3269results about "Thermometer applications" patented technology

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 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 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 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 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 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 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 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 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.

The invention discloses a low-voltage distribution box temperature rise abnormity identification method based on an improved fuzzy clustering algorithm. The method comprises the following steps: S1, generating a temperature-current synchronous acquisition data set according to a time sequence; s2, constructing a load-temperature difference weight matrix in combination with the current current amplitude; s3, obtaining a current membership matrix and a cluster center set; s4, calculating the membership slip amount of each measuring point according to the current membership matrix and the membership matrix at the previous moment, and generating a temperature rise trend state sequence; s5, judging whether the temperature rise trend state sequence has a measuring point which continuously slides from the normal cluster to the risk cluster or not; if yes, corresponding cluster centers in the corresponding cluster center sets are extracted and mapped to the physical coordinates according to the measuring point index table, and positioning information containing the measuring point numbers, the types of the electrical parts and the risk levels is generated. According to the invention, the fault positioning accuracy and the operation and maintenance efficiency are greatly improved.

The invention discloses a cold-chain logistics temperature control supervision method and system based on GPS linkage, and relates to the related technical field of cold-chain logistics, and the method comprises the steps: installing a vehicle-mounted terminal on a cold-chain logistics transport vehicle, and collecting the vehicle position information and carriage temperature data in real time; encrypting and uploading to a monitoring processing center, and calling cold-chain logistics temperature control analysis dual channels of the target cold-chain goods; obtaining a temperature comparison result with a preset safe temperature threshold value, and matching and determining a target temperature control analysis channel; and performing temperature control analysis, determining a temperature control adjustment strategy parameter, and performing cold-chain logistics temperature control supervision. The technical problems that in the prior art, temperature control supervision lags behind, deep fusion analysis with the vehicle position and the transportation state is not carried out, specific reasons of temperature abnormity cannot be distinguished, and the regulation and control strategy is lack of accuracy and predictability are solved, and real-time, accurate and intelligent cold chain transportation temperature control abnormity recognition and supervision are achieved. And the reliability, the safety and the efficiency of cold-chain logistics are improved.

The invention discloses a photovoltaic power station fault diagnosis method based on digital twinning and transfer learning, and belongs to the technical field of photovoltaic power generation fault detection. According to the method, a photovoltaic power station digital twin model based on a physical mechanism is constructed, operation behaviors in normal and multiple fault states are simulated, and a simulation data set with an accurate label is generated; in combination with the acquired real operation data, a fault diagnosis model is trained by adopting a transfer learning framework containing a domain adaptation loss item, so that the distribution difference between simulation data and real data is effectively reduced; a physical consistency loss item is innovatively introduced, and a physical rule is used as a constraint embedded model, so that the reliability and interpretability of a diagnosis result are improved; and finally, performing accurate fault diagnosis on the real-time operation data by using the trained model. According to the method, the model training problem caused by scarcity of real fault data is solved, the diagnosis precision and generalization ability are remarkably improved, and the method is suitable for intelligent operation and maintenance of the photovoltaic power station.

The invention relates to the technical field of cable fault positioning, in particular to a cable fault accurate positioning method based on multi-channel synchronous acquisition, which comprises the following steps: S1, constructing a multi-channel adaptive acquisition system which comprises a main controller, a multi-channel acquisition module, a synchronous control module and a data transmission module; s2, multi-channel data synchronous acquisition is carried out through the synchronous control module, wherein the multi-channel data comprises impedance data, temperature data, partial discharge signals and insulation resistance data; during use, through a multi-channel adaptive acquisition design, monitoring channels can be flexibly selected according to cable materials, operation years and environmental conditions, different aging fault characteristics are comprehensively covered, the fault omission ratio and the false alarm rate are reduced, different types of low-voltage cables are adapted, a synchronous scheme of constant-temperature crystal oscillator and hardware triggering is adopted, and the reliability of the system is improved. The method does not need to depend on a GPS / PTP time service module, is favorable for saving cost input, and eliminates environmental interference and single parameter false triggering.

The invention relates to the technical field of cable monitoring, and discloses a cable joint temperature monitoring method and a cable joint, and the method comprises the steps: obtaining the real-time insulating layer temperature data of the cable joint based on a miniature temperature sensor, and obtaining the real-time environment data of the cable joint based on an auxiliary sensor; comparing the real-time insulating layer temperature data with a real-time environment temperature value, and determining the thermal radiation direction of the cable joint; determining a temperature difference value between the real-time insulating layer temperature data and the real-time environment temperature value, and determining a predicted temperature value of the conductor crimping layer according to the temperature difference value based on the heat radiation direction; determining a predicted temperature change rate of the conductor crimping layer according to the predicted temperature value; the real-time load current of the cable connector is obtained, and the real-time temperature change rate is determined; and determining a difference ratio between the predicted temperature change rate and the real-time temperature change rate, and judging whether the temperature monitoring of the cable joint is abnormal or not. According to the invention, accurate temperature prediction and reliable abnormity judgment of the cable joint are realized.

The invention discloses a vehicle-mounted power supply short-circuit heat prediction method based on big data analysis, and the method comprises the following steps: collecting real-time operation data, and storing the data in a data cache, thereby obtaining a cache time sequence signal; filtering the cached time sequence signal, and extracting a basic feature sequence; constructing an RC network model based on the basic feature sequence, and converting a fractional order operator into an equivalent integer order model; performing parameter estimation on the equivalent integer order model to generate a thermal prediction model; taking the thermal prediction model and the basic feature sequence as joint input and output power loss features; inputting the power loss characteristics into a quantile regression forest model to form a heat prediction result; comparing the heat prediction result with a heat threshold value and a temperature rise threshold value, and outputting a control trigger signal; and calling parameters in a control strategy library according to the control trigger signal to generate a control strategy. According to the invention, vehicle-mounted power supply short-circuit heat prediction is realized.

The invention discloses a transformer monitoring method and system based on a multi-dimensional signal, and relates to the technical field of transformer monitoring, and the method comprises the steps: obtaining the multi-dimensional signal and working condition parameters of a transformer, carrying out the time domain feature extraction of the multi-dimensional signal, obtaining a corresponding time domain correlation feature, carrying out the frequency domain feature extraction of the multi-dimensional signal, obtaining a corresponding frequency domain correlation feature, and carrying out the time domain feature extraction of the multi-dimensional signal; fault analysis is carried out according to the working condition parameters and the frequency domain correlation characteristics, and a corresponding initial fault detection result and an electromagnetic coupling residual value are obtained; and if so, carrying out fuzzy evaluation on the time domain correlation feature, the frequency domain correlation feature, the electromagnetic coupling residual value and the multi-dimensional signal based on a pre-trained fault detection model to obtain a corresponding fault monitoring result. The technical problems that a traditional transformer monitoring method mainly depends on electrical signal analysis, but key parameters such as oil temperature are not included in a monitoring system, so that potential fault hidden dangers cannot be found in time, and the operation reliability of the transformer is reduced are solved.

The invention discloses a workstation electrical parameter real-time monitoring and fault early warning method, which belongs to the technical field of electrical equipment monitoring, and comprises the following steps: acquiring voltage parameter data, current parameter data and temperature parameter data through a multi-source sensor deployed in a workstation, and performing timestamp alignment processing; initial electrical parameter data are obtained and preprocessed, and preprocessed electrical parameter data are generated; historical electrical parameter data are acquired and analyzed and calculated in combination with the preprocessed electrical parameter data, and a dynamic early warning threshold value is generated; extracting multi-parameter characteristics based on the preprocessed electrical parameter data, calculating a correlation coefficient, performing collaborative analysis in combination with a dynamic early warning threshold, and generating a fault prediction result; and performing fault diagnosis according to the fault prediction result, and generating and outputting an early warning instruction. According to the invention, a mode of generating the dynamic early warning threshold and carrying out multi-parameter collaborative analysis is adopted, so that accurate prediction and intelligent graded early warning of the electrical fault of the workstation can be realized.

The invention provides a heavy-duty truck vehicle-mounted safety monitoring method and device, and the method comprises the steps: obtaining the original monitoring data of a wheel-rail force through a wheel-rail force monitoring module which is installed on an axle and has a low power consumption characteristic, and carrying out the edge calculation of the original monitoring data, and obtaining a wheel load shedding rate, a derailment coefficient and a wheel axle transverse force; the framework acceleration and the bearing temperature are obtained through the low-power-consumption vibration acceleration sensing module and the bearing temperature sensing module; performing multi-modal fusion on each index to obtain a multi-modal operation state representation vector; obtaining a dynamic threshold value of each index based on an enhanced feature vector and a multi-modal operation state representation vector obtained by an offline self-learning model; and giving current situation analysis and trend analysis of safety monitoring based on a comparison result of each index and a dynamic threshold value. According to the invention, the limitation of independent analysis of single sensor data in the prior art is overcome, the dynamic adjustment of the safety threshold is realized, and the accuracy and reliability of safety monitoring are improved.

A vehicle smart energy center includes an array of electronic fuses coupling a DC power bus to a plurality of loads and primary and supplemental electrical power sources. Electronic fuses self-diagnose health and report current, voltage and temperature data which is used by a smart energy center control module to manage the diagnostic, current, voltage and temperature data and electronic fuses for fault remediation whereby the vehicle remains operable through various smart energy center anomalies.

The invention provides a primary resonance elimination device of a power distribution network voltage transformer and a management and control method, and relates to the technical field of voltage transformer resonance elimination. The primary resonance elimination device comprises a primary resonance elimination resistor, a primary resonance elimination power electronic device, a standby resonance elimination resistor and a standby resonance elimination power electronic device; the primary resonance elimination resistor and the primary resonance elimination power electronic device are connected in series and then are connected in series between a neutral point of the power distribution network voltage transformer and a ground wire; the standby resonance elimination resistor and the standby resonance elimination power electronic device are connected in series, one end of the standby resonance elimination resistor and the standby resonance elimination power electronic device which are connected in series is connected with a neutral point of the power distribution network voltage transformer, and the other end is grounded; the primary resonance elimination device further comprises a current transformer and a temperature sensor; the temperature sensor is used for monitoring the temperature values of the primary resonance elimination resistor and the standby resonance elimination resistor; the current transformer is assembled on a line where the primary resonance elimination resistor and the standby resonance elimination resistor are grounded together and used for monitoring the value of current flowing through the primary resonance elimination device.

The invention discloses a capacitor multi-stage adaptive flexible aging test method and system, and belongs to the technical field of industrial process advanced control. According to the method, voltage, current and temperature response data in the aging process of the capacitor are collected in real time by defining charging and discharging time sequences and initial parameters of four stages of preheating, constant current, constant voltage and resting; and comparing real-time data based on a preset model (an internal resistance change rate model and a temperature rise curve model), and dynamically adjusting power supply parameters in the next stage or triggering a protection mechanism. According to the method, the overstress or understress problem of a traditional fixed parameter test is solved, intelligent and personalized control of the aging process is realized, the test accuracy and the capacitor reliability are improved, and the method is suitable for aging test scenes of various capacitors.

The invention discloses a Bridgman method growth gallium oxide single crystal abnormity detection method based on multi-mode perception, and relates to the technical field of single crystal preparation, and the method comprises the following steps: S1, collecting optical signals of a melt surface area in a Bridgman method growth gallium oxide single crystal process through a sensor array, synchronously acquiring temperature field data or infrared thermal image data of the region; the method comprises the following steps: acquiring an original spectral data set covering a local area according to a radiation signal under a micron-order spatial resolution, and obtaining initial radiation distribution information; according to the anomaly detection method for growing the gallium oxide single crystal by the Bridgman method based on multi-mode perception, dynamic tracking and accurate positioning of component anomaly in the growth process of the Bridgman method are realized, and an effective technical means is provided for improving the growth quality of the gallium oxide single crystal, reducing crystal defects and improving the process control capability.

The invention provides a mass concrete temperature monitoring method and device for ultra-wide section open cut tunnel construction, and relates to the technical field of constructional engineering.The method includes the steps that geometric parameters of a tunnel are collected, a three-dimensional model is constructed, grid division is conducted in combination with concrete material attributes, and a simulation model is generated; the temperature gradient modulus of a node under each time step is calculated by simulating the whole process of concrete from pouring to cooling; a gradient extraction neighborhood is constructed with the initial temperature monitoring point as the center, the maximum gradient intensity in the neighborhood is extracted, a gradient integral index is calculated, and alternative monitoring points are screened out; and finally, by calculating representative weights between the alternative monitoring points and the nodes, selecting a final monitoring point with the best representativeness to carry out temperature sensor arrangement, so that scientific and dynamic monitoring of the concrete temperature field is realized, and the monitoring precision and efficiency are effectively improved.

A temperature probe of a temperature monitoring system includes a circuit board substrate, a first temperature sensor disposed on a first side of the circuit board substrate, and a second temperature sensor disposed on a second side of the circuit board substrate opposite the first side. The temperature probe also includes a controllers disposed on the circuit board substrate. The controller is configured to receive a first temperature signal from the first temperature sensor; receive a second temperature signal from the second temperature sensor; generate comparison data based on the first temperature signal and the second temperature signal; and transmit a data signal based on the comparison data to a network monitoring system.