8269 results about "Electric equipment" patented technology

The invention provides a multi-dimensional electrical equipment insulation analysis and evaluation method, and relates to the technical field of electrical equipment insulation online detection and intelligent diagnosis. According to the method, an electrically isolated direct current detection channel is constructed in a neutral point small resistance grounding system, controllable direct current detection signals are injected, micro direct current leakage response signals are collected, and after zero drift correction, temperature compensation and power frequency ripple suppression are carried out, layered attribution is carried out in combination with primary system topology and an equipment ledger, and a multi-dimensional response matrix is formed. According to the method, environment and working condition normalization is realized through factor rejection and robust pull-back, a mechanism model containing leakage channel equivalent parameters and path constraints is established, multi-working-condition data is fused to calculate equivalent insulation parameters and credibility indication quantity, and a multi-dimensional insulation representation vector is generated. And performing time sequence analysis on the representation vector, extracting degradation and abrupt change characteristics, outputting a health index and a risk level, and realizing quantitative evaluation and intelligent early warning of the insulation state.

The invention relates to the technical field of image processing, in particular to a high-voltage electrical equipment surface defect identification method based on image processing. The method comprises the following steps: analyzing the gradient of pixel points in a to-be-analyzed image of a to-be-detected area on the surface of the high-voltage electrical equipment to obtain the weight of each pixel point; weighting the gray value of each pixel point in the to-be-analyzed image subjected to Laplacian filtering by using the weight of each pixel point of the to-be-analyzed image to obtain a first feature map; calculating a local standard deviation of each pixel point in the to-be-analyzed image so as to obtain a first parameter and a second parameter; constructing two Gaussian kernels based on the first parameter and the second parameter to filter the to-be-analyzed image to obtain a second feature map; fusing the first feature map and the second feature map of the to-be-analyzed image to obtain a defect saliency map of the to-be-analyzed image; and recognizing a surface defect area of the high-voltage electrical equipment based on the defect saliency map of each to-be-analyzed image. According to the invention, the accuracy of high-voltage electrical equipment surface defect identification can be improved.

The invention relates to the field of on-line monitoring of electrical equipment, in particular to an on-line monitoring method of the electrical equipment, which comprises the following steps: acquiring multi-dimensional monitoring parameters of the electrical equipment, including an electrical parameter, a temperature parameter and an insulation parameter; performing feature extraction on the collected multi-dimensional monitoring parameters to obtain feature parameters corresponding to the monitoring parameters, and constructing a feature vector set; based on historical normal operation data and real-time acquisition data of the electrical equipment, establishing a dynamic threshold model, and calculating a threshold corresponding to the characteristic parameter through the dynamic threshold model; feature parameters in the feature vector set are compared with corresponding thresholds output by the dynamic threshold model, and whether the electrical equipment is abnormal or not is judged; when the abnormity exists, outputting an abnormity monitoring result; and if not, returning to S1. According to the invention, by collecting multi-dimensional parameters, calibrating, correcting, de-noising and extracting features, and combining historical and real-time data to build a model and verify, data preprocessing, threshold adaptation and anomaly judgment are realized, and the monitoring reliability is improved.

The invention discloses a load decomposition method based on fusion feature data enhancement, and the method comprises the steps: synchronously collecting the low-frequency power data of a bus end of an electrical loop of a building and the low-frequency power data of all electric equipment ends, and generating a confusion power sequence of similar equipment through Beta distribution mixing, so as to enhance the recognition capability of a model for power overlapping features; based on the power time sequence data, extracting a mutation feature, an equipment state feature and a time coding feature to construct a multi-dimensional feature vector; a CNN-BiLSTM double-branch neural network is adopted, spatial-temporal characteristics are fused through a dynamic weight attention mechanism, and equipment state classification and power decomposition tasks are jointly optimized. In practical application, bus end power data is input, and the operation state and power distribution of each electric device are obtained. According to the method, an adversarial training strategy and a gating feature fusion mechanism are innovatively introduced, the load decomposition performance in a complex power utilization scene is remarkably improved, and the method is particularly suitable for identification and power prediction of equipment with similar rated power.

The invention relates to the technical field of intelligent power distribution and electrical equipment state monitoring, in particular to an intelligent power distribution cabinet data acquisition control system and method, and the method comprises the steps: presetting a temperature sensor and a Hall sensor at the lap joint of a circuit breaker contact and a copper bar of an intelligent power distribution cabinet; calibrating inherent parameters of the system; in response to starting operation of the intelligent power distribution cabinet, the following steps are triggered: obtaining real-time load current, real-time environment temperature and actual temperature through filtering processing; generating a theoretical healthy temperature through a thermoelectric coupling evolution equation; calculating aging resistance increment through dynamic impedance differential evolution processing; generating an aging resistance estimated value through contact state multi-dimensional evaluation; generating a dynamic current limit value through an intelligent current limiting control model; outputting a control signal to adjust the operation state of the intelligent power distribution cabinet; according to the invention, the problem that a traditional single threshold scheme cannot accurately identify a real health state is solved, and the sensitivity and accuracy of contact aging detection are remarkably improved.

The invention discloses a low-voltage cabinet fault prediction method and system based on artificial intelligence, and relates to the technical field of low-voltage electrical equipment state monitoring, and the method comprises the steps: obtaining operation data, such as current and voltage, collected by a sensor in a low-voltage cabinet; a multi-dimensional tensor structure containing spatial topological codes is constructed through preprocessing such as normalization; inputting a fault prediction model to obtain fault type probability and trend evaluation parameters, and generating a dominant abnormal factor index set through an interpretable module; analyzing a root cause path by using a fault causal knowledge graph in combination with the data, and determining a fault evolution chain; and finally generating interpretable early warning prediction information. The technical problems that a traditional low-voltage cabinet fault detection mode is difficult to process multi-dimensional complex data, cannot efficiently mine key fault information and is difficult to accurately predict are solved, the prediction model is constructed by using the deep learning technology, and then efficient processing of the multi-dimensional complex data of the low-voltage cabinet and deep mining of the key fault information are achieved. The technical effect of accurate fault prediction is achieved.

The invention discloses a wiper motor health state early warning system based on big data, and particularly relates to the technical field of electrical equipment state monitoring and fault diagnosis, which comprises a data acquisition module, a state interface module, a data preprocessing module, a data analysis module, an early warning decision module and an early warning information output module, electrical parameters such as current, voltage, temperature and rotating speed are collected through a sensor, and a feature set is generated through preprocessing in combination with vehicle bus working condition data; comparing the real-time electrical characteristics with a pre-stored baseline model and a fault mode library, calculating a health score based on a mahalanobis distance algorithm, identifying a fault and predicting the life; and generating graded early warning according to a multi-level threshold strategy, and outputting the graded early warning through a vehicle-mounted interface and a remote monitoring center. According to the invention, early, accurate and predictive early warning of the health state of the motor is realized, and the accuracy of state evaluation and the reliability of the system are improved.

The invention provides a coal mine cable aging degree analysis and evaluation method and system, a medium and a product, and relates to the technical field of coal mine electrical equipment state monitoring and safety early warning, and the method comprises the steps: collecting cable aging data in real time through a distributed monitoring terminal arranged in an underground coal mine; inputting the aging data characteristics corresponding to the cable aging data into a dynamic weighted fusion evaluation model, and calculating to obtain a comprehensive aging index of the current cable; when the comprehensive aging index exceeds a preset early warning threshold value, inputting a comprehensive aging index sequence and an environmental load feature sequence in a preset historical time window into a pre-trained long-short-term memory network model, and outputting an aging index change curve in a preset future time period; and calculating the residual life of the cable according to the curve, and triggering cable replacement early warning when the residual life is smaller than a preset threshold value. According to the method, the accuracy of cable aging state evaluation under complex working conditions can be improved, and the misjudgment rate caused by fluctuation of a single parameter is reduced.

The invention discloses an explosion-proof and intrinsic safety type combined feeder switch state monitoring method based on big data, and relates to the technical field of industrial electrical equipment state monitoring and fault diagnosis, and the method is realized based on a three-level architecture constructed by an edge computing node, a region computing node and a cloud platform. Comprising the following steps: S1, collecting multi-source sensing data of a feeder switch in real time by an edge computing node, and performing feature extraction and fusion calculation on the multi-source sensing data; according to the method for monitoring the state of the explosion-proof and intrinsic safety type combined feeder switch based on the big data, the contradiction between the real-time performance and the accuracy in multi-source heterogeneous data fusion processing is effectively solved, abnormity can be found in the first time, early warning can be triggered, and the reliability of a diagnosis conclusion is improved; and the accuracy and foresight of fault diagnosis are ensured. According to the grading processing mechanism, computing resources are reasonably distributed, the network transmission load is greatly reduced, and the system can stably operate in a complex industrial environment.

The invention relates to the technical field of power equipment monitoring, in particular to a multi-mode sensor power equipment state monitoring system based on AI smell. The data acquisition module is used for acquiring multi-dimensional sensing data around the electrical equipment; the AI olfaction processing module is used for converting original sensor data into standardized odor feature fingerprints by performing spatial-temporal feature extraction and multi-modal feature fusion on the multi-dimensional perception data; the overheating state analysis module is used for generating an overheating comprehensive index and an equipment state evaluation result based on the smell characteristic fingerprints; the multi-stage early warning and response module is used for carrying out early warning grade judgment based on the overheating comprehensive index and the equipment state evaluation result and generating graded early warning information; and the storage module is used for storing historical monitoring data and early warning event records. According to the invention, through system integration of the multi-mode sensor array of the bionic olfaction mechanism, AI feature fusion processing and intelligent analysis decision, active sensing and accurate identification of the overheating state of the power equipment are realized.

The invention discloses a method and a system for determining optimal parameters of an X-ray excited extra-high voltage composite apparatus, and relates to the technical field of extra-high voltage electrical equipment detection. The method comprises the following steps: remotely operating the X-ray machine and calculating partial discharge intensity; for the current irradiation position, the tube voltage and the tube current are gradually adjusted, parameters are dynamically optimized based on the quantitative relation between the partial discharge intensity and the radiation dose rate change rate, and the optimal parameters are recorded; and changing position repetition parameter optimization, and constructing a BP neural network model after data set training optimization so as to predict an optimal irradiation position. The optimized BP neural network adopts an improved sparrow search algorithm to optimize an initial weight and a threshold value, and combines Bayesian to optimize a learning rate and a hidden node number. Accurate optimization of excitation parameters and irradiation positions is achieved, detection accuracy and safety are both considered, the optimal irradiation position positioning efficiency is improved, and the method is suitable for ultra-high voltage GIS detection of different voltage grades.

The invention relates to the technical field of power system equipment state monitoring and fault diagnosis, and discloses an arc light online monitoring method and system based on a spectrum sensing technology, and the method comprises the steps: arranging a first spectrum sensor and a second spectrum sensor corresponding to a detected target, and respectively detecting optical signals in different wavelength ranges; collecting optical data according to a set period, and identifying abnormal features according to a preset template; when the double abnormal features exist, the duration of the double abnormal features is calculated, and if the duration exceeds the corresponding reference duration, a duration reference value is generated; and performing weighted calculation on the time length analysis value, if the time length analysis value is within a preset range, giving a partial discharge abnormal prompt, and resetting the reference value. Multi-wavelength spectrum monitoring is utilized, non-contact and high-sensitivity detection of partial discharge is achieved, electrical equipment is prevented from being damaged, fault hidden dangers are found in time, and safe and stable operation of the equipment is guaranteed.

The invention relates to the technical field of fault detection, in particular to an electrical equipment fault diagnosis method and system, and the method comprises the following steps: constructing a ratio group of a trigger incremental sequence quantity and a frequency spectrum cluster set based on a partial discharge pulse interval, deducing impedance frequency boundary migration, analyzing a conductor heating and cooling rate difference, and forming thermal response lag information. According to the method, frequency spectrum energy evolution characteristics are extracted through partial discharge pulse interval dynamic behaviors, a frequency migration path is constructed to reflect an impedance boundary fluctuation process, hysteresis effect characteristics are extracted in combination with thermal inertia response differences in a temperature rise stage, and the impedance boundary fluctuation is determined. And analyzing a voltage and current phase fluctuation rule and deducing a change trend of a combined feature, establishing a layer-by-layer progressive judgment logic between multi-stage dynamic coupling features, realizing evolutionary process tracking from local disturbance to a global abnormal state, and improving a fine-grained identification level of an abnormal mechanism in an electrical equipment operation behavior.

The invention relates to the technical field of electrical equipment state monitoring and fault early warning, and particularly discloses an electrical cabinet condensation risk online monitoring method and system, and the method comprises the steps: synchronously collecting the physical parameters of the same microscopic space point in an electrical cabinet; calculating the classical dew point temperature by adopting a Magnus formula; performing quantification processing on the air pressure micro-fluctuation data in the time window, and calculating information entropy of micro-air pressure fluctuation; the wall surface temperature gradient and the air flowing state are fused, and the equivalent dew point temperature is calculated; real-time safety margin is calculated, and the instantaneous change rate of the safety margin is solved; constructing a condensation risk trend index; comparing with a preset threshold value, generating an early warning signal and a risk diagnosis label, and completing condensation risk monitoring; the system comprises a multi-physical field sensing unit, an embedded processing unit, a self-adaptive control unit, a man-machine interaction unit and a communication unit which are integrated in the same shell.

The invention discloses a vacuum breaking type GIS operating mechanism multi-scale fatigue damage real-time monitoring and residual life prediction method and system, equipment and a medium, and relates to the technical field of intelligent state monitoring and operation and maintenance management of power equipment, and the method comprises the specific steps: collecting and preprocessing a signal, and constructing a multi-dimensional feature vector; establishing a multi-scale fatigue damage identification model to obtain a damage grade classification result; and collecting an accumulated damage historical sequence for preprocessing, calculating through a calculation module, constructing a life prediction model, and predicting the life of the GIS operating mechanism. Through multi-source data collaborative analysis, multi-scale fatigue damage identification and life prediction model construction, the problems that a traditional method cannot realize continuous tracking and prediction of the damage evolution process of the operating mechanism and cannot fully capture multi-frequency response and local damage signals in the operating load are solved; the problems of diagnosis lag, low accuracy and weak generalization in the prior art are solved, and the operation safety and the maintenance scientificity of the high-voltage electrical equipment are remarkably improved.

The invention relates to the technical field of electrical equipment state monitoring, and discloses a power distribution cabinet safety monitoring method and system based on image recognition. The method comprises the following steps: deploying a dual-band image acquisition device in a key area of the power distribution cabinet and firstly acquiring an infrared thermal image; carrying out preprocessing and temperature analysis on the image, and triggering an alarm when an abnormal area is found; adjusting the posture of the equipment to synchronously collect high-definition visible light and infrared images of the abnormal area; registering the two images, respectively extracting appearance and temperature distribution feature vectors, and fusing; and finally, inputting the fusion feature vector into a diagnosis model for fault identification, and generating a diagnosis conclusion after a high-confidence result and a reference image are compared and analyzed. The reliability and efficiency of power distribution cabinet monitoring can be improved.

The invention relates to the technical field of high-voltage electrical equipment, and particularly discloses a high-insulating-property thermoplastic sleeve and a method for preparing the same based on a co-extrusion process, the surface of a nonlinear layer is wrapped with an insulating material, so that the corona discharge phenomenon is avoided; and meanwhile, the same thermoplastic material is adopted as the insulating layer (including the first thermoplastic layer and the second thermoplastic layer) material and the base material of the nonlinear layer, so that no obvious interface exists between the insulating layer and the nonlinear layer, charge accumulation is avoided, insulation failure is avoided, the insulation and voltage resistance of the sleeve is improved, and the operation reliability is improved.

The invention discloses a battery and electric equipment, and belongs to the technical field of batteries. The battery includes a first battery module including a plurality of battery cells stacked in a second direction, and an isolation member. The isolation part is located on one side of the first battery module in the first direction. A pressure relief mechanism is arranged on one side, facing the isolation part, of each battery monomer in the first battery module, a collection cavity is formed in the isolation part, and the collection cavity is used for collecting emissions of the battery monomers when the pressure relief mechanism is actuated. And in the second direction, the pressure relief mechanisms of the two adjacent battery monomers in the first battery module are staggered from each other, and the second direction is intersected with the first direction. The battery has relatively high reliability.

The invention relates to the technical field of electrical equipment fault diagnosis, in particular to an electrical equipment fault diagnosis method and system based on a knowledge graph, and the method comprises the steps: obtaining the multi-mode sensing data of electrical equipment, and constructing a fault diagnosis knowledge graph; mapping entities and relationships in the atlas to a low-dimensional continuous vector space by adopting a knowledge representation learning algorithm, and extracting semantic feature vectors; inputting the data into a graph neural network model to learn neighborhood information, executing link prediction and knowledge completion, and determining a potential fault causal chain in a preset operation environment; and matching the causal chain with the logic rule base, performing causal logic derivation on the diagnosis result in combination with a large-scale language model, and outputting a fault root cause analysis report and an operation and maintenance management decision suggestion. According to the method, multi-modal perception data and knowledge representation learning are deeply fused, implicit fault association which is not explicitly recorded in historical operation and maintenance data is mined by using a graph neural network, and interpretable analysis is provided in combination with a language model.

The invention belongs to the technical field of batteries, and particularly relates to a negative electrode material, a secondary battery and electric equipment. The second coating layer with the porous carbon material in the negative electrode material provides a sufficient buffer space for volume expansion of silicon, so that cracks and pulverization of the negative electrode material are effectively reduced, and the structural integrity of the negative electrode material is further ensured. And meanwhile, the solid electrolyte arranged in the second coating layer can maintain the smoothness of an ion transmission path in the silicon expansion process, so that the rate capability and the quick charge performance of the battery are ensured. Besides, a third coating layer (SEI film) formed by the ionic liquid has good ionic conductivity, and dynamic covalent bonds existing in some ionic liquids can be fractured and recombined under the action of external stress, so that a self-repairing function is realized. Therefore, when cracks are generated on the surface of the negative electrode material due to volume expansion, the third coating layer can quickly repair the cracks, prevent electrolyte permeation and lithium dendrite growth and inhibit side reactions.

The invention provides a compact intelligent switch cabinet, which belongs to the technical field of intelligent switch cabinets and comprises an intelligent switch cabinet body, a switch cabinet back plate is arranged on the outer wall of one side of the intelligent switch cabinet body, and a switch cabinet door is hinged to the outer wall of the other side of the intelligent switch cabinet body. A transverse partition plate is arranged in the middle of the inner side wall of the intelligent switch cabinet body, a concave hole is formed in the outer wall of one side of the transverse partition plate, and an electrical equipment compact installation assembly and a locking linkage assembly are arranged on the inner side of the intelligent switch cabinet body. According to the electrical equipment compact installation assembly, electrical equipment can be adaptively and horizontally arranged and installed in the intelligent switch cabinet according to the thickness of the electrical equipment, so that the space occupied by tiled installation of the electrical equipment is reduced, and the electrical equipment installed in the intelligent switch cabinet can be conveniently overhauled, replaced and installed; and the intelligent switch cabinet can be manufactured to be more compact and can be flexibly expanded so as to adapt to frequently-changed power utilization requirements.

The utility model provides a cover plate assembly, a battery and electric equipment. The cover plate assembly comprises a terminal and a body, the body comprises a first connecting part and a second connecting part, the first connecting part is connected with the terminal, the second connecting part is connected to the side, away from the terminal, of the first connecting part, and an explosion-proof structure is formed at the joint of the first connecting part and the second connecting part. According to the cover plate assembly, when the battery is subjected to thermal runaway, the terminal can be separated from the collector plate of the battery, so that the battery can be powered off, the situation that the thermal runaway is aggravated due to the fact that the battery is still discharged when being subjected to thermal runaway can be avoided, and the safety of the battery is improved.

The embodiment of the invention provides a heat exchange assembly, a battery device, electric equipment and energy storage equipment. The battery device comprises a box body assembly, a battery monomer assembly and a heat exchange assembly, a containing cavity is formed in the box body assembly. And the battery monomer assembly is arranged in the accommodating cavity. The heat exchange assembly is arranged in the containing cavity. Wherein the heat exchange assembly comprises at least two flexible parts, the at least two flexible parts are stacked, a heat exchange flow channel is formed between the flexible parts, the heat exchange flow channel is used for conducting a heat exchange medium, and the heat exchange medium is used for performing heat exchange with the battery monomer assembly. According to the battery device provided by the embodiment of the invention, the energy density of the battery device is favorably improved, and the fitting degree between the heat exchange assembly and the box body assembly and / or the battery monomer assembly can be improved, so that the effective heat exchange area between the heat exchange assembly and the box body assembly and / or the battery monomer assembly is increased; and the heat exchange efficiency and the heat exchange effect of the heat exchange assembly are improved.

The invention discloses an anti-condensation and humidity control system of a low-voltage switch cabinet, and relates to the technical field of environment regulation and control of low-voltage electrical equipment, and the system comprises a data collection module which collects data through a multi-region temperature and humidity sensor; the humidity prediction module is used for predicting 30-minute humidity by using a long-short-term memory neural network in combination with cabinet body parameters and historical data, and pre-evaluating the condensation risk; the anti-condensation execution module is used for starting a PTC heating device, an axial flow ventilation device or a semiconductor dehumidification device according to the prediction result; the system linkage module interacts with an electric power monitoring system and adjusts parameters of the execution device when the load is high; the parameter self-learning module is used for optimizing the prediction model and recording optimal threshold values of the device in different environments; and the safety protection module is used for monitoring the heating temperature and the condensate water level and switching an emergency strategy when a fault occurs. The system has the advantages of comprehensive monitoring, accurate prediction, cooperative operation of execution devices, adaptation to different environments and loads, reduction of condensation faults, prolongation of the service life of elements, and improvement of the operation reliability of the switch cabinet.

The invention discloses a virtual reactive power plant user single-phase asynchronous motor reactive power regulation method, and relates to the technical field of electrical equipment control, and the method comprises the steps: collecting the real-time state data of a key node of a power distribution network, and building a short-term prediction model based on the state data; setting a judgment condition according to a prediction result of the model, generating a preliminary reactive power regulation instruction, and adjusting the priority and the regulation amplitude of the preliminary reactive power regulation instruction in real time based on dynamic updating of a prediction trend; issuing the adjusted reactive power adjustment instruction to a user terminal, and performing cooperative processing based on distributed coordination control to generate a final cooperative adjustment instruction; executing an action of cutting off the split-phase capacitor of the single-phase asynchronous motor according to the coordinated adjustment instruction; according to the invention, the split-phase capacitance of the single-phase asynchronous motor at the user side is regulated and controlled by using the virtual reactive power plant, so that low-cost, quick-response, continuous and accurate reactive power regulation is realized, and the problems of reactive power excess and high voltage of a power grid are effectively solved.

The invention discloses an automatic welding mechanism of a foil winding machine, and relates to the technical field of electrical equipment manufacturing, the automatic welding mechanism comprises a supporting plate, and first supporting rods are symmetrically arranged at the top of the supporting plate; the traction mechanism is arranged on the first supporting rod; the supporting frame is arranged on the supporting plate, and a feeding belt is arranged at the top of the supporting frame; and the foil winding mechanism is arranged on one side of the traction mechanism and comprises a pair of mounting plates and a pair of mounting plates which are respectively arranged on the pair of mounting plates. By integrating the feeding and traction mechanism, the foil winding mechanism, the induction mechanism, the cutting and tensioning adjusting device, the positioning and welding device and the limiting mechanism and optimizing the cooperative relation and action logic of all the components, full-process automation from foil belt feeding, tension control, fixed-length winding, automatic cutting to precise positioning and welding of the tail end of a foil roll is achieved.

The invention provides a battery pack and electric equipment, the battery pack comprises a plurality of batteries, each battery is provided with a first surface, the first surface is provided with a pressure relief piece and a pole, and the pressure relief piece is provided with a weak part; the busbars are connected with the pole posts in a welding manner to form welding parts; a guard; the shortest distance between the weak part and the pole is D, the overlapping area of the orthographic projection of the area defined by the weak part on the first surface and the orthographic projection of the protection piece on the first surface is A, the overlapping area of the orthographic projection of the pole on the first surface and the orthographic projection of the protection piece on the first surface is B, and the area of the protection piece is S2, the shortest distance between every two adjacent welding parts is M, A + B = S1, S1 / S2 = Y, and 3.2 mm < 2 > < = D * Y * M < = 8897 mm < 2 >. Compared with the prior art, the method has the advantages that the formula value is limited in the preset range, the polar column can be prevented from being influenced by the pressure relief part, the thermal runaway risk of the adjacent battery is reduced, and meanwhile, the pressure relief part can timely relieve pressure.

The invention discloses a solid-state battery, an electrode layer, a battery cell, a single battery and electric equipment, the solid-state battery is composed of a solid-state positive pole piece, a solid-state electrolyte layer and a solid-state negative pole piece, the edges (at least one pole piece) of the positive pole piece and the negative pole piece contain an insulating coating, and the solid-state electrolyte layer can contain or does not contain the insulating coating; the short-circuit risk of the solid-state battery due to dislocation of positive and negative plates is solved, the problems of powder falling and material falling at the edges of positive and negative electrodes and electrolyte of the solid-state battery can be solved, particles are prevented from entering the battery, and the safety is further improved.

The invention discloses an insulated wire, a coil and electronic / electrical equipment, and relates to the technical field of insulated wires. The modified polyamide-imide insulating layer is composed of an insulating material obtained by reacting diisocyanate with mixed anhydride, wherein the mixed anhydride comprises short-chain anhydride and long-chain anhydride. According to the insulated wire coated with the polyamide-imide insulating layer, the wire flexibility is improved, the insulated wire is suitable for closer arrangement of complex groove types, and then the motor groove fullness rate is improved, so that the size of a stator iron core is reduced under the same power, the motor weight is indirectly reduced, and the requirement for light weight is met.

The invention discloses a new energy unmanned ship battery power control method and system, and the method comprises the steps: an unmanned ship total system sends a start-stop instruction to a BMS, and carries out the automatic awakening and starting of a battery pack; after the BMS is started, the voltage of a battery module, the current of a main loop and the temperature of key components are detected, and if normal, a pre-charging circuit is started to pre-charge a high-voltage loop capacitor; after pre-charging is completed, the BMS disconnects the auxiliary contactor and closes the main contactor, and the battery module supplies power to the electric equipment; during operation, the BMS acquires operation data in real time and uploads the operation data to the unmanned ship total system for monitoring through an external CAN bus; when the multiple battery packs are connected in parallel, cooperative charging and discharging control is carried out through an external CAN bus; the unmanned ship main system sends a shutdown instruction, and the BMS cuts off the main contactor; when abnormity is monitored, the BMS triggers the main fuse to fuse and cuts off self power supply. According to the method, the problem of safe starting is solved, the battery state monitoring precision is improved, a multi-dimensional safety protection system is constructed, and multi-battery-pack cooperative control is optimized.