76results about How to "Addressing slow performance" patented technology

The invention discloses a transformer fault diagnosis method based on an adaptive fault attention residual network, and the method comprises the steps: employing an MEMS photoacoustic sensor with a capacitor resonator, analyzing a sound wave signal through a photoacoustic spectrum and a signal processing technology, collecting soluble gas in oil, and forming a parameter sequence; introducing an adaptive activation function and a fault index function; the feature distribution of a source domain and a target domain is adjusted by using an LMMD and a domain discriminator, the data of the source domain and the data of the target domain are from different distributions but share the same label space, and the edge distribution difference of the features and conditions is minimized through the LMMD and the domain discriminator to realize cross-domain invariant feature learning; the performance of the model is optimized by balancing hyper-parameters, and the loss meaning in all aspects is balanced to realize dynamic balance. The method can be effectively applied to the fault diagnosis of the transformer, and the diagnosis accuracy is greatly improved.

The invention discloses a hemispherical resonator gyroscope scale factor self-compensation system and method. An X-axis electrode and a Y-axis electrode are respectively connected with a buffer amplifier, an analog-to-digital converter and a demodulation unit through switches; one output of the demodulation unit is connected with the excitation signal demodulation unit and the scale factor compensation unit, the other output of the demodulation unit is connected with the control unit through the low-pass filtering unit, the output of the control unit is connected with the scale factor compensation unit and the two signal modulation units, and the two signal modulation units are respectively connected with an X-axis electrode and a Y-axis electrode through the digital-to-analog converter; and the output of the excitation signal generator is connected with the Y-axis signal modulation unit and the excitation signal demodulation unit. A Y-axis sine excitation signal response amplitude is extracted through an excitation signal demodulation unit, and a scale factor change relation can be obtained according to a ratio relation between the amplitude and an amplitude reference value. According to the invention, the self-compensation of the scale factor can be completed, the problem that the performance of the gyro scale factor is reduced due to temperature change and aging is solved, and the temperature stability and long-term working stability of the gyro scale factor are improved.

The invention belongs to the technical field of non-ferrous metal profile forming, and relates to a shaping method and device for titanium and titanium alloy arc-shaped profiles. According to the shaping method, the problems of large curvature radius change, profile distortion, poor flatness and the like caused by rebound of the bent titanium and titanium alloy arc-shaped profiles are solved through hot shaping treatment, and the annealing device is used for annealing treatment to solve the problem of secondary deformation of the arc-shaped profiles in the annealing process of eliminating residual stress. Before the shape correcting machine is used for correcting the shape, the arc-shaped profiled bar is firstly placed in a space and is heated to a preset temperature on line, so that the problems of high resistance, poor plasticity, high resilience and difficulty in deformation of the profiled bar in a normal temperature state are solved, and meanwhile, various problems caused by secondary transfer of a hot material after offline heating and clamping and re-positioning of the arc-shaped profiled bar are also solved. According to the annealing device, the arc-shaped profiled bar, the bottom plate and the fixing block assembly form a coating whole, so that the possibility of secondary deformation of the arc-shaped profiled bar in the annealing process of eliminating residual stress is effectively avoided.

The invention discloses an optical remote sensing image target detection domain adaptation method based on multi-view adaptive feature alignment. A multi-view adaptive feature alignment module provided by the invention firstly adaptively captures domain specific features in an image by using a cross attention mechanism; subsequently, the method adopts a training mode of comparative learning to enhance the difference of aggregation features of the feature alignment module so as to realize feature aggregation of different view angles. Compared with the prior art, the method has the advantages that the domain specific features in the optical remote sensing image can be captured more effectively, and more accurate feature alignment is realized.

The invention belongs to the technical field of vibration control, and relates to a sensor arrangement and feedback control algorithm joint optimization method in structural vibration control, which adopts a continuous time recurrent neural network CTRNN as a basic neuron, and adopts an NEAT algorithm to optimize a network structure to obtain an optimal network structure; the optimal network structure serves as a control strategy of the vibration control system, the positions of the sensors are obtained, when external excitation is applied to the structure, the optimal control force is output by the optimal network structure through state response measured by the sensors at the corresponding positions, and then an execution mechanism is controlled to generate the corresponding control force. According to the invention, the position of the sensor can be accurately optimized, a neural network architecture with the simplest structure is evolved, and the overall efficiency of the system is improved.

The invention discloses a method for treating a graphite boat by plasma. The method comprises the following steps: S1, transferring the graphite boat subjected to dry cleaning to a PECVD (Plasma Enhanced Chemical Vapor Deposition) boat plating machine; s2, putting the graphite boat into a PECVD furnace tube, and inserting an electrode into the graphite boat; s3, the PECVD furnace tube is vacuumized and heated; s4, when the pressure and the temperature in the PECVD furnace tube reach preset values, introducing process gas; s5, applying a radio frequency power supply to the graphite boat, ionizing the process gas to form plasmas, and carrying out chemical reaction on the plasmas and the fluorocarbon film on the surface of the graphite boat; and S6, a product formed by reaction in the PECVD furnace tube is pumped out, and cleaning of the graphite boat is completed. The graphite boat cleaning device has the advantages of being simple in principle, convenient to operate, high in cleaning efficiency and the like, online graphite boat treatment can be achieved, the graphite boat treatment efficiency is greatly improved, and timeliness is improved.

The invention relates to the technical field of performance evaluation, in particular to a computer performance evaluation system and method based on big data and AI. The system comprises a request processing unit, a calculation time unit, a subset influence unit and a performance evaluation unit, and the subset influence unit calculates response time length subset feature values according to extracted feature values and then calculates the comprehensive influence degree according to the calculated response time length subset feature values. The performance evaluation unit evaluates the computer performance delay probability according to the calculated comprehensive influence degree and the calculated average response time, and when the evaluated performance delay probability is larger than 1, it shows that the influence of the extracted feature value on the calculated average response time in the response time long subset is obvious; the computer performance delay is evaluated according to the comprehensive influence degree, the obvious influence of the features on the response time can be accurately recognized, the influence of subjective judgment is reduced, and more accurate delay probability evaluation is provided.

The application discloses a visual language navigation method based on adaptive prompt information during testing, comprising the following steps: obtaining natural language-based navigation instructions and 360-degree panoramic visual observation information of an intelligent agent; extracting low-frequency visual features of sub-images and selecting valuable information from an image memory bank for prompting; extracting a text feature vector corresponding to the navigation instructions and an initial state vector of the current environment; selecting historical information from the visual feature memory bank using the state vector for prompting; inputting the state vector and the instruction features into a visual-language interaction module to generate navigation action selection; updating the normalization layer in the full connection layer according to the prediction score of the navigation action selection; and repeating the above steps until the intelligent agent stops selecting or reaches the maximum number of movement steps. The application can effectively enhance the navigation performance of the pre-trained intelligent agent model in unknown test environments.

The invention discloses low-carbon polypropylene fiber concrete with high toughness and crack resistance, and belongs to the field of concrete. The raw materials comprise, by mass, 100-110 parts of coarse aggregate, 60-70 parts of fine aggregate, 20-30 parts of cement, 80-120 parts of fly ash, 15-20 parts of modified polypropylene fiber, and 2-3 parts of a water reducing agent; the use amount of the water meets the condition that the water-cement ratio is 0.3-0.4; according to the method, the surface of the polypropylene fiber is subjected to controllable micro-etching through concentrated phosphoric acid, a microscopic gully and micropore structure is formed, effective roughening of the surface of the fiber is achieved, the specific surface area of the fiber is greatly increased, the fiber can generate a stronger mechanical meshing effect in concrete, pulling out of the fiber is effectively prevented, and the tensile strength of the fiber is improved. And in the subsequent silane coupling agent treatment, a stable'molecular bridge 'is constructed between the fiber surface subjected to acid etching activation and an inorganic cement matrix, and firm Si-O-Si covalent bond connection is formed, so that the problem of weak bonding force between the common polypropylene fiber and the matrix interface is solved.

This invention relates to a robust adaptive beamforming method based on group sparse reconstruction for radar and communication systems. The method includes: establishing an array steering vector by combining the geometry of a uniform linear array, and constructing a sparse signal model for practical engineering applications with accompanying amplitude and phase errors; subsequently, establishing a joint optimization model by performing operator transformations on the received signal model to minimize the Frobenius norm of the interference signal and the fitting error of the observed data; based on this, introducing auxiliary variables and reconstructing the original objective function into an unconstrained optimization problem centered on minimizing the augmented Lagrangian function, and using convex constraints to avoid zero solutions, decomposing the optimization problem into three related sub-problems using the alternating direction multiplier method framework and solving them sequentially; finally, reconstructing the interference plus noise covariance matrix using the acquired interference signal and amplitude and phase error parameters, and using this to estimate the optimal weight vector to achieve robust adaptive beamforming. This invention not only accurately estimates array amplitude and phase errors and interference signals, effectively solving the performance degradation problem caused by the limited number of snapshots in practical applications; but also, the group sparse prior effectively utilizes the temporal correlation of signals between snapshots, further improving the reconstruction performance of interference signals.

The invention discloses a multi-mode signal real-time self-adaptive recognition and extraction method for a complex scene, solves the problem that an existing interference suppression and signal extraction algorithm cannot adapt to gravitational wave measurement requirements, belongs to the technical field of digital signal processing, and adopts a fixed sequence of main carrier clock sideband pseudo code signals to perform signal extraction and separation. According to the sequence, the processing efficiency is optimized, the weak signal is prevented from being covered by the strong signal, and the interference of subsequent processing is remarkably reduced. Compared with parallel or disordered processing in the background technology, the method provided by the invention has the advantages that strong interference components are stripped in sequence, so that rapid convergence in real-time operation is ensured, and the robustness and precision of overall signal extraction are improved.

The application discloses a domain self-adaption image classification method based on self-training and centroid alignment, comprising the following steps: obtaining data sets of a source domain and a target domain and performing simple data preprocessing; through a self-training process, the network obtains simple classification capability; through a consistency regularization loss, the consistency of outputs of two classification modules is improved; the distance between target domain data and corresponding class centroids of the source domain is shortened, and the target domain data features are promoted to move to the direction of the source domain class centroids, so that the features are aligned at a structure level; and in the learning process, the classification model effectively aligns the knowledge of different domains gradually. The application combines feature information and structure information, considers two angles of distance and direction, guides the model to learn domain-invariant features, and effectively solves the problem of performance decline of an image classification network caused by large differences between data distribution of the source domain and the target domain.

The embodiment of the application provides a reference signal configuration method, a storage medium, an electronic device and a computer program product, and the method comprises the following steps: a first node determines a target modulation mode of a reference signal in multiple reference signal modulation modes. According to the application, the modulation mode of the reference signal is no longer fixedly configured, and the communication node can select a suitable modulation mode from the multiple reference signal modulation modes according to the needs, so that different communication scenes and needs can be more flexibly adapted, and the problem that, in the related art, the nonlinear difference between the reference signal and the data signal fixedly using low-order modulation in the scene of high-power transmission and high-order data modulation can cause the performance of the receiver to decline when the receiver performs channel estimation and data recovery is solved, and therefore the effect of improving the efficiency and performance of the communication system is achieved.

The present invention relates to a lithium secondary battery including a negative electrode including a negative electrode active material, a positive electrode including a positive electrode active material, a separator interposed between the negative electrode and the positive electrode, and an electrolyte, wherein the negative electrode active material includes silicon particles, a Si charge depth represented by Equation 1 below is 30% to 60%, and a Si discharge depth represented by Equation 2 below is 10% or greater. Si charge depth (%) = {(positive electrode loading amount+pre-lithiation capacity of negative electrode) / negative electrode loading amount} ×100 Si discharge depth (%) = {(positive electrode loading amount + pre-lithiation capacity of negative electrode - discharge loading amount) / negative electrode loading amount} ×100 In Equations 1 and 2 above, the positive electrode loading amount indicates capacity per unit area of the positive electrode (unit: mAh / cm2), the negative electrode loading amount indicates capacity per unit area of the negative electrode (unit: mAh / cm2), the pre-lithiation capacity of the negative electrode indicates capacity per unit area (unit: mAh / cm2) of lithium (Li) inserted into the negative electrode through pre-lithiation, and the discharge loading amount indicates a value obtained by dividing discharge capacity of a secondary battery by positive electrode area at discharge cut-off voltage.

The invention discloses a Tesla turbine with an adjustable nozzle incident angle, which comprises a shell, a nozzle structure and a turbine disc assembly which are sequentially arranged from outside to inside, and further comprises an angle adjusting device, the nozzle structure comprises an inner shield and a plurality of nozzles rotationally embedded in the inner shield; the turbine disc assembly is arranged on the inner side of the inner protective cover and comprises a rotating shaft and a plurality of turbine discs fixedly installed in the axial direction of the rotating shaft. A total incident port is formed in the outer side of the nozzle structure on the shell; the angle adjusting device comprises a total torque, a total torque connecting shaft and a main gear which are coaxially arranged, and transmission gear assemblies and auxiliary gears which are in one-to-one correspondence with the nozzles, the auxiliary gears and the nozzles are coaxially arranged and fixedly connected, the main gear is driven to rotate by rotating the total torque, and the auxiliary gears are driven to rotate through the transmission gear assemblies; therefore, the nozzle is driven to rotate to change the incident angle of the nozzle. By introducing the adjustable nozzle, the incident angle of the fluid can be conveniently adjusted, so that the performance of the turbine is optimized, and the efficiency is improved.

The invention provides a heat exchange plate structure and a plate heat exchanger, relates to the technical field of heat exchangers, and solves the technical problem that the performance of the heat exchanger is reduced due to non-uniform flow distribution of the heat exchanger in the prior art. The heat exchange plate structure and the plate heat exchanger are provided with a plurality of first inter-plate channels and a plurality of second inter-plate channels which are sequentially arranged at intervals, two sets of inlet and outlet angle hole channels are further formed in the heat exchange plate structure, and the two sets of inlet and outlet angle hole channels are used for being communicated with the first inter-plate channels and the second inter-plate channels respectively. The heat exchange plate structure is further provided with at least one distribution channel, and the at least one distribution channel is used for communicating the at least two first inter-plate channels or the at least two second inter-plate channels. According to the heat exchange plate structure and the plate heat exchanger, the uniformity of flow distribution in the heat exchange plate structure is improved, and the heat exchange performance of the heat exchanger is improved.

The invention relates to the technical field of kitchen utensils, and discloses a range hood which comprises an air inlet channel, a hurricane cavity, a high-speed fan, an oil smoke suction inlet, an air filtering device, a smoke exhaust pipe connector and an air curtain module, the air inlet channel is arranged on the outer side of the hurricane cavity and the inner side of a range hood shell, and the air filtering device is arranged on the air inlet channel; the oil smoke suction inlet, the hurricane cavity and the smoke exhaust pipe connector are communicated, the high-speed fan comprises a high-speed fan main body and a high-speed fan air outlet pipe, the high-speed fan main body is arranged on the lower side of a bottom plate of the hurricane cavity, and the high-speed fan air outlet pipe penetrates through the bottom plate of the hurricane cavity and points to the smoke exhaust pipe connector. According to the range hood, through the combined action of the high-speed fan, the hurricane cavity and the air filtering device, lampblack is directly discharged along with high-speed airflow, and the problem that the performance of the range hood is reduced due to the fact that oil stains adhere to a wind wheel and a volute is solved.

This invention relates to the fields of unmanned aerial vehicles (UAVs) and computer vision technology, and particularly to a UAV-based method for classifying bridge components. The technical problem is that existing point cloud-based automatic classification methods for bridge components suffer from issues such as scarce samples, high annotation costs, insufficient model generalization ability, and low processing efficiency. The technical solution is a UAV-based method for classifying bridge components, comprising a data acquisition step, a data augmentation step, a data preprocessing step, a model training step, and a component classification step. In the model training step, preprocessed 3D point cloud data and its corresponding component category labels are used to train a deep learning-based point cloud semantic segmentation model, enabling the model to learn the feature representations of different bridge components. This invention uses a systematic data augmentation strategy to simulate real-world acquisition interference, combined with an efficient point cloud deep learning model, to achieve accurate, robust, and efficient classification of various bridge components.

Disclosed are a system and method for verifying a power amplifier of a vehicular audio system. The system includes: a TDM converter for converting an audio signal into a TDM signal; an audio signal processor for receiving the TDM signal and generating and outputting a test signal for transmitting to the power amplifier, the audio signal processor having N audio channels, N>1; a channel configurator having M configuration units, M≥N, each of the audio channels corresponding to one of the configuration units, and each of the configuration units having a selected state and a non-selected state; and a controller for determining that each of the configuration units is in the selected state or non-selected state and controlling the audio signal processor to place the TDM signal in audio channel(s) corresponding to configuration unit(s) in the selected state.

This invention provides a cross-modal anomaly detection and localization method and system based on context and dual text. The method includes: extracting global semantic visual features, spatial fine-grained visual features, and fragment-level visual context information from the video frame to be detected; generating instance-level conditional cue vectors using the fragment-level visual context information as conditions; mapping and generating a first text prototype feature representing a normal state and a second text prototype feature representing an abnormal state; performing cross-modal comparison between the generated dual text prototype features and the extracted global semantic visual features and spatial fine-grained visual features, generating frame-level anomaly scores in parallel to complete anomaly detection, and generating pixel-level anomaly localization heatmaps to complete anomaly localization. This invention enables the generated normal and abnormal text prototype features to adaptively adjust with the context of the current video scene, overcoming the shortcomings of static text cue's lack of scene adaptability and susceptibility to environmental interference.

This application provides a communication method, a communication apparatus, and a terminal, applied to autonomous driving, assisted driving, and the like. The method includes: A first node scans another primary node based on a scanning parameter, where the first node is a primary node of at least one communication domain; and when the another primary node is not discovered through scanning, the first node listens to a first broadcast message based on random listening duration, where the first broadcast message is a broadcast message from one or more primary nodes in the another primary node, and the random listening duration is used by the first node to listen to the another primary node. The random listening duration is not fixed. Broadcast time periods of the first node and the another primary node are staggered, so that the first node can receive a broadcast message of the another primary node in the random listening duration. This can avoid a problem that the first node and the another primary node cannot discover each other because the first node and the another primary node are both in a scanning state or a broadcast state. The method may be applied to an internet of vehicles, for example, V2X, LTE-V, and V2V.

This invention relates to a wide common-mode sampling circuit, specifically integrated circuit technology. The circuit comprises: a first switch positioned between the non-inverting input terminal and a non-inverting reference point; a second switch positioned between the inverting input terminal and an inverting reference point; a third switch positioned between the non-inverting reference point and an inverting reference point; a first capacitor positioned between the non-inverting reference point and a non-inverting output terminal; a second capacitor positioned between the non-inverting reference point and an inverting output terminal; a third capacitor positioned between the inverting reference point and an inverting output terminal; a fourth capacitor positioned between the inverting reference point and a non-inverting output terminal; a fourth switch positioned between the non-inverting output terminal and a common-mode voltage input point; a fifth switch positioned between the inverting output terminal and the common-mode voltage input point; an equivalent capacitance of the non-inverting capacitor array positioned between the non-inverting output terminal and a ground connection terminal; and an equivalent capacitance of the inverting capacitor array positioned between the inverting output terminal and a ground connection terminal. This invention provides a wide input common-mode range.

The application relates to a layer-by-layer manufacturing method of a dense permanent magnet, which comprises the following steps: 1) laying a layer of samarium iron nitride powder on the bottom of a lower mold; 2) vibrating the powder layer; 3) applying downward mechanical pressure on the powder by a pressing block to compact the powder; 4) using ultra-short laser pulses to impact the compacted samarium iron nitride powder with a certain coverage rate to solidify the samarium iron nitride powder layer; 5) laying a next layer of samarium iron nitride powder on the surface of the impacted samarium iron nitride material, and re-executing steps 2) to 4) to solidify the samarium iron nitride powder of the current layer by using the ultra-short laser pulse impact; and 6) repeatedly executing steps 2) to 5) to obtain a dense samarium iron nitride block material. The application can realize layer-by-layer manufacturing of a permanent magnet block material, and meanwhile, can avoid the decline of magnetic performance caused by a thermal process.

The disclosed embodiment is about a method and device for dynamic collaborative cluster selection based on multi-agent deep reinforcement learning. The method includes: establishing a system model; decomposing the global state action value Q function; collecting the state, action, reward and next state of the agent as the training data of the network, and storing them in the buffer; training the Q network; using the optimal collaborative cluster selection scheme to serve the user. The disclosed embodiment combines a multi-agent system with a deep reinforcement learning algorithm to solve the combinatorial optimization problem between AP and users in a decellularized large-scale MIMO system. The value function decomposition network is used to improve the environmental non-stationarity problem of the multi-agent system. The non-steady-state environment caused by independent learning of the agent is avoided by value decomposition, the amount of calculation is reduced, and the training process of each agent is more targeted. The best collaborative cluster is selected to serve multiple users in the system at the same time, so that the spectrum efficiency of the system is improved.

The invention provides an anti-interference track insulation on-line tester integrated with a signal generator and a method, and relates to the field of track insulation tests.The anti-interference track insulation on-line tester comprises a handset, a controller arranged in the handset, a signal generation assembly and an insulation resistance tester; the signal generating assembly and the insulation resistance tester are electrically connected with the controller, the controller is used for controlling the signal generating assembly to apply alternating current signals for track insulation testing to the two ends of a tested track, and the insulation resistance tester comprises a signal collecting assembly and a filtering assembly. The signal acquisition assembly is used for acquiring signals of a tested track in the process of applying alternating current signals for track insulation testing to the two ends of the tested track, and the filtering assembly is used for filtering the signals of the tested track acquired by the signal acquisition assembly. And the controller is used for calculating the insulation resistance value of the tested track according to the filtered signal of the tested track. The method has the advantage of improving the accuracy and efficiency of the track insulation online test.

The invention relates to the technical field of machine learning, in particular to a time series data analysis system and method based on deep learning, and the system comprises an input preprocessing module which is used for carrying out the standardization processing and segmentation processing of input time series data; the sliding window recurrent neural network module is used for processing the input time sequence data in each sliding window of eight thousand time steps to generate a short-term time sequence feature vector; the dynamic updating recurrent neural network module is used for processing long-term time sequence data; the meta-learning optimization module is used for dynamically updating initialization parameters of the recurrent neural network module through outer loop optimization; and the output fusion module is used for fusing the feature vectors and generating a final time sequence analysis result. According to the method, a weight updating mechanism which only updates the last quarter layer of the recurrent neural network is innovatively designed, the calculation complexity is remarkably reduced, and the contradiction between the calculation efficiency and the model adaptability in long-sequence time sequence analysis is effectively solved while high precision is kept.

The invention belongs to the technical field of broadband array signal processing, and particularly relates to a broadband signal direction-of-arrival estimation method based on virtual interference suppression. The method comprises the following steps of: constructing a data set: generating array observation covariance matrixes # imgabs0 # with different signal-to-noise ratios and different directions and corresponding virtual interference matrixes # imgabs1 # based on analogue simulation data to perform preprocessing and post-processing on # imgabs2 # and # imgabs3 #, and taking the processed # imgabs4 # and # imgabs5 # as samples and labels of the data set; model training: performing machine learning model training by using the constructed data set; and estimating the direction of arrival: preprocessing an actually measured array output vector covariance matrix # imgabs6 #, inputting the preprocessed array output vector covariance matrix # imgabs6 # into the trained machine learning model, obtaining a virtual interference matrix # imgabs7 # according to the output of the machine learning model, obtaining a rank-1 covariance matrix in a manner of loading # imgabs9 # by # imgabs8 #, and finally estimating a spatial spectrum by combining a subspace orthogonal method. The direction corresponding to the peak position of the spatial spectrum is the direction of arrival.

The invention discloses a successive approximation type analog-to-digital converter based on a dual-threshold comparator. The successive approximation type analog-to-digital converter comprises a switched capacitor DAC array, the dual-threshold comparator and an SAR logic circuit. After an input voltage passes through the switched capacitor DAC array, a differential signal is output to a differential input end of the dual-threshold comparator. The double-threshold comparator divides three comparison intervals by setting two thresholds, judges an interval corresponding to an input differential input signal, obtains an output code and sends the output code to the SAR logic circuit; and the SAR logic circuit controls the switch of the capacitor at the corresponding position in the switched capacitor DAC array to convert, so that the differential input signal jumps towards the approaching direction gradually. According to the successive approximation type analog-to-digital converter, the problem that the ADC performance is reduced due to the fact that a traditional comparator is prone to making mistakes when an input signal is close to 0V is solved, and meanwhile the same conversion precision is achieved with fewer conversion periods, lower power consumption and smaller area.

The present invention relates to a uniformly modified cathode material for a silicon-based lithium ion battery, a preparation method therefor and an application thereof. The structure of cathode material for a silicon-based lithium ion battery is that carbon atoms are uniformly dispersed and distributed in a silicon (II) oxide matrix on an atomic scale. In a focused ion beam-transmission electron microscope (FIB-TEM) test of the cathode material for the silicon-based lithium ion battery, an energy spectrum surface sweep of a particle section shows that the carbon elements, oxygen elements and silicon element inside the particles are uniformly distributed; the average particle diameter D50 of the particles in the cathode material for the silicon-based lithium ion battery is 1 nm to 100 µm, and the specific surface area is 0.5 m2 / g to 40 m2 / g; and the mass of the carbon atoms accounts for 0.1% to 40% of the mass of the silicon (II) oxide matrix. In the present invention, a carbon-containing gas source is introduced during the preparation process of silicon (II) oxide, and the distribution of carbon atoms in the silicon (II) oxide is bulk phase distribution, so that the cathode material has the advantages of carbon bulk phase doping, which improves the electrical conductivity of the material and the cycling stability of lithium ion batteries.

Disclosed (or Provided) are a negative electrode for a secondary battery, including: a current collector; a first negative electrode active material layer provided on the current collector; and a second negative electrode active material layer provided on the first negative electrode active material layer, in which the first and second negative electrode active material layers include a silicon-based active material and natural graphite, the natural graphite has an average particle diameter (D50) of 10 µm or less, and the OI (004 / 110) of the first and second negative electrode active material layers is 8 or less, and a secondary battery including the same.