5820 results about "Data signal" patented technology

A BIM-based engineering information management system of the present invention relates to the field of constructional engineering informatization management, and comprises a data processing module, a model construction module, a block chain evidence storage module, a verification module, a data fusion module and an authority management module. The data processing module receives original engineering data and generates a structured data signal. And the model construction module generates a BIM model according to the structured data signal. And the block chain evidence storage module encrypts and stores the metadata signal, generates an evidence storage completion signal and feeds back the evidence storage completion signal to the model construction module. And the verification module analyzes the dynamic operation log signal, and generates a risk early warning signal and a repair suggestion signal when abnormality is detected. And the data fusion module generates a fusion data signal, transmits the fusion data signal to the model construction module and triggers BIM model updating. And the authority management module adjusts the user authority according to the risk early warning signal. According to the invention, the problems of data islands, safety risks, low cooperation efficiency and insufficient intelligence in engineering information management can be solved.

The invention provides an earth surface anomaly monitoring data fusion system based on vibration signal processing, and relates to the technical field of earthquake precursor monitoring, and the system comprises a data collection module which is used for carrying out the real-time monitoring of the earth surface through a distributed multi-parameter sensor network, and obtaining the time sequence monitoring data related to the earthquake precursor; the signal processing module is used for processing the time sequence monitoring data by adopting a two-parameter joint optimization algorithm and outputting a separation signal component containing precursor information; the data fusion and state inversion module is used for carrying out fusion processing on the separated signal components by adopting a dual-time scale analysis framework and outputting a multi-scale precursor state estimation result and an uncertainty quantitative index; and the monitoring and early warning module is used for outputting earthquake precursor grading early warning information through a three-layer anomaly recognition architecture and probabilistic risk assessment. According to the invention, high-precision separation and identification of earthquake precursor related signals can be realized, and the false alarm phenomenon is reduced through an intelligent early warning mechanism.

The invention discloses a geotechnical engineering intelligent investigation system and method based on big data. The system comprises a field sensing module, a multi-source fusion module, an intelligent analysis module, a modeling early warning module and a report generation module. The field sensing module collects drilling parameters, in-situ test data and geological images in real time; the multi-source fusion module receives an original investigation signal and accesses a historical engineering investigation database and a regional geology database; the intelligent analysis module receives the fused data signal, constructs a knowledge graph containing a rock-soil entity relationship, predicts rock-soil parameters and stratum distribution of an unexplored area based on a machine learning model, and generates a parameter prediction signal; the modeling early warning module receives the parameter prediction signal; and the report generation module receives the parameter prediction signal and the risk early warning signal. The geotechnical engineering intelligent investigation system and method based on big data can solve the problems of discrete investigation data, strong experience dependence and low intelligent degree.

A voltage mode driver circuit has a first driver slice has a pullup structure that includes a first transistor and a second transistor and a pulldown structure that includes a third transistor and a fourth transistor. The first transistor has a gate that receives a first version of a first data signal. The second transistor has a gate that receives a first version of a second data signal. The third transistor has a gate that receives a second, complementary version of the first data signal. The fourth transistor has a gate that receives a second, complementary version of the second data signal. The first data signal encodes data in a first mode and enables the pullup and pulldown structures in a second mode of operation. The second data signal encodes data in the second mode of operation and enables the pullup and pulldown structures in the first mode of operation.

The main object of the invention is an antenna module for an acoustic point defence system (SAD) and an acoustic system comprising said module. The antenna module comprises at least one array of electroacoustic transmitting transducers (TX) and at least one array of electroacoustic receiving transducers (RX). The arrays of transmitting transducers and the arrays receiving transducers have the characteristic of being housed in a watertight container adapted to be submerged in water when the module is in operation, for example by means of a hoist or winch device (SMMR - Launching and Recovery System). The container has watertight accesses, for example via watertight connectors, for at least one cable for a power supply signal, connected or connectable with a power supply unit external to the container, and for at least one cable for a data signal, connected or connectable to a remote control unit adapted to send commands and receive target detection states and / or signals. The antenna module comprises a local control and calculation unit housed inside the container, which local control and calculation unit is equipped with an input for the power supply signal and a port for the data signal, and is interfaced with the arrays of transducers for controlling the signal transmitted by the arrays of transmitting transducers and processing the signals received by the arrays of receiving transducers on the basis of commands received by the data port.

The invention discloses a tangential eddy current-based pipeline welding seam array type detection device and method. The device comprises a detection probe module, a signal excitation and acquisition module and a signal processing module, the detection probe module comprises a flexible probe unit, the flexible probe unit comprises a plurality of tangential eddy current probes, each tangential eddy current probe comprises an excitation coil and two detection coils, and the excitation coils and the detection coils are coaxially and orthogonally distributed; the axis of the tangential eddy current probe and the surface of the weld form an included angle of 0-20 degrees, the excitation magnetic field is tangentially distributed along the weld, and the eddy current field is perpendicular to the weld and penetrates through the inside. The method comprises the following steps: A, detecting by using the detection probe module; b, a signal excitation and acquisition module acquires and records original data in real time; and C, the signal processing module processes, analyzes, identifies and positions defects according to the original data. Through tangential eddy current excitation and multi-channel signal fusion, the method has the advantages that interference of weld reinforcement on detection signals is eliminated, and the defect recognition capability is improved.

Methods and systems are disclosed for collecting EMG speech signals using a speech signal detection device. The methods and systems collect a combination of signals comprising electromyograph (EMG) data signals and one or more non-EMG data signals. The methods and systems process the combination of signals by a machine learning (ML) model to detect inner speech of the user, the ML model trained to establish a relationship between training signals comprising training EMG data signals and training non-EMG data signals and ground-truth inner speech data and, in response, performing one or more operations associated with the speech signal detection device.

The invention discloses a display panel and a driving method thereof, the display panel comprises a plurality of first switches and a plurality of second switches, N columns of pixels are arranged in a first display area, and M columns of pixels are arranged in a second display area; the first scanning line is connected with N columns of pixels in the first display area and M columns of pixels in the second display area, the second scanning line is connected with the N columns of pixels in the first display area, and each first switch is connected with two data lines corresponding to sub-pixels with the same color in the first display area and the second display area; each second switch is connected with a data driving chip and a data line corresponding to a pixel in the first display area; wherein N and M are natural numbers greater than or equal to 2. The two data lines corresponding to the sub-pixels with the same color in the first display area and the second display area respectively receive the same data signal or different data signals to realize display in different display modes by switching on or switching off the first switch and the second switch, so that insufficient charging under a high refresh rate is improved.

The invention discloses a sound acquisition and processing system based on cooperation of multiple microphone arrays. The system comprises a sound acquisition module, a multi-channel signal preprocessing module, a sound signal feature extraction module, an abnormal sound recognition module, a sound source positioning module and an alarm module. A multi-channel mixed data signal is collected through a circularly-arranged multi-microphone array formed by a plurality of microphones, after echo cancellation, wave beam domain noise reduction and multi-sound-source separation, a single-sound-source feature vector is extracted, according to the single-sound-source feature vector, abnormal sound including explosion, screaming or glass breakage is recognized through a BiLSTM and an attention mechanism model, and the abnormal sound is recognized through an attention mechanism model. And the GCC-PHAT and MDS-MUSIC algorithms are combined to position abnormal sound production, and alarm information is generated. According to the invention, accurate identification, positioning and alarm of the abnormal sound can be realized, and the real-time performance, the accuracy and the multi-target processing capability of abnormal sound monitoring in a complex environment can be obviously improved.

The invention discloses a multi-mode optical signal transmission method and device, electronic equipment and a storage medium, and relates to the technical field of computers.The transmitted data stream is divided into a control signal and a data signal, the control signal is a visible light wave band, the data signal is an infrared wave band, and the control signal and the data signal are combined to a single multi-core optical fiber through a combiner; the optical network controller allocates subcarrier resources as required according to the change of network flow in a spectrum dynamic allocation mode to perform data transmission, so that the subcarrier resource allocation adapted to the change of network flow improves the dynamic adaptability, and the allocation of the subcarrier resources as required improves the equipment cooperation efficiency, and in the data transmission process, the data transmission efficiency is improved. Clock synchronization and signal encryption are realized through a quantum key distribution mode, and the security is improved.

The invention discloses a sleep monitoring method and system based on a non-contact video data sequence. The method comprises the steps that S1, video data streams of human body sleep are collected through a camera system; utilizing a YOLOv11 network to obtain face video data and thoracoabdominal video data; s2, the RPPG signal extraction model extracts and obtains an RPPG signal by using the face video data; s3, the breathing signal extraction model extracts thoracic and abdominal micro-motion change characteristics in the thoracic and abdominal video data by using an optical flow method, and noise filtering processing is carried out to obtain thoracic and abdominal motion signals as breathing signals; s4, the oxyhemoglobin saturation extraction model detects and outputs an oxyhemoglobin saturation signal by using the RPPG signal; and S5, performing multi-modal fusion analysis on the multi-physiological index fusion recognition model according to time slice T1 division to obtain a long-time-sequence sleep stage staging result. According to the invention, the physiological index signals are extracted and recognized by adopting the non-contact video data sequence, and the sleep stage staging result with a long time sequence is obtained, so that high-precision sleep monitoring and sleep stage recognition are realized.

The grouting drilling orifice reinforcement construction method based on intelligent control comprises the five steps that S1, drilling machine parameter signals are collected through operation of a drilling machine, and meanwhile geological data signals are collected through in-hole detection equipment; s2, the drilling machine parameter signals and the geological data signals are input into an intelligent decision-making center for fusion processing, and geological sensing signals are generated; s3, based on the geological sensing signal, calling a preset decision-making model through the intelligent decision-making center for calculation, and generating a grouting control instruction signal; s4, grouting operation is executed according to the grouting control instruction signal, and meanwhile a grouting process monitoring signal is collected and fed back to the intelligent decision center in real time; and S5, after grouting is completed, hole opening stable monitoring signals are continuously collected, and when the effect evaluation signals do not reach the standard, reinforcing grouting signals are automatically triggered, and grouting operation is executed again. According to the intelligent die adjusting method for the die-casting machine, the problems that drill holes are prone to collapse, reinforcement depends on experience, and quality is difficult to control can be solved.

The invention discloses a driving motor fault diagnosis method and device based on an artificial intelligence technology, and relates to the technical field of fault diagnosis, and the method comprises the steps: arranging a multi-modal sensor at a preset part of a driving motor, and obtaining a multi-dimensional data signal collected by the multi-modal sensor; performing real-time filtering, amplification and noise reduction processing on the multi-dimensional data signal, and performing feature extraction on the multi-dimensional data signal by using a feature extraction algorithm to obtain a plurality of key feature parameters; based on a preset dynamic weighted fusion algorithm, fusing the plurality of key feature parameters to obtain fused key feature parameters; and establishing a hybrid deep network architecture consisting of a long-short term memory network and a convolutional neural network, and inputting the fusion key feature parameters into the pre-trained hybrid deep network architecture to obtain corresponding fault information of the driving motor. The problem of low fault diagnosis accuracy of the driving motor in the prior art is solved.

The invention discloses an agricultural environment monitoring method and system based on digital agriculture. The method comprises the steps that S1, original environment data signals are collected through a heterogeneous sensor network deployed in a farmland area; s2, performing space-time fusion processing on the original environment data signal to generate a standardized environment state signal; s3, inputting the standardized environment state signal into a pre-trained multi-mode diagnosis model to generate an environment-crop coupling diagnosis signal; s4, according to the environment-crop coupling diagnosis signal, an agricultural operation decision signal is generated, and the agricultural operation decision signal comprises quantitative parameters of the irrigation amount, the fertilization formula or the disaster prevention and control instruction; and S5, converting the agricultural operation decision signal into an equipment control instruction signal, and driving an execution mechanism to complete precise agricultural operation. According to the agricultural environment monitoring method and system based on digital agriculture, the problems of data splitting, extensive decision making and execution lagging in agricultural environment monitoring can be solved.

The invention relates to cable manufacturing process optimization, and discloses a cable conductor process parameter optimization method and device. The method comprises a multi-source data acquisition step, a parameter decision step and a process control step. According to the multi-source data acquisition, a multi-source data signal is formed through data fusion; in the parameter decision-making stage, performance prediction, defect risk signals, optimal process parameters and safety process thresholds are output through sub-steps of prediction, optimization and regulation; and in the process control stage, dynamic parameter adjustment, equipment cooperative control and risk comprehensive early warning are realized through sub-steps of adaptive execution, equipment group control and interactive early warning. According to the method, through closed-loop control of multi-source data driving and intelligent decision making, precise optimization of cable conductor process parameters is achieved, the adaptability and stability of the production process are enhanced, the defect occurrence rate is effectively reduced, and the product quality consistency is improved.

The invention discloses a refrigerator defrosting control method based on BP (Back Propagation) neural network and fuzzy control coupling, which is characterized by comprising the following steps: realizing deep perception of dynamic characteristics of a refrigerating system through data-driven modeling: learning actual data signals of a compressor and an evaporator by the BP neural network, and indirectly estimating recessive parameters such as refrigerant charge and frost layer form distribution; and the fuzzy controller dynamically adjusts a defrosting strategy according to a neural network prediction result, and collaborative optimization of refrigeration cycle parameters and the frosting process is achieved. Environment parameters are learned online through a neural network, and the fuzzy rule base is optimized in real time; the neural network directly maps bottom layer physical characteristics such as a compressor operation mode and evaporator structure parameters, and outputs a dynamic compensation signal; and the model is continuously iterated by accumulating historical data to adapt to regional / seasonal differences.

A system may be configured to receive and process various signals to generate a natural language description of a user's environment, called situational context data. The signals may include sensor data, device status, user activity, user input, and / or inferences made using such data. The situational context data may express a user-centric description of the user's environment; for example: “User is taking a walk in the park on a sunny afternoon” or “activity: driving location: highway”, etc. The system may send the situational context data to various system components that may, for example, process speech, select applications / skills for handling user inputs, and / or that implement those applications / skills. The applications / skills may use the situational context data to provide recommendations, generate responses, and / or perform actions that are more relevant to the user's current environment.

The invention discloses a motorcycle instrument fuel quantity display calibration method based on multi-sensor fusion. The method comprises the following steps: acquiring a data signal of a sensor, fusing and preprocessing data, calculating a fuel consumption rate by adopting a dynamic self-adaptive fuel consumption model, predicting the remaining fuel quantity, and dynamically adjusting the remaining mileage; and self-adaptive calibration: based on a dynamic self-adaptive fuel consumption model, displaying the remaining fuel quantity, the instantaneous fuel consumption, the average fuel consumption and the estimated endurance mileage by an instrument, and displaying a historical fuel consumption trend in a thermodynamic diagram form. The method has the advantages that multi-sensor data and dynamic environment compensation are fused, the liquid level measurement error is smaller than + / -1%, the endurance prediction error is smaller than + / -5%, the overall oil mass calculation precision is improved to 98%, the DAFCM model supports millisecond-level response, the method dynamically adapts to complex driving scenes, resource occupation is low, the method is suitable for a low-end vehicle-mounted controller, and the method is suitable for large-scale popularization and application. And reliable data support is provided for intelligent travel.

The invention discloses a recirculating ball type electric power steering system fault self-checking method and system and a readable medium, and relates to the technical field of vehicles, and the method comprises the steps: storing a signal transmission path and each function node, determining the type of an influence factor which influences the instruction execution accuracy of the function node, and determining the instruction execution accuracy of the function node; analyzing and acquiring the relationship between the fluctuation amplitude of the data signal at each function node and the numerical value of the influence factor, storing the relationship as a correction model, and storing a first relationship model for reflecting the data association relationship among the function nodes on the same signal transmission path; acquiring real-time data and source data of various impact factors, and generating theoretical response data according to the first relation model, the real-time impact factor data and the correction model; the actual response data is collected, the deviation amplitude between the actual response data and the theoretical response data is calculated, whether the function node breaks down or has a potential fault is judged according to the deviation amplitude, early warning is output, and the fault detection accuracy and the potential fault pre-judgment capability are greatly improved through the technical scheme.

The invention discloses a precise and synchronous collaborative data signal analysis method based on dual-channel optical fiber sensing, and relates to the field of collaborative data signal analysis, polarization compensation and nonlinear noise decoupling analysis are carried out on time domain optical signals collected by independent A and B sensing channels to obtain polarization dimension-increasing coherent collaborative signals, and the collaborative data signal analysis is carried out on the time domain optical signals. Performing quaternion mapping and transformation compensation analysis to obtain a pure polarization state of strain immunization, performing fractional differential, HOSVD decomposition and time varying Jacobian matrix propagation disturbance based on the pure polarization state of strain immunization to obtain a non-equilibrium entanglement feature vector, combining quantum Bayesian update and a KLD weight model, generating a particle set through state sampling, and obtaining a non-equilibrium entanglement feature vector. And performing directional transfer entropy calculation on the particle set, constructing a weighted Dirichlet clustering model and directional causal energy flow analysis, comparing dual-channel energy values to obtain a channel fault judgment result, and performing channel fault judgment timely and accurately.

Systems and methods for predicting problem behavior in individuals with developmental and behavior disabilities. A plurality of sensors are configured to collect multimodal data signals of a subject individual including a wearable upper body motion sensing device with a plurality of inertial measurement units (IMUs). An electronic controller is configured to receive output signals from each of IMUs and to model an upper body position of the subject individual based on the output signals from the IMUs. A trained machine-learning model is then applied by providing an input data set that includes multimodal signal data (e.g., including signal data from at least one IMU) and / or features extracted from the multimodal signal data. The machine-learning model is trained to produce as output an indication of whether a precursor to the problem behavior is detected and, in response to detecting the precursor, a notification (or alarm) is generated.

The invention relates to the technical field of data signal processing, in particular to a distribution box electric spark monitoring method which has the advantages of not depending on optical imaging conditions, adapting to complex environments and being capable of effectively distinguishing difference between mechanical vibration and discharge signals. According to the invention, non-intrusive monitoring of electric sparks in the distribution box can be realized, and the limitation of a traditional visual method in a closed space is overcome. And the fusion of the multi-dimensional acoustic features improves the recognition accuracy and reduces the false alarm rate. The introduction of environmental parameters enables the system to adapt to different working conditions, and improves the robustness of monitoring. The method does not need to reform the structure of the distribution box, can be widely applied to state monitoring of various low-voltage distribution equipment, and effectively prevents equipment faults and safety accidents caused by electric sparks.

The invention discloses a variable refresh rate control circuit and method, a display panel and a display device, and relates to the technical field of display, the circuit comprises pixel units arranged in an array and a corresponding refresh control module, and the refresh control module comprises a mode judgment unit, a refresh enable unit and a data write-in unit. The mode determination unit determines a pixel refresh mode through a row strobe signal of a scan line and a data signal of a source line, and outputs a first / second potential of a first / second potential line. The refresh enabling unit turns off the data write-in unit based on a first potential output by the mode judgment unit in a static refresh mode so as to maintain pixel data of the corresponding pixel unit, and turns on the data write-in unit based on a second potential output by the mode judgment unit in a dynamic refresh mode so as to write a data signal into the corresponding pixel unit. Pixel-level variable refresh rate adjustment is achieved, high-refresh-rate and low-refresh-rate display can be supported at the same time, and power consumption is effectively reduced while the display effect is guaranteed.

The invention discloses a reference-clock-free clock and data recovery circuit, a use method and an integrated circuit. The circuit comprises a first circuit and a second circuit, the first circuit comprises a voltage-controlled oscillator, the second circuit comprises a comparison module and a frequency capture module, and the comparison module is used for comparing a first clock signal with a high-speed serial data signal, generating a first signal and outputting the first signal to the frequency capture module. The frequency capture module outputs a first control signal, the first control signal is used for adjusting the first oscillation frequency so as to enable the frequency deviation between the first oscillation frequency and the second oscillation frequency to be smaller than a first deviation threshold value, and the second circuit generates a second clock signal and outputs the second clock signal to the first circuit; the first circuit adjusts the voltage-controlled oscillator using a second clock signal such that the frequency offset is less than a second offset threshold. According to the invention, the CDR and PLL functions are fused and an external reference clock is replaced by the CDR recovery clock, so that a clock and data recovery system without an external clock is realized.

The present disclosure relates to a 5G communication system or a 6G communication system for supporting higher data rates beyond a 4G communication system such as long term evolution (LTE). The present disclosure provides a method performed by a second node in a communication system, comprising: receiving first configuration information for first data from a first node; obtaining first predicted data signals of various layers of the first data based on the first configuration information; determining a first set including the first predicted data signal of at least one layer of the various layers and a second set including the first predicted data signals of one or more layers of multiple layers based on an accuracy of the first predicted data signals of the various layers; obtaining a third set based on the first configuration information and the first set, wherein the third set includes second predicted data signals corresponding to the one or more layers; obtaining a fourth set based on the second set and the third set, wherein the fourth set includes third predicted data signals corresponding to the one or more layers; obtaining soft bits of the first data based on the first set and the fourth set.

The invention discloses a clock domain crossing circuit timing sequence closed-loop optimization method and system, and relates to the technical field of integrated circuit design, and the method comprises the steps: reading a circuit netlist and timing sequence constraints, recognizing a converged path, and carrying out the clustering analysis to form a plurality of path groups; establishing a time sequence analysis model, and dynamically calculating the maximum delay constraint of the data signal in the path group; matching and executing at least one time sequence optimization strategy for each path group; and carrying out closed-loop time sequence verification, retaining a legacy path set which does not meet the constraint, and repeatedly executing the time sequence optimization strategy until a convergence condition is reached or the maximum preset number of iterations is reached. According to the method disclosed by the invention, the cross-domain path needing to be optimized in the DMUX structure can be accurately positioned without manual intervention, so that the path identification efficiency is improved, constraint omission or grouping deviation caused by manual operation is avoided, design defects are reduced from the source, and the design reliability of a chip is improved.

A display device includes a data driver for supplying a reference voltage and a plurality of data signals to each of output lines, a divider including demultiplexers for supplying the reference voltage and the plurality of data signals, which are supplied from the each of the output lines, to a plurality of data lines, corresponding to control signals, a timing controller for supplying the control signals, and pixels connected to scan lines and the data lines. The pixels located on at least two different horizontal lines are simultaneously supplied with the reference voltage.

The invention discloses a motion state monitoring and feedback system and method based on electroencephalogram signals, in the system, an electrode array is arranged corresponding to a forehead part and used for collecting the electroencephalogram signals, a motion sensor is used for collecting motion data signals, and a vibration motor is used for sending out vibration feedback; the electroencephalogram collection hair band sends the electroencephalogram signals and the motion data signals to the mobile terminal, and the mobile terminal sends the electroencephalogram signals and the motion data signals to the cloud processing platform; the cloud processing platform is used for predicting input electroencephalogram signals and motion data signals by adopting a physical and mental state prediction model based on a deep neural network to obtain physical and mental state indexes; triggering a self-adaptive feedback intervention strategy according to the physical and mental state indexes, and controlling a vibration motor to send vibration feedback in a preset mode. According to the technical scheme of the invention, the recognition accuracy of the physical and mental state characteristics of the movement of the user is improved, and the wearing comfort and convenience of the signal acquisition device are improved.

Disclosed is a display substrate, comprising: a base substrate having a first display region and a second display region, a plurality of first light-emitting elements, a plurality of second light-emitting elements, a plurality of first pixel circuits and a plurality of second pixel circuits. The plurality of first light-emitting elements are located in the first display region, and the plurality of first pixel circuits, the plurality of second pixel circuits and the plurality of second light-emitting elements are located in the second display region. At least one first pixel circuit is configured to use a first data signal to drive at least one electrically connected first light-emitting element to emit light, and at least one second pixel circuit is configured to use a second data signal to drive at least one electrically connected second light-emitting element to emit light. The first data signal is different from the second data signal.

The invention provides a UCIe-based data transmission method, a storage medium and an artificial intelligence chip. The data transmission method comprises the following steps: communicating with a second core particle through a first core particle; receiving a bypass receive data signal and a bypass receive clock signal from the transmit module of the first core particle through the receive module of the second core particle; the serial-to-parallel conversion module is used for transmitting the bypass receiving data signal and the bypass receiving clock signal to the second core particle through the receiving module; counting the bypass receiving clock signal through a serial-to-parallel conversion module to obtain a bypass receiving frame count value, and sampling a bypass receiving data signal according to the bypass receiving frame count value; a serial-to-parallel conversion module decides to count a bypass burr removal count value according to a bypass burr removal counter starting signal; and when the bypass glitch removal count value is equal to the bypass glitch removal counter threshold value, resetting the bypass receiving frame count value through the serial-to-parallel conversion module. According to the invention, good data transmission quality among the core particles can be realized.