1038 results about "Clock synchronization" patented technology

The invention provides a gas leakage time-space correlation early warning method and system based on multi-modal data fusion, and relates to the technical field of data processing, and the method comprises the steps: collecting real-time concentration data and a historical leakage accident database through an NB-IoT combustible gas sensor network disposed at a key node of a gas pipe network; according to the real-time concentration data and a historical leakage accident database, a UTM projection coordinate system is adopted to carry out registration on geographic space data, a three-dimensional space mapping model of a pipe network topological structure is established, timestamp alignment is carried out on all dynamic monitoring data streams through an NTP clock synchronization protocol, and a time-space correlation standardized data set is formed. According to the invention, full-chain intelligent management of the gas leakage risk from sensing, prediction to disposal is realized.

The invention provides a VDSL ultra-low time delay communication method and system, and relates to the technical field of communication, and the method comprises the steps: encrypting a clock synchronization channel through a quantum key distribution protocol, dynamically dividing micro time slot resources in an orthogonal frequency division multiplexing symbol period, and generating a dynamically adjusted micro time slot resource distribution result; calculating an optimal phase offset matrix of the metasurface intelligent reflecting surface through a depth deterministic strategy gradient algorithm to obtain an optimized electromagnetic wave propagation path; generating a global optimization check matrix by aggregating the locally trained lightweight error correction model gradient of each node to obtain a compensated data stream; and constructing a causal graph model dynamic pruning high-entropy path to minimize causal entropy, through multi-agent reinforcement learning, taking time delay-energy efficiency as a game target to decide an optimal modulation order and a subcarrier switching strategy, and obtaining an optimized stable communication link. According to the invention, high-reliability and low-delay communication basic support is provided for high-precision intelligent manufacturing.

The invention provides a multi-modal sentiment analysis method and device based on multi-agent collaboration, and the method comprises the steps: carrying out the clock synchronization collection and preprocessing of multi-modal data, and obtaining a multi-modal data package with a time sequence mark; and performing feature extraction on the multi-modal data packet by using a plurality of intelligent agents, and mapping the extracted feature vector and a preset emotion ontology to obtain a multi-modal emotion vector. And detecting the difference degree between the vectors in the multi-modal emotion vectors, and starting a conflict detection algorithm based on feature credibility to perform evaluation when a significant difference is detected to obtain an evaluation result. And performing feature fusion operation on the multi-modal emotion vector according to the evaluation result and a preset weight coefficient to obtain an emotion recognition result, and performing timestamp association storage on the emotion recognition result and the corresponding multi-modal data. By introducing a difference detection and evaluation mechanism, the abnormal feature vector is effectively prevented from influencing the fusion result, and the reliability of modal sentiment analysis is improved.

The invention relates to a multi-sensor fusion intelligent actuator fault self-diagnosis method and system, and belongs to the technical field of actuator fault diagnosis. The method comprises the following steps: realizing multi-sensor clock synchronization through a unified clock source, generating a synchronous time sequence for asynchronous data such as Hall, pressure and temperature by adopting an interpolation method, and intensively acquiring signals such as rotating speed, torque and vibration; based on a current sensor and temperature data, primary fault identification is carried out through a current overload protection model, and an adaptive processing mechanism is triggered; demodulating the noise-reduced vibration signal, extracting high-frequency energy and detecting the deviation degree between the fault frequency of the bearing and a base line; bearing energy consumption abnormity is detected, and a fusion weight is dynamically set by combining the deviation degree, the energy consumption coefficient and an overload result; and carrying out weighted fusion on the multi-modal features, inputting an intelligent diagnosis model to carry out fault mode identification, and finally outputting a fault type / position and triggering a processing strategy. Accurate and rapid self-diagnosis of the actuator fault is realized.

The invention relates to the technical field of data processing, in particular to an industrial environment data acquisition device and method, and the device comprises a sensing layer, an edge calculation layer, a cloud platform layer, and an application layer. Compared with a traditional scheme in which a single sensor is adopted to work independently, software synchronization is relied on, and signal conditioning parameters are fixed, the technical scheme of the invention realizes significant breakthrough through collaborative design of a multi-mode sensor array and an intelligent signal conditioning system, microsecond-level clock synchronization is realized by adopting a hardware-level PTP protocol, and the signal conditioning precision is improved. The program control amplifier is combined with the self-adaptive anti-aliasing filter, so that the conditioning circuit can be self-adaptive to the working condition change of equipment; meanwhile, nanosecond-level real-time processing of signals is achieved through an FPGA + ARM heterogeneous processor architecture, abnormal high-speed sampling is dynamically triggered in cooperation with an ARM, compared with a traditional scheme, the real-time performance is improved by three orders of magnitude, meanwhile, invalid data transmission is reduced by 80%, and a high-timeliness and low-redundancy intelligent monitoring solution is provided for early failure early warning of industrial equipment.

The invention relates to the technical field of audio signal processing, and discloses a high-sampling-rate audio analysis optimization method and system, a storage medium and equipment, and the method comprises the steps: upgrading an FFTW (Fast Fourier Transform) library, and building an optimized memory management mechanism; carrying out adaptive framing processing on the input audio data, and optimizing the data access efficiency by adopting a multi-stage cache system; the calculation precision of the FFT fast Fourier transform is dynamically adjusted, and the calculation of the FFT fast Fourier transform is optimized by pre-calculating and caching an FFTW plan; performing clock synchronization optimization and jitter elimination on the COAX digital audio signal; establishing a multi-dimensional result cache matrix and an intelligent multiplexing decision tree, and reducing repeated operation through intelligent cache prediction and similarity calculation; system performance indexes are monitored in real time, adaptive parameter adjustment is realized based on an optimization objective function, audio quality and system stability are monitored in combination with subjective and objective evaluation mechanisms, repeated operation is remarkably reduced, and calculation resource allocation is optimized on the premise of ensuring sound quality.

The invention discloses a building engineering progress automatic identification early warning system based on computer vision, which relates to the field of building engineering informatization and comprises a synchronous calibration module, a joint calibration module, a pose generation module, a mapping construction module, a resampling module, a mapping registration module and a comparison early warning module. Clock synchronization and rolling readout calibration are carried out on a camera and an inertial measurement unit, a continuous time pose is established in a frame, sampling is carried out according to rows, and an equivalent global shutter frame is generated in combination with plane and pixel-level geometric mapping; outputting a camera pose track and a local measurement map by adopting back-end optimization containing a rolling mechanism, and registering with the building information model; component detection, instance segmentation and state discrimination are completed on an equivalent global shutter frame, pixel domain measurement is unified into engineering quantity under pose and registration constraints, the engineering quantity is mapped to a work decomposition structure and a progress plan, deviation is calculated, and graded early warning is output according to a multi-threshold rule. Long-term stable and reliable operation and evidence traceability of the system are guaranteed through whole-course quality monitoring and threshold grading.

The invention relates to an immersive VR psychological detection system and method based on multi-modal AI. The system comprises a data acquisition and processing module which is used for acquiring a multi-modal data set of a user in a virtual reality scene based on unified clock synchronization, performing time-space alignment and noise reduction standardization processing on the multi-modal data set, and extracting key biological characteristics. And the correlation model construction module performs space-time correlation mapping through a spatial transformation network, constructs a three-dimensional space attention model, and generates a real-time fluctuation curve after inputting the key biological characteristics into the trained model. And the state report generation module identifies a real-time fluctuation curve by using a time sequence analysis model, performs backtracking analysis in combination with the psychological state conversion node and a multi-modal cross validation result, and finally generates a three-dimensional interactive report. By adopting the method, multi-modal data fusion can be realized, the dynamic change of the psychological state of the user can be effectively captured, the psychological state of the user can be comprehensively and deeply analyzed, and a scientific basis is provided for psychological health assessment and intervention.

The invention relates to the technical field of data acquisition, and discloses a distributed photovoltaic data acquisition method and device based on time synchronization. The method comprises the following steps: performing distributed transmission processing on nanosecond synchronous acquisition reference signals through a time delay compensation network to obtain trigger synchronization signals of a plurality of acquisition nodes, and performing data acquisition processing through a multi-channel parallel sampling circuit to obtain original voltage and current data; and carrying out data verification processing on the original voltage and current data through a digital filtering algorithm to obtain effective acquisition data, carrying out string matching analysis processing to obtain string feature distribution, and carrying out data screening processing through a state evaluation algorithm to obtain real-time performance data of the photovoltaic module. Through high-precision clock synchronization and parallel sampling technologies, nanosecond sampling synchronization of a plurality of acquisition nodes is realized, and the acquisition data is ensured to have a strict time sequence corresponding relation, so that an accurate data basis is provided for performance evaluation and fault diagnosis of the photovoltaic module.

The invention discloses a cross-modal data real-time acquisition and analysis system based on multi-sensor fusion, and relates to the technical field of data acquisition and analysis, comprising: acquiring cross-modal data in real time through various types of sensors; data synchronous acquisition is carried out among various types of sensors through a high-precision clock synchronization technology; preprocessing the synchronously collected original data; constructing a cross-modal feature mapping model according to the preprocessed data, mapping cross-modal data features to the same feature space, and performing deep fusion of the data; and performing real-time analysis and decision according to the fused data. Through a high-precision clock synchronization technology, synchronous acquisition of various modal data is realized, and the time consistency of the data is improved; data fusion is carried out by adopting a deep learning algorithm, so that features of multi-modal data can be effectively extracted, deep fusion of the data is realized, and the accuracy and efficiency of data fusion are improved; the system can realize real-time acquisition and analysis of data.

The invention relates to the technical field of intelligent manufacturing decisions, in particular to a digital twinning-based manufacturing system decision analysis method and system, and the method comprises the steps: deploying a plurality of Internet of Things sensors on a physical manufacturing system, and collecting a physical real-time data stream of equipment in real time; the method comprises the following steps: establishing a virtual data acquisition channel aligned with a physical manufacturing system clock, injecting a physical real-time data stream into a digital twinning creation model, and performing data preprocessing based on distributed edge calculation to delay and compress original data acquisition of the physical real-time data stream to 10ms level, the time sequence database and the NTP / GPS clock are synchronized to ensure the state alignment error lt of the physical-virtual system; compared with the prior art, the deep space-time prediction network is fused with a CNN-LSTM-attention mechanism, the accuracy of multivariable coupled KPI prediction is improved, in addition, model failure is recognized in real time through Page-Hinkley inspection, and a prediction error reaches a relatively stable state through an adaptive retraining mechanism.

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 belongs to the technical field of production monitoring systems, and discloses a numerical control machine tool dynamic fault diagnosis method and system. The method comprises the following steps: generating a global time reference signal through a main shaft encoder and a clock synchronization protocol; the method comprises the following steps: collecting vibration data of a main shaft bearing in each unit time, current data of an electric cabinet and process parameters, and generating a preprocessed data sequence through transmission delay compensation and multi-rate frequency raising processing; inputting the vibration data and the current data into a preset mechanical-electrical transfer function model, and calculating a time delay parameter; performing phase alignment on the preprocessed data sequence based on the time delay parameter to generate an aligned data sequence; inputting the aligned data sequence into a time sequence neural network, and outputting a fusion feature vector; calculating a cross correlation coefficient of the fusion feature vector, and generating a fault diagnosis result based on a preset cross correlation threshold value; the problem of failure of fault feature extraction caused by data asynchronization in the prior art is solved.

The invention relates to the technical field of integrated circuits, and provides a clock domain crossing synchronization circuit and method based on a multiphase clock, and the circuit comprises a selector, an asynchronous clock synchronization module and a digital signal processor. Wherein serial data to be transmitted and a low-frequency clock are input into the selector, and the selector is used for outputting corresponding RX parallel data to the asynchronous clock synchronization module according to the serial data and the low-frequency clock; the asynchronous clock synchronization module is used for outputting corresponding alignment data to the digital signal processor according to the input RX parallel data, the clock of the digital signal processor and the low-frequency clock; the asynchronous clock synchronization module is used for performing phase alignment on the RX parallel data and a clock of the digital signal processor; and the digital signal processor is used for carrying out data processing on the alignment data. According to the invention, stable transmission of low-delay cross-clock domain data can be realized.

The invention relates to a multi-channel acquisition module, system and method based on trigger synchronization automatic calibration, and belongs to the field of electronic measurement, the acquisition module comprises a channel conditioning and acquisition circuit, a trigger and input and output feedback circuit, a clock synchronization and calibration signal generation circuit and an FPGA receiving and synchronous calibration circuit; the method has the advantages that the multi-channel synchronization precision is high, convenient channel expansion is supported, pairwise interconnection delay calibration can be carried out between modules in parallel, the system calibration time does not increase linearly along with the increase of the number of the channels, the interconnection relation is realized through annular connection, and a large fan-out cable is not needed.

The invention relates to the technical field of combustion equipment state monitoring, in particular to a combustion state monitoring method and system based on vibration and noise fusion analysis, and the method comprises the steps: collecting a combustion signal in real time through a vibration sensor and a broadband noise sensor, extracting a characteristic frequency band through noise reduction and filtering, and obtaining a combustion state; fPGA hardware-level clock synchronization is adopted to realize time alignment of bimodal signals, a vibration-noise joint feature matrix is constructed and multidimensional coupling is carried out, a combustion state feature vector is generated through a mixed deep learning model, a combustion state is determined based on a bimodal confidence weighting decision, and a linkage control instruction is generated during interruption. The system comprises a signal acquisition synchronization unit, a feature fusion and modeling unit, a state decision and instruction generation unit and a safety linkage execution unit, executes a method and activates a standby safety system, solves the problems of asynchronous bimodal data and insufficient monitoring precision, and improves the combustion state monitoring accuracy and safety response efficiency.

The invention discloses a multi-mode fusion time service method and system based on dynamic calibration of DCP equipment. The method comprises the following steps: completing initial time reference data acquisition based on a multi-mode satellite signal; meanwhile, time data of a ground time service network are collected, and motion parameters of DCP equipment are monitored in real time through an inertial navigation unit; establishing a correlation model of the motion parameter and the time service error; quantitative analysis of error sources is completed based on the correlation model; fusing the multi-source time data; dynamically adjusting the fusion weight of each data source according to the real-time motion state of the DCP equipment; redundant time slots are automatically distributed to high-error equipment; outputting the calibrated time signal to DCP equipment through an interface to complete clock synchronization; and continuously monitoring the time deviation of the DCP equipment, and dynamically updating the calibration parameters to form closed-loop optimization. And the environmental adaptability and the precision stability of the timing system are improved.

The invention provides a method for automatically calibrating collected data of high-precision measurement and control deformation monitoring equipment, and belongs to the technical field of wind tunnel inner profile measurement. Multi-equipment clock synchronization is realized by establishing a unified time reference system, and measurement data of different frequencies are processed by adopting a CPU-GPU heterogeneous parallel processing architecture, so that the measurement accuracy is improved. The method comprises the following steps: performing three-level alignment on time, space and precision dimensions by using a multi-level clustering algorithm, eliminating clock differences among equipment by using a clock drift correction equation set, establishing a space coordinate system conversion matrix to unify a global coordinate system, and performing adaptive data fusion according to a precision weight by using a Kalman filtering fusion algorithm. And a data quality evaluation system is established to ensure the accuracy of a calibration result, a high-precision calibration data set is finally output, and the technical problem that the measurement precision is reduced due to space-time inconsistency of heterogeneous data of multiple devices is solved.

The invention relates to the technical field of data processing of organic displays, in particular to an interaction control method of a double-screen organic display system, which acquires touch control, deformation and ambient light data in real time through a multi-mode sensor array, and realizes cross-screen heterogeneous data space-time alignment by adopting an improved clock synchronization protocol and a sliding window mechanism. And generating a time sequence feature vector set with weight. And based on an LSTM-Transform hybrid model, fusing historical communication delay data, analyzing space-time relevance by using a multi-head attention mechanism, and outputting a cross-screen communication delay probability distribution diagram. And generating a multi-target task allocation strategy in combination with a deep reinforcement learning model, and iteratively calculating a shortest path allocation scheme of a rendering task through an improved ant colony optimization algorithm. The space-time synchronization precision of double-screen interaction is effectively improved, signal misalignment caused by communication delay is reduced, the distribution efficiency of distributed rendering resources is optimized, and the environmental adaptability and operation robustness of a flexible display system are enhanced.

The invention relates to the technical field of on-load voltage regulation, in particular to a transformer on-load voltage regulation control method. According to the technical scheme, the transformer on-load voltage regulation control method comprises the following steps that S1, a transformer cluster running in parallel is decomposed into distributed intelligent nodes, and each node executes local voltage prediction and constraint condition management; s2, adopting a distributed model prediction control framework, and cooperatively solving a voltage regulation optimization target through an edge calculation unit; s3, a harmonic disturbance signal with a preset amplitude is injected into the transformer, equivalent impedance is dynamically identified based on response data, and virtual impedance compensation is triggered; and S4, establishing a hierarchical cooperative control architecture, and realizing clock synchronization, dynamic weight distribution and multi-objective optimization decision among nodes. Through a cooperative mechanism of distributed predictive control and dynamic impedance compensation, the response speed and the regulation precision of the on-load voltage regulation system to the power grid voltage fluctuation are remarkably improved, and the voltage out-of-limit problem under the complex working conditions of new energy grid connection, load sudden change and the like is effectively solved.

The invention discloses a pump set vibration monitoring method and system based on distributed sensing. The method comprises the steps that vibration collection signals and real-time working condition parameters of distributed sensing nodes are obtained, multi-node clock synchronization correction is executed, and synchronous vibration data frames are formed; time-frequency feature extraction is carried out on the multi-channel vibration data set, abnormal channels are identified through inter-channel consistency analysis, a local fault correlation frequency band is extracted, and a vibration feature vector is constructed; associating the vibration feature vector with a real-time working condition parameter to form a working condition-feature mapping table, identifying a feature mutation rotating speed point for the feature baseline, adaptively dividing a rotating speed interval to generate an adaptive working condition threshold value, and implementing out-of-limit detection to generate an abnormal trigger identifier; determining a traceability analysis window based on the abnormal trigger identifier, and executing multi-measuring-point coherence calculation and energy attenuation gradient analysis to construct an energy transfer link diagram; and finally, vibration source position positioning is performed to form fault source probability distribution, a fault mode label is matched, pump set vibration monitoring is completed, and quantitative positioning of a fault source is realized.

The invention discloses a cross-chain meteorological and hydrological data consistency verification method oriented to multi-block-chain collaboration, which effectively solves the problem of data alignment caused by inconsistent time standards in a multi-chain environment by collecting multi-source data and generating a data tuple with a geographic identifier and distributing a unified reference timestamp for the data in combination with a physical clock synchronization mechanism. And furthermore, the time deviation is extracted through a layered verification structure and inter-chain cross check, and the timestamp weight is dynamically adjusted, so that the time synchronization precision of the cross-chain data is improved. Finally, a standardized time sequence is generated through segmented aggregation of the data, cross-chain storage is completed, regular backtracking verification is performed in combination with a supervision chain, time consistency and content credibility of the meteorological and hydrological data in the cross-chain circulation process are guaranteed, and efficient collaboration and traceability between heterogeneous block chain systems are achieved.

The invention discloses a wind power generation energy storage load intelligent prediction and power distribution management method, and relates to the technical field of new energy power generation, and the method comprises the steps: generating a clock synchronization signal when a phase gradient quantity exceeds a stable threshold value, correcting the node voltage phase deviation of a power distribution network according to the clock synchronization signal, and outputting a whole network synchronization voltage waveform; inputting an energy storage charging and discharging control instruction into the power flow optimization model, and generating a voltage suppression control vector through a dynamic power deviation compensation algorithm; and extracting stability parameters in the wind power operation data, carrying out weight distribution and state matching on the energy storage charging and discharging control instruction and the voltage suppression control vector, and outputting a cooperative control instruction. According to the method, heterogeneous data such as wind speed, power and voltage are fused into multi-dimensional sequence parameters through a space-time dynamic coupling method, the phase gradient quantity is generated through phase field gradient extraction, a quantitative correlation model of wind speed fluctuation and power grid response is established, and the load prediction accuracy is improved.

The invention relates to the technical field of copper pipe flaw detection, in particular to a flaw detection ink jet line time sequence synchronous control method, system and equipment, and the method comprises the following steps: flaw detection equipment and an ink jet head establish a master-slave node relationship according to a communication network protocol, the flaw detection equipment is used as a master node, and the ink jet head is used as a slave node; the master node acquires flaw detection signals of the copper pipe in real time, encapsulates the flaw detection signals into a data packet carrying a timestamp, and transmits the data packet to the slave node according to a communication network protocol; after the slave nodes receive the data packets, the timestamps are analyzed, and ink jet trigger compensation time is calculated based on a network transmission delay prediction model; a synchronous ink jet instruction is generated according to the ink jet trigger compensation time, and an ink jet head is controlled to mark the defect position of the copper pipe; and periodically checking clock skew data of the master and slave nodes based on a communication network protocol, and dynamically updating the network transmission delay prediction model. According to the invention, the core problem of mark position offset caused by insufficient clock synchronization precision is effectively solved.

The invention relates to the technical field of content transmission methods, in particular to a content transmission method based on a multi-modal streaming media fusion technology system, which comprises the following steps: S1, multi-source data acquisition and time-space alignment: adopting an IEEE 1588PTP protocol, deploying a hardware clock synchronization module at a camera, a microphone and IMU equipment, improving the timestamp precision to a mu s level, and performing time-space alignment on the camera, the microphone and the IMU equipment; carrying out sampling rate normalization on the sensor data; s2, feature level cross-modal fusion: extracting each modal feature by using a lightweight Transform model, executing cross-modal attention calculation in an edge server, and generating a fusion feature tensor Ffusion belonging to RN * D; the system content transmission method based on the multi-modal streaming media fusion technology solves the problems that a traditional multi-modal transmission system usually adopts a separated transmission architecture, video streams are transmitted through an RTMP protocol, audio streams are encoded through Opus and are transmitted through WebRTC, sensor data are sent through an MQTT protocol in a JSON format, ABR only adjusts the video code rate, and the transmission efficiency is low. And joint optimization of multi-modal data is not coordinated.

The invention provides a signal decoding method based on a PSI5 interface, and relates to the technical field of signal decoding, and the decoding method comprises the following steps: processing and adjusting a received PSI5 signal to obtain an accurate identification signal; and extracting a clock signal from the PSI5 signal, designing a clock compensation algorithm according to the quantum neural network, and dynamically adjusting the clock signal to obtain clock synchronization data. And determining a data frame boundary, and decoding the accurate identification signal to obtain original data. And performing comparative analysis on the original data and the PSI5 signal to obtain comparative difference data, judging whether the comparative difference data accords with a preset error type or not, and performing corresponding processing. According to the invention, fractional calculus processing is carried out on the received signal, then signal distortion is corrected by using a channel equalization method, and the quantum neural network is constructed to dynamically adjust the clock signal, so that clock synchronization is realized, a reliable time reference is provided for accurate processing of the signal, and the accuracy of signal decoding is improved.

This application relates to the field of communication technologies, and discloses a communication method and apparatus. A first communication apparatus receives capability information, where the capability information indicates that a second communication apparatus supports at least one of encrypting or decrypting a first message. The first communication apparatus encrypts the first message to obtain a second message, and sends the second message with a first timestamp, where the first timestamp indicates the sending time of the second message. According to the foregoing method, the first communication apparatus sends an encrypted message (that is, the second message) and the timestamp when the encrypted message is sent to the second communication apparatus, so that encrypted transmission can be performed on a message (for example, a clock synchronization message) that needs to be stamped, thereby improving security of the message transmission.

The invention relates to the field of physiological parameter detection and medical diagnosis measurement, in particular to a multi-mode psychological state quantitative evaluation method and device and a medium thereof. The method comprises the following steps: acquiring multi-source physiological and behavior signals and situation metadata, performing clock synchronization and equipment identifier mapping processing, performing quality feature extraction, artifact detection and slice completion processing, and generating a quality passing fragment set; de-noising normalization, time windowing and feature stack construction, modal gating and attention weighting processing are executed, and unified time sequence embedding is generated; through domain alignment parameter estimation, double-domain drift correction and multi-task inference, a psychological state quantitative index and uncertainty thereof are output; and finally, generating a structured evaluation report and updating parameters through consistency comparison, online self-distillation updating and individual baseline refreshing. According to the method, the anti-interference performance, the individual suitability and the result credibility of psychological state assessment in a natural scene are effectively improved.

The present application belongs to the technical field of communications, and discloses a clock synchronization method, a device, and a computer readable storage medium. The method comprises: selecting a master clock source inside of a base station to be a clock synchronization source of a fusion control system; on the basis of the clock synchronization source, constructing a wired synchronous master clock message and an air interface synchronous master clock message; and sending the wired synchronization master clock message to a wired connection device, and sending the air interface synchronization master clock message to a wireless connection device.

The invention provides a digital twinborn monitoring, regulating and controlling method for a copepod bait biological domestication process. The method comprises the following steps: establishing a micro-ecology and population dynamics coupled twinborn model, and defining a monitoring parameter and control object unified term system; collecting multi-source sensing data at an edge end, and performing clock synchronization, signal denoising and state estimation to obtain a micro-ecology, group and water body state joint estimation result; executing parameter online identification based on state estimation, and constructing a safety constraint set of ammonia nitrogen, dissolved oxygen, death rate, environment change rate and the like; the breeding efficiency improvement and the system stability balance are realized through a multi-objective optimization function; and generating a micro-ecological pulse and environment gradual change cooperative control action set according to a solving result, and when a set condition is met, outputting a domestication prescription and terminating the process. Intelligent, low-stress and high-stability regulation and control of a copepod domestication process are realized through digital twin modeling, rolling predictive control and a self-evolution updating mechanism.