637 results about "Time space" patented technology

The invention provides a mine abnormal event real-time identification method and system based on time sequence characteristics, and relates to the technical field of mode identification, and the method comprises the steps: carrying out the time-space alignment and semantic annotation of multi-modal monitoring data, and constructing a time sequence knowledge graph; calculating a dynamic association weight between entities, and analyzing a risk propagation path; predicting a risk situation based on a historical evolution rule; and dynamically generating a differential early warning strategy and establishing a closed-loop tracking system. According to the invention, early identification, accurate prediction and efficient disposal of mine safety risks can be realized, and the mine safety management level is improved.

The invention relates to the technical field of high-precision satellite navigation and positioning, and discloses a multi-modal sensor space-time synchronization method for high-precision data acquisition, which comprises the following steps: constructing a hardware delay observation loop to calculate physical response delay in real time; establishing a receiver clock drift model and extracting a clock drift coefficient; generating a phase lead trigger instruction in response to the delay mean value and the clock drift coefficient cumulant based on the GNSS whole-second target moment; according to the method, a feed-forward control closed loop is constructed by using GNSS clock difference parameters, the intrinsic alignment of the heterogeneous sensor physical sampling action and GNSS atomic time is realized, the real-time alignment of the sensor physical sampling action and the GNSS atomic time is realized, and the real-time alignment of the sensor physical sampling action and the GNSS atomic time is realized. And a strict physical time reference is provided for high-dynamic multi-source data fusion.

The invention discloses a vehicle-mounted cross-protocol data time sequence cooperative processing method, which comprises the following steps: establishing a unified time reference, and providing an accurate space-time coordinate system for heterogeneous network nodes by electing a global master clock and constructing a clock relation model and a periodic correction mechanism; systematic cognition and management of communication resources are realized, and a verified protocol capability table is generated by automatically identifying protocol types and variants; constructing an automatic mapping system from demand to execution, performing formalized packaging on a target scene by introducing a unified intermediate model, generating a cross-protocol mapping rule based on the model and a protocol capability table, and then forming a global scheduling table in combination with a unified time reference; the cooperative communication of all nodes is finally realized by using the global scheduling table, and the end-to-end delay is constrained within the preset range, so that the problem that the end-to-end time sequence in the vehicle-mounted heterogeneous network is uncontrollable is solved, and the controllability and reliability of the time sequence of the vehicle-mounted network are improved.

The invention discloses an electric connector fault prediction method, and the method comprises the steps: synchronously obtaining multiple types of time series data of a contact area of a target electric connector in a preset time window based on a preset data collection device, and the time series data comprise temperature spatial and temporal distribution time series data, contact resistance transient time series data and a surface image sequence; performing feature analysis on the multi-source time sequence data by using a preset time sequence coupling sign extraction mechanism to obtain a multi-source phase change feature vector of the target electric connector in the current time window; inputting the multi-source phase change feature vector into a pre-trained electric connector health state prediction model, and outputting a prediction result of the health state of the target connector at the current moment, including a health state index; and performing differential early warning based on the predicted health state index of the target connector. Therefore, early and quantitative accurate prediction of the state of the electric connector is realized.

The invention provides a long video understanding method capable of relieving time sequence illusion in a video language large model. The long video understanding method is based on a static bias adaptive frame selection mechanism and a cross-modal feature fusion strategy. According to the static bias mechanism, inter-frame similarity is evaluated through a discriminator, redundant frames are identified, key frames are selected or a complete sequence is reserved, so that calculation overhead is reduced, and spatio-temporal information integrity is kept; a video frame and a text are mapped to a shared semantic space, the single-frame semantic understanding ability is enhanced, then an embedded sequence serves as a soft prompt to be input into a large language model, and a final answer is generated in an autoregression mode. According to the method, the efficiency and accuracy of long video understanding and video question and answer tasks can be remarkably improved; the problem of low training and reasoning efficiency caused by time sequence dependence redundancy and excessive computing resource consumption is effectively relieved; and through a dynamic multi-modal task processing framework and a space-time memory bank compression mechanism, the modeling capability and generalization performance of the model on a long video sequence are further improved.

The invention provides a video monitoring abnormal behavior identification and tracking linkage method based on artificial intelligence, which relates to the technical field of artificial intelligence, and comprises the following steps: carrying out space-time registration on a multi-camera video stream, and establishing a unified coordinate system; in the system, static objects are detected, and suspected remnants are marked; constructing a time sequence backtracking window to determine the association between the article and the person in charge; establishing a topological graph model containing a camera switching probability and a spatial adjacency relation; and predicting a motion track of an uncovered area according to the current motion state, dynamically distributing a tracking weight and generating an alarm. According to the invention, the remnant tracking efficiency and accuracy are improved.

Certain aspects of the present disclosure provide flow estimation techniques for dynamic scenes. A method generally includes obtaining a first time series sequence of sample sets of a scene (including a first and a second set of samples) over a first period of time associated with a reference time; processing, with a spatial-temporal attention module, the first time series sequence, to generate a modified first time series sequence of sample sets of the scene comprising the first set and not the second set of samples; processing, with a first neural ordinary differential equation (ODE) and a first ODE solver, the modified first time series sequence to predict a second time series sequence of sample sets; and generating a first output indicating a predicted flow of the scene from the reference time to a first selected time based on the modified first time series sequence and the second time series sequence.

The invention relates to the technical field of construction resource scheduling, in particular to a water conservancy construction equipment scheduling method and system, and the method comprises the following steps: obtaining on-site point cloud and soil data to construct an environmental resistance field; calculating a dynamic work efficiency matrix by combining the equipment power parameters and the resistance field; generating an initial time sequence chain based on the work efficiency matrix, and calculating an overlapping volume of a three-dimensional space envelope body of the equipment; if there is overlap, the job time is adjusted according to the priority to generate a conflict-free scheduling instruction set. According to the method, the rated work efficiency parameters of different machine types in a specific operation area are corrected in real time by constructing the equipment passing resistance model fusing the terrain gradient and the soil moisture content, the problem that static scheduling cannot adapt to environmental changes is solved, and meanwhile space-time conflict detection logic based on the three-dimensional operation envelope surface is introduced; and when it is recognized that the multi-machine moving tracks are overlapped, avoiding or peak shifting instructions with priorities are automatically generated, and conflict-free collaborative operation of the equipment cluster in a complex water conservancy scene is achieved.

The invention discloses a quality monitoring abnormity positioning method and system for multi-source heterogeneous media data, and particularly relates to the technical field of quality monitoring of the multi-source heterogeneous media data.The method comprises the steps that through access and time base normalization, sources such as RTSP, HLS / DASH, offline files and message buses are mapped to a unified time reference, and out-of-order rearrangement and late cutting are carried out; basic quality elements are extracted from A / V / S in a sliding window, a dynamic threshold value is formed by adopting a trimming mean value and a median absolute deviation, and a window-level quality event is generated; using event anchor points as common reference to estimate cross-modal time delay and fuse evidences, outputting scene / mode labels by means of a lightweight online classifier, entering a confidence reduction mode when the evidences are missing, and outputting an availability bitmap and a reason code; and then event confirmation, time-space range refinement and hierarchical root cause posterior determination are completed, influence measurement and priority are calculated, and finally an anomaly positioning and root cause analysis record set including an event timeline, severity / influence degree, root causes and disposal suggestions is formed.

The invention discloses an SLAM-oriented multi-sensor space-time synchronization and online calibration method, and relates to the technical field of computer vision and robot perception. Comprising the steps that S100, initialization is carried out, and an initial pose and an external parameter transformation matrix initial value are obtained through coarse alignment; s200, data acquisition and preprocessing: acquiring IMU data, LiDAR data and Odom data in real time, and performing data filtering, distortion removal, time sequence correction and point cloud downsampling processing on the acquired data; and S300, performing time synchronization processing, estimating the time offset of the LiDAR and Odom data in real time, and performing unified correction to generate a unified timestamp. According to the method, an online time offset estimation and compensation mechanism with an IMU as a main clock is established, accurate synchronization of sensor data of different frequencies is achieved, and the problem of data alignment caused by sampling frequency difference and clock drift is effectively solved; and meanwhile, tight coupling fusion is realized by using an IMU pre-integration factor, a LiDAR residual factor and an Odom constraint factor, so that the space-time consistency of multi-sensor data is remarkably improved.

The invention provides a self-adaptive cache evaluation method and device based on multi-dimensional evaluation parameters and a medium, and relates to the technical field of data storage. The method comprises the following steps: acquiring an evaluation access frequency f of to-be-cached data, evaluation semantic popularity h and evaluation space-time demand density d of the to-be-cached data by a to-be-cached terminal, and evaluation terminal performance e of the to-be-cached terminal; obtaining an access frequency evaluation weight alpha, a semantic popularity evaluation weight beta, a space-time demand density evaluation weight gamma and a terminal performance evaluation weight delta; and obtaining the cache value of the to-be-cached data in the to-be-cached terminal, wherein the cache value is equal to alpha * f + beta * h + gamma * d + delta * e. According to the method, the cache value of the to-be-cached data in the to-be-cached terminal is evaluated from the four dimensions of the access frequency, the semantic popularity, the space-time demand density and the terminal performance, deep demands such as semantic association, space-time distribution and terminal suitability are considered while high-frequency access is captured, resource mismatching is effectively avoided, and the cache content value can be evaluated more accurately.

The invention discloses a multi-modal robot control method and system based on space-time fusion and a liquid neural network. The method comprises the following steps: collecting asynchronous time sequence data of a multi-source heterogeneous sensor, and establishing a unified reference clock and time grid; through a neural phase-locked loop and soft-DTW, learnable synchronization is realized, and an alignment sequence, confidence and a residual error are output; after modal feature coding, fusion features are formed through confidence-gated layered space-time cross attention; a nominal control instruction is generated by the liquid neural network of the self-adaptive time constant; and the command is optimized and corrected in combination with CLF / CBF and QP, and a safe and feasible control command is output. According to the method, uncertainty caused by asynchronization, shielding and the like is effectively inhibited, the path deviation and the collision rate are remarkably reduced, and high precision, high robustness and provable safety are kept in a dynamic unstructured environment.

The invention discloses an intelligent security and protection management system and method based on abnormal target tracking, and relates to the field of security and protection management, which analyzes the space-time accessibility of a target in an environment topology after the target is lost, models the dynamic evolution process of target characteristics, and compares the judgment reference of identity re-identification from the static global appearance, so that the identification accuracy of the target is improved. And transferring to a dynamic probabilistic spatio-temporal search model with perspectiveness. And furthermore, based on the model, actively predicting a time-space window where a target may appear and an attenuated appearance portrait, performing targeted search and matching in a specified real-time video stream, and realizing reliable re-association of the identity according to the confidence of a matching result. Therefore, two key variables, namely environment topology and time decay, are introduced into a tracking mechanism, so that active prediction of the space-time position and the appearance state of the missing target can be realized, and the continuity and the accuracy of identity tracking can be guaranteed under the complex condition that the target is separated from the visual field for a long time.

A traffic flow prediction method based on a dynamic space-time fusion attention mechanism is characterized in that a space-time decoupling embedding strategy, a dynamic weighting time attention module and a self-adaptive dynamic graph modeling module are innovatively adopted, so that space and time characteristics can be effectively decoupled and independently modeled; and meanwhile, the long-term dependency relationship of the time dimension and the spatial relevance of the road network are captured. According to the method, the accuracy and generalization ability of the model in a complex traffic flow prediction task are remarkably improved, a more reliable and more efficient solution is provided for flow prediction in an intelligent traffic system, and the method has important application value and prospect.

The invention belongs to the technical field of time synchronization, and provides a multi-sensor time synchronization method and system and application, and the method comprises the steps: carrying out the calibration of a local clock of each sensor based on a reference clock signal provided by a dual-mode reference clock, so as to correct the time deviation; measuring the original transmission delay of data transmission of each sensor in real time, and generating a dynamic compensation coefficient based on a delay fluctuation model; based on the calibrated local clock and the dynamic compensation coefficient, performing correction and alignment verification on the timestamps of the sensor data, and calculating an alignment error; and if the alignment error exceeds a preset threshold value, reversely adjusting the dynamic compensation coefficient and / or the reference clock signal to form closed-loop control. According to the method, the problems of low synchronization precision, poor flexibility and insufficient real-time performance of the existing multi-sensor time synchronization method under complex working conditions can be solved, and the space-time matching reliability of a multi-sensor fusion system is improved.

The invention discloses an energy operation load prediction and early warning method and system, and belongs to the technical field of energy prediction. The method comprises the following steps: firstly, performing multi-source data fusion and preprocessing, acquiring various data such as energy system sensor data, cleaning to remove abnormal data, aligning and unifying a time sequence, extracting spatio-temporal characteristics, and performing standardization processing to obtain input data; a mixed deep learning model is constructed, spatial local features are extracted in combination with a convolutional neural network, time long-term dependence features are extracted in combination with a long-short-term memory network, an attention mechanism is introduced to enhance key data attention, and after features are fused, a load prediction result is output; then establishing a real-time prediction and early warning mechanism, deploying a model to access real-time data flow dynamic adjustment parameters, setting a multi-stage early warning threshold, monitoring load abnormity in combination with an abnormity detection algorithm, and triggering early warning when the load exceeds the threshold; and finally, absorbing new data through online learning to update the model, dynamically adjusting the feature weight, regularly training and optimizing hyper-parameters offline, and continuously improving the performance of the model.

The invention relates to the technical field of multi-section and multi-BIM model file boundary connection review, in particular to a BIM model-based comprehensive consultation method and system, which can be used for quickly reviewing boundary connection and developing a corresponding review system. The problems that large-scale project boundary connection is complex, multi-section and multi-model file collaboration is not smooth, and more disassembly and modification are generated in the construction process are solved. According to the method, the BIM components related to boundary connection are pre-selected for highlight display or specific color setting, and the boundary connection of the model is pointedly examined. Through a multi-segment model boundary connection review opinion and model view angle association function, a space-time label and a VR view angle synchronization function, a problem component in a multi-segment multi-model file is quickly searched, and a review model modification condition is quickly checked; a new and old component mapping relation is automatically established by comparing characteristic values such as vertex coordinates and normal vectors, and automatic inheritance of highlight setting of updated components is achieved; and collision and disconnection pre-check is performed by using a spatial index algorithm, so that the review efficiency is improved.

The invention discloses an intelligent satellite communication terminal beam scheduling method based on space-time path, which comprises the following steps: constructing a space-time extension resource map for mapping a connection state in a future time window based on a terminal track prediction sequence and a whole network gateway station resource capacity model; solving a global space-time path meeting service constraints in the graph, and generating resource reservation contract data containing a specified time slice, a target beam and a reserved share; and issuing the contract to a second-level network manager, executing a local schedule based on contract agreement by the second-level network manager, and triggering re-planning only when the deviation between the actual state and the execution of the contract exceeds a threshold value. According to the method, greedy beam selection is converted into full-time-domain path planning, and the decision result is packaged into a rigidly executed resource contract, so that the problems of decision short view and difficulty in cross-gateway coordination in the prior art are solved, and resource certainty guarantee and scheduling robustness of cross-domain switching of the mobile terminal are realized.

The invention discloses a three-dimensional motion estimation method based on an event camera. The method comprises the following steps: 1, constructing a time-space decoupling event time sequence and spatial representation; 2, geometric structure and context features are extracted through a space encoder, and time sequence evolution features are extracted through a time sequence encoder; 3, respectively constructing a spatial correlation graph and a time sequence correlation graph based on the geometric structure characteristics and the time sequence evolution characteristics; 4, constructing a pixel track curve according to the event density; 5, geometric structure and time sequence evolution characteristics are sampled along the pixel track curve, and a space-time correlation graph is generated; 6, iteratively optimizing pixel track curve parameters according to the space-time correlation graph and the context features; and 7, utilizing the optimized pixel track curve and the derivative thereof to accurately predict the three-dimensional motion. The method obviously improves the three-dimensional motion estimation precision of the event camera in a complex scene, is suitable for the fields of automatic driving and robots, and has a good engineering application value.

The invention discloses a secondary equipment hidden fault identification method and system based on transient state-steady state data space-time alignment. The method comprises the following steps: collecting transient state recording data and steady state SCADA (Supervisory Control And Data Acquisition) data; unified time service is carried out on transient state and steady state data, and a time mark error is corrected; performing Lagrange interpolation resampling on the transient data to generate a virtual sequence aligned with a time axis of the steady-state data; constructing a cost matrix for the resampled transient sequence and the resampled steady-state sequence, and backtracking a shortest path to obtain an aligned fusion sequence; inputting the fusion sequence into a multi-scale feature fusion network, and outputting a fault type and confidence; the system comprises a data acquisition module, a clock drift correction module, a resampling alignment module, a dynamic time warping module and a fault feature extraction module. According to the method, the fusion problem caused by sampling rate difference, clock drift and dimension difference of transient and steady data is solved, millisecond-level space-time alignment is realized, and the precision of data fusion is remarkably improved.

The invention discloses a video restoration method, a video restoration model and a video restoration model construction method. The method comprises the following steps: multiplexing complete parameters and architecture of a pre-training image restoration model, and carrying out unified time sequence expansion processing on the model: adding a time sequence convolution layer and an attention layer in an image encoder and a decoder to construct a time sequence encoder and a time sequence decoder; integrating a time sequence attention module and position embedding in the image restoration network to construct a video restoration network; and finally, combining the expanded components and inheriting pre-training parameters to complete low-cost construction of the high-performance video repair model. When the model is applied, time-space features are extracted through time sequence coding, global time-space cooperative enhancement is carried out through a video restoration network, and then a high-definition video is reconstructed through a time sequence decoder. The problems of inter-frame hopping, detail smearing and high training cost during video restoration are solved, and the high-quality restored video with rich details and coherent time sequence is output while the training resource consumption is reduced.

The invention discloses a computing power load space-time migration regulation and control method and a related device. The method comprises the following steps: acquiring power regulation information required by a power grid frequency modulation service; according to the power regulation information required by the power grid frequency modulation service, constructing a space-time migration characteristic model of a data center computing power load, and calculating the computing power load based on the space-time migration characteristic model of the data center computing power load; constructing an optimization problem by taking the minimum economic cost as an optimization target according to the computing power load capacity, constructing constraint conditions, solving the optimization problem under the constraint conditions to obtain an optimal regulation and control scheme, and carrying out computing power load space-time migration regulation and control according to the optimal regulation and control scheme. According to the method and the related device, the space-time multi-dimensional adjustment potential of the computing power load can be mined for multi-service requirements, the space-time migration regulation and control of the computing power load are improved, and safe and economical operation of a power grid is ensured.

The invention discloses a cooperative control method for a multi-domain unmanned system. The method comprises the following steps: acquiring an original sensor sequence and a late message set; according to the original sensor sequence, calculating a dominant function and anti-fact regret to obtain a value signal; generating a time delay weight according to the late message set; constructing a space-time source item based on the value signal and the time delay weight; constructing a neural pheromone field by solving partial differential equation dynamics by using space-time source items and pheromone field parameters; under the guidance of the neural pheromone field, sampling to generate a candidate path set; and selecting a cooperative path from the candidate path set for the unmanned system to execute. According to the method, the problems of value characterization and timeliness under asynchronous communication are solved, deep coupling of a decision strategy and a physical model is realized, and the robustness and the adaptive ability of collaborative decision are improved.

The invention belongs to the technical field of geotechnical engineering disaster prevention and reduction and earthquake engineering tests, and relates to a vibration table test method considering time-varying deterioration of anti-seismic performance of an anchored rock slope. The method comprises the following steps: firstly, constructing a dynamic performance time-varying deterioration model based on an anchoring system interface bonding force and a free section steel bar yield resistance attenuation rule; physical equivalent simulation of interface degradation and component yield resistance reduction is realized by regulating and controlling the proportion of rock mass similar materials and utilizing a graded rigidity anchoring system. The core of the method is that before a vibration table applies seismic waves, attenuation of the anchoring performance of a supporting structure is actively simulated in advance, so that a real evolution path that the bearing capacity of an anchoring system is reduced firstly, slope stress redistribution and accumulated damage are caused, and then sudden seismic loads are suffered is reproduced; the limitation that only the static low bearing capacity is simply simulated in a traditional test is broken through. According to the method, space-time coupling of static time-varying degradation accumulation and transient earthquake dynamic action is effectively achieved, the problems of interface degradation simulation distortion and the like are solved, and a reliable test method is provided for dynamic stability evaluation and reinforcement design of a long-term service anchoring slope.

The invention discloses a space-time decoupling efficient electroencephalogram basic model construction method and device, and relates to the technical field of brain signal processing. The method comprises the following steps: constructing an electroencephalogram basic model based on linear attention and spatio-temporal data decoupling; segmenting the acquired electroencephalogram signal data, inputting the segmented electroencephalogram signal data into an electroencephalogram embedding layer, and outputting an embedding vector; inputting the embedded vector into a space-time mixing module for separation and decoupling to obtain a spatial feature and a time feature; performing coding calculation on the spatial features and the time features based on an enhanced bidirectional weighted key value algorithm of a linear attention mechanism to obtain new spatial hidden layer features and time hidden layer features; mixing the two hidden layer features to obtain spatial-temporal features; inputting the spatial-temporal characteristics into a forward propagation network, and pre-training the model to obtain a trained model; and inputting a downstream task to be executed into the trained model for processing, and outputting a task result. According to the invention, the brain signal processing efficiency can be improved.

The invention provides a data management method and device based on a space-time hybrid index and a program product. The method comprises the steps of obtaining to-be-queried data; querying the double-layer index structure according to the to-be-queried data to obtain a query result; wherein the double-layer index structure comprises a first-layer index and a second-layer index, and the first-layer index is obtained by dividing a minimum containing area corresponding to each physics teaching space on the basis of a teaching behavior data set and constructing a concept hierarchy tree on the basis of each minimum containing area; the minimum containing area represents a minimum area which completely covers all activity ranges of the corresponding physics teaching space geometrically, the second-layer index is obtained on the basis of constructing a balance tree in each teaching unit, and the teaching units are used for representing the minimum containing area after the concept hierarchy tree is constructed in the first-layer index; and updating the double-layer index structure according to the teaching behavior data when determining that the to-be-queried data is the teaching behavior data based on the query result that the to-be-queried data is the non-queried data. The method can support complex space-time semantic query.

The invention provides an intelligent marking and analyzing method for steel production quality based on multi-process space-time mapping. The method comprises the following steps: acquiring a time sequence curve of characteristic parameters, material tracking data and material quality data of a target material in each process in production; according to the material tracking data, establishing a mapping model of the processing position, the process processing time and the cross-process processing position; standardized marking is conducted on the machining position and the machining time interval based on the mapping model; extracting corresponding time sequence curve features based on the annotation interval; analyzing the difference between the target material and the reference material based on feature comparison; and generating a diagnosis result according to the analysis result. The technical problems that in the prior art, the process curve feature extraction mode is single, cross-process data are difficult to associate, and the quality problem tracing efficiency is low are solved.

The invention discloses a physical equation constraint and long short-term memory network combined NDVI (normalized difference vegetation index) time sequence prediction method, which comprises the following steps of: acquiring meteorological variables, vegetation indexes and soil data related to NDVI change in a historical time period of a target area from a plurality of data sources, and preprocessing to obtain a spatio-temporal data set; extracting statistical features and spatial geometric features of an NDVI time sequence from the spatio-temporal data set, and constructing an NDVI spatio-temporal cube of which the dimension is a time-space-environment factor; the space-time cube is flattened after being subjected to seasonal trend decomposition, a trained mixed type multivariable LSTM-PINN model is input for time sequence prediction, and an NDVI time sequence prediction result of the target area in the target time period is obtained. The invention relates to the technical field of NDVI (normalized difference vegetation index) time sequence prediction, and realizes high-precision prediction of NDVI time sequence change of a target area by fusing multiple technical means such as multi-source three-dimensional space-time combination construction, physical-data dual-drive LSTM-PINN network modeling, constraint condition construction based on a vegetation ecological mechanism and the like.

The invention relates to the technical field of distributed online reasoning cluster scheduling, and discloses an operator fusion and grouping fragmentation collaborative scheduling optimization method, which comprises the following steps: S1, collecting operator execution events under a unified time base, recording operator identifiers, topological relations, fragmentation positions and resource occupation, and forming a joint execution event flow; and S2, constructing an operator layer graph based on the joint execution event flow, and identifying and fusing candidate fragments according to adjacent calling and intermediate data multiplexing. A joint execution event flow is collected under a unified time base, a space-time collaboration graph for superposing fragmentation boundaries and node resource states is constructed, and an operator fusion mode, a grouping fragmentation scheme and a scheduling strategy are jointly determined on the graph by taking a collaboration execution unit as granularity. Therefore, the same fusion candidate slice paragraph is explicitly constrained in the same slice and the same node in a multi-model mixed operation scene, cross-slice data migration and cache jitter are reduced, the balance of cluster resource utilization is improved, and the tail delay fluctuation of a service request is reduced.

The invention belongs to the technical field of intelligent humanoid robots, and discloses a remote operation data acquisition and data closed-loop system for an intelligent humanoid robot, which builds an instruction, action and acquisition ternary time sequence synchronization mechanism through a time sequence coordination module, realizes dynamic coordination of the three parts by using a time sensitive network technology, effectively reduces time sequence deviation, and improves the system performance. Meanwhile, a space-time alignment algorithm of the data processing module is combined, so that the consistency of the multi-modal data in time and space dimensions is guaranteed; acquired data can accurately fit human operation logic and a robot action state, the optimization efficiency of a data closed loop is improved, and the requirements of high-precision operation scenes such as complex assembly and flexible material processing are successfully met; a quantitative association system of data quality and control effects is established by means of the evaluation module, invalid data such as sensor faults and action fuzziness can be screened in real time, and occupation of redundant storage and computing resources is reduced.