4246results about "Thermometer applications" patented technology

The invention relates to the technical field of circuit detection, discloses a power transmission line thermochromic wire clamp heating early warning method and system, effectively solves the problem of data acquisition distortion in a strong electromagnetic environment, and improves the accuracy of state evaluation through multi-source data fusion. The dynamically adjusted early warning model reduces the risk of false alarm and missing alarm caused by equipment aging, the intelligent decision support module shortens the fault handling response time, the data closed-loop mechanism ensures the reliability of the system in the whole life cycle, and the reliability of the system in the whole life cycle is improved through multi-sensor cooperative monitoring and edge calculation processing. And the influence of environmental factors on data acquisition is reduced. The two-channel transmission architecture guarantees the data transmission integrity under different network conditions, the CRC verification mechanism effectively recognizes and corrects transmission errors, a high-quality data basis is provided for a subsequent early warning model, the abnormal data recollection mechanism avoids data missing caused by single collection failure, and continuous and stable operation of the monitoring system is ensured.

The invention discloses a power battery health state on-line monitoring and early warning method and system based on multi-data fusion. The method comprises the following steps: synchronously acquiring multi-dimensional heterogeneous data in a battery operation process through a multi-sensor data acquisition module; carrying out standardization processing and preprocessing on the multi-dimensional heterogeneous data, extracting a comprehensive feature vector and carrying out multi-sensor data fusion; a multi-sensor data fusion algorithm is adopted to carry out weight distribution on the fused comprehensive feature vector, a dynamic weight coefficient is calculated according to the sensitivity and reliability of each parameter to the health state of the battery, and a fused battery state feature descriptor is obtained; and performing mode recognition and state classification on the fused battery state feature descriptors by using a pre-established evaluation model to realize quantitative evaluation of the health degree of the battery. According to the invention, real-time monitoring, evaluation and early warning of the health state of the battery in the whole life cycle are realized, and a reliable basis is provided for safe operation and maintenance decision of a battery system.

The invention relates to the technical field of power transformation equipment state monitoring, in particular to a power transformation equipment multi-parameter intelligent sensing and state monitoring system which comprises a sensing unit, a data processing unit, a state analysis and diagnosis unit and an upper computer monitoring and management unit. Through data collection preprocessing, lightweight AI model anomaly preliminary screening and hierarchical edge cloud cooperative transmission strategies, efficient cleaning of original data, rapid edge end anomaly identification and optimal utilization of network resources are realized, data transmission bandwidth occupation is greatly reduced, monitoring real-time performance is improved, and the method is suitable for large-scale popularization and application. The method integrates technologies such as digital twinning, federated learning and a time-space attention network, realizes equipment cross-time-space fault accurate positioning, fault type reliable identification and residual life dynamic prediction in combination with a quantification algorithm, triggers hierarchical early warning through hierarchical health assessment, and provides scientific and accurate decision support for refined operation and maintenance of power transformation equipment.

The invention discloses an electricity meter terminal temperature anomaly identification method, system and device and a storage medium, and relates to the technical field of intelligent electricity meter monitoring and anomaly detection.The method comprises the steps that currents of a live line loop and a zero line loop of an intelligent electricity meter are sampled, and the deviation rate of the live line current relative to the zero line current is calculated; estimating the temperature of the terminal of the electric energy meter based on the deviation rate and by using the resistance characteristic metering error change trend of the manganese-copper shunt; and when the terminal temperature estimated value of the electric energy meter exceeds a preset threshold value, determining that the temperature of the live wire terminal is abnormal, and recording an alarm event. The method realizes real-time acquisition of current difference characteristics, provides a reliable data basis for subsequent terminal temperature estimation, realizes a method of indirectly reflecting terminal temperature rise through electrical characteristics, ensures that alarm can be triggered and a complete tracing chain can be formed when temperature abnormity occurs, and improves the accuracy of terminal temperature estimation. Therefore, the controllability of the operation safety of the electric energy meter and the completeness of post analysis are improved.

The invention relates to the technical field of industrial temperature measurement, in particular to a high-temperature heating body hearth temperature distribution measurement method, which comprises the following steps of: establishing a quantitative mapping relation model between real-time input electric power and theoretical heating intensity of a heating body; a limited number of temperature sensor arrays are arranged at key positions of the internal space of the hearth; synchronously acquiring a real-time temperature measurement value of the temperature sensor array and real-time input electric power data of the heating body; calculating and generating theoretical temperature field distribution covering the three-dimensional geometric model; obtaining a corrected temperature value; calculating the temperature residual error of the corrected temperature value and the theoretical temperature field distributed at the corresponding position of the sensor; expanding the discrete temperature residual error into a full-space three-dimensional residual error distribution field; and outputting the three-dimensional real-time temperature distribution field of the hearth. Dynamic lag compensation and convective heat transfer compensation algorithms are adopted, lag and errors in measured values can be corrected in real time, and it is ensured that measured data at high temperature have high response speed and accuracy.

The invention belongs to the field of asphalt mixture performance analysis, and particularly discloses a fatigue performance analysis method for an asphalt mixture of an asphalt pavement, which comprises the following steps of: synchronously acquiring a dynamic strain field and a temperature field in a material by fusing an embedded sensor in a paving and rolling construction process; a temperature-strain coupled fatigue damage evolution model is combined to construct an on-line fatigue performance analysis system oriented to the whole construction process, so that a complex heat-force coupling environment and a non-uniform compaction process in actual paving operation are effectively restored; scale effect deviation and boundary condition distortion caused by difference between laboratory test piece preparation and on-site working conditions are remarkably reduced, and meanwhile, in the online detection process of the fatigue performance of the asphalt mixture, instant quantitative judgment of the performance state of the pavement structure is realized by positioning a damage remarkable overrun area; and the in-situ monitoring capability on the initial fatigue damage is fully reflected, so that the abnormal strain accumulation process before microcrack initiation can be captured.

The invention relates to a cold chain transportation intelligent early warning and abnormity processing method based on temperature sensor data, and the method comprises the following steps: carrying out the temperature data collection of different regions in a cold chain transportation carriage, and obtaining the temperature collection data; performing temperature fluctuation analysis based on the temperature acquisition data to obtain a temperature fluctuation trend parameter; based on the temperature fluctuation trend parameters, temperature threshold value judgment is conducted on the cold chain transportation compartment, and a temperature judgment result is obtained; and when the temperature judgment result is that the temperature of the core cargo area in the cold-chain transportation carriage exceeds the preset threshold range, temperature early warning grade information is determined according to the temperature judgment result, and an abnormity processing instruction is generated based on the temperature early warning grade information, so that the problem that potential temperature risks cannot be predicted in advance is solved, and the safety of the cold-chain transportation carriage is improved. And the exception processing is often lagged.

The invention discloses a pump set vibration monitoring method and system based on distributed sensing. The method comprises the steps that vibration collection signals and real-time working condition parameters of distributed sensing nodes are obtained, multi-node clock synchronization correction is executed, and synchronous vibration data frames are formed; time-frequency feature extraction is carried out on the multi-channel vibration data set, abnormal channels are identified through inter-channel consistency analysis, a local fault correlation frequency band is extracted, and a vibration feature vector is constructed; associating the vibration feature vector with a real-time working condition parameter to form a working condition-feature mapping table, identifying a feature mutation rotating speed point for the feature baseline, adaptively dividing a rotating speed interval to generate an adaptive working condition threshold value, and implementing out-of-limit detection to generate an abnormal trigger identifier; determining a traceability analysis window based on the abnormal trigger identifier, and executing multi-measuring-point coherence calculation and energy attenuation gradient analysis to construct an energy transfer link diagram; and finally, vibration source position positioning is performed to form fault source probability distribution, a fault mode label is matched, pump set vibration monitoring is completed, and quantitative positioning of a fault source is realized.

The invention relates to the technical field of state monitoring, in particular to an intelligent fuse comprising a monitoring module, and the fuse comprises a data analysis module, a steady state recognition module, a temperature analysis module, a coupling calculation module and a state calibration module. According to the method, time consistency verification and corresponding matching are carried out on temperature and current sampling, abnormal interference is eliminated in a stable current-carrying interval, a continuous sequence is formed, and a definite time relation is established between thermal response and a current-carrying state by combining analysis of accumulated deviation and change turning of the temperature along with time; the abnormal trend of slow evolution is amplified and presented, correction is carried out according to the direction consistency of multi-segment time difference change, progressive features before fusing are recognized in advance, early warning continuity and operation decision reliability are improved, misjudgment is reduced, continuous updating of state evaluation is supported, and stability of a control closed loop is improved.

The invention relates to the technical field of state monitoring, in particular to an electric drive system overheating protection monitoring method and system based on big data, and the method comprises the following steps: obtaining a power rising section and extracting a temperature curve, judging whether the temperature rise lags behind or not, marking response delay, recognizing thermal fluctuation lag through an expansion window, and extracting peak valley to judge trend deviation. And the positioning node tracks the heat path, identifies the interrupt structure and generates an early warning identifier. In the electric drive system operation process, the coupling relation between the power change trend and the temperature rise time sequence is combined, accurate recognition of temperature rise response delay is achieved, the position distribution of a heat path interruption structure is defined through continuous tracing of the heat node temperature rise sequence relation, and linkage performance between heat interruption structures is combined, so that the reliability of the electric drive system is improved, and the reliability of the electric drive system is improved. Concurrent characteristics of overheating abnormity are identified, the response capability and early warning accuracy of an electric drive system in the early stage of overheating risks are improved, and rapid positioning of potential thermal fault risks and intelligent division of risk levels are achieved.

The invention provides a chip test calibration device and method, and relates to the technical field of chip test. The chip test calibration device comprises a base, a cooling air supply device, a control device, a chip and two groups of manipulators. According to the invention, the chip is heated by setting different temperature environments in stages, and the multi-point synchronous temperature measurement of each temperature stage is combined, so that the heat distribution on the surface of the chip can be comprehensively captured, and the calibration error caused by non-uniform temperature or a detection blind area is avoided; the chip is automatically circulated among the heating station, the detection station and the cooling station, synchronous processing and continuous detection of multiple chips are achieved, the single-batch detection period is greatly shortened, the surface of the chip is directly blown by directional airflow, an efficient air return channel is matched, the temperature of the chip can be rapidly reduced after detection is completed, idle waiting for station switching is reduced, and the overall production efficiency is improved.

The invention belongs to the technical field of transformer operation loss monitoring, and particularly relates to a transformer operation loss condition optimization monitoring method, which comprises the following steps of: calculating an anomaly degree based on oil temperature and vibration characteristics, generating a real-time working condition in combination with load intensity, and associating accumulated operation duration to analyze a real-time operation loss anomaly index; the method comprises the following steps: realizing dynamic correlation between loss and the actual state of equipment, analyzing and extracting fluctuation parameters through a load characteristic change curve, calculating a comprehensive load fluctuation degree, correcting an operation loss abnormal index to eliminate load fluctuation interference, finally determining a dynamic reference threshold according to a historical loss curve, judging continuous overrun through double rules, and triggering early warning. Meanwhile, guide factors such as overload and internal heating abnormity are accurately recognized, corresponding adjustment instructions are output, loss monitoring accuracy and scene adaptability are improved, operation and maintenance closed loop of early warning, guide factors and disposal is achieved, and the method is suitable for transformer full-life-cycle loss monitoring.

The invention discloses a temperature measurement sensor data processing method and system in a chemical synthesis environment. The method comprises the steps of original data acquisition and preprocessing, sensor drift compensation, dynamic response optimization and anomaly detection and adaptive calibration. The system comprises an original data acquisition and preprocessing module, a sensor drift compensation module, a dynamic response optimization module and an anomaly detection and adaptive calibration module. Through multi-module deep fusion of LSTM drift compensation and adaptive Kalman filtering, + / -0.5 DEG C measurement precision is realized in a range of 80-300 DEG C; the MPC predictive control compresses the thermal inertia lag of the sensor from 500ms to less than or equal to 80ms, so that the sudden rising / dropping temperature in the synthesis reaction can be effectively captured, and the product decomposition caused by local overheating is avoided; isolated forest anomaly detection and least square calibration are combined, so that the maintenance period of the sensor in a strong corrosion environment is prolonged from 7 days to 90 days, and the annual maintenance cost is reduced by 65%.

The invention discloses a multi-dimensional data-based temperature prediction method and system for a power integrated module, particularly relates to the technical field of temperature measurement, and is used for solving the problems of temperature field distortion and unreliable measurement result caused by thermal disturbance introduced by a sensor in temperature monitoring of an existing power integrated module. Multi-dimensional temperature data are formed by acquiring measurement data of a plurality of temperature sensors, a virtual heat flow path is reconstructed based on temperature gradient distribution, deviation degree analysis is carried out on the virtual heat flow path and an inherent structure heat flow path of a module so as to judge thermal disturbance influence, and reference isothermal surface topological structure features are acquired when disturbance occurs. A real-time isothermal surface is constructed, a thermal disturbance space position and an influence range are identified by detecting a curvature distribution sudden change area, a local thermal conductivity correction field is established to perform thermal field reconstruction on multi-dimensional temperature data to generate correction temperature field data, and finally a temperature change trend is analyzed according to the correction temperature field data to predict a future temperature state.

The invention discloses a lithium battery thermal runaway risk dynamic regulation and control method and system, and the method comprises the steps: collecting temperature data in a new energy automobile battery module, and constructing a temperature field matrix; judging whether the lithium battery has a temperature risk based on the temperature field matrix; when the temperature risk exists in the lithium battery, risk grades are divided; and based on the divided risk levels, dynamically regulating and controlling the thermal runaway risk of the lithium battery. The differential cooling parameters are determined according to the risk level and the hot spot position, and the control strategy is optimized in real time through feedback data, so that the flow velocity adjustment and heat dissipation power distribution of the liquid cooling system are more scientific and effective, the overall performance of battery thermal management is improved, the risk of thermal runaway of the lithium battery is effectively reduced, and the service life of the lithium battery is prolonged. The method has wide application prospects in the fields of new energy automobiles and the like, and stable operation of the system and personnel safety guarantee can be remarkably enhanced.

The invention belongs to the technical field of power equipment state monitoring, and discloses a cable branch box operation state intelligent evaluation system based on multi-sensor fusion, which comprises a state acquisition module used for acquiring a partial discharge signal, a voltage fluctuation signal, a terminal temperature signal and a terminal contact resistance signal of a cable branch box in an operation process, constructing a multi-source operation state parameter set of the cable branch box through time synchronization and physical quantity normalization processing; the mutual coupling analysis module is used for performing transient correlation deduction on the partial discharge signal and the corresponding voltage fluctuation signal based on the multi-source operation state parameter set to obtain a partial discharge-voltage coupling amount; when the partial discharge-voltage coupling amount exceeds a preset partial discharge-voltage coupling amount threshold value, determining that partial discharge abnormity caused by insulation degradation exists in the cable branch box; comprehensive and accurate evaluation of the operation state of the cable branch box is realized, and reliable support is provided for safe operation and maintenance of equipment.

The invention relates to the field of lithium battery health state prediction, in particular to a lithium battery health state prediction method based on TKAN and PxLSTM. According to the scheme, the method comprises the steps of collecting multi-dimensional time sequence data, including voltage, current and temperature, of a lithium battery, forming an input sequence by the multi-dimensional time sequence data, and performing denoising and standardization processing on the data; hilbert-Huang transform is applied to the input sequence, the input sequence is decomposed into a plurality of intrinsic mode functions and residual errors, the time-frequency domain joint information entropy of each intrinsic mode function is calculated, the first k intrinsic mode functions and corresponding entropy values are selected according to entropy value sorting, and a compact feature vector is constructed in combination with the residual errors; the coded compact feature vectors are input into a TKAN module and a PxLSTM module at the same time; and performing weighted combination on the first hidden state sequence and the second hidden state sequence through a fusion weight to obtain a fusion feature, and outputting a lithium battery health state prediction result according to the fusion feature. The method is suitable for battery health state prediction.

The invention relates to the technical field of energy storage cabinet safety monitoring, in particular to an energy storage cabinet multi-parameter intelligent monitoring device based on an MEMS sensor array, a sensor array unit in the energy storage cabinet multi-parameter intelligent monitoring device comprises distributed MEMS temperature and vibration sensors, the temperature sensors are installed in a non-vertical mode, detection deviation caused by direct blowing of airflow is avoided, and the energy storage cabinet multi-parameter intelligent monitoring device based on the MEMS sensor array is obtained. The airflow disturbance correction unit corrects temperature original data in real time and eliminates interference of airflow on module body temperature detection by means of a heat dissipation airflow vector field dynamic model and a compensation algorithm, and the multi-parameter coupling analysis unit processes vibration and temperature signals in a double-channel mode, extracts tab loosening high-frequency micro-vibration characteristics and correlates the temperature rising rate. Early fault logic is triggered through two-stage judgment, the fault decision and output unit fuses the correction temperature, the coupling strength coefficient and the speed-up ratio, graded early warning is generated through a fault tree model and transmitted to an external monitoring system, and accurate monitoring of early faults of the energy storage cabinet is achieved.

The invention relates to the technical field of water quality monitoring, in particular to an aquaculture water quality monitoring and self-adaptive regulation and control platform and method.The aquaculture water quality monitoring and self-adaptive regulation and control platform comprises a data processing module used for obtaining water quality parameter data of aquaculture at multiple monitoring points and multiple time points and conducting preprocessing and time sequence serialization fitting on the water quality parameter data to obtain a data processing module; obtaining a water quality parameter time sequence; the feature extraction module is used for carrying out space-time semantic recognition and feature extraction on the water quality parameter time sequence and constructing a feature sequence of each water quality parameter; and the risk early warning module is used for carrying out inter-parameter space-time correlation calculation and dynamic risk threshold value adaptation according to the feature sequence to obtain water quality risk early warning features. By combining the water quality parameters and the culture organism state data, the system can accurately evaluate the risk level of the water quality, timely warn potential risk factors, help a culturist to take effective precautionary measures, and avoid the death or health problem of the culture organism.

The invention discloses a battery risk judgment method based on lithium battery life prediction and computer equipment, and the method comprises the steps: obtaining multi-source heterogeneous data of a whole life cycle of a battery, achieving the alignment of a time domain and a space domain through a self-alignment engine, extracting data which is strongly related to capacity attenuation, carrying out the fusion of the data to generate a health factor, and carrying out the calculation of the health factor. Realizing characteristic standardization: inputting data into a co-evolution framework, performing co-evolution through a dual model formed by a materialization channel model and a data driving channel model, predicting the service life of the battery, and outputting an SOH predicted value; calculating the confidence degree based on the difference degree of the dual-model prediction distribution P and Q; according to the confidence coefficient, different methods are adopted to determine an SOH final prediction value and the confidence coefficient; and according to the final SOH prediction value and the confidence coefficient, combining a historical label comparison result, and outputting a risk warning of the battery. According to the invention, the battery life prediction precision is improved.

The invention discloses a power equipment temperature monitoring and early warning system and method based on wireless communication, and relates to the technical field of temperature monitoring, the system comprises a wireless temperature measurement terminal box and a mobile terminal APP, the wireless temperature measurement terminal box and the mobile terminal APP establish communication connection based on a wireless network, and the wireless temperature measurement terminal box is fixedly installed on power equipment; the wireless temperature measurement terminal box comprises an electromagnetic environment judgment module which monitors and judges whether interference exists in the environment where power equipment is located in real time based on an electromagnetic sensor and synchronizes a result into a temperature drift compensation analysis module; and the temperature monitoring module monitors temperature data in the power equipment in real time based on a temperature sensor. Through electromagnetic environment judgment and temperature drift compensation, historical data and a model are combined to dynamically correct interfered temperature data, the influence of electromagnetic interference is eliminated, real-time monitoring is matched with graded alarm, hidden dangers can be found in time, response can be made according to risk grades, accidents are avoided, and wireless connection adapts to complex scenes.

The invention provides a cabinet three-dimensional temperature field monitoring method and device, and the method comprises the steps: arranging four temperature sensors at the positions of left and right columns at the rear part of a cabinet and the left and right middle positions of the rear side of the top of the cabinet respectively, and forming vertical and horizontal coordinate distribution points covering a key heat distribution region of the cabinet; collecting temperature data of the four temperature sensors at preset coordinate positions, and inputting the temperature data into a three-dimensional temperature field reconstruction algorithm model; processing the temperature data by adopting a bilinear interpolation method, and calculating the temperature value of any position in the cabinet; and according to a hot air convection rule, introducing height direction temperature gradient correction into the three-dimensional temperature field reconstruction algorithm model so as to eliminate the influence of heat convection on a temperature monitoring result. The three-dimensional temperature distribution in the cabinet can be accurately reflected, the temperature monitoring precision and real-time performance are improved, and the installation and maintenance cost is reduced.

The invention discloses a high-voltage switch cabinet partial discharge fault identification method and a storage medium. The method comprises the following steps: synchronously acquiring acoustic, electric, magnetic and thermal signals through a multi-physical field sensor array; performing preprocessing and feature extraction on the signals; using a cascade deep learning network to fuse multi-modal features to identify fault types and levels; realizing accurate positioning of the discharge source in the digital twinborn model by adopting a dynamic simulation coherent positioning method; and risk assessment and early warning are carried out by integrating diagnosis and positioning results. According to the method, deep learning and physical mechanism simulation are deeply fused through a data and model dual-drive normal form, so that the defects of shallow fusion level, low positioning precision, lack of interpretability and the like in the prior art are overcome, and intelligent, precise and prospective identification and early warning of the partial discharge fault of the high-voltage switch cabinet are realized.

The invention relates to the technical field of electric vehicle charging, in particular to a charging gun short-circuit fault detection method, and aims to solve the problems that in the prior art, charging gun short-circuit fault detection mainly depends on two technical paths of current threshold triggering and single parameter monitoring, when it is detected that the current exceeds a preset value, a circuit is cut off, detection hysteresis exists, and the detection time is shortened. And pre-judgment cannot be carried out before short circuit occurs, so that the equipment is damaged before the protection action is executed. According to the method, the digital twinning pre-diagnosis model is constructed by fusing multiple physical quantity data, early warning and graded protection of the short-circuit fault of the charging gun are realized by combining multi-dimensional feature fusion and a dynamic risk assessment mechanism, and the method has the advantages of improving the detection timeliness, reducing the misjudgment and missed judgment rate and enhancing the environmental adaptability.

The invention discloses a geotechnical engineering slope stability real-time monitoring method and a geotechnical engineering slope stability real-time monitoring system, which are characterized in that a blind source separation technology combining independent component analysis and physical constraint is introduced, an original displacement time sequence and an environment temperature time sequence of a plurality of GNSS (Global Navigation Satellite System) measuring points are regarded as multi-channel mixed signals, and statistical independence among signal sources is utilized to monitor the stability of a slope in real time. Periodic environment noise, instrument random noise and drift and real slope deformation signals are effectively separated from the mixed observation signals; and then, through correlation verification with physical quantities such as the field environment temperature and the like, automatic calibration is performed on the separated source signals, and temperature effect source signals and long-term creep source signals with physical labels are accurately identified and extracted, so that accurate elimination of noise components and high-fidelity reconstruction of pure deformation signals are realized. Therefore, the accuracy of real-time monitoring of the slope stability of geotechnical engineering can be effectively improved, and a solid data basis and a decision basis are provided for early warning and prevention of slope disasters.

According to the power cable fault diagnosis method based on multi-feature fusion, accurate diagnosis of partial discharge, an early fault and a short-circuit fault of a power cable is realized by comprehensively considering feature parameters such as partial discharge, temperature, current and voltage. Samples are obtained by adopting a simulation analysis method, cable fault identification is realized through an artificial intelligence algorithm, and a rapid and accurate method is provided for cable fault identification and positioning. For cable fault type identification, the CNN-BiLSTM-based cable fault identification method is provided, and the method comprises the following specific steps: firstly, obtaining fault current of cable monitoring points M and N, taking the root mean square of the current after the fault as a feature value, inputting the feature value into an artificial intelligence algorithm, training through the artificial intelligence algorithm, and finally carrying out algorithm iteration to obtain an optimal solution; and a cable fault type is obtained through algorithm identification. In order to consider fault types under different conditions, different fault types of the cable are identified by using an artificial intelligence algorithm.

The invention discloses a high-precision transformer fault detection method and system, relates to the technical field of transformer detection, is used for solving the problems of insensitive fault characterization and delayed diagnosis under a light load condition, and judges whether to enter a load judgment mechanism or not by acquiring input side access voltage information and counting low-load accumulated duration. When entering a load discrimination mechanism, detecting the start-stop behavior of a load branch and the temperature rise trend of a winding, calculating a light load index, identifying a light load state, carrying out region division on an iron core, collecting magnetic flux density data, and calculating iron loss balance characteristics aiming at iron loss deviation which is easy to occur under a light load condition; the insulation degradation coefficient is calculated by fusing the oil temperature peak value and the dielectric loss factor, and the abnormal index is generated with the iron loss balance characteristic, so that the early warning of the early hidden fault of the transformer can be realized in a light load scene in which the traditional temperature rise and current indexes are insensitive, and the operation reliability and maintenance efficiency of the transformer in a power distribution system are improved.

The invention discloses a temperature real-time monitoring method and system based on mass concrete continuous pouring, and the method comprises the steps: collecting data in a mass concrete continuous pouring process in real time through the arrangement of sensors, forming a global temperature set of temperature values at each collection moment, obtaining the temperature value of each layer at each collection moment according to pouring layering, and carrying out the real-time monitoring of the temperature of each layer; forming a layered temperature set of the temperature values of each layer; determining a concrete pouring abnormal index; determining a dynamic early warning threshold value in the concrete continuous pouring process, determining a multi-stage early warning and response mechanism according to comparison between the concrete abnormal index and the threshold value, and generating an early warning control signal; and based on the early warning control signal, controlling the water cooling equipment and carrying out real-time monitoring feedback. According to the method, the specific temperature conditions of each layer at different acquisition moments can be precisely known, the positions of the layers with abnormal temperature can be more precisely positioned, structural cracks caused by temperature incoordination between the layers are avoided, and real-time monitoring of the temperature of mass concrete continuous pouring is achieved.

The invention discloses a high-voltage cable grounding current abnormity monitoring system, which belongs to the technical field of electric power facilities and comprises an inspection acquisition module, an association analysis module, a collaborative reasoning module and a self-optimization module. The inspection acquisition module is used for synchronously acquiring multi-source monitoring parameters and acquiring position information so as to generate a grounding parameter matrix; the association analysis module is used for screening suspected defect types, dividing a local overheating area, performing overheating cause judgment, constructing a coupling association matrix of a multi-physical field, performing evolution stage judgment in combination with historical data, and generating a preliminary mechanism hypothesis in combination with credibility rating; the collaborative reasoning module is used for constructing a dynamic knowledge graph and a constraint prediction network, constructing a collaborative reasoning mechanism based on a two-way real-time interaction channel, performing preliminary reasoning, and dividing early warning levels in combination with comprehensive confidence so as to perform operation and maintenance feedback; and the self-optimization module is used for carrying out directional iteration on the double engines by utilizing manual feedback and carrying out cross-line migration optimization according to different lines.

The invention discloses a quick response flexible temperature sensor with low thermal crosstalk and a preparation method thereof. The sensor comprises a contact layer, a sensing layer, an electrode layer and a substrate layer, wherein the contact layer comprises a heat conduction layer and a heat insulation network; the heat-conducting layer is made of a heat-conducting composite material and comprises polydimethylsiloxane and flake carbon powder; the heat insulation network and the substrate layer are made of a heat insulation composite material which comprises polydimethylsiloxane and hollow glass microspheres; the sensing layer comprises a plurality of sensing units arranged in an array, the sensing units are connected with the electrode layer and output resistance signals, the heat conduction layer and the substrate layer are arranged over and under the sensing units respectively, and the heat conduction and heat insulation synergistic structure can enhance the quick response of the sensing units to the temperature; a thermal insulation network is embedded in the contact layer to block thermal crosstalk between adjacent sensing units. The temperature sensor has the advantages of quick response, low thermal crosstalk and high reliability in a wide range, and large-scale production and preparation can be realized.