595 results about "Signal transformation" patented technology

The invention relates to the technical field of data processing, in particular to a metal wire data analysis method and system. The method comprises the following steps: acquiring wire interface data of composite metal; acquiring a wire rod interface characteristic matrix based on the wire rod interface data; constructing a wire rod interface characteristic model according to the wire rod interface characteristic matrix; differential signal conversion processing is carried out on the wire rod interface characteristic model, and wire rod interface change characteristics are extracted based on a differential signal conversion result; constructing a wire rod interface defect initial indication diagram according to the wire rod interface change characteristics; performing multi-scale decomposition processing on the basis of the wire rod interface defect initial indication graph to obtain interface defect feature data; and identifying wire interface defect type features according to the interface defect feature data. According to the method, a closed-loop optimization mechanism of defect characteristics and production process parameters is established, and the interface quality and reliability of the high-performance composite metal wire are remarkably improved.

The invention relates to the field of data signal processing, in particular to a network algorithm cable terminal discharge early warning method, device and system based on dual-channel waveform identification. The method comprises the following steps of: firstly, acquiring a periodic ultrasonic signal and an ultrahigh frequency electromagnetic field signal generated by discharging of a cable terminal, converting the two signals into a corresponding time-frequency diagram by utilizing short-time Fourier transform, and performing normalization processing to serve as input of a subsequent network; a dual-channel high-order feature extraction network formed by cascading a ConvNeXt backbone network and a convolution block attention module is constructed, and is used for extracting time-frequency diagram detail fault features of ultrasonic signals and high-frequency electromagnetic field signals. And finally, by constructing an adaptive feature fusion network, fusing the fault features of the two channels, and obtaining cross-channel fault joint features by using an adaptive weight coefficient vector, thereby outputting a diagnosis result, and realizing accurate evaluation of a defect state of an adjacent cable terminal.

The invention requests to protect a rotating machine noise robust fault diagnosis method based on a mixed domain graph neural network. Comprising the following steps: collecting vibration data, and dividing an original vibration signal into a plurality of sections of subsamples with the length of 1024 by using non-overlapping sampling; converting the constructed subsample signal vibration signal into a frequency domain signal by using fast Fourier transform (FFT); taking the subsample frequency domain signals as nodes, the frequency spectrums as node features and the fault types as node labels, and then constructing the nodes into a graph structure; dividing the generated graph structure data set into a training set and a test set; and performing fault diagnosis on a test set by using the trained model, realizing self-adaptive weight adjustment by using a gating mechanism to fuse mechanical features, outputting a diagnosis result, calculating a performance index and performing result visualization analysis. The result shows that compared with a single-domain graph neural network, the method has higher accuracy and noise immunity, and end-to-end rotating machine fault diagnosis under the strong noise condition can be effectively achieved.

The invention provides a high-quality 3D Mesh reconstruction method based on brain EEG (electroencephalogram) signals. The electroencephalogram signals can be quickly and efficiently converted into a high-fidelity three-dimensional mesh model. Firstly, an EEG encoder extracts time domain and frequency domain features from an EEG signal to enhance the representation capability of the signal. The time-frequency embedding is then input to a two-stage image generation module guided by the EEG, generating a high-quality two-dimensional image. And finally, through a multi-view diffusion model and a sparse view reconstruction technology, the generated two-dimensional image is used as input, and reconstruction of the three-dimensional grid is rapidly completed. According to the scheme, the technical problem of converting the EEG signal into the three-dimensional object is effectively solved, an important breakthrough is made in the fields of electroencephalogram signal processing and three-dimensional reconstruction, and a new view angle and a new tool can be provided for research in the fields of acknowledging science, psychology and neuroscience.

The invention provides an audio-visual voice separation method and system for a scene with a flexible number of speakers, and the method comprises the steps: extracting mixed voice signals of all speakers in audio data, converting the mixed voice signals into audio features for representation; according to the method, a multi-head self-attention branch and a multi-layer perceptron branch with convolution gating are used for coding from a mixed voice signal to obtain dependency relationships of different scales, the audio feature representation is combined to obtain final mixed voice features and block the mixed voice features, each block is separated in parallel and independently, a mask is estimated for each potential speaker, and a multi-level perceptron branch with convolution gating is obtained. The visual features are fused to obtain cross-modal fusion features of the blocks, modeling is carried out on the cross-modal fusion features on the inter-block level, and mask estimation of the blocks is obtained; and reconstructing the separated signal in the time domain according to the estimated mask, judging whether the extracted voice is effective or not, and estimating the number of speakers. The problem that most voice separation methods cannot separate speakers with the number changing dynamically can be solved at the same time.

The invention discloses a self-adaptive and flexibly-expanded centralized protocol conversion device and method suitable for heterogeneous peripherals of an aircraft engineering simulator. Compatible communication between the peripherals and the host is achieved through the control self-adaption module and the communication self-adaption module, and the problem that heterogeneous peripherals are not compatible is solved. The control adaptive module comprises a hardware interface module, a bus identification module, an electrical conversion module, a master control MCU and an interface expansion module, and is responsible for electrical signal conversion and identification of a multi-bus protocol. The communication self-adaption module is composed of a configuration management module, a data receiving module, a protocol identification module, a data preprocessing module, a JSON coding and decoding library module, a data coding and decoding module and a data sending module, and data analysis and transmission are achieved. Multi-bus self-adaptive conversion is carried out by adopting a uniform interface, resource waste caused by fixed interface limitation is avoided, and equipment can be used after being inserted. And the data is analyzed through the JSON decoding library separated from the development platform, so that the system compatibility is improved, and the communication requirements of different devices are met.

The invention discloses a multi-mode sensing electromechanical actuator fault prediction system and a control method thereof, and relates to the field of intelligent maintenance of electromechanical integrated equipment. Comprising the following steps: S1, multi-mode sensor deployment and signal acquisition: cooperatively deploying a vibration sensor, a current sensor and a temperature sensor at a bearing, a motor winding and a driver of an electromechanical actuator; and S2, bionic pulse coding and time synchronization: converting the three-mode signal into a sparse pulse stream with time synchronization through a self-adaptive integral distribution model with dynamic threshold adjustment. Through bionic multi-mode sensing, brain-like edge calculation and dynamic fault-tolerant control, real-time health management of the high-precision electromechanical system is realized, the equipment reliability is remarkably improved, and the operation and maintenance cost is reduced. The method is suitable for industrial application with precise assembly requirements such as automobile welding lines, aerospace machining and metal 3D printing.

The invention belongs to the technical field of intelligent fault diagnosis, and discloses an aero-engine bearing fault diagnosis method based on clustering enhancement domain generalization. Firstly, a time domain vibration signal is converted into frequency domain representation through fast Fourier transform, a two-stage convolutional neural network architecture comprising a domain alignment encoder and a classification encoder is constructed, and cross-working-condition feature alignment under adversarial training is realized by using a domain discriminator; introducing a multi-source domain maximum mean difference statistical alignment strategy and a clustering enhanced triple loss mechanism, and synchronously optimizing intra-class feature compactness and inter-class separability through pseudo label clustering center anchoring and dynamic interval constraint; and gradient inversion adversarial training and multi-modal feature fusion are combined. The method provided by the invention can effectively improve the accuracy and robustness of generalization judgment when the aero-engine bearing fault diagnosis lacks target domain data.

The invention discloses a temperature effect monitoring method and system for a metal roof, particularly relates to the technical field of building structure health monitoring, and is used for solving the problems of decoupling of temperature monitoring data and structural mechanical response, failure of a static early warning threshold and the like in the prior art. Acquiring three-dimensional temperature field data through a distributed temperature sensing network, and extracting a temperature gradient characteristic value from the three-dimensional temperature field data; a piezoelectric film sensor is used for collecting constraint point micro-vibration signals, and dynamic constraint reaction force spectrum characteristic values are extracted through signal transformation and manifold learning; establishing a temperature gradient and mechanical characteristic coupling model to generate a temperature stress evaluation value; dynamically adjusting an early warning threshold range according to the environmental parameters; when the evaluation value exceeds a threshold value, a structure safety early warning signal is output; multi-physical field coupling analysis of temperature field distribution and structural response is achieved, an environment-adaptive dynamic early warning mechanism is constructed, and the accuracy and reliability of metal roof temperature effect monitoring are remarkably improved.

The invention provides an intelligent rotating machine fault diagnosis method, and belongs to the technical field of mechanical fault diagnosis, and the method comprises the steps: firstly, obtaining a vibration signal to be subjected to fault diagnosis under a source domain working condition and a target domain working condition, and carrying out the noise reduction processing; secondly, converting the denoised signal into a two-dimensional time-frequency image through short-time Fourier transform, and respectively making a source domain data set and a target domain data set; thirdly, constructing an attention enhancement network (SE-CNN-BiLSTM-MSMHA), and performing pre-training on the model based on the source domain data set to obtain a source domain working condition model; then, freezing part of parameters of the source domain working condition model, and finely adjusting the parameters of the source domain working condition model by using the target domain data to obtain a target domain working condition model; and finally, inputting target domain data to realize fault diagnosis of target domain working conditions. The method can provide a scientific basis for improving the cross-working-condition fault diagnosis accuracy under the strong noise background.

The invention relates to the technical field of data collection control, in particular to a connection transmission control method and system for PCB transmission, and the method comprises the steps: carrying out the data collection of a PCB card when the PCB card is detected to reach a preset scanning region, and obtaining the identification information of the PCB card; the identification information is transmitted to the MES subsystem to obtain a first instruction, and the first instruction is an instruction which is sent to the PLC subsystem after the MES subsystem receives the identification information and carries out processing based on the identification information; performing signal conversion on the first instruction to obtain a target instruction; and controlling the PCB card to carry out transmission according to the target instruction. According to the method, through cooperative work of the PLC subsystem and the MES subsystem, automatic data acquisition and transmission control of the PCB card are realized, manual intervention is reduced, continuity and real-time performance of a production process are ensured, and the automation level of a production line is improved.

An SAR ADC includes: at least one sub-ADC, configured to convert a corresponding input signal to a corresponding SAR code; and a tuning control unit, configured to adjust a full-scale voltage (VFS) of each of the sub-ADC to a corresponding predetermined target level in a VFS tuning mode. The tuning control unit generates a tuning code to control an adjusting capacitor array coupled to the sub-ADC for tuning the VFS. The tuning control unit controls the sub-ADC to convert plural reference voltages in the VFS tuning mode and extrapolating the conversion result to determine a corresponding calibrating VFS. The tuning control unit determines whether the calibrating VFS meeting the target VFS and loops the adjusting process in a linear search method or in a SAR method.

The invention provides a multi-model fusion asynchronous motor fault diagnosis method. The method comprises the following steps: converting a collected asynchronous motor time domain signal into a frequency domain signal by adopting fast Fourier transform; extracting a plurality of characteristic values related to the motor fault from the frequency domain signal; the current working mode of the motor is determined by calculating the power frequency and the load rate of motor operation; using all normal samples to train a main model as a reference model; and for each working mode, training the sub-model by using the corresponding sample data. According to the working mode of the to-be-predicted sample, the corresponding sub-model is preferentially selected for calculation, and if the matched sub-model cannot be found, the main model is used for calculation. And calculating the fault probability of each component of the motor according to the abnormal probability of the characteristic value predicted and calculated by the model. The fault diagnosis method can effectively meet the fault diagnosis requirements in different working modes, is particularly suitable for fault diagnosis of the variable frequency motor under complex working conditions, and improves the accuracy and adaptability of diagnosis.

The invention provides a bus signal processing method and device of a server, equipment and a storage medium, and relates to the technical field of servers. According to the specific implementation scheme, multiple paths of first signals, based on a first protocol, of a CPU are transmitted to a CPLD of a mainboard; converting the first signal into a second signal based on a second protocol by using a CPLD (Complex Programmable Logic Device) of the mainboard; transmitting the second signal to a CPLD (Complex Programmable Logic Device) of the SCM through a communication interface; and converting the second signal into a third signal based on the first protocol by using the CPLD of the SCM, and transmitting the third signal to the BMC chip. According to the scheme, when the number of certain standard signal interfaces cannot meet the design requirement of the server, another signal interface can be reused for signal transmission, so that the cost of the server is reduced, and the convenience of assembly, operation and maintenance is improved.

The invention relates to the technical field of multi-core processors, in particular to a protocol conversion device, method and equipment based on multi-type transaction processing, a medium and a product. A conversion component receives a write channel transaction which is transmitted by a processor and conforms to a first bus protocol; receiving a monitoring request which is transmitted by the main node and accords with a second bus protocol; an address to listen to the request access is transmitted to the transaction management component. When there is a write channel transaction having the same address as the listening request, the transaction management component transmits a conflict signal to the translation component. The conversion component transmits the conflict signal and the write channel transaction to the generation component. The generation component generates a monitoring response message conforming to a second bus protocol based on the write channel transaction and the monitoring request, and transmits the monitoring response message to the main node in a monitoring response mode; and after transmission is completed, field zero clearing operation is performed on the monitoring response message, and the obtained invalid message is transmitted to the main node, so that conflict processing between different protocol devices is realized.

Disclosed in the present invention are a power grid impedance measurement method and apparatus. The method comprises: generating a characteristic harmonic current instruction on the basis of a harmonic-signal generation algorithm, and inputting the characteristic harmonic current instruction into a direct-current voltage feedback control loop; receiving the characteristic harmonic current instruction to generate a control signal, and performing current tracking control to obtain a harmonic current to be injected; converting an information electronic signal of a main circuit into a driving signal in a device control loop, and selecting a preset harmonic injection mode in real time on the basis of a harmonic injection target frequency; when active harmonic injection is selected, controlling the turning-on and turning-off of a switch tube, and injecting said harmonic current into a power grid; when passive harmonic injection is selected, controlling the turning-on and turning-off of an IGBT, controlling a time and a current flow direction of when a capacitor is connected to the power grid for operation, and injecting said harmonic current into the power grid; and selecting voltage and current signals to perform signal decomposition, processing and analysis, so as to obtain a power grid impedance. By means of the present embodiment, a power grid impedance is effectively measured, the disturbance to a power grid during impedance measurement is reduced, and the measurement precision is improved.

The invention discloses a non-contact electrocardiosignal monitoring reconstruction method and system based on time-frequency domain combination, and relates to the technical field of radar signal processing, and the method comprises the following steps: obtaining a chest wall vibration signal S of a to-be-detected person by adopting a frequency-modulated continuous wave radar; separating a heart mechanical motion signal SH from the chest wall vibration signal S; and converting the heart mechanical motion signal SH into an electrocardiosignal ECG through a pre-established ResNet architecture neural network model. Time domain loss and frequency domain loss are used for training a ResNet architecture neural network model, and the error between the calculated heart rate and the real heart rate is quantified through the mean absolute error and the root-mean-square error. According to the method, the neural network capable of being used for ECG signal conversion is designed based on the ResNet architecture, after the neural network is subjected to data training, more detailed signal features can be learned, the fitting accuracy is improved, then the ECG waveform can be accurately predicted, and non-contact electrocardiosignal monitoring and reconstruction are achieved.

A secure system for sending and receiving remote signals that control semiautonomous critical systems and infrastructure. The secure system includes a plurality of independent operator stations to provide command signals to the remote critical systems and infrastructure by means of wired and / or wireless communications. Decoupling of the secure system from raw signals is performed by a data broker that serves to check for time-dependent and signal-dependent corroboration between the raw signals and then provide limited-scope instructions to the secure system controller. Further security is afforded to the secure system through encryption of the incoming command signals, convoluting the encrypted command signals within data structures, and padding with erroneous information with the implementation of a synthesized command signal that renders successfully decrypted and deconvoluted command signals neither readable by humans or machines. The final method of data security is appropriated through the synthesis of transmitter / receiver unique “languages” that serve to translate decrypted and unconvoluted signals into their final human / machine readable instructions.

The invention provides a high-precision trigger signal and ADC sampling data alignment method and device, and the method comprises the steps: inputting a serial trigger signal to a high-speed transceiver, converting the serial trigger signal into parallel trigger data through the high-speed transceiver based on a line rate and a user data bit width, and outputting user data and a corresponding user clock frequency; performing parallel edge detection on the user data, and identifying an edge detection result; and synchronizing parallel trigger data comprising the user data and the edge detection result in the user clock domain to an ADC clock domain, and synchronizing the edge detection result in the parallel trigger data based on a matching result of the parallel trigger data and the sampling rate of the ADC and the edge detection result in the synchronized parallel trigger data. And determining a target trigger signal matched with the sampling data point time of the ADC from the user data, and aligning the target trigger signal with the ADC sampling data, thereby realizing accurate alignment of the external trigger signal and the high-speed ADC sampling data.

The invention belongs to the technical field of feeder terminal communication, and particularly relates to a digital feeder terminal digital quantity transmission protocol self-adaptive analysis system and a digital feeder terminal digital quantity transmission protocol self-adaptive analysis method. The differential signal conversion module is used for converting an input signal into a single-ended level signal; the transmission rate detection module is used for detecting single-end level signal overturning and recording the level holding time between two adjacent overturning; the data transmission rate is judged according to the level holding time, and the serial data and the judged transmission rate are transmitted to the decoding module; the decoding module is used for decoding the serial data based on the transmission rate; retrieving a start character from the decoded data; according to the position of the start character, analyzing the data set length and the data set field in the decoded data, and verifying the correctness of the whole message content; if verification is correct, the type of the corresponding data frame format is judged according to the data set length and the data set field, effective data content extraction is carried out, and message content analysis is completed.

To provide a dimension measurement device which can efficiently perform dimension measurement of a work material in one direction with high accuracy, and to provide a cutting tool system and a dimension measurement method.SOLUTION: A dimension measurement device includes a sensor unit and a measurement device body. The sensor unit includes: a first distance sensor using an eddy-current sensor; and a signal conversion unit which outputs output signals of the distance sensor as detection values at a preset time interval. The measurement device body includes: a detection control unit which detects a distance to a work material with the sensor unit while rotating the work material; an acquisition unit which acquires the multiple detection values output from the sensor unit at the time interval in a preset specified time period; a calculation unit which calculates an average value of the detection values acquired by the acquisition unit; and a result output unit which outputs a measurement result of the work material based on the average value of the detection values calculated by the calculation unit.SELECTED DRAWING: Figure 1

The invention relates to the technical field of microelectronics, semiconductors and communication, and discloses a radio frequency switch chip, and the circuit structure of the radio frequency switch chip comprises a transformer type differential inductor T-L1, a transformer type differential inductor T-L2, a capacitor C1, an NMOS transistor M1, an NMOS transistor M2, a gate resistor RG1, and a gate resistor RG2. The radio frequency switch chip provided by the invention adopts a differential structure, so that the parasitic inductance effect of a grounding key and a wire during packaging and testing can be eliminated, and the insertion loss and the isolation performance are improved; compared with a single-end port, the lower impedance of the differential port is more beneficial to improving the power capacity of the switch; the differential structure allows the transformer type differential inductor to use differential signal characteristics, the equivalent quality factor of the inductor is improved, and the circuit performance is improved; the differential structure can improve the linearity of the circuit, especially the second-order intermodulation effect; the differential structure can be compatible with a modern silicon-based CMOS radio frequency system, and an additional balun is not needed for signal conversion.

The invention discloses a multi-sensor cooperative partial discharge waveform identification method and system, and the method comprises the steps: extracting an envelope line of an oscillation waveform through Hilbert transformation for a collected partial discharge signal, carrying out the Logistic function fitting and exponential decay function fitting of each sensor signal, and carrying out the recognition of the partial discharge waveform through the Logistic function fitting and exponential decay function fitting. Taking the characteristic parameters extracted by each sensor as node characteristics in a graph structure, establishing an edge connection relation in a graph according to the maximum value starting time difference and the similarity of signals between the sensors, constructing a graph neural network model, performing training and reasoning by utilizing the graph structure, and finally outputting a recognition result of whether a partial discharge event occurs or not; the system comprises a signal conversion module, a feature extraction module, a data fusion module and a partial discharge diagnosis module. According to the invention, the accuracy and adaptability of partial discharge signal identification are improved, and the on-line monitoring requirement of the transformer in a complex electromagnetic environment is met.

The invention relates to the technical field of optical current transformer detection, and provides an optical current transformer real-time calibration and health diagnosis method and system, and the method comprises the steps: carrying out the harmonic analysis of an initial digital signal sequence obtained through the conversion of a continuous modulation optical signal of a target optical current transformer, and obtaining harmonic component data; physical state evaluation is carried out according to the higher harmonic component data and preset health state baseline data to generate a physical state evaluation result, nonlinear disturbance compensation is carried out according to an error control signal generated based on the higher harmonic component data, and after a linear working point is reached, nonlinear disturbance compensation is carried out; and calculating an electrical measurement value according to the output target fundamental component data, and performing comprehensive analysis according to the physical state evaluation result and the electrical measurement value to generate a diagnosis report. According to the invention, through a measurement calibration and state diagnosis mechanism based on harmonic analysis, while the electrical performance measurement precision is effectively improved, reliable quantitative diagnosis and accurate traceability of different physical faults in the equipment are realized.

The invention discloses a power electronic device fault early warning method based on multi-physics field coupling, and the method comprises the steps: (1) collecting aging data of a power electronic device under the condition of electric field and thermal field coupling, and dividing the aging data into a source domain data set and a target domain data set; (2) establishing a power electronic device aging parameter screening method, selecting a parameter which can best represent an aging rule as an aging precursor, and converting discrete voltage and current signals into time sequence signals which can be accepted by a neural network; (3) compressing the time sequence signal set into sliding window graph nodes, calculating a distance relationship between the graph nodes, and obtaining a graph matrix containing data structure information; (4) building a space-time fusion fault predictor, wherein the space-time fusion fault predictor comprises a feature extractor, a time sequence predictor and a domain discriminator; and (5) a weak supervision adversarial learning strategy is adopted, a small amount of target domain label data participates in the model training process, and the source domain prediction loss, the target domain prediction loss and the discrimination loss are combined for training.

The invention relates to the field of rotating machine fault diagnosis, in particular to a rotating machine data enhancement method based on an SCF-CVAE-GAN network, and the method comprises the steps: obtaining rotating machine vibration signals in different fault modes; the rotating machinery vibration signals are converted into a two-dimensional time-frequency image, and the SCF-CVAE-GAN network is trained by adopting the two-dimensional time-frequency image; generating a two-dimensional time-frequency image sample by adopting the trained SCF-CVAE-GAN, calculating the structural similarity and the distance score of the generated sample, and evaluating the quality of the generated sample; mixing the generated sample meeting the quality requirement and the two-dimensional time-frequency image to obtain an enhanced data set; according to the method, a double-self-correction network is provided, a frequency domain regularization strategy is integrated, global information and local textures of data are deeply mined, and the diversity and fidelity of generated samples are ensured by constraining distribution of the samples in a frequency domain.

The invention discloses a neural stress injury diagnosis method based on electroencephalogram data channel selection and a deep learning neural network, and relates to the technical field of neural stress injury diagnosis, comprising the following steps: S1, a data preprocessing module; s2, an adaptive channel selection module; and S3, a feature classification module and a classification module. According to the neural stress injury diagnosis method based on electroencephalogram data channel selection and the deep learning neural network, EEG signals are converted into time-frequency diagrams through short-time Fourier transform (STFT), frequency characteristics changing along with time are effectively captured, signal characterization is enriched, and the accuracy of stress injury diagnosis is improved. By introducing an attention mechanism, key EEG channels related to stress injury are automatically identified and focused, personalized channel selection for different individuals is realized, and the diagnosis precision is improved.

The invention discloses an intelligent start-stop control method and system for a magnetic suspension refrigeration compressor, and the method comprises the steps: obtaining a position perception enhancement source through collecting and analyzing the current response data of a magnetic bearing coil, and extracting the current response change characteristics; rotor position estimation is carried out based on current response change characteristics, a positioning precision driving source is extracted from estimation deviation, and a non-impact starting excitation current instruction is generated; a multi-sensor fusion data network is established, configuration is reconstructed through an abnormal response activation signal, and a degradation operation fault-tolerant mode is formed; converting a response preparation gain according to the shutdown instruction signal, performing shutdown state evaluation in combination with signal reconstruction configuration, and generating an emergency shutdown trigger condition; and the power supply state of a bus capacitor is detected, gradient control potential is released, the active damping control opportunity is determined, a damping force control instruction is generated, finally intelligent start-stop control over the magnetic suspension refrigeration compressor is achieved, and safe and reliable operation of the system under various working conditions is ensured.

A data storage system for managing an operation of a solid-state device (SSD) includes the SSD, an adaptor coupled to the SSD via a first bus protocol and including a non-volatile memory storing a power configuration table. The protocol converter is configured to: couple to the adaptor using the first bus protocol, convert first signals of the first bus protocol to second signals for a second bus protocol, and couple to a host using the second bus protocol. The power configuration table includes power profiles for the SSD each corresponding to an interface type for the second bus protocol. The protocol converter is further configured to: determine an interface type of the second bus protocol, read the power configuration table from the non-volatile memory to determine a power profile corresponding to the determined interface type, and cause the SSD to operate using the determined power profile.

The invention discloses a water level early-warning device for a water conservancy project. The water level early-warning device comprises a floater, a limiting mechanism, a mounting frame, a signal conversion and processing device and display equipment, wherein a pressure sensor is arranged at the bottom of the floater; the floater is sleeved with a flow blocking pipe, the flow blocking pipe is rotationally connected with the mounting frame, a plurality of leaf plates are evenly arranged on the outer side of the lower portion of the flow blocking pipe along the rotating shaft, the leaf plates are vertically arranged, and the floater is located in the flow blocking pipe; the processor is electrically connected with an alarm and wirelessly connected with a mobile terminal. The floater is limited through the limiting mechanism, so that the floater can only move up and down to drive the sliding piece to move up and down, water level information is directly obtained through the signal conversion and processing device and displayed on the display device, and the water level sensor is simple and reasonable in structure, low in manufacturing cost, compact in structure, small in resistance in the working process, smooth and sensitive in action and high in measurement result accuracy; the measurement result is directly displayed; whether the floater works normally or not is judged through pressure information fed back by the pressure sensor; and a flow blocking pipe is arranged, so that the device is also suitable for large water waves.