82 results about "Time distribution" patented technology

This invention relates to a signal transmission control method for a mobile phone back clip with infrared transmission function, belonging to the field of data transmission technology. The method analyzes the infrared signal sequences received by each receiving node of the infrared receiving array on the mobile phone back clip during a preset time period. Based on the signal fluctuation degree and rarity of each sampling point in each sequence, the suspected anomaly degree of each sampling point is determined. Then, based on the suspected anomaly degree, suspected anomalous sampling points are screened from all sampling points of all infrared signal sequences. Subsequently, by analyzing the spatial and temporal distribution characteristics of each suspected anomalous sampling point during the preset time period, the method distinguishes between normal signal changes and interference-induced changes during the preset time period and quantifies the interference degree. The quantified interference degree guides the subsequent signal transmission intensity of the transmitting end, ensuring the stability of infrared signal transmission while avoiding power waste at the transmitting end, thus improving the signal transmission and reception performance of the mobile phone back clip with infrared transmission function.

The application discloses a park energy-carbon coordination early warning management and control method, relates to the technical field of energy saving and emission reduction and intelligent decision support, and comprises the following steps: establishing a unified atomic time reference, setting a unified time reference for a plurality of energy subsystems, generating a cross-source time anchor set under the unified time reference, and using the time anchor set to calibrate the sampling start time of each energy subsystem. The application realizes time alignment and real time sequence restoration of energy consumption data of multiple energy subsystems by establishing a unified atomic time reference and a cross-source time anchor set, and improves energy consumption fusion accuracy. By introducing conflict isolation and breathing time domain patching mechanism, the time distribution and load balance of peak value aggregation section are dynamically controlled, the energy consumption superposition amplification effect is inhibited, and the accuracy and stability of carbon emission reduction simulation and economic analysis results are ensured.

The application relates to the technical field of electric machines, and particularly discloses an electric machine driving and control anti-interference method and system based on an isolator, which can accurately perceive the space-time distribution characteristics of the ground potential difference by constructing a global potential difference dynamic matrix in real time and calculating the common-mode voltage drift amount; based on this, the system no longer maintains the fixed working state of the isolator, but iteratively optimizes the bias current and the gain coefficient of the photoelectric isolator according to the real-time common-mode voltage drift amount, and dynamically reconstructs the transmission parameters; so that the isolator always works in the optimal linear region, effectively avoids signal saturation or cutoff distortion caused by ground potential difference fluctuation, and fundamentally solves the problem that the fixed working point cannot adapt to dynamic common-mode interference.

The present application relates to a kind of time series prediction methods under multi-scale double clustering, the present application can simultaneously capture the data pattern and relevance under different time scales by multi-scale input mechanism, improve the modeling ability to complex real-world signal.Effective decoupling and modeling TDS and CCT: by parallel patch intra-domain clustering module and mask attention module, respectively, special modeling is carried out on two core challenges of time distribution drift and channel correlation conversion, avoids causal confusion, and improves the robustness and generalization ability of the model.The model carries out twice dynamic, hybrid expert-based dependency filtering at patch level, can accurately capture instantaneous and time-varying correlation, avoids the information loss or noise interference caused by traditional coarse-grained or static dependency modeling.

The present application relates to the technical field of artificial intelligence, and more particularly to an AI interaction system for ornithological scientific research, which comprises a collection unit, a trigger determination unit, a path determination unit, a dominant determination unit, a prompt generation unit and an adjustment unit. The present application constructs a multi-level behavior determination process through joint analysis of bird song spectrum, image motion characteristics, flight trajectory and environmental factors, and through statistical analysis of the time distribution and spatial distribution of these behavior indicators within a preset period, and correlation analysis with environmental factors such as temperature, humidity, wind speed and illumination, the influence of environmental changes on bird activity frequency and spatial aggregation degree can be identified, so that the acoustic trigger threshold can be dynamically adjusted, so that the system can continuously maintain the sensitive recognition ability of bird activity with the change of the habitat environment, and then realize high-reliable behavior recognition and interaction prompt for ornithological scientific research.

The present application relates to the technical field of optical processing, and particularly relates to a polishing device and method for cylindrical optical elements integrated with a multi-joint robot. The polishing method is realized based on the integration of a multi-joint robot and a cylindrical polishing tool. Firstly, according to the cylindrical surface shape characteristics of the optical element to be processed, the surface shape error distribution and the removal function are obtained. Then, the dwell time distribution is solved according to the target removal function. Finally, the multi-joint robot is used to convert the dwell time into the actual processing parameters of the cylindrical polishing tool at each processing position. The polishing device comprises a surface shape error detection module, a removal function calibration module, a dwell time solving module, a cylindrical polishing tool and a multi-joint robot. The present application converts the dwell time into the actual processing behavior of the cylindrical polishing tool through the high integration of the multi-joint robot and the cylindrical polishing tool, realizes the high-precision correction of the surface shape error of the cylindrical optical element, and effectively improves the predictability and processing consistency of the polishing process.

The application discloses a kind of compartmentalization type wave overtopping measurement method and measuring device.The device includes a plurality of cascading arrangement measuring cabin, each measuring cabin is divided into front pool and rear store by the partition with communication hole, wave height instrument is arranged in rear store.The method includes: obtaining the water level data of rear store;Based on the time rate of change of water level data and the preset partition hydraulic transfer function, the instantaneous total amount of water storage of measuring cabin is determined;According to the time rate of change of instantaneous total amount of water storage, the instantaneous wave overtopping flow entering measuring cabin is obtained by inversion;Based on the time distribution characteristics of instantaneous wave overtopping flow, single-wave wave overtopping event is identified and single-wave wave overtopping volume is calculated.The application solves the contradiction between large-capacity trapping and high-precision single-wave trapping by the combination of physical compartmentation and algorithm inversion, and realizes the synchronous measurement of average wave overtopping and single-wave maximum wave overtopping.

The application provides a DAS flow velocity identification method based on one-dimensional time sequence feature extraction guided by physical priori, and belongs to the technical field of fluid acoustics, and comprises the following steps: collecting DAS original time sequence acoustic signals related to fluid flow, and obtaining DAS original time sequence data distributed according to spatial positions and time; performing flow velocity sensitive input sample construction on the DAS original time sequence data based on flow related physical priori, so as to obtain flow velocity sensitive one-dimensional time sequence input samples used for flow velocity identification; inputting the flow velocity sensitive one-dimensional time sequence input samples into a time sequence feature identification model, so as to obtain time sequence features related to fluid flow velocity; inputting the time sequence features into a regression output layer, so as to obtain fluid flow velocity identification results; and outputting the flow velocity identification results, so as to represent the actual flow velocity state of the measured pipeline or wellbore fluid. The application further discloses a system and a storage medium for executing the method, and is suitable for a wellbore or pipeline flow velocity identification scene.

This invention discloses a deterministic ultra-precision chemical mechanical polishing method and apparatus for shaft-type parts. The invention includes: acquiring roughness error data of the outer annular surface of the shaft-type part before polishing by uniformly sampling points using a white light interferometer and fitting the data; selecting time-controlled polishing parameters; calculating the dwell time distribution; and performing chemical mechanical polishing on the outer annular surface of the shaft-type part using a polishing machine; acquiring measurement data obtained from uniformly sampling points on the outer annular surface of the shaft-type part using a white light interferometer and fitting the data to obtain the roughness data of the outer annular surface of the shaft-type part after polishing; and determining whether the roughness data of the entire shaft-type part after polishing meets the requirements to select iterative processing or terminate the processing. This invention aims to achieve highly efficient automated processing of sub-nanometer roughness of the entire shaft-type part, realizing high-efficiency and high-precision shaft-type part processing.

A computer-implemented method maintains time synchronization over a wireless connection through variable operational conditions. The wireless device determines an operating mode based on the operational conditions of the wireless device. When the wireless device receives a time synchronization message from a remote device, the wireless device determines a clock offset between its local clock and a remote clock of the remote device. The clock offset is based on the operating mode and information in the time synchronization message. The wireless device adjusts its local clock to mitigate the clock offset from the remote clock. The wireless device also determines timing window(s) based on the local clock and receives a wireless data message beginning within a first timing window.

This invention relates to the field of nondestructive testing technology, solving the problem of large thickness assessment errors caused by the combined effects of the cladding layer lift-off effect and the shielding effect of the metal protective layer in existing technologies. Specifically, it relates to a pulsed eddy current thickness assessment method based on multi-feature fusion. This method extracts multi-dimensional feature parameters such as centroid time, information diffusion energy, and information diffusion width as inputs, and performs normalization and weighted fusion on each feature to construct a fused feature. This fused feature comprehensively characterizes the time distribution, energy distribution, and diffusion characteristics of the signal, achieving high-precision quantitative assessment of the wall thickness of clad equipment. This invention uses multi-dimensional features such as centroid time, information diffusion energy, and information diffusion width as inputs, and comprehensively characterizes the signal through feature normalization and weighted fusion methods. It fully utilizes the overall information of the pulsed eddy current signal, improving the accuracy and stability of wall thickness assessment, thereby achieving accurate measurement of the wall thickness of clad pipes.

The application provides a meta-learning enhanced burst order multi-constraint distribution emergency scheduling method, comprising: acquiring the time distribution mode and intensity change trend of continuous burst order flow through historical order data collection, and determining the regularity difference capture result; according to the regularity difference capture result, using meta-learning model training to obtain evolution trend judgment parameters of subsequent demand prediction; if the evolution trend judgment parameters exceed the preset threshold, starting resource allocation optimization to obtain an acceleration processing means allocation scheme under a dynamic coordination mechanism; for the preliminary scheduling sequence, using genetic algorithm iteration to determine the minimum path of the chain delay situation, and obtaining the optimization output of order mode analysis; through the final execution plan, using particle swarm optimization algorithm fusion to determine the compensation measures of resource allocation lag, and obtaining the complete sequence of overall distribution emergency scheduling.

The application discloses a kind of compartmentalization type wave overtopping measurement method and measuring device.The device includes a plurality of cascaded measurement cabin, each measurement cabin is divided into front pool and rear store by the partition with communication hole, wave height instrument is arranged in rear store.The method includes: obtaining the water level data of rear store;Based on the time rate of change of water level data and the preset partition hydraulic transfer function, the instantaneous total amount of water storage of measurement cabin is determined;According to the time rate of change of instantaneous total amount of water storage, the instantaneous wave overtopping flow entering measurement cabin is obtained by inversion;Based on the time distribution characteristics of instantaneous wave overtopping flow, single-wave wave overtopping event is identified and single-wave wave overtopping volume is calculated.The application solves the contradiction between large-capacity trapping and high-precision single-wave trapping by the combination of physical compartmentation and algorithm inversion, and realizes the synchronous measurement of average wave overtopping and single-wave maximum wave overtopping.

The application provides a quick connection system and method based on the Internet of Things, which comprises an Internet of Things device, a remote control device and an Internet of Things device management server; the remote control device is connected with the Internet of Things device management server, and sends control instructions to the Internet of Things device through the Internet of Things device management server to control the work of the Internet of Things device; the Internet of Things device management server is used for generating the sleep time length of the Internet of Things device corresponding to different time periods according to the control time distribution data of the Internet of Things device controlled by the remote control device, and sending the sleep time length to the Internet of Things device, so that the Internet of Things device executes a sleep strategy according to the sleep time length when no control instruction is received for a long time, the delay time of the Internet of Things device responding to the remote control instruction is reduced, and the user experience is improved.

This invention belongs to the technical field of magnetic levitation motion control and conveying systems. It provides a method and system for real-time distribution of six-degree-of-freedom generalized forces in a magnetic levitation planar motor. First, a physical model of the magnetic levitation planar motor is established, and the real-time position coordinates of the mover in the stator coordinate system are obtained. A constraint coefficient matrix representing the balance of the six-degree-of-freedom generalized forces is constructed. A quadratic convex optimization projection model is used, with minimizing coil copper loss as the objective function and generalized force balance as the constraint condition. Based on the Lagrange multiplier method, the forces on each coil in the model are optimized to obtain the minimum force on each coil group at any position of the mover throughout its stroke, thereby reducing the coil current and achieving low power consumption for the six-degree-of-freedom motion of the mover. This invention has the advantages of low computational complexity, real-time processing of force distribution tasks at any position of the mover throughout its stroke, and effective reduction of system heat dissipation.

This invention relates to the field of goods storage scheduling technology, and discloses an intelligent scheduling system and method for goods storage inbound and outbound operations. The method includes acquiring a real-time distribution feature set and a historical scheduling feature set of goods in a target warehouse. The real-time distribution feature set includes spatial distribution and morphological feature sets of goods, while the historical scheduling feature set includes historical spatial distribution and morphological feature sets. A scheduling sensitivity matrix is ​​generated by comparing the two types of real-time feature sets with their corresponding historical feature sets. A dataset of warehouse location movement trajectories matching this matrix is ​​retrieved. When the dataset dispersion reaches a dispersion threshold, the scheduling time percentage from the initial inventory time to the current scheduling time is calculated. A warehouse location reorganization instruction set is generated based on this time percentage. This method combines real-time and historical data to optimize warehouse location trajectory planning and warehouse location reorganization, adapting to complex warehousing scenarios and improving the level of scheduling intelligence.

The application discloses a radioactive separation laboratory environment radiation field dynamic modeling method and system, and belongs to the field of chemical process modeling. Step 1, the concentration distribution of a non-radioactive process tracer physically associated with a target radionuclide in a process fluid is obtained; step 2, based on the concentration distribution and the macroscopic residence time distribution of the material, the pre-judgment source term of the radionuclide is obtained through time translation and decay scaling; step 3, the radiation field observation waveform collected by a heterogeneous radiation sensitive field sensor network and independent of the pre-judgment source term is obtained; step 4, the theoretical radiation field change waveform calculated based on the pre-judgment source term is obtained; the application can obtain the concentration distribution of the non-radioactive tracer physically associated with the target radionuclide, eliminate the inherent bias of the source term from the source, and accurately depict the time-varying characteristics of the source term caused by the continuous migration of the material without making steady-state or quasi-static simplification assumptions on fluid turbulence, two-phase interface dynamic change and other processes.

The application belongs to the technical field of network security, and specifically relates to a network operation and maintenance monitoring method and system based on intelligent analysis, which comprises the following steps: acquiring abnormal behavior data flow and environment information, identifying abnormal behavior based on a hierarchical environment baseline to obtain a preliminary abnormal behavior sequence; extracting abnormal behavior time distribution characteristics and constructing an initial entity influence relationship graph; combining environment change information and historical background information to correct the relationship graph and determine risk assessment deviation; updating entity risk scores and performing risk propagation to obtain risk level distribution; and then performing feedback correction to output a risk propagation vector and a threat response priority sequence. The application can improve risk assessment accuracy and rationality of threat response order.

The application relates to the technical field of power grid intelligent control, and discloses a user power consumption behavior prediction method and system based on peak-valley time-of-use electricity price. The method comprises the following steps: obtaining user historical power consumption data information, establishing a time index table, logically layering the data based on the peak-valley time-of-use electricity price, and decoupling user behavior into a first data set of two dimensions of peak time and valley time; cleaning and normalizing the first data set for pretreatment, calibrating discrete sampling points into standard input vectors by using a preset mapping table to obtain a second data set; inputting the second data set into pre-constructed peak-time and valley-time user power consumption behavior prediction models, and determining the power consumption behavior score of the user by combining a time distribution correction function and an instantaneous volatility difference. The application can accurately capture user behavior characteristics, effectively solve the problem of low load prediction accuracy, realize dynamic interactive regulation of power grid energy, and thus improve user power consumption reliability.

The application discloses a large model and hydrological prediction model collaborative coupling calling method and system, the method is through the large model to complete the demand intention recognition, rainfall space-time distribution analysis and hydrological prediction model parameter adaptation, calling hydrological prediction model to execute the core calculation of runoff yield, and combining the error evaluation and correction mechanism to realize accurate prediction. The application constructs four kinds of functions of intention recognition, model adaptation, parameter mapping and error evaluation, which guarantees the intelligence and stability of the coupling process. Taking the future water inflow prediction and flood prediction of a certain basin as an example, it is verified that the method can efficiently complete the collaborative coupling calling, solve the problems of complex calling and poor adaptability of traditional hydrological prediction model, and improve the prediction efficiency and accuracy.

The present application relates to the technical field of intelligent supply chain, and more particularly to a method and system for supply chain optimization decision based on artificial intelligence, comprising the following steps: S1, obtaining forward logistics historical data and product life cycle parameters, performing time series analysis through a machine learning model, and outputting reverse material space-time distribution prediction data; S2, matching the reverse material actually arriving at the collection hub according to the reverse material space-time distribution prediction data, collecting images of the reverse material, evaluating the physical damage degree and determining the packaging state by using a computer vision model, and generating containerization classification instructions and corresponding effective containerization material data.In the present application, for the reverse logistics scene of ship, railway and highway intermodal transportation, the dynamic transport capacity and road conditions of different transportation modes are converted into quantitative resistance, multi-objective path optimization is performed to generate a switching sequence, the reasonable combination planning of cross-transportation modes is realized, and the operation efficiency of reverse material in the multimodal network is improved.

The application discloses a kind of flood risk map element automatic identification extraction method and system, comprising: collecting the basic geography and hydrodynamic data of study area, flood evolution simulation is carried out based on hydrodynamic model, and the flood submergence process data and submergence maximum data of each grid unit in simulation area are output;The submergence maximum data is converted into format, and the standardized vector geographic data file with spatial topology structure and attribute information is generated;Based on the standardized vector geographic data file, the submergence range of flood, submergence water depth distribution, submergence duration distribution and the arrival time distribution of flood are identified and extracted by spatial analysis and hydrology identification;The submergence range of flood, submergence water depth distribution, submergence duration distribution and flood arrival time distribution are integrated, and flood risk map is generated.The practicability and timeliness of the flood risk map production process are significantly improved, so that it can better serve the scientific decision of emergency response and disaster prevention and reduction.

The application discloses a highway fog elimination method and system based on the principle of air cannon, and belongs to the technical field of air cannon control, which comprises the following steps: acquiring multi-source environment and real-time traffic flow data of a target road section; extracting the visibility change trend and the wind field space-time distribution characteristics to construct an environment dynamic characteristic vector; establishing an environment energy field assistance model to calculate a coupling gain coefficient, and combining historical spraying events for optimization; dynamically generating air cannon cooperative control instruction sets, generating executable driving instructions to trigger spraying after system safety boundary verification and mechanism adaptability verification; and updating data to evaluate the effect and feed back the model for correction after spraying. The multivariate energy field model of the coupled traffic environment and the parameter self-learning correction strategy based on closed-loop feedback are adopted, so that the optimal cooperative control of the air cannon array spraying timing, angle and intensity can be realized.

The application relates to a UAV multi-source data fusion method and system based on data grading and a computer, the method comprising collecting heterogeneous sensor data pairs which have been subjected to space-time matching; constructing joint confidence according to the confidence of each data in the heterogeneous sensor data pairs, and performing grading processing on the heterogeneous sensor data pairs according to the joint confidence; performing a fusion operation on high-confidence data pairs, performing a preferential retention operation on medium-confidence data pairs, and performing an elimination operation on low-confidence data pairs, so as to output a high-confidence data stream which has been subjected to space-time optimization; and inputting the high-confidence data stream into an extended Kalman filtering framework for filtering optimization; the application effectively improves the data quality and optimizes the space-time distribution of the data, so as to facilitate subsequent filtering, and the application has high positioning accuracy and strong real-time performance compared with existing methods in a complex urban environment.

The application relates to the technical field of water environment monitoring and water quality prediction, and particularly relates to a regional water quality prediction method and system. The application can perform modal decomposition according to the frequency distribution of detection data, extract time characteristics of modal components respectively, and couple the time characteristics through a self-adaptive updating graph network structure, so that the time characteristics and spatial characteristics are combined to obtain space-time characteristics, and finally, the space-time characteristics of two different modes are fused to obtain a prediction result. Compared with the prior art, the embodiment of the application can start from the characteristics of water quality data, consider the long-term volatility, short-term volatility and spatial part changeability of the water quality data, extract multiple features of the data, and optimize the time characteristics and spatial characteristics, finally obtain fused characteristics reflecting the space-time distribution of the water quality data, and realize the accuracy of the regional water quality prediction result based on the fused characteristics.

The application discloses a continuous fluid chemical product carbon tracking method and system based on RTD dynamic compensation, comprising: reactor RTD mathematical characterization modeling and generating a probability density sequence; the reactor is abstracted into a one-stage or multi-stage series stirred tank model, and a residence time distribution density function is used to describe the residence probability of fluid microgroups in the reactor; an instantaneous carbon load factor is constructed; reactor inlet flow, raw material implicit carbon intensity and multi-path energy consumption are collected in real time, and dispersed energy consumption and material data are integrated into a carbon flow signal changing with time; and output carbon accounting based on time domain convolution. The application applies mixing theory in chemical engineering to carbon accounting, realizes a leap from average carbon to instantaneous carbon, and can provide a precise carbon single with a time stamp for downstream customers.

The application relates to the technical field of human-computer interaction devices, and discloses an anti-interference filtering and instruction recognition method for touch feedback signals, which comprises the following steps: acquiring a touch sensing array capacitance frame stream and establishing a space-time distribution matrix; determining an orthogonal axial capacitance evolution vector with a capacitance peak node as an origin, and extracting an asymmetric characteristic value representing anisotropy of deformation; calculating a first-order capacitance gradient between frames to generate a phase state conversion criterion; inputting the asymmetric characteristic value and the phase state conversion criterion into a touch recognition state machine; when a transition state of flexible fitting, rigid support and stable contact is met in turn and conforms to an asymmetric expansion law, the method determines that a real touch signal is obtained; the application converts a deformation process into a state machine logic sequence by using nonlinear impedance mutation characteristics of biological tissue pressing, removes symmetric expansion interference caused by water stains or mechanical vibration in principle, and enhances instruction recognition accuracy of the device under complex working conditions.

The application provides a DAS system audio adaptive noise reduction method, device, medium and product, the method comprises the following steps: demodulating the light signals of each spatial sampling channel of the DAS system to obtain vibration signals, and calculating short-time energy values; constructing a global dynamic noise floor baseline reflecting the energy space-time distribution characteristics based on the short-time energy values of all channels; comparing the short-time energy values of each channel with the baseline, determining the target channel when the difference is greater than the threshold value, and using it to delimit the reference channel search range and the protection interval area; after eliminating the channels in the protection area, selecting the reference channels from the candidate set; using the reference channels to determine the environmental noise characteristics, and performing frequency domain noise reduction on the target channel. By implementing the application, the spatial correlation of the DAS array can be fully utilized to construct a robust noise reference, the effective signal of the adjacent channel can be prevented from leaking into the noise reference by setting the protection interval, and the signal-to-noise ratio and the listening clarity of the DAS restored audio in a complex environment can be improved.

The application provides a cluster distributed efficient management method and system for optimization and acceleration of large model training, relates to the technical field of large model training, and first constructs a node resource state space-time distribution graph according to real-time running state monitoring data flow of a computing node in a distributed cluster; extracts a training subtask and a data dependency relationship of a large model training task to generate a task topology association structure graph; calls a cluster resource state prediction model to generate a future resource state space-time distribution graph; inputs the above two graphs into a task resource collaborative arrangement model to obtain an initial scheduling scheme; and finally globally optimizes the initial scheduling scheme to generate a task resource collaborative scheduling instruction. The application can be applied to vertical large model training such as smart park, disaster prevention and reduction, smart energy, government affair question and answer, can improve training efficiency and cluster resource utilization rate, and promotes achievement transformation of vertical large models.