259 results about "Global time" patented technology

The invention discloses an edge computing assisted wireless communication system, which relates to the technical field of edge computing, and comprises an edge computing node cluster which is deployed at a base station and an access point and provides distributed computing and storage capability; the resource sensing module is used for monitoring computing resources, communication resources and task queue states of the edge nodes in real time; the dynamic scheduling controller is used for dynamically deciding a task unloading path based on reinforcement learning and game theory algorithms, wherein the task unloading path comprises local processing, edge node cooperative processing, cloud unloading and global time delay and energy consumption optimization; the cross-layer communication interface supports multi-protocol fusion of 5G NR and Wi-Fi 6, and realizes low-delay data interaction between edge nodes; and the security verification module is used for ensuring credibility and integrity of data between edge nodes through a lightweight block chain and a TEE (Trusted Execution Environment). According to the invention, through a hybrid decision framework of reinforcement learning and game theory, the problem that a single algorithm in a traditional scheme cannot give consideration to dynamic environment adaptability and multi-node benefit coordination is solved.

The invention discloses a Transform-based traffic flow prediction method and device capable of sensing a local-global time-space relationship, and the method comprises the steps: firstly obtaining the historical traffic data of all to-be-predicted road sections, the historical data comprising the traffic flow feature data of the predicted road sections, the static adjacent matrix data between road network nodes, and the sampling timestamp data of the historical data; secondly, a spatio-temporal information embedding layer is constructed to provide multiple types of embedding input for a model trunk, the learning ability of the model is enhanced, and three different types of embedding are respectively historical data information embedding, time information embedding and space node self-adaptive embedding; then, constructing a local-global time dependence extraction module, respectively learning short-time and long-time time dependence relationships in the data by using a multi-scale TCN and a self-attention mechanism in a time dimension, and meanwhile, introducing a double-path self-adaptive information gating fusion technology to realize effective fusion of time features of different hierarchies; then constructing a local-global spatial dependency extraction module, respectively learning local and global spatial dependency relationships in the data by using a dynamic-static graph convolutional network and a self-attention mechanism in spatial dimension, and realizing effective fusion of spatial features of different levels based on a two-way adaptive information gating fusion technology; and finally, mapping the potential spatial-temporal feature representation into a prediction result through a full connection layer network.

The invention provides a seasonal power load prediction method based on dual-scale feature fusion, relates to the technical field of power information, and is used for solving the technical problem that an existing power load prediction method is insufficient in prediction precision in a seasonal fluctuation scene. The method comprises the following steps: acquiring original power load data, and preprocessing the original power load data; generating structured graph data according to the preprocessed original power load data; inputting the graph data into a pre-trained power load prediction model, and executing the following operations; extracting spatial distribution characteristics among different nodes in the graph data by using a graph convolutional network; extracting short-term dependency features between adjacent time steps in the feature variables by using a time convolutional network; extracting long-term dependence features of the feature variables in global time by using a bidirectional long-short-term memory network; and predicting a power load value of the target time step according to fusion features of the spatial distribution features, the short-term dependency features and the long-term dependency features.

The invention relates to the technical field of time synchronization, and discloses a hybrid time service method based on Beidou satellite navigation and a TSN, which comprises the following steps: acquiring network topology structure information and historical time service error data of each synchronization node in a target distributed system, and constructing a dynamic time delay state set for jitter identification; based on the dynamic time delay state set, a current global time reference is extracted through a time calibration algorithm, a time difference reference vector sequence is generated, and initial synchronization adjustment is carried out on the TSN main node; constructing an inter-node clock convergence function curve according to the slave node response stability, and judging whether the synchronous network is in a low jitter interval or not based on the time offset rate and the time drift trend change; in combination with the node time state after directional offset suppression, a multi-channel redundant time synchronization mechanism in a time window is applied, a weight adjustment coefficient is extracted, and an unbalanced calibration factor is introduced; and according to a regression correction result, evaluating a time service stability index in real time. The method has the advantage of improving the time service stability.

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 discloses a multi-head time sequence intelligent risk control method and system based on weighted trend and fluctuation, and relates to the technical field of financial risk management. Comprising the following steps: S1, collecting multi-channel time sequence data in real time, and carrying out data preprocessing; s2, calculating the global time weight, quantifying the multi-scale fluctuation stability of the channel pair, and judging the asynchronous alignment degree of the channel pair; s3, extracting an effective frequency band interval, calculating frequency domain characteristic parameters of the signal, evaluating frequency domain energy phase characteristics of a channel signal, and quantifying fluctuation states of a channel under different scales; s4, constructing a sparse coupling relation graph, quantifying an edge weight in the sparse coupling relation graph, and obtaining a network average coupling weight; and S5, evaluating the dynamic evolution characteristics of the channel risk state, and generating risk trend prediction and control suggestions. The problem that risk control accuracy is affected due to the fact that trend and fluctuation feature extraction of multi-source heterogeneous high-noise multi-head time series data is unstable under the condition of concept drift and multi-scale coexistence is solved.

The invention discloses a command and control system resource trend prediction method based on fusion of long and short time sequence characteristics. The method comprises the following steps: acquiring a public power load or similar time sequence monitoring data set, and preprocessing the data in the data set; a deep learning network model based on a TCN-Transformer hybrid model is constructed, a TCN model and a Transformer model are adopted for parallel computing to achieve feature extraction, the TCN model extracts short-term information, the Transformer model extracts long-term features, then fusion features are obtained through a cross attention mechanism and multi-layer perceptron (MLP) weighting, and finally prediction output is generated through full connection layer mapping. Taking data in the training set as input, training the constructed TCN-Transform hybrid model, and continuously optimizing the model until convergence meets a set requirement; and performing prediction by using the trained network model. According to the method, the TCN-Transform hybrid model is constructed, so that local fine-grained features are reserved, the global time trend is effectively captured, and the accuracy of command decision making is improved.

The invention provides an intelligent fan running state control system based on multi-source data, and relates to the field of wind power generation, and the intelligent fan running state control system comprises the steps: firstly, collecting a time sequence set of running parameter data of a target fan object and a time sequence set of electric field outlet power data, and then constructing a field domain-equipment two-stage information fusion mechanism; and performing mutual information guided deep interaction on the global time sequence characteristics of the electric field outlet power and the local time sequence characteristics of the single machine operation parameters, thereby optimizing the expression of the self-information of the fan operation state by using the time sequence characteristics of the electric field outlet power, determining the detection result of the fan operation state, and determining the early warning level and the control strategy. The method is used for breaking through the technical bottleneck of decoupling of global context information and local equipment state of wind power plant operation in a traditional method, and is beneficial to more accurate detection and control of the operation state of the fan.

The invention relates to the technical field of new energy power generation prediction, and discloses a t-SNE visualization and depth time sequence attention model-based wind and light power prediction method and system, and the method comprises the steps: obtaining historical power data and corresponding historical meteorological data of a wind power station and a photovoltaic station, and constructing a historical data set; inputting the preprocessed high-dimensional meteorological data into a t-SNE dimension reduction module, and mapping the high-dimensional data to a low-dimensional space through symmetric joint probability density calculation to obtain low-dimensional visual data distribution; the low-dimensional data are input into a parallel model composed of a TCN-SENet branch and a BiGRU-GlobalAttention branch, and space-time local features and global time sequence features are extracted respectively; and fusing the feature vectors output by the two branches, generating a wind-solar power prediction result through a full-connection layer, and performing evaluation. According to the method, the limitation of traditional single energy independent modeling is broken through, the adaptability of the model to a complex power generation mode of a distributed station is optimized, and the precision and generalization ability of multi-energy joint prediction are remarkably improved.

The invention provides a servo press control system and method based on a time-sensitive network, and relates to the technical field of intelligent manufacturing and industrial automation control, and the system comprises a time-sensitive network TSN which is used for providing global time synchronization and deterministic data transmission; a main controller of the multi-axis servo control unit is used for issuing a motion control instruction and a feeding and discharging control instruction through a TSN. A robot controller in the robot feeding and discharging unit is used for driving an industrial robot to execute plate taking and placing operation under the action of the feeding and discharging control instruction. The visual monitoring unit comprises an industrial camera accessed to the TSN and is used for collecting images and transmitting image data to the edge computing node through the TSN; the edge computing node is used for processing the image data to generate a compensation instruction and feeding back the compensation instruction through the TSN; and the upper monitoring system is used for subscribing real-time data published by the edge computing nodes, so that the control range of the servo pressure control system is expanded, and the convenience of data transmission and interaction is improved.

The invention discloses a self-supervised traffic flow prediction method based on multi-scale space-time-frequency fusion. The method comprises the following steps: acquiring enhanced data; performing multi-scale spatial-temporal feature coding; performing frequency domain residual filtering; generating a traffic flow prediction result; and carrying out joint target optimization. According to the invention, the multi-scale space-time frequency encoder is designed, local and global time features are captured at the same time through the mixed time encoding module in the time dimension, the multi-scale space encoding module aggregates space features under different distances in the space dimension, and the prediction precision is significantly improved. A frequency domain residual filtering module is embedded in the encoder to adaptively purify frequency domain features in an end-to-end mode, enhance key periodic features and suppress irrelevant noise, space-time features are fused through residual connection, space-time-frequency three-dimension collaborative modeling is achieved, meanwhile, frequency domain consistency loss is introduced in the optimization stage, and time-frequency three-dimension collaborative modeling is achieved. And the method is more robust when facing real traffic data containing noise.

The invention relates to the field of cloud computing and cross-border supply chain management, and discloses a cross-border supply chain real-time monitoring method in a cloud computing environment. Comprising the following steps: deploying data acquisition and access modules for supply chain nodes of different countries or regions on a cloud computing platform, and establishing a global time index based on node reporting time to realize time sequence correction of multi-source data; performing multi-modal fusion processing on the corrected data set to form a comprehensive feature vector with a unified dimension; constructing a supply chain digital twin map to associate logistics, information flow and fund flow, and realizing node state prediction and risk identification; executing cross-border data synchronization and privacy protection according to compliance requirements of each country; after the risk is identified, risk traceability and feedback adjustment are carried out through graph structure simulation, so that self-adaptive updating and periodic self-correction of system parameters are realized. According to the invention, real-time monitoring, risk prediction and compliance data synchronization of the cross-border supply chain can be realized, and the safety and stability of the system are improved.

The invention discloses a distributed multi-screen synchronization method, and relates to the technical field of video synchronous display, and the method comprises the steps: forming an output node cluster by a plurality of devices, dynamically electing a main node in the output node cluster, and enabling the main node to serve as a global synchronization controller; the interrupt time sequence of the distributed nodes is synchronized, and alignment of frame taking moments of a video output module VO of the equipment is achieved; and a frame synchronization controller is selected from the distributed nodes, the frame synchronization controller collects the data of the distributed nodes and carries out frame synchronization control, so that multiple devices can synchronously display the same frame of data. Based on a dynamic main node election mechanism, decentralized distributed deployment is achieved, and distributed deployment can be achieved without intervention of an upper computer or input node equipment; by constructing a frame synchronization mechanism based on a global time reference, the tearing and dislocation phenomena of a moving picture are effectively eliminated; and realizing multi-screen synchronization in a sub-millisecond error range through an interrupt synchronization mechanism.

The invention relates to the technical field of signal processing, and discloses a multichannel data acquisition system and method based on an FPGA (Field Programmable Gate Array), an acquisition module drives a global counter by using a global synchronous clock, writes acquired data into an annular buffer area with a physical address and a counter value in a linear mapping relationship, and establishes implicit time index storage. When a trigger event occurs, the system broadcasts the locked global trigger timestamp, and the acquisition module backtracks and reads historical data according to the global trigger timestamp, and packages the historical data into a sparse matrix type data packet in combination with the channel validity mask. And after receiving the data packet, the data processing module directly calculates a memory mapping address by using global time information in the data packet, and writes a data load into a corresponding position of the waveform reconstruction buffer area. According to the method, through strict binding of the physical address and the absolute time, independent time label redundancy is eliminated, high-precision synchronization of distributed multiple channels is ensured, and out-of-order automatic in-situ recombination and efficient waveform reconstruction of data of a receiving end are achieved.

The invention relates to the technical field of industrial automation control and dynamic simulation, and discloses a centralized control console and 3D model linkage virtual-real simulation system, which comprises a control signal sampling interface, a load response modulation unit, a dynamic model evolution unit and a virtual-real closed-loop feedback unit, the load response modulation unit performs energy consumption weight accumulation on all the virtual controlled objects in transient action to generate a global time scaling factor so as to correct an inertia time constant of the system in real time; the dynamic model evolution unit introduces a hard limiting boundary by using an amplitude limiting link and calculates a process variable; the virtual-real closed-loop feedback unit extracts a control deviation signal and drives a physical instrument to generate a nonlinear damping action, and by establishing a global load modulation model based on energy constraint, a discrete control system spontaneously emerges a nonlinear hysteresis characteristic conforming to physical energy conservation under the low calculation power condition without complex fluid network solution.

The invention provides a social robot detection method and system, computer equipment and a storage medium, and belongs to the technical field of social robot detection.The method comprises the steps that user multi-source data on a social platform and the social relation between users are collected; a hybrid encoder is adopted to capture local dependence and global time sequence dynamic states of behaviors, and user behavior characteristics are generated; aggregating structure attention features between the initial node features of the target account and the initial node features of the social relation account to obtain multi-modal relation aggregated structure features; introducing a multi-modal adversarial training strategy, adaptively adjusting the disturbance intensity of each modal based on gradient sensitivity, and aligning the characterization of the clean sample and the adversarial sample in combination with a content discriminator and a behavior discriminator; and outputting a target user classification result through the multi-task target function optimization model. According to the method, the problems of insufficient modal fusion and insufficient adversarial robustness of an existing method can be effectively solved, and the accuracy and stability of social robot detection in a complex and adversarial scene are improved.

The invention discloses a robot end effector trajectory planning method and device, and relates to the technical field of robots. The method comprises the steps that the first initial joint state, the pre-grabbing posture and the grabbing posture of an end effector of the robot are obtained; on the basis of a preset first constraint, the first initial joint state and the pre-grabbing pose, a feasible preparation track is obtained; on the basis of a preset first constraint, a preset second constraint, the final joint state of the feasible preparation trajectory and the grabbing pose, a feasible grabbing trajectory is obtained, and the second constraint comprises a direction constraint and a linear path constraint; and obtaining a final track based on the feasible preparation track and the feasible grabbing track. Directional-linear joint constraint of part of tracks in a grabbing task can be achieved, the efficiency bottleneck of a traditional two-step method is broken through, and the track planning efficiency is improved. The dynamics consistency of the two sections of tracks can be ensured, and the final track can be smoother. The real optimization of global time and smoothness can be realized, and the track quality is improved.

The invention provides a multi-sensor domain controller dynamic management method and a domain controller system, and the method comprises the following steps: S1, taking an external Ethernet gateway as a main clock source, and carrying out the time service of an MCU module in a domain controller based on a GPTP protocol; s2, the MCU module serves as a secondary clock source, distributes synchronous clock signals to the SOC module and an external sensor and collects clock skew data in real time; s3, when the main clock source fails, automatically switching to an internal GNSS module and an IMU module as standby clock sources, and dynamically compensating a time sequence error based on historical clock skew data; s4, controlling a global time synchronization error to be less than 150 microseconds through hardware-level timestamp marking and a software-level error correction algorithm; and S5, based on the synchronized time sequence data, performing timestamp alignment and fusion on the heterogeneous data input by the multiple sensors. According to the invention, the problems of clock redundancy and insufficient error compensation in the prior art are solved.

The invention relates to the technical field of state prediction of hydroelectric power generation equipment, and discloses a cooler blockage prediction method based on deep learning coupling physical constraint, which comprises the following steps of: acquiring temperature, pressure difference and flow parameters during operation of a cooler, and generating time sequence data; preprocessing the time sequence data to generate a time sequence sample vector; constructing a deep learning hybrid model, extracting local features through the deep learning hybrid model, and capturing global time sequence dependence; combining a Navier-Stokes equation and a cross entropy to construct a comprehensive loss function; and inputting the processed data into the deep learning hybrid model, and outputting a cooler blockage state classification result. According to the method, early accurate recognition of the blockage state of the cooler can be achieved, the problem of response lag of a traditional method is solved, early warning can be given out in the early stage of blockage, the blockage type is recognized in advance, and non-planned shutdown is avoided.

The invention provides a time synchronization method and device and a time synchronization system of an intelligent driving system, and relates to the technical field of data processing, in particular to the technical fields of automatic driving, electronic technology and time synchronization. The specific implementation scheme of the time synchronization method comprises the following steps: acquiring second-level world time and a corresponding second pulse signal; determining a second global timestamp corresponding to a predetermined edge of the pulse per second signal according to a first local timestamp of the captured pulse per second signal, a second local timestamp of the execution moment of the interrupt service function and the first global timestamp; wherein the interrupt service function is triggered by a second pulse signal; obtaining the second time of the current moment according to the global timestamp of the current moment and the second global timestamp; and obtaining the accurate time of the current moment according to the second-level world time and the within-second time. According to the invention, errors can be reduced, and high-precision global time can be provided.

The invention discloses a TCN-Transform-based renewable energy power grid load prediction method and device. The method comprises the steps of constructing a data set containing historical load data, meteorological data, renewable energy output data and electricity price data of a renewable energy power grid; inputting the data set into a time convolutional network, and extracting local features in a time sequence by using an expanded convolutional layer; inputting local features in the time sequence into a Transform network, and extracting global features in the time sequence by using a multi-head self-attention mechanism; and the local features extracted by the time convolution network and the global features extracted by the Transform network are fused, a renewable energy power grid load prediction model is constructed, and a load prediction value at a future time point is generated. According to the method, the local and global time sequence features are extracted, and dynamic features such as renewable energy output and electricity price are combined, so that the precision and reliability of load prediction are improved, and the scheduling requirement of a high-proportion renewable energy power grid is met.

The invention discloses a video understanding large model optimization and evaluation method, system and device and a storage medium, which are in one-to-one correspondence, and in the scheme, a new connector structure is designed to improve the global time sequence understanding capability, and the new connector structure comprises a space-time downsampler, a local bidirectional Mama structure and a linear layer. The time-space downsampler can reduce the token storage overhead; meanwhile, the local bidirectional Mama structure makes up for the problem that the receptive field is limited on one hand, and on the other hand can model intra-frame features and inter-frame features at the same time; in addition, training of the connector is low in cost, a three-stage progressive training strategy is used, and catastrophic forgetting can be resisted. Moreover, a semi-automatic data generation pipeline is constructed, and global time sequence understanding data is provided based on the pipeline, so that the evaluation vacancy of the existing reference field in the capability is made up.

The invention belongs to the technical field of semiconductor design automation, and provides a digital circuit engineering correction method and related equipment. The method comprises the following steps: after wiring of a digital circuit is completed, performing static time sequence analysis based on actual circuit resistance-capacitance parasitic parameters of a time sequence path in the digital circuit to obtain global time sequence information of the time sequence path, and screening out an abnormal time sequence path containing an abnormal logic unit from the digital circuit based on the global time sequence information; performing logic unit change on the abnormal time sequence path containing the abnormal logic unit, and performing wiring change on the digital circuit after the logic unit change; and if the wiring violation does not exist in the digital circuit, returning to execute the step of performing the static time sequence analysis based on the actual circuit resistance-capacitance parasitic parameter of each time sequence path in the digital circuit until the abnormal time sequence path does not exist in the digital circuit. According to the technical scheme provided by the invention, the correction efficiency of the digital circuit engineering can be improved.

The invention discloses an intelligent e-commerce data recommendation method and system based on a time sequence diagram neural network and an attention mechanism, and relates to the technical field of e-commerce data analysis, and the method comprises the steps: constructing a dynamic time sequence diagram through user behavior time sequence data, calculating the weight of an edge in the time sequence diagram through a time decay function, and calculating the weight of the edge in the time sequence diagram; meanwhile, local time sequence features among commodities are captured in combination with a gating graph neural network; the method comprises the following steps of: introducing a Star-transform network structure, wherein the structure realizes long-range information interaction of non-adjacent commodities through a relay node; meanwhile, a multi-head dynamic attention mechanism is combined, the influence of key behaviors is highlighted in the process of integrating long-range information, and global time sequence features are extracted; and designing an adaptive feature fusion mechanism, dynamically integrating local and global time sequence features to obtain fusion features, and then further fusing real-time behavior preferences of the user and commodity attribute information to generate a recommendation list meeting personalized requirements of the user. According to the method, the individuation degree, the accuracy and the user satisfaction of the e-commerce product recommendation result can be improved.

The invention discloses a space-time elastic weight consolidated edge federal continuous learning method, which is applied to a system comprising a server and a plurality of edge devices, is used for processing space-time heterogeneity time sequence data, and comprises the following steps: initializing training, broadcasting a previous time sequence global time Fisher diagonal matrix (the first time sequence is not broadcasted, the second time sequence is not broadcasted, and the third time sequence is not broadcasted) by the server; however, the global model needs to be randomly initialized and broadcasted); in the model training stage, based on local data, a global time Fisher diagonal matrix and the like, an edge device updates a local model through a loss function containing a time / space regular term, calculates a local space Fisher diagonal matrix, uploads the local space Fisher diagonal matrix, and then a server weights and aggregates the global model according to the data volume and issues the global model, and circulates until convergence; and in the global time Fisher diagonal matrix calculation stage, the equipment calculates a local time Fisher diagonal matrix based on a convergence model, and uploads and aggregates the local time Fisher diagonal matrix for the next time sequence. Historical data does not need to be stored, original data does not need to be transmitted, storage calculation / communication overhead is reduced, privacy is protected, and the model convergence speed and precision are improved.

The invention discloses a Modbus multi-axis real-time synchronous control method for a numerical control machine tool, which relates to the technical field of numerical control, and realizes multi-axis microsecond-level synchronous control by establishing innovative mechanisms such as global time reference, dynamic time slot allocation, instruction pre-caching synchronous execution, feed-forward-feedback dual-mode fault tolerance and the like. The technical limitation of a traditional Modbus protocol is broken through, the cost is greatly reduced while high performance is guaranteed, the synchronization precision reaches + / -0.8 microseconds, the roundness machining error is smaller than or equal to 9.5 micrometers, and the communication reliability reaches 99.998%. The method is particularly suitable for high-precision equipment such as a five-axis linkage numerical control machine tool and a high-speed machining center.

The embodiment of the invention provides a mobile mapping system and a time synchronization method, and is applied to the technical field of positioning and map construction. The system comprises a device to be synchronized, an industrial personal computer and a controller. The controller is used for determining a global time reference and determining a reference time point for acquiring a hardware trigger signal; the to-be-synchronized device is used for acquiring data according to the hardware trigger signal; and the industrial personal computer is used for associating the data acquisition time of the to-be-synchronized equipment with the global time reference according to the global time reference and a receiving time point for receiving the data sent by the to-be-synchronized equipment. The time service mode depends on a hardware trigger signal, and effective time service of data acquired by the multi-source sensor can still be realized in a scene with weak GNSS signals or without GNSS signals, so that the synchronization precision of data acquisition time of the multi-source sensor is improved.

The invention discloses a 5G base station traffic prediction method and device, equipment, a medium and a product, and relates to the field of 5G base station traffic prediction. The method comprises the following steps: inputting a traffic data set of a current time period of a target 5G base station into a traffic prediction model composed of a gating cycle unit, a time convolutional network, a patch long-short term memory network, a cross attention fusion module and a full connection layer; the gating circulation unit obtains a hidden state set; the time convolution network and the patch long-short-term memory network extract local and global time features respectively; the cross attention fusion module performs weighted fusion on the two features; and the full connection layer outputs a traffic prediction value set in the prediction time period of the target 5G base station. According to the method, the prediction precision is improved by comprehensively capturing the time sequence characteristics and dynamic changes of the traffic data. Meanwhile, the complexity and diversity of the traffic prediction model not only enhance the generalization ability, but also support long-term prediction through the combination of multiple modules such as the GRU, the TCN, the PLSTM and the CA.

The invention discloses an AI generated video detection method and system based on double-branch attention fusion. The method comprises the following steps: synchronously extracting a Patch-level feature sequence and a global-level feature sequence of a video frame by using a pre-trained visual Transform; carrying out local time sequence modeling and independent judgment on the Patch feature sequence through a local space-time artifact branch by adopting a pixel tube Transform; carrying out overall narrative modeling and independent judgment on the frame-level global feature sequence by adopting a global time sequence Transform through a global time sequence consistency branch in parallel; and finally, dynamic weighted fusion is carried out on double-branch discrimination results through an attention fusion module, and a final detection probability is output. According to the method, the AI video generated by advanced models such as Sora and Pika can be effectively identified from two complementary dimensions of local space-time details and global time sequence dynamic, and the method has strong robustness and cross-model generalization ability for interference such as video compression and fuzziness, and is suitable for security scenes such as network content auditing and media evidence obtaining.

The invention discloses a multi-experiment task production scheduling method, device and equipment and a storage medium, and the method comprises the steps: carrying out the sampling of a preset number of times for each production scheduling scheme based on the prior distribution of the duration of each experiment stage, and determining the starting time of each experiment operation, constructing a global time resource scheduling problem under the constraint conditions that the time window constraint of the experimental operation, the time period of the experimental operation belongs to the idle time period of the operation equipment and the time periods of the experimental operation distributed on the same operation equipment are not overlapped, and calculating the ratio of the infeasible times of the global time resource scheduling problem to the preset times; and if the risk value is lower than the risk threshold value, taking the production scheduling scheme as a target production scheduling scheme, and performing online scheduling of the experimental tasks according to the target production scheduling scheme, so that the method is suitable for production scheduling of high-throughput and diversified experimental tasks, and the risk of equipment resource conflict is reduced.