330533 results about "Electrical and Electronics engineering" patented technology

The invention relates to the technical field of equipment fault diagnosis, and discloses an equipment fault diagnosis and prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-modal data in real time through a plurality of sensors installed on equipment; s2, preprocessing the collected data; s3, constructing a hybrid deep learning model; s4, dynamic weighted fusion is performed on the features of different modal data by using an attention mechanism, and comprehensive feature representation is generated; s5, using the marked fault data and normal data to supervise and train the model; s6, inputting equipment operation data acquired in real time into the trained model, and judging the state of the equipment; and S7, generating a potential fault early warning signal based on a prediction result of the model. A piezoelectric vibration sensor and a thermal infrared imager are arranged on a motor bearing through vibration, temperature and sound sensors, vibration waveforms, thermal imaging slices and time-frequency diagrams are synchronously captured, and composite state characteristics such as mechanical wear and temperature anomaly of equipment are comprehensively reflected.

The invention belongs to the technical field of electromechanical equipment operation and maintenance, and discloses an electromechanical equipment self-adaptive intelligent early warning system based on multi-source sensing data. The system is composed of a multi-source sensing module, an edge data acquisition and preprocessing module, a data cleaning and multi-dimensional feature extraction module, an equipment state dynamic modeling module, an intelligent fault prediction and trend analysis module, a self-adaptive early warning threshold generation and dynamic adjustment module, and an intelligent decision and remote cooperation module. The system is composed of a multi-source sensing module, an intelligent low-carbon operation and maintenance management and control module and a digital twin system integration and full-period mapping module, multiple sensors are deployed through the multi-source sensing module to acquire multi-dimensional data of equipment, cleaning and calibration are performed through the edge data acquisition and preprocessing module, deep processing is performed through the data cleaning and multi-dimensional feature extraction module, and multi-dimensional feature extraction is performed through the multi-source sensing module. The data integrity and accuracy are ensured; and data are quickly transmitted among the modules, so that the monitoring system can accurately present the running state of the equipment in real time.

The method can include: recording a neural state, determining a latent array, and determining an output based on the latent array. In variants, the method can function to enable a subject to use neural signals to navigate within a high-dimensional concept space to control an output (e.g., image generation, motor control, communication, etc.).

Various wearable health monitoring devices are described herein. Some implementations include one or more emitter chamber covers injection molded out of a first material comprising transparent glass, one or more detector chamber covers injection molded out of a second material comprising transparent glass, and a light barrier construct injection molded out of a third material over the one or more emitter chamber covers and the one or more detector chamber covers. The third material may be opaque and can comprise glass and an optically absorbing pigment. Some implementations include a circuit board, emitter(s) mounted to the circuit board and arranged within the emitter chamber cover(s), and detector(s) mounted to the circuit board and arranged within the detector chamber cover(s). The circuit board, emitter(s), detector(s), emitter chamber cover(s), and detector chamber cover(s) can form part of a sensor assembly connected to a housing of the wearable health monitoring devices.

Various wearable health monitoring devices are described herein. Some implementations include a housing including a front side and a back side configured to face tissue of the wearer when the device is worn; and a sensor assembly positioned by the back side of the housing. The sensor assembly can include emitters and detectors positioned on a surface of a substrate and a frame positioned adjacent to the surface of the substrate and configured to inhibit transmission of optical radiation through the frame. The frame can comprise an emitter chamber housing the emitters; and one or more detector chambers housing the one or more detectors.

The method can include: recording a neural state, determining a latent array, and determining an output based on the latent array. In variants, the method can function to enable a subject to use neural signals to navigate within a high-dimensional concept space to control an output (e.g., image generation, motor control, communication, etc.).

The invention discloses a primary and secondary fusion complete ring main unit fault diagnosis method, and particularly relates to the technical field of power distribution fault diagnosis, and the method comprises the steps: collecting multi-path original data, carrying out the frequency domain and time domain combined calibration, carrying out the comprehensive evaluation according to two preset discrimination factors, namely, a data stability deviation amplitude and a multi-path waveform time deviation degree, and obtaining a fault diagnosis result. Two types of feature data sets are constructed subsequently, input data of two interference modeling networks are calculated respectively, then the interference modeling networks are input to output interference grade values, sampling precision dynamic adjustment and fault recognition strategy switching operation are executed according to the interference influence grade values, and diagnosis accuracy and stability in a complex interference environment are improved. According to the method, unified normalization processing of multi-path sensing data is realized, and the sensing accuracy of fault features is improved; through double-factor triggering and feature fusion evaluation, the stability of interference identification is enhanced; and sampling adjustment and strategy switching are executed based on the interference level value, so that the robustness and reliability of diagnosis are improved.

Disclosed are techniques for wireless communication. In an aspect, a reconfigurable intelligent surface (RIS) transmits a sounding reference signal (SRS) transmission request message to a base station serving a user equipment (UE), the SRS transmission request message including one or more SRS transmission properties to be used by the base station to configure the UE to transmit SRS, receives a response message from the base station, the response message indicating one or more SRS configurations the UE is configured to use to transmit SRS, the one or more SRS configurations based on the one or more SRS transmission properties, and measures a plurality of SRS transmissions from the UE with a plurality of receive beams to determine a best receive beam for receiving uplink transmissions from the UE, wherein the plurality of SRS transmissions is based on at least one SRS configuration of the one or more SRS configurations.

A wireless device receives a configuration for a layer 1 or layer 2 triggered mobility (LTM) of the wireless device and / or an early timing advance (ETA) acquisition of the wireless device. The wireless device transmits, to a base station, a report indicating an elapsed time between a first event associated with the ETA and / or the LTM and a second event associated with the ETA and / or the LTM.

The invention relates to the technical field of cable fault monitoring, in particular to a communication cable fault monitoring system and monitoring method. The method comprises the following steps: injecting a time sequence structure excitation code into a communication cable, obtaining a multi-node excitation record, carrying out full-path phase difference tracking, obtaining phase response trajectory data, constructing a cable response matrix, carrying out structure mapping on the response matrix, positioning a response difference region, and calculating a response distortion feature vector. The method comprises the following steps of: deducing an internal abnormal section of a cable, acquiring geometrical characteristics of the cable, performing radial mapping fitting, reconstructing an internal section of the cable, generating a three-dimensional communication cable model, expanding the three-dimensional model layer by layer, identifying an external damage mode, deducing an external abnormal section, performing interactive authentication by combining the internal and external abnormal sections, and judging a cable fault. And abnormal joint positioning is carried out to form a cable fault section. According to the invention, the accurate identification of the fault source is realized, and the intelligent management efficiency of the communication infrastructure is improved.

Embodiments of the invention provide a write head for perpendicular recording in which the magnetic field intensity applied at a read element is reduced with larger write-field gradients and the distance between a write element and a read element is small. In one embodiment, a pair of magnetic bodies for shielding a read element are provided on the leading and trailing sides of the read element respectively and whichever one of the two shields is the longer in the length from the air bearing surface is shorter than the length up to the back contact position PBK at which a main pole and an auxiliary pole are connected.

The invention provides an underground cavern surrounding rock parameter collaborative sensing method and system based on multi-source data fusion, and the method comprises the steps: firstly deploying a sensor group comprising an optical fiber strain sensor, a micro-seismic monitoring array, a three-dimensional laser scanner and an acoustic emission sensor, the optical fiber strain sensor is arranged along the axis of a cavern, the micro-seismic monitoring array is deployed according to a triangular network, and the three-dimensional laser scanner is arranged along the axis of the cavern; performing time synchronization on the sensor group through a synchronization pulse signal, establishing a local coordinate system at a central point of a cavern entrance, uniformly collecting data, generating a time-aligned data stream and a space-unified data set, and triggering associated sensors to enter a hierarchical response collection mode according to a strain rate and a vibration signal; and generating a multi-modal monitoring data set, finally carrying out dynamic weight distribution and conflict resolution processing on the multi-modal monitoring data set, extracting surrounding rock deformation characteristics and fracture event density, and outputting a surrounding rock stability evaluation result, thereby realizing underground cavern surrounding rock parameter collaborative perception and stability evaluation.

The invention discloses an LCD display screen display fault judgment method, and relates to the related technical field of LCD display screen detection. Comprising the steps of synchronous acquisition and preprocessing of multi-source signals, feature extraction, establishment of a fault judgment model, generation of a dynamic test mode, dynamic generation of a targeted test image according to a preliminary detection result to excite a potential fault, iterative optimization of a test sequence by a Q-learning algorithm, and fault diagnosis. The invention also discloses a system for judging the display fault of the LCD screen. The system comprises a multi-source data acquisition module, a central processing unit, a memory and a user interface. Various data including display data, driving voltage / current monitoring data, temperature distribution, environment parameters and the like are synchronously collected through the optical sensor, the electric signal probe, the thermal imaging module and the environment sensor, all-directional information of the LCD display screen in the operation process can be obtained, and limitation and misjudgment possibly caused by a single data source are avoided.

The invention belongs to the technical field of batteries, and discloses a battery cover plate and a single battery, the battery cover plate comprises a plate body assembly and a pole assembly, the plate body assembly is used for covering an opening of a battery shell, the plate body assembly comprises a first plate body, a second plate body and a connecting part, the first plate body comprises a first surface and a second surface which are oppositely arranged, the first surface is configured to be arranged towards the battery shell, the direction from the first surface to the second surface is a first direction, the second plate body comprises a first side and a second side which are oppositely arranged, the second plate body inclines in the direction deviating from the first direction from the direction from the first side to the second side, and the first side is connected with the first plate body through a connecting part; the battery cover plate comprises a first plate body and a second plate body, a connecting part is arranged between the first plate body and the second plate body, the connecting part protrudes towards the first direction, a pole assembly penetrates through at least one of the first plate body and the second plate body, the connecting part protrudes out of the pole assembly along the first direction, the structural strength of the plate body assembly can be improved, and the probability that the pole assembly is bumped in the manufacturing process can be reduced.

The invention belongs to the technical field of batteries, and discloses a battery top cover which comprises a plate body assembly and a pole assembly, the plate body assembly is used for covering an opening of a battery cell shell, the direction from the battery cell shell to the plate body assembly is configured to be a first direction, and the plate body assembly comprises a first plate body, a second plate body and a first reinforcing part; the first plate body is perpendicular to the first direction, a reinforcing part is arranged on the edge of the side, facing the first direction, of the first plate body, one side of the second plate body is connected with the reinforcing part, the second plate body inclines in the direction away from the first direction from the side facing the reinforcing part to the side away from the reinforcing part, and the first reinforcing part is connected with the reinforcing part. According to the battery top cover, at least one of the first reinforcing piece and the reinforcing part protrudes in the first direction, a pole assembly penetrates through at least one of the first plate body and the second plate body, the first reinforcing piece protrudes out of the pole assembly in the first direction, and the plate body assembly of the battery top cover is high in structural strength and can also play a role in protecting the pole assembly.

The invention discloses a deep learning-combined power transformer partial discharge signal extraction and diagnosis method. The method comprises the following steps of S1, setting a multi-channel synchronous acquisition system in a power transformer body area to acquire a multi-dimensional original partial discharge data set; s2, preprocessing the acquired multi-dimensional original partial discharge data set; s3, performing time alignment and amplitude matching on the processed signal, and dividing the processed signal into a sliding time window to construct a standard input tensor; s4, constructing an attention enhancement model fused by the convolutional neural network and the bidirectional gating circulation unit; s5, performing supervised training on the attention enhancement model by using the labeled sample; s6, inputting the real-time signal into the training model, and outputting a discharge type label; s7, risk grade evaluation is carried out in combination with statistical characteristics; and S8, generating a structured diagnosis report and uploading the structured diagnosis report to a monitoring platform. According to the invention, multi-source signals and a depth model are fused, and intelligent diagnosis and risk assessment of transformer partial discharge are realized.

The invention relates to an intelligent power distribution harmonic monitoring and dynamic compensation system which comprises a monitoring unit, a correction unit and a compensation unit. The monitoring unit continuously collects high-frequency harmonic voltage and current data in a distribution line at a high sampling frequency, extracts transient harmonic components through wavelet packet transformation and empirical mode decomposition, and generates low-dimensional feature vectors based on sparse representation. And the correction unit decodes the low-dimensional feature vector, recovers harmonic time-frequency features, calculates a phase drift rate, predicts a harmonic propagation path and an accumulation node by combining real-time power distribution network topology construction and adopting a nonlinear dynamic prediction model, and generates a correction instruction when abnormality is detected. And the compensation unit adopts pulse sequence density modulation to dynamically adjust a compensation current phase according to the correction instruction, and meanwhile, an inductive coupling device is utilized to transfer harmonic energy to a low-risk node, so that harmonic voltage distortion of a target node is quickly recovered to a stable level in a fundamental wave period after early warning.

A sample holder includes a first member featuring a first retaining mechanism configured to retain a first substrate that includes a sample, a second member featuring a second retaining mechanism configured to retain a second substrate that includes a reagent medium, and an alignment mechanism connected to at least one of the first and second members, and configured to align the first and second members such that the sample contacts at least a portion of the reagent medium when the first and second members are aligned.

Described herein is an electric aircraft comprising a battery pack to power the aircraft. The battery pack comprises a housing, an arrangement of battery cells within the housing and a crumple structure associated with the battery pack. The crumple structure is beneath the arrangement of battery cells and is adapted to compress in the event of an impact from beneath the aircraft. A venting channel is fluidically coupled to the battery cells and arranged to vent gasses away from the battery cells. The venting channel passes through the crumple structure and is adapted to deform with the crumple structure in the event of an impact from beneath the aircraft.

A battery pack according to one or more embodiments of the present disclosure includes a plurality of battery cells; and a housing including a bottom member, a plurality of side walls, and a top member, the bottom member, the plurality of side walls, and the top member form an interior accommodation space configured to accommodate the plurality of battery cells, wherein at least one among the plurality side walls, the top member, and the bottom member are formed by a plurality of metal sheets that are roll bonded to each other, wherein at least one cooling channel is formed between bonding areas of the plurality of metal sheets, and wherein at least one crash channel is formed between bonding areas of the plurality of the metal sheets.

The invention provides an intelligent water conservancy digital twinborn simulation system based on multi-source data, and belongs to the technical field of digital monitoring. Minute-level data acquisition and transmission are realized by constructing a space-air-ground three-dimensional sensing network and combining edge intelligent preprocessing, data acquisition, noise reduction and abnormity identification are realized by utilizing sensors such as millimeter wave radar and laser radar, and the system is used for realizing data acquisition and transmission. Dynamic data fusion and intelligent calibration are carried out, and second-level alignment and credible verification of data are realized by means of a space-time calibration algorithm, Kalman filtering and a block chain evidence storage technology; a hybrid simulation and intelligent decision model is established, a physical model, a machine learning architecture and a dynamic threshold decision tree are adopted, flood routing minute-level prediction and emergency response are realized, and the system improves water conservancy monitoring prediction precision and emergency response efficiency.

The invention discloses a battery capacity attenuation prediction method and device, equipment and a medium. The method comprises the following steps: acquiring operation parameter information and current working condition information of a to-be-detected battery; performing feature extraction according to the operation parameter information, and determining a battery feature parameter set; according to the current working condition information, the pre-training state evaluation model set and the battery characteristic parameter set, determining a health state evaluation value of the to-be-detected battery; and determining a capacity attenuation prediction value according to the health state evaluation value, the battery characteristic parameter set and the pre-training time convolutional network model. A pre-training state evaluation model corresponding to the current working condition of a to-be-detected battery is firstly determined, a health state evaluation value is determined in combination with a battery characteristic parameter set, and then a capacity attenuation prediction value is determined in combination with a pre-training time convolutional network model. The influence of working condition fluctuation on prediction is reduced, the prediction precision, robustness and long-term adaptability are improved, and a foundation is laid for subsequently prolonging the service life of the battery and reducing the operation and maintenance cost.

The invention provides a photovoltaic module fault detection system, method and device based on infrared image hot spot detection, and a storage medium, and belongs to the field of photovoltaic module fault detection. The system comprises a temperature difference boundary extraction module, an image sequence registration module, a hot spot track identification module, an abnormal dynamic screening module and a fault hot spot confirmation module. Pixel difference screening is carried out by setting a temperature difference threshold value, a boundary communication structure is established, frame-level displacement calculation between time sequence images is introduced to realize coordinate alignment, hot spot center points in continuous frames are extracted to form a motion path, abnormal point screening and time node labeling are carried out in combination with path displacement characteristics, and the time sequence image is obtained. According to the method, the boundary area growth rate and temperature change double factors are fused to screen fault areas, dynamic tracking and accurate detection of abnormal hot spots are achieved, the logic relevance between abnormal behavior recognition and fault judgment is enhanced, and the integrity of photovoltaic module fault information extraction and the accuracy of hot spot recognition are guaranteed.

The invention relates to the technical field of lithium battery management, in particular to a lithium battery pack dynamic balancing method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: predicting battery cell state parameters of each battery cell based on a preset long-short-term memory neural network model to obtain a capacity attenuation trend of each battery cell; building a health state evaluation index based on the capacity attenuation trend; based on the health state evaluation indexes, performing health grade classification on each battery cell by adopting a preset clustering algorithm; obtaining the temperature gradient, the state of charge deviation and the charge-discharge rate of each battery cell, and determining the balance priority of each battery cell based on the health grade category division result, the temperature gradient, the state of charge deviation and the charge-discharge rate of each battery cell; and on the basis of the equalization priority, a target dynamic equalization adjustment strategy is generated, and each battery cell is adjusted according to the target dynamic equalization adjustment strategy, so that the energy scheduling accuracy of the lithium battery pack is improved.

The invention discloses a TSV packaging defect detection method and system, and the method comprises the steps: carrying out the multi-dimensional excitation scanning of a TSV packaging structure through employing an eddy current pulse thermal imaging device, and generating a three-dimensional eddy current field distribution feature matrix; inputting the three-dimensional eddy current field distribution characteristic matrix into a topological manifold decomposition module for defect characteristic separation, and constructing three characteristic components of a structure deformation topological ring, an interface fracture chain and a thermal stress abnormal curved surface based on a continuous coherence analysis method; performing cross-domain fusion processing of a dynamic heterogeneous neural network on the three feature components, and generating a defect topology fingerprint spectrum through a double-path attention gating mechanism; and inputting the defect topology fingerprint into a multi-scale entropy evaluation module for defect evolution simulation, and outputting a quantitative detection report containing defect geometric parameters, failure probability and reliability threshold. According to the embodiment of the invention, a comprehensive defect detection system can be constructed, and the accuracy and efficiency of defect identification are improved.

The invention provides a self-adaptive error compensation method and system for focused ion beam equipment, and the method comprises the steps: obtaining a real-time error signal set generated by the focused ion beam equipment in a machining process, training a self-adaptive error compensation model according to the real-time error signal set, and generating an error compensation parameter set; inputting the error compensation parameter set into a control unit of the focused ion beam equipment, and generating a real-time compensation control instruction set; adjusting an ion source parameter and a deflection electrode voltage parameter of the focused ion beam equipment according to the real-time compensation control instruction set, and outputting an adjusted ion beam path parameter set; and performing ion beam processing on the target processing area according to the adjusted ion beam path parameter set, and monitoring morphology feature data of the processed area in real time to verify an error compensation effect. According to the invention, harmful effects caused by thermal effects, electric fields or magnetic fields can be eliminated, and the self-adaptability and long-term reliability of a compensation strategy are ensured.

A method for manufacturing an electric vehicle battery case includes: preparing a frame configured to define a through space inside and a flat plate made of resin; superposing and disposing the flat plate on the frame; and applying pressure to the flat plate from an opposite side from the frame to press the flat plate against the frame to cause the flat plate to swell in the through space, thereby deforming the flat plate into a tray having a bathtub shape including a bottom wall and a peripheral wall provided at a peripheral edge of the bottom wall and configured to define an opening portion, and joining by press-fitting the tray to the frame.

In wireless communication, a device may change, add, or handover between cells of network access for inter-cell mobility. This may include Layer 1 and / or Layer 2 (L1 / L2) signaling for a user equipment (UE) moving between cells in a network. There may be a configuration message that includes configurations for one or more Layer 1 (“L1”) or Layer 2 (“L2”) Triggered Mobility (“LTM”) candidate cells. A measurement report with L1 measurements for the LTM candidate cells is used for a cell switch command to indicate a target LTM candidate cell to trigger execution of a LTM cell switch to the target LTM candidate cell. The LTM cell switch is from a source cell to the target LTM candidate cell.

The invention discloses an electrical fire monitoring method based on inherent residual current automatic compensation, and relates to the technical field of electrical safety monitoring, and the method comprises the following steps: S100, collecting a residual current signal, executing multi-component time-frequency deconstruction processing, extracting an energy distribution characteristic through wavelet packet transformation, and extracting a mutability index in combination with short-time spectrum entropy analysis, and constructing a preliminary distribution map of higher harmonic interference, and determining boundary features of interference signals in a time domain and a frequency domain. According to the method, high-frequency interference signals are accurately positioned through time-frequency deconstruction, wavelet packet analysis and spectral entropy indexes, non-fault harmonic components are effectively eliminated in combination with amplitude-frequency coupling recognition and a recursive rejection strategy, closed-loop control is constructed by introducing an adaptive compensation and stability backtracking mechanism, accurate recognition and dynamic correction of real electric leakage risks are achieved, and the method is suitable for large-scale popularization and application. The identification precision and the safety reliability of the monitoring system in a complex industrial environment are obviously improved, and the method has good engineering adaptability.