1157 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 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.

The invention relates to a multivariate time series data abnormal root cause identification method based on a space-time cause and effect diagram, and belongs to the technical field of anomaly detection. According to the method, multi-window expansion causal convolution is adopted for multivariate time series data, mutual information screening is combined, and time embedding covering short-term mutation and long-time dependence at the same time is extracted; non-local space correlation is learned through multi-head self-attention, the directional causal intensity is measured through conditional entropy, and a sparse and interpretable space-time causal graph is generated through normalization-pruning; introducing a causal enhancement graph attention network on the space-time causal graph, and performing multiple rounds of causal propagation updating on node embedding; and calculating a root cause score by integrating the abnormal degree and the causal influence, identifying a key source node in an abnormal propagation path, and realizing accurate root cause positioning of the system abnormality. According to the method, the adaptability to the dynamic behavior mode and the capturing capability to the abnormal driving factor are enhanced, and the modeling precision and the root cause identification capability of the abnormal propagation process are improved.

The invention discloses an electronic jacquard machine control method and system based on multi-channel collaboration, and the method comprises the steps: calculating a delay compensation value of each channel in real time according to the hardware performance difference and signal transmission delay data of each channel, and dynamically adjusting the time sequence offset of a control instruction of an electronic jacquard machine by using the delay compensation value, generating a multi-channel cooperative control signal; according to input fabric pattern data, decomposing the fabric pattern data to generate a layered instruction set, and binding the layered instruction set to a multi-channel cooperative control signal to form a time-space synchronization instruction set; according to channel operation state data acquired in real time, identifying an abnormal channel and a fault type thereof, and generating a reconstruction instruction set after fault immunization; and dynamically allocating computing resources and storage bandwidth according to the reconstruction instruction set, generating an optimization execution instruction set, and issuing the optimization execution instruction set to each channel for execution in real time. By utilizing the embodiment of the invention, the control capability of the electronic jacquard machine and the fabric production efficiency can be improved, and the intelligent development of the textile industry is promoted.

The invention relates to the technical field of ocean data interpolation filling, in particular to a space-time deficiency filling method and system based on context association and physical guidance. The method comprises the following steps: acquiring seawater dissolved oxygen data and context data; multivariable space-time dependence extraction is carried out based on the obtained seawater dissolved oxygen data and context data; gaussian noise diffusion is carried out based on the obtained seawater dissolved oxygen data; noise prediction is carried out based on double-view space-time correlation; and the prediction error is constrained based on the joint loss function. According to the method, a physical consistency constraint mechanism based on a partial differential equation is introduced in a model training process, so that model output better conforms to a physical coupling rule among variables in a marine environment. The constraint effectively inhibits non-physical fluctuation possibly occurring in the interpolation result, enhances the physical credibility and interpretability of the result, and provides a more reliable data basis for subsequent scientific analysis and process modeling.

The invention discloses an engineering machinery fault prediction and intelligent maintenance method based on deep learning, and belongs to the technical field of intelligent operation and maintenance of engineering machinery. The method comprises the following steps: firstly, performing timestamp synchronization and feature enhancement on multi-source sensor data to generate a space-time alignment tensor; fusing the image and time sequence features through a multi-modal feature distillation network, and constructing a cross-modal unified feature vector; thirdly, calculating a fault probability and residual life distribution, and constructing a Markov decision model in combination with a resource state; and finally, dynamically optimizing the maintenance instruction by using deep reinforcement learning, and continuously updating the model through closed-loop feedback. According to the method, early-stage accurate prediction of the fault and dynamic optimization of the maintenance strategy are realized, the problems of inaccurate prediction, decision lag, resource waste and the like in a traditional method are effectively solved, and the availability rate and the maintenance economy of equipment are remarkably improved.

The invention is suitable for the technical field of earth surface monitoring, and provides an earth surface deformation monitoring method and system based on a time sequence InSAR, and the method comprises the following steps: carrying out the SAR image screening and interferogram generation, and collecting corresponding meteorological data and thermal infrared data; performing space-time adaptive atmospheric phase correction, taking the air pressure vertical gradient and the temperature anomaly as driving factors of atmospheric delay, and separating an atmospheric phase through a space-time weighted model; dynamic deformation modeling is carried out, and deformation is decomposed into linear deformation and nonlinear deformation; the method comprises the following steps: taking a mining area road network as a geometric constraint, unwrapping a coherent region by adopting a minimum cost flow algorithm, converting an unwrapping phase into sight-line-direction deformation, and calculating horizontal and vertical deformation components in combination with InSAR sight-line-direction deformation and a digital elevation model. The method adapts to a complex deformation mechanism of a mining area by capturing linear and nonlinear deformation. Through sight line deformation and DEM geometric projection, vertical and horizontal deformation separation is realized, and the deformation direction is determined.

The invention discloses a real-time interaction and intelligent management method for a multi-dimensional data space, and relates to the technical field of data interaction management, and the method comprises the following steps: I, collecting multi-source data, including structured data, unstructured data and spatio-temporal data, in real time through a diversified collection terminal, and dynamically adjusting the sampling frequency based on a data entropy value, carrying out cross-device space-time synchronization calibration and edge end lightweight processing on the acquired data; iI, preprocessing the data flow in real time, fusing the multi-modal data, and the like. According to the method, the sampling frequency is dynamically adjusted based on the data entropy value, and in combination with edge end lightweight processing, invalid data transmission is reduced, and the key data acquisition efficiency is improved; hot data is stored in a time sequence database and a memory database, real-time monitoring of an emergency scene and second-level response of emergency command are supported, dynamic capacity expansion and shrinkage of computing resources are matched, edge-cloud end collaboratively distributes tasks, network delay is reduced, and system stability and response speed in a high-concurrency scene are guaranteed.

The invention provides a multi-dimensional multi-spatio-temporal data base plate construction method and system, and the method comprises the steps: carrying out the standardization processing of obtained original multi-source heterogeneous data, and generating a standardized data set; performing multi-level space fusion and dynamic time weighting processing based on the standardized data set to generate a multi-level fusion spatio-temporal data set; performing space-time grid aggregation and adaptive node screening on the multi-level fusion space-time data set to generate an optimized space-time node set; based on the optimized space-time node set, dynamic association is constructed, and an association model is generated and comprises event rules, topological relations and dynamic weights; and generating a multi-dimensional multi-spatiotemporal data base plate based on the optimized spatiotemporal node set and the association model. By adopting the method, the data base plate which can cover global features and has real-time response capability can be constructed, and the decision-making precision and efficiency of scenes such as water conservancy disaster prevention and control and environment monitoring can be improved.

The invention provides a seismic event space-time density peak value clustering method and device, a medium and electronic equipment. The seismic event space-time density peak value clustering method comprises the steps of determining a space-time neighbor set of samples in a space-time data set; calculating a space weight coefficient and a time weight coefficient of the samples, and calculating the local density of each sample under space-time constraint; calculating the relative distance of the samples; calculating a decision value of each sample for selecting a potential cluster center of the spatio-temporal data set; eliminating potential cluster centers with merging potential according to the comprehensive similarity of the cluster centers to obtain cluster centers of various clusters; distributing the remaining samples after the cluster centers are determined; and carrying out abnormal point identification in each cluster to obtain a final clustering result of the sample data set. By applying the method, time and space constraints are introduced into a cluster center merging rule at the same time, so that a class cluster center with higher accuracy and consistency is obtained; a more flexible multi-step distribution strategy is adopted to replace a single distribution mode, and an abnormal point identification mechanism based on double criteria is combined, so that the clustering effect is improved.

The invention provides a multi-data center computing power-electric power cooperative scheduling and demand response declaration capacity optimization method, which comprises the following steps of: firstly, acquiring multi-dimensional historical time sequence data of a data center; a machine learning algorithm is used to predict the workload of each data center in each time period of a future single day in a future scheduling period and the power market price of the place where each data center is located, and then a space-time coupling-oriented multi-data center computing power-power cooperation model considering uncertainty is constructed; the collaborative model comprises a joint optimization framework of computing power scheduling and power scheduling, and takes actual profit maximization as a target, then the established collaborative model is converted into a mixed integer linear programming problem and solved, and the optimal declaration capacity of each data center participating in demand response is obtained; and each data center carries out scheduling according to the task load of space migration and time migration, the charging and discharging power of an energy storage system and the power generation amount of renewable energy sources, so that overall optimization of demand response declaration capacity of the multiple data centers is realized.

The invention discloses a universal multi-modal target tracking method based on space-time propagation and modal cooperation, and belongs to the technical field of computer vision. The method comprises the following steps: converting RGB and X modal images into a token form, and constructing initial features in combination with modal specific time tokens; the method comprises the following steps of: extracting a multi-level enhanced feature through a Transform encoder and a Mama collaborative prompt block; generating discriminative fusion features by using a gating fusion and context sensing module; a time-guided attention mechanism is adopted to strengthen search area features, and a result is output through a tracking prediction head; and transmitting the fusion time token as historical information to the next frame, and dynamically updating the template by combining a long-short time template updating strategy. According to the method, complementarity and space-time dependence between modes are effectively mined, tracking robustness and generalization ability in a complex scene are improved, and the method is suitable for various mode combination tasks such as RGB-D, RGB-T and RGB-E.

The invention discloses a server temperature control method and system based on deep learning, and belongs to the technical field of server temperature control, and the method comprises the steps: firstly, synchronously collecting server hardware temperature, business load and environment parameters, capturing a heat conduction relation through combining a thermal imaging technology, and constructing a dynamic relation network through time-space fusion processing; a heat flow prediction model is constructed based on the network, model parameters are dynamically corrected in combination with thermal response characteristic attenuation coefficients generated by service load characteristic analysis, and temperature distribution changes are predicted; generating a candidate control strategy set, screening an optimal strategy through composite scene simulation, and generating a control instruction; and comparing the actual temperature after the control instruction is executed with the model prediction temperature in real time, switching to a safety control mode when the deviation exceeds a threshold value, and extracting an incremental sample to update model parameters after the deviation is recovered, thereby realizing accurate temperature control.

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 discloses a time series data lossless compression method and system based on dynamic context awareness, and relates to the field of data lossless compression, and the method comprises the steps: extracting target data from a device port, and carrying out the preprocessing of the target data through a data processing technology according to the business dimension corresponding to the target data, constructing a structured data set based on a preprocessing result; capturing local time sequence features based on the structured data set, distributing attention weights in combination with a hidden state, generating a feature vector used for sensing a context, and outputting a dimension vector by using a three-layer full-connection network; and according to the dimension vector, analyzing the compression performance score value of each compression algorithm in the resource library, sorting the compression performance score values, and selecting the compression algorithm meeting the target requirement to compress the target data. According to the method, the global periodic features and the local mutation features of the time series data are analyzed through the space-time attention mechanism, and real-time driving algorithm switching is achieved.

The invention relates to the technical field of data processing, in particular to a deep learning fusion-based marine hydro-meteorological data fusion processing method, which comprises the following steps: S1, data acquisition: monitoring marine hydro-meteorological data through a distributed marine monitoring network; s2, constructing a regional collaborative fusion network; s3, space-time consistency optimization: introducing a multi-scale space-time attention mechanism, capturing a local short-term change and a global long-term trend which exist in the data at the same time, and dynamically adjusting weights of different time scales; and optimizing a data fusion result by taking marine physical characteristics as constraint conditions, wherein the marine physical characteristics comprise tide and ocean current periodicity laws. According to the method, through introduction of a multi-scale space-time attention mechanism, accurate capture of short-term dynamic change and long-term trend is realized on the basis of global fusion data, and through a dynamic time window adjustment mechanism, the method can adapt to change characteristics of different time scales.

The embodiment of the invention provides a time-space synchronization method and device, electronic equipment and a medium, which are used for solving the problem of inaccuracy of a local clock in related technologies. In the embodiment of the invention, the electronic equipment combines the average deviation pseudo-range, the RTT jitter and the target frequency offset into the multi-dimensional observation vector, so that the error source of clock synchronization is fully covered. The time deviation and the frequency deviation of the clock are accurately and effectively determined through a Kalman filtering algorithm, recursive processing of observation vectors and dynamic estimation of the time deviation and the frequency deviation of the clock, and high-precision synchronization of the local clock in a complex environment is achieved in combination with the optimization capability of the Kalman filtering algorithm. And the time of the local clock is the time determined according to the time of the multiple clocks, so that the clocks are more accurate.

The invention provides a digital twinning-based scene information reverse transmission method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the space-time alignment and abnormal value processing of collected multi-source data, forming a standardized data sequence, selecting a reference data segment from the standardized data sequence, and carrying out the calculation of the reference data segment; generating two feature subsequences based on the reference data segment, and calculating the dynamic correlation of the two feature subsequences to form a feature interval; selecting a key data unit in the feature interval, and selecting two key data units outside the feature interval; constructing a nonlinear data track according to the time continuity of the three key data units; generating a correction factor according to the nonlinear data track; and jointly inputting the correction factor and the standardized data sequence into a generative adversarial network to generate extreme fault time sequence data under multi-source interference. According to the invention, the adaptability and security of reverse transmission are improved.

The invention discloses a spatio-temporal data compression method and system based on lightweight processing, and relates to the technical field of image data processing, and the method comprises the steps: based on spatio-temporal data to be compressed, establishing standardized data entries, forming a window set through time axis segmentation, carrying out the periodic discrimination of windows according to the frequency domain energy distribution, and constructing spatio-temporal blocks, establishing an error control parameter table based on the global error budget; generating a time coding stream, performing integerization on the three-dimensional coordinates and the attribute values, generating a space coding stream through double difference and run length coding, performing edge folding lightweight and texture compression on the grid data, and generating a joint coding result; and based on a joint coding result, dividing the compressed data into a base layer and a plurality of enhancement layers, and establishing a relevance hierarchical storage structure and a block-level index table to generate the compressed data capable of being transmitted in a streaming manner. According to the method, collaborative compression can be carried out by utilizing spatial-temporal data internal relevance.

The invention provides a structure health early warning method and system based on space-time correlation characteristics and digital twinning, and relates to the technical field of data processing. The method comprises the following steps: acquiring multi-source heterogeneous data of a target structure; performing dynamic sampling alignment and wavelet packet decomposition on the multi-source heterogeneous data to extract energy features to obtain synchronous data, and performing abnormal data filtering on the synchronous data to obtain cleaned fusion data; performing wavelet decomposition on a high-frequency vibration signal in the fused data to obtain a damage impact feature, performing time sequence processing on low-frequency temperature data in the fused data to obtain a temperature time feature, and performing dynamic graph convolutional network processing on strain data in the fused data to obtain a spatial correlation feature; constructing an input vector; calculating a damage degree index; and according to the damage degree indexes, early warning grades are divided, and corresponding control instructions are triggered for different early warning grades. By implementing the technical scheme provided by the invention, the accuracy of structural health early warning can be improved.

The invention relates to the technical field of Internet of Things industrial control. The invention relates to an AGV cluster scheduling method and system, and more specifically relates to a scheduling method and system for an AGV cluster. The method comprises the steps of calculating a production rhythm disturbance coefficient; carrying out weighted calculation on the production rhythm disturbance coefficient and the comprehensive execution adaptability of the AGV to obtain a distribution utility value; constructing a task allocation model with the goal of maximizing the sum of all possible allocation utility values, and solving the model to obtain an optimal AGV-task allocation pair; for the determined AGV-task allocation pairs, according to the sequence of the production rhythm disturbance coefficients of the tasks from high to low, driving paths are planned for the AGVs in sequence; wherein an improved A * algorithm is adopted, and when the passing cost of the path segment is calculated, a time-space conflict probability cost is superposed; and issuing the driving path to a corresponding AGV for execution. According to the invention, the success rate of path planning and the passing efficiency of the AGV are improved, and the operation of the whole logistics system is smoother and more efficient.