy project multi-source heterogeneous monitoring data through the deployment of a sensor network, and extracting multi-scale space-time fusion features from the data; performing structural state modeling, anomaly prediction, risk assessment and early warning by using a long-short-term memory neural network model integrated with a multi-head attention mechanism; and intelligent suggestions oriented to maintenance decisions are generated, so that comprehensive, accurate and prospective evaluation and early warning of the structural state of the water conservancy project are finally realized, the exception identification and risk prediction capabilities are effectively improved, the false alarm rate is reduced, refined and initiative intelligent maintenance decisions are provided, resource allocation is optimized, and the service life of the project is prolonged.

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a high-standard farmland intelligent irrigation system based on Internet of Things and data analysis, and the system comprises a soil moisture content sensing module which collects data through a multi-source sensor to construct a three-dimensional soil moisture content distribution model, and generates a soil moisture content characteristic spectrum; the soil moisture content prediction module generates water demand prediction data based on the soil moisture content characteristic spectrum, the meteorological data and the crop growth stage; the irrigation strategy module fuses terrain elevation and pipe network pressure parameters to generate an irrigation control map; the equipment state monitoring module collects water pump current waveform and other data to generate equipment health degree parameters; the pipe network optimization module optimizes pipe network topology and generates an adjusting instruction; the multi-source data fusion module generates fusion evaluation indexes by using an evidence theory, an entropy weight method and the like; and the intelligent execution module generates an execution control instruction accordingly. All the modules cooperate to achieve precise irrigation, and the utilization efficiency of water resources and the intelligent level of farmland management are improved.

The invention discloses a wetland ecological restoration dynamic monitoring method based on deep learning, and relates to the technical field of ecological restoration, and the method comprises the following steps: obtaining multi-source wetland ecological sensor data and remote sensing image flow in real time, constructing a space-time fusion data cube, and extracting an ecological feature tensor; performing degradation mode analysis on the ecological characteristic tensor, generating an ecological state dynamic topological graph, and calculating an ecological connectivity index; carrying out restoration demand identification based on the ecological connectivity index, positioning a degradation hot spot region through a multi-modal graph convolutional network, and generating a restoration priority region coordinate set; through multi-source data space-time fusion and deep crossing of deep learning and landscape ecology, a whole-process technical system from ecological state dynamic perception to restoration scheme intelligent optimization is constructed. The problems that in traditional wetland restoration, data scales are not matched, degradation area positioning is fuzzy, restoration path ecological adaptability is poor, and multi-target cooperation is difficult are effectively solved.

The invention discloses a health degree evaluation system and method based on a photovoltaic string, and belongs to the technical field of photovoltaic operation and maintenance intellectualization, and the method comprises the steps: firstly collecting SCADA operation data of a plurality of strings of a photovoltaic power station, including voltage, current, assembly temperature, environment irradiance and power factors; secondly, constructing a whole-station feature reference model, and generating a state vector fusing environment and load characteristics through a nonlinear projection algorithm; aiming at the target group string, extracting a health reference track, calculating a disturbance propagation factor, and identifying a health abnormal state; when the deviation degree exceeds a threshold value, dynamically screening heterogeneous reference group strings, constructing a health score distribution model, and further calculating a confidence score and a prediction residual error; if the score is low and the residual error is unstable, determining that the string is in a sub-health or hidden fault state, and generating an intervention instruction; the method has high accuracy, strong adaptability and good interpretability, and can be widely applied to intelligent diagnosis and refined operation and maintenance management of the photovoltaic power station.

The invention relates to the technical field of pile foundation monitoring, and discloses a pile foundation state real-time monitoring and diagnosis system based on a digital twinborn technology. The system comprises a multi-source data acquisition module, a data confidence evaluation module and an acoustic emission monitoring decision module. The multi-source data acquisition module comprises a plurality of sensor groups deployed at different depths of a pile foundation, each group comprises a strain sensor, an acceleration sensor, an acoustic emission sensor and a temperature sensor, and pile foundation data can be acquired in multiple dimensions; the data confidence evaluation module receives original data, generates a correction data sequence through time sequence noise separation and reconstruction, and calculates data confidence according to correction data distribution dispersion; the acoustic emission monitoring decision module judges whether acoustic emission monitoring is started or not according to the data confidence coefficient, and controls the acoustic emission sensor array at the top of the pile foundation to collect acoustic emission signals during starting. The system can comprehensively obtain pile foundation data, improve data accuracy, achieve early damage recognition and guarantee pile foundation safety.

The invention relates to the technical field of optical fiber communication monitoring, in particular to a passive optical fiber multi-parameter digital twin drive abnormal root cause positioning method and system. Collecting temperature, stress, acoustics and polarization signals, and constructing a time domain, frequency domain and energy domain coupling feature tensor and time sequence data set; performing nonlinear dimension reduction and feature decoupling by using a beta-VAE model, and dynamically quantifying contribution of each parameter to anomaly by using an integral gradient to form a contribution degree vector group; constructing a PINN digital twinborn model embedded with heat conduction and elasto-optical effects, and predicting a normal fluctuation range under contribution vector weighting and data-physics dual constraints; modeling measurement and prediction deviations under the guidance of contribution vectors, and outputting an abnormal measurement score, confidence, a position and a time sequence; and a causal graph neural network is constructed, topology and abnormal events are fused for tracing causes, a fault source is identified, and the model is subjected to closed-loop calibration. According to the invention, data driving and a physical mechanism are fused, and accurate detection and root cause positioning of the abnormity of the optical fiber system are realized.

The invention provides a crop growth state evaluation method based on the agricultural Internet of Things, and the method is characterized in that the method specifically comprises the following steps: S1, collecting the multi-dimensional data of a crop growth environment in real time, and outputting an original data set; s2, aligning the multi-dimensional data according to geographic coordinates, extracting features, and constructing a multi-modal fusion feature vector; s3, automatically identifying the current growth stage of the crop and the confidence of the current growth stage according to the fused feature vector; s4, calculating a crop health degree score and grading according to the growth stage information and the feature data, and identifying a stress state detection result at the same time; s5, according to a quantitative evaluation result, predicting a growth trend and generating a suggested decision scheme; and S6, the decision is monitored, the decision execution effect is fed back to the system, and the model parameters and the decision rules are continuously optimized. A closed-loop mechanism of evaluation, decision, feedback and optimization is formed, the applicability and evaluation precision of the system are continuously improved, and agricultural production is promoted to be upgraded to precision and intelligence.

The invention relates to the technical field of equipment health state prediction, in particular to a multi-source sensing driven equipment health prediction method and system. The method comprises the following steps: synchronously acquiring equipment temperature, vibration, current and acoustic data through a multi-source sensor, carrying out denoising and standardization processing, dynamically distributing each signal weight to adapt to an equipment operation stage, generating a high-dimensional dynamic feature vector, and embedding a historical smoothing mechanism to realize continuous updating; performing standardization and nonlinear mapping on the features, constructing a dynamic coupling factor matrix to quantify a cooperative relationship between the features, fusing interaction information and adaptively enhancing abnormal features; three-layer progressive health prediction from a local part, a middle-layer subsystem to global equipment is implemented based on coupling characteristics, a trend consistency verification mechanism is introduced, global and middle-layer prediction differences are quantified through residual errors, weights are adaptively corrected, and the equipment health state evolution trend and the risk level are output. According to the method, the multi-working-condition adaptability, the feature coupling sensitivity and the prediction result reliability are remarkably improved.

The invention provides a multi-branch network and cross attention multi-source data fusion slope displacement prediction method, which comprises the steps of collecting meteorological data, GNSS node data and radar three-dimensional data, and performing preprocessing and standardization processing on multi-source data; constructing a multi-branch network to perform feature extraction on each type of data; fusing the feature vectors of the multi-source data through a cross attention mechanism; a recent trend is captured by combining a multi-scale memory network with LSTM, periodic features are extracted by a one-dimensional expansion convolutional neural network, and a displacement predicted value is obtained through adaptive fusion; and training the model by adopting a course learning mechanism, and outputting a displacement predicted value. According to the method, the multi-branch network is constructed to carry out targeted feature extraction on different modal data, deep fusion is carried out on multi-source data by using a cross attention mechanism, different modal specific feature extraction, cross-modal association modeling and multi-scale prediction of the multi-source data are realized, potential information in the multi-source data is fully mined, and the multi-source data extraction efficiency is improved. And the accuracy and reliability of slope displacement prediction are improved.

The invention relates to a reservoir dam operation safety sky-land project intelligent sensing system and an operation method, and relates to the technical field of hydraulic engineering safety monitoring. The system is composed of a sky-land water conservancy project integrated monitoring and sensing system, a self-adaptive sampling module, a layered distributed architecture and a software and hardware integrated module, and multi-source data such as deformation, seepage, stress strain, vibration and environmental quantity are cooperatively collected through five dimensions of sky domain, airspace, territory, water domain and work domain. The monitoring frequency is dynamically adjusted by using an adaptive sampling strategy, and data cleaning, standardization, space-time registration and fusion processing are completed through a distributed architecture to generate a high-quality comprehensive data set. The system can realize total-factor and whole-process refined monitoring, effectively eliminates data islands, improves data quality and monitoring efficiency, has high reliability, real-time performance and expandability, and provides powerful data support and decision basis for dam safety assessment and intelligent early warning.

The invention relates to a building module data interface intelligent monitoring system based on the Internet of Things and a deployment method, and belongs to the technical field of building information monitoring. The system is composed of a distributed sensing unit, an edge computing gateway, a cloud platform and a visual terminal, a self-adaptive filtering algorithm and an abnormal mode recognition model are built in the edge computing gateway, the cloud platform adopts a time sequence database to construct a multi-dimensional data warehouse, and structural health degree evaluation is carried out by fusing an LSTM neural network and a random forest algorithm. The deployment method comprises the steps of optimizing a sensor distribution strategy based on a BIM model, establishing a wireless Mesh ad hoc network communication architecture, and configuring a grading early warning mechanism and a fault tracing function. The innovation point is that a dynamic threshold adjustment algorithm and an interface performance degradation prediction model are provided, and real-time monitoring of the connection state of the building module and life prediction are realized. The system has the advantages of flexible deployment, high detection precision and low maintenance cost, and the intelligent level of building structure safety monitoring is effectively improved.

The invention discloses a quality management and control system for fabricated decoration construction, and the system comprises a sensing layer which constructs a multi-modal data collection network, carries out the three-dimensional real-time data synchronous collection through combining logistics API docking and an OCR recognition system, and constructs a construction process digital twin bottom plate; in the edge calculation layer, an edge node carries out lightweight processing on the original data; the cognitive layer is used for calling a Prolog rule through a process knowledge graph engine to reasone the feature snapshots, carrying out defect instant diagnosis and dynamic constraint propagation, outputting a root cause path with probability weight through three-stage verification, and quantifying intervention influence; the decision-making layer is used for constructing a dynamic prediction model based on a bidirectional LSTM and an attention mechanism, automatically activating a compensation mode when key interference is detected in combination with an anti-fact memory bank and a case-based reasoning compensator, and generating an alternative scheme of optimal cost / optimal construction period / comprehensive balance through a multi-target optimizer; and in the application layer, a Unity engine is utilized to develop the digital twinborn billboard.

The invention relates to the technical field of environment monitoring, in particular to an intelligent acquisition method based on environment monitoring data fusion, which comprises the following steps: S1, constructing a multi-sensor distributed monitoring network, and acquiring atmosphere, water quality, soil and meteorological environment data; s2, performing data preprocessing, including smoothing, anomaly detection, interpolation and time alignment; s3, carrying out data source, feature and decision three-level fusion, and outputting an environment quality level; s4, constructing a quality index system, monitoring data quality and adaptively optimizing fusion parameters when the data quality is abnormal; s5, performing environment trend prediction and pollution tracing based on a fusion result, and generating early warning information; and S6, constructing a cross-modal causal diagram, reasoning a multi-source causal path, and identifying pollution key factors and source responsibility subjects. According to the invention, through multi-source environment data fusion and cross-modal causal reasoning, high-precision early warning of environment abnormity and intelligent traceability identification of pollution sources are realized.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

The invention discloses a railway vehicle test data management and analysis system, and the system comprises a multi-modal data collection module which collects the heterogeneous data of tests such as airtightness and weighing in real time; the block chain credible evidence storage module is used for performing time-space stamp marking and hash encryption on the data and verifying the integrity; the space-time atlas analysis module is used for constructing a space-time heterogeneous atlas and generating vehicle-level feature vectors through graph convolutional network fusion data; the predictive maintenance decision module is used for predicting subsystem performance degradation and fault risks and generating a hierarchical maintenance strategy; and the adaptive visual platform integrates a large screen and a mobile terminal to display data and decisions. According to the system, efficient integration and credible evidence storage of multi-source data are realized, the data utilization efficiency and analysis depth are improved, accurate maintenance decision is supported, and safe operation of railway vehicles is guaranteed.

The invention discloses a nondestructive testing method for internal defects of a concrete structure fusing big data feature extraction and deep learning. According to the method, through multi-source data collaboration and dynamic feature fusion, the accuracy and robustness of concrete structure defect detection are remarkably improved. In a data processing link, ultrasonic electromagnetic induction infrared thermal imaging data and the like acquired by a multi-source nondestructive testing technology are subjected to collaborative preprocessing, so that the influence of noise interference and environmental fluctuation is eliminated, and standardized input is provided for feature extraction. The dynamic weight distribution network further combines the relevance of each modal feature in a historical defect sample, adjusts fusion weights of different modals in real time, reinforces ultrasonic features with great contribution to cavity recognition or infrared features sensitive to cracks, effectively compresses redundant information, and improves the accuracy of cavity recognition. According to the method, features and data-driven deep features of the fused feature vectors are manually designed at the same time, so that the limitation of single-modal data is avoided, and a model can more accurately capture multi-dimensional features of defects.

The invention discloses a micro-grid dynamic scheduling method based on deep learning, and the method comprises the steps: fusing industrial Internet of Things collection and GIS positioning, and constructing a multivariable original spatio-temporal data set covering multiple nodes; extracting multi-scale features through multi-resolution wavelets and Fourier transform, combining the multi-scale features with a dynamic adjacency matrix, and realizing feature adaptive distribution and nonlinear dynamic modeling by using multi-scale attention gating, graph convolution and a time sequence neural network model; the micro-grid load and state prediction accuracy, the system generalization ability and the abnormal response level can be effectively improved, and powerful support is provided for intelligent scheduling and abnormal analysis.

The invention discloses an equipment fault intelligent early warning system based on abnormal voiceprint AI analysis of energy equipment, and relates to the technical field of equipment health management, the equipment fault intelligent early warning system comprises an equipment fault early warning platform, and the equipment fault early warning platform is in communication connection with the following modules: a data sensing fusion module, a voiceprint AI analysis module and a voiceprint AI analysis module; the data acquisition module is used for acquiring high-frequency voiceprint signals, temperature field distribution and vibration data in real time during operation of energy equipment through a distributed sensor network to form a comprehensive data set; and the voiceprint AI analysis module is used for extracting voiceprint features from the comprehensive data set, and identifying whether the equipment emits abnormal voiceprints or not by using a pre-trained voiceprint AI model. According to the method, early abnormity is identified through the high-precision AI model, sudden equipment faults are effectively prevented, equipment physical field interaction is simulated in combination with the digital twin technology, and a fault evolution path is dynamically deduced, so that operation and maintenance personnel can take intervention measures at the initial stage of the faults, the stability and reliability of equipment operation are remarkably improved, and the non-planned downtime is shortened.

The invention relates to the technical field of carbon emission accounting, in particular to a road engineering carbon emission analysis method based on multi-source heterogeneous data fusion. The method comprises the following steps: collecting carbon emission factor multi-source heterogeneous data; performing multi-source heterogeneous data anomaly identification and correction processing on the carbon emission factor multi-source heterogeneous data to generate carbon emission factor multi-source heterogeneous standard data; performing carbon emission factor cross-modal global connection fusion processing on the carbon emission factor multi-source heterogeneous standard data to generate carbon emission factor global fusion feature data; establishing an optimization relation model of actual working condition carbon emission accounting based on the carbon emission factor global fusion feature data, and generating an optimization carbon emission accounting relation model; and performing carbon emission intelligent accounting operation on the road construction project based on the optimized carbon emission accounting relation model. According to the method, the accurate accounting of the carbon emission of the road engineering is realized by carrying out fusion analysis on the multi-source heterogeneous data.

The invention relates to the technical field of civil engineering monitoring, and discloses a structure health monitoring system based on civil engineering. A high-density sensing network of the system collects strain field distribution, vibration spectrum and environmental corrosion parameters of a structure through a distributed multi-mode sensor array; performing clock drift compensation and space coordinate normalization on the asynchronous sampling data by a space-time alignment engine to generate an original feature tensor of a unified space-time reference; the semantic modeling unit is combined with a design drawing and a material parameter library, the original feature tensor is mapped to a component semantic space, and hierarchical structure features with topological marks are output; the adaptive fusion core executes dynamic weight distribution, eliminates sensor conflict data and generates an anti-interference fusion diagnosis index; the edge computing node operates a lightweight damage detection model according to the fusion diagnosis index, and outputs a local component health state level; and the cloud collaborative analyzer aggregates multiple edge node results and predicts the overall residual life of the structure in combination with historical degradation data.

The invention discloses ultra-high performance concrete and a preparation method thereof. The ultra-high performance concrete comprises the following components in percentage by mass: 15%-25% of cement, 3%-6% of nano SiO2, 30%-35% of fly ash or steel slag powder, 35%-40% of recycled aggregate, 1%-4% of fiber, 0.5% of a microbial remediation agent and 1%-3% of nano TiO2. The preparation method comprises the following steps: S1, pretreating the recycled aggregate; and S2, packaging the microbial remediation agent: mixing the bacillus pasteurii spores with calcium lactate, and wrapping the mixture in calcium phosphate microspheres. S3, step-by-step stirring; S3.1, dry mixing: uniformly stirring the cement, the fly ash, the nano SiO2, the nano TiO2 and the recycled aggregate; s3.2, wet mixing is carried out, water solution fibers containing the polycarboxylic acid water reducing agent are added, uniform stirring is carried out, and the water-binder ratio of the water solution containing the polycarboxylic acid water reducing agent is 0.18; s3.3, final mixing: adding the packaged microbial remediation agent, and uniformly stirring; and S4, directional curing: performing steam curing at 80 DEG C for 48 hours, performing ultraviolet irradiation, and performing constant-temperature and constant-humidity curing for 7 days. The ultra-high performance concrete has the effects of low cost, low carbon emission, multi-function integration and repeatable crack self-repairing.