1366 results about "Time space" patented technology

The invention discloses a cross-regional virtual power plant cooperative scheduling method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring real-time characteristic data such as space-time positions, output / demand prediction and the like of distributed energy resources and loads, and determining dynamic weights of characteristic dimensions based on a global optimization target and data of a current scheduling period; generating a dynamic resource cluster division instruction containing a member list and a coordination constraint condition according to the dynamic weight and the real-time data, and sending an initial cross-regional coordination scheduling instruction containing a net exchange power target value and the like and a compensation price signal to each dynamic resource cluster local agent; after aggregation response boundary information returned by the local agent is received, an instruction and a signal are updated, a target collaborative scheduling instruction is obtained and finally sent to each dynamic resource cluster for execution, and effective control over cross-regional virtual power plant resources is achieved. According to the method, the problem that the adaptability of the cross-regional virtual power plant collaborative scheduling instruction and the actual resource capacity is insufficient can be relieved.

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 belongs to the technical field of time series data processing, and particularly relates to a time series data anomaly detection method based on a variable time converter. According to the method, an anomaly detection model VT-GAN is provided, the model designs a parallel generator group, and each generator extracts mode features of different time scales through expansion causal convolution; time self-attention, variable self-attention and cross attention layers are fused in the VTT architecture, and a time-space interaction relationship is explicitly modeled through a learnable gating weight; and a loss function of the VT-GAN model is adjusted and optimized in combination with temperature parameters, and data anomaly detection is carried out based on the constructed VT-GAN model. The method solves the problems that a traditional model is difficult to meet millisecond-level response requirements due to sequence calculation characteristics, the calculation complexity of a global attention mechanism in high-dimensional time series data limits the real-time performance, the false alarm rate is high, and the anomaly detection coverage rate is low.

The invention belongs to the technical field of power system load prediction, and discloses a cross-dimension multi-scale fusion load prediction method based on multi-user load time-space correlation, which comprises the following steps of: firstly, preprocessing user load statistical data, extracting time sequence dependence and periodic characteristics in a time sequence, and calculating the time sequence dependence and periodic characteristics of the user load statistical data; introducing a channel attention mechanism to adaptively mine key variable information; then, a multi-scale space-time fusion module is combined with frequency domain analysis and a graph convolutional network to realize depth feature interaction under different time scales and space levels; and finally, outputting a load prediction result under a plurality of time granularities in the future through a linear projection structure. Compared with an existing method, the method has the remarkable advantages in the aspects of capturing a complex load mode, improving model prediction precision and enhancing generalization ability, and is suitable for various application scenes such as power consumer energy consumption management and power grid load dispatching.

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 provides a space-time decoupling feature fusion method based on space-time bidirectional Vision Mama, and the method comprises the steps: S1, extracting multi-scale space detail features from an input time sequence image through employing a plurality of cascaded 2D CNN blocks, and obtaining a feature graph; s2, a bidirectional space-time Mama structure is applied to the feature maps of the low and middle layers so as to compensate for loss of time information in the 2D CNN block; wherein for the feature map obtained in the step S1, time sequence features are extracted by applying time bidirectional Vision Mama, and then space features are extracted by using space bidirectional Vision Mama. The invention further provides a space-time decoupling feature fusion system based on the space-time bidirectional Vision Mama. According to the method, efficient and accurate feature extraction and information fusion are performed on a special time sequence multi-band multi-channel remote sensing image, and efficient and high-precision semantic segmentation of crops on a large-area scale remote sensing image is realized.

The invention discloses a building engineering full life cycle resource intelligent scheduling system and method, and belongs to the technical field of building management, and the method specifically comprises the steps: collecting dynamic physical parameters of a component installation process in a construction stage; constructing a spatio-temporal index engine, associating the dynamic physical parameters to corresponding component nodes of the BIM model, and generating a spatio-temporal binding data unit; in an engineering operation and maintenance stage, monitoring operation state data of the component, and when an abnormal state is recognized, extracting current space coordinates of the abnormal component and activating a reverse traceability channel; taking the space coordinates of the abnormal components as retrieval anchor points, matching the space-time binding data units with the same space coordinates, and obtaining a dynamic physical parameter sequence in a construction stage; comparing and mapping the dynamic physical parameter sequence and the abnormal state, generating an operation and maintenance decision instruction set with a construction defect positioning identifier, and inputting a resource scheduling sequence; according to the method, the utilization rate of operation and maintenance decisions on construction stage data is remarkably improved.

The invention discloses an intelligent ship situation awareness prediction method and system, and relates to the technical field of intelligent ship situation awareness and trajectory prediction, and the method comprises the steps: carrying out the time-space synchronization of collected operation environment data of a ship through a timestamp alignment method, and generating a ship situation data set; based on the ship situation data set, the course angle change rate and the Euclidean distance of the obstacle relative to the ship are extracted, and weighted fusion is carried out on the dynamic collision probability and the approaching time of the obstacle to obtain an obstacle threat degree index; inputting the obstacle threat degree index into a ship trajectory prediction model, and outputting the minimum meeting distance between the ship and each obstacle; and carrying out risk grade division on the minimum encounter distance according to an international maritime collision avoidance rule, outputting a graded early warning signal, and formulating a navigation strategy to execute obstacle avoidance and route adjustment. According to the method, the technical problems of risk perception lag, single and rough avoidance action and insufficient trajectory prediction accuracy in the prior art are effectively solved.

The invention discloses an intelligent visual tracking method fusing memory distillation and space-time alignment, and belongs to the technical field of artificial intelligence, computer vision and target tracking. Firstly, feature extraction is carried out on an input target template frame and an input search frame through a shared backbone network, an image is divided into a non-overlapping image block sequence, and feature representation is generated. Then, the features are processed by adopting an adaptive space-time consistency attention module, the module comprises time and space attention branches, and cross-frame time sequence dependence and intra-frame target-background association are respectively modeled; then, a space-time memory distillation network is constructed, target features are maintained through a dynamic memory library and a gating read-write mechanism, and feature representation is optimized through knowledge distillation; and finally, designing a space-time consistency loss function, and carrying out joint optimization by comprehensively considering trajectory smoothness, motion consistency and feature time sequence coherence. Through the combination of the technical schemes, the performance and robustness of visual target tracking can be effectively improved.

The invention relates to the technical field of data processing systems, and discloses a command and dispatch management method and system based on artificial intelligence, and the method comprises the steps: generating a space-time occupancy statement containing an occupancy area, a time window and a task state through an edge node, and broadcasting the space-time occupancy statement to an adjacent node; and a receiver performs local conflict verification according to a state mutual exclusion rule, and triggers priority-based lightweight negotiation to adjust an occupied time window when detecting that static observation and dynamic intervention states coexist in an overlapped space-time. Postward conflict processing of traditional centralized scheduling is converted into beforehand conflict avoidance, the response speed and the operation toughness of the system are remarkably improved, and meanwhile, through physical reachability verification and a channel self-adaption mechanism, the cooperation reliability in a complex environment is guaranteed.

The invention discloses a multi-stage spatial-temporal clustering method and system based on a fused mahalanobis distance. The method comprises the following steps: acquiring a spatio-temporal data set, and determining a spatio-temporal neighbor relation of samples in the data set; calculating a space communication distance and a time decay distance of the sample; fusing the space communication distance and the time decay distance by using a mahalanobis distance to obtain a relative distance of the sample; selecting a class cluster center from the data set according to the local density of the sample and the relative distance; adopting a multi-stage distribution strategy to distribute non-class-cluster center samples to corresponding class clusters; wherein the multi-stage allocation strategy comprises an inevitable allocation stage based on space-time shared neighbor and a similarity allocation stage based on a weighted similarity matrix. According to the method, the key problems that an existing space-time clustering algorithm is insufficient in space-time attribute coupling processing and sensitive to distribution errors are solved, and the clustering accuracy, robustness and practicability in the fields of intelligent traffic analysis, seismic sequence recognition and the like are remarkably improved.