559 results about "Digital mapping" patented technology

The invention provides a flood control project virtual simulation and risk rehearsal method based on digital twinning, and the method comprises the following steps: obtaining drainage basin multi-source data collected by a space-air-ground integrated monitoring network, the multi-source data comprising a satellite remote sensing image, an unmanned aerial vehicle LiDAR point cloud and ground sensor monitoring data; inputting the multi-source data into a pre-constructed digital twin model, wherein the model comprises a spatio-temporal data fusion layer, a physical process modeling layer, a multi-scale simulation deduction layer and a risk assessment decision-making layer which are connected in sequence; the spatio-temporal data fusion layer is used for performing spatio-temporal registration and feature fusion on input multi-source data, and constructing a total element digital backplane; through the space-air-ground integrated monitoring network and the spatio-temporal data fusion technology, high-precision digital mapping of basin total elements is realized. Compared with a traditional single data source, time-space consistency of flood routing simulation is remarkably improved through multi-source data fusion, and prediction errors of key physical processes such as river channel scouring are greatly reduced.

The invention relates to the technical field of unmanned aerial vehicle flight attitude control, in particular to an unmanned aerial vehicle flight attitude adjustment method and system based on reinforcement learning. Comprising the following steps: constructing a dynamic environment grid map based on a digital map and a real-time semantic segmentation result, and generating an initial track by introducing a space-time constraint fast search random tree algorithm; collecting flight state information, environment perception information and image definition indexes of the unmanned aerial vehicle, inputting the flight state information, the environment perception information and the image definition indexes into a space-time attention encoder, and generating a semantic-fused state tensor; performing importance distribution on the state tensor through an information entropy weighting mechanism to obtain a weighted state vector; inputting into a Meta-SAC model with meta-learning ability, and outputting a target flight reference pose and an LQR controller dynamic gain coefficient; the control module drives the LQR controller to generate a flight control instruction based on the information; and a reward function is constructed based on the image quality and the energy consumption efficiency, and a reinforcement learning strategy is fed back in real time.

The invention discloses a traffic protection facility state detection method and system based on digital twinning, and relates to the field of data processing methods for prediction purposes. Constructing a characteristic parameter matrix in the virtual model layer; performing adaptive decomposition on the characteristic parameter matrix to obtain a parameter subspace; establishing a parameter subspace index chain; analyzing the change correlation degree among the state parameters in each parameter subspace, and constructing a correlation degree network; calculating correlation evolution characteristics of the parameter subspace to obtain an evolution characteristic sequence; identifying an associated abnormal region, and positioning an abnormal source; performing state prediction simulation on the virtual model layer to obtain a state prediction simulation result; and predicting a parameter change curve, and when any value in the parameter change curve exceeds a preset safety threshold, outputting an early warning signal to the physical entity layer through the data interaction layer. The method and the device are used for improving the accuracy of traffic protection facility state detection, so that the preventive maintenance effect of the traffic protection facility is improved.

The invention relates to the field of unmanned ship technology control, in particular to an unmanned ship formation path planning method and system based on cloud control. Comprising the following steps: acquiring initial position information and target position information of each unmanned ship in an unmanned ship formation and environment information in a navigation area through a cloud platform; constructing a digital map of the navigation area through the cloud platform; planning a plurality of candidate paths for each unmanned ship through a digital map; calculating a cost function value of each candidate path through a preset cost model, and screening out an initial optimal planning path; the position information of each unmanned ship and the environment information in the navigation area are received in real time through the cloud platform, and the optimal planning path is updated. According to the invention, the position information of all unmanned ships in the formation and the environment data of the navigation area are acquired in a centralized manner through the cloud platform, a unified digital map is constructed and updated in real time, and the limitation of local sensing of a single ship in distributed control is broken through.

The invention discloses an intelligent control and optimization system and method for the whole process of a photovoltaic power station. The method comprises the following steps: constructing a component-level high-fidelity digital twin through multi-source heterogeneous data fusion and an attention enhancement deep learning model; injecting annotated historical fault data, carrying out joint training by combining a feature embedding layer and an adversarial generative network, and generating a digital mapping body with fault evolution deduction capability; a three-layer architecture is introduced, reinforcement learning and a multi-objective optimization algorithm are fused, and autonomous decisions of power station level power output maximization, equipment loss minimization and maintenance cost optimization are realized in a virtual simulation environment constructed by a digital mapping body. According to the method, accurate dynamic mapping of the physical entity and the virtual space, fault pre-judgment and deduction and closed-loop control of global autonomous optimization are realized, the intelligent level and the full-life-cycle economy of the photovoltaic power station are improved, and the problems of insufficient fidelity, lack of fault deduction capability and low global optimization efficiency in the prior art are solved.

The invention discloses a multi-communication link management platform and method for a wireless ad hoc network, and relates to the technical field of wireless communication, and the platform comprises a situation awareness module which is used for collecting data and generating situation data in combination with a three-dimensional digital map; the link occlusion risk prediction module is used for generating a predictive occlusion index based on the situation data; the multi-link comprehensive efficiency evaluation module is used for receiving the predictive shielding index and calculating and outputting a link comprehensive efficiency score in combination with the operation cost data; and the predictive switching decision and execution module is used for responding to the switching condition and switching the service data flow from the currently activated link to the inactivated link with the highest link comprehensive performance score. According to the method, communication interruption is avoided by actively predicting switching, expensive links are used as required, a multi-target model is adopted, the cost is reduced, and the method can flexibly adapt to service requirements.

The invention relates to the technical field of power system monitoring, in particular to a power system parameter dynamic verification method, system and device based on digital twinning and a storage medium. Constructing a digital twinborn model of the power system, establishing a state mapping relation between a physical system and a digital model, realizing bidirectional dynamic mapping between the physical system and the digital model through a digital mapping mechanism, and obtaining system operation characteristics; constructing a parameter verification model based on the operation characteristics, obtaining a verification objective function by fusing physical constraints and data driving, and dynamically adjusting parameter values to generate parameter correction values by evaluating model errors in real time by adopting an adaptive parameter correction algorithm; based on a dynamic optimization mechanism of multi-objective optimization, an optimization strategy is adaptively adjusted according to the running state, and the verification process is continuously optimized; and performing multi-dimensional evaluation on the verification result through the verification precision evaluation system to generate a parameter verification result. The technical problems that a traditional method is inaccurate in modeling, low in response speed and poor in adaptability are effectively solved.

The invention relates to the technical field of digital twinning, in particular to a smart park digital twinning construction method and system based on big data, and the method comprises the steps: constructing a multi-source heterogeneous data fusion collection system, deploying an Internet of Things sensor in a smart park to collect data, and carrying out the data preprocessing; constructing a distributed big data storage architecture to store the collected data, and extracting data depth features in combination with machine learning and deep learning algorithms; constructing a park high-precision digital twin three-dimensional model, and accurately associating the extracted data depth features with a model entity; a real-time data monitoring mechanism is set, a message queue and edge calculation are adopted for data transmission and processing, a multi-terminal human-computer interaction interface regular evaluation model is developed, and an optimization algorithm is used for iteratively upgrading the digital twinborn body. Through full-process data driving and model iteration, digital mapping of the smart park is realized, and the management efficiency and decision scientificity of the smart park are improved.

The invention relates to the technical field of 5G communication, in particular to a 5G network signal low-altitude coverage test method and system, and the method comprises the steps: obtaining regional geographic information and base station layout information; based on geographic information and base station layout, constructing a three-dimensional digital map, dividing a dynamic test grid, and determining an initial flight path; according to a multi-source sensor of the unmanned aerial vehicle, in combination with the initial flight path, collecting 5G signal parameters and environmental data of the grid points through a preset frequency; a wavelet transform filtering algorithm is adopted for calibration, abnormal values are removed, and feature vectors are generated; according to the feature vectors, a coverage evaluation model is constructed in combination with a three-dimensional digital map, and a low-altitude coverage blind area and an interference type are identified; and if a low-altitude coverage blind area or abnormal interference is detected, dynamically adjusting the flight path of the unmanned aerial vehicle through an A-star algorithm, testing a target area, and generating a coverage optimization scheme based on a test result. Therefore, the problems of low test efficiency, lagging data processing and analysis and the like in the prior art are solved.

The invention relates to the technical field of petroleum drilling engineering, and particularly discloses an analysis and prediction system based on petroleum drilling and production data, which realizes virtual reconstruction of a physical drilling system by constructing a digital mapping body, and adopts a multi-source sensor network to collect drill string vortex frequency and wellbore temperature field gradient data. Forming a time sequence feature set through time sequence alignment and fusion processing; performing differential transformation and modal decomposition on the feature set to extract transient response feature vectors and generate a risk distribution diagram; establishing a parameter-risk correlation model, and decoupling mechanical vibration and thermal stress interference by solving a physical equation to obtain a dynamic risk index; the risk indexes and the process parameters are mapped to a three-dimensional grid to construct a multi-dimensional feature space, a feature fusion grid is adopted to achieve information aggregation and reconstruction, and comprehensive risk assessment indexes are generated; a dynamic early warning threshold value is established according to the evaluation indexes, and drilling parameters are optimized in real time through closed-loop control.

The invention discloses a high-precision intelligent distance measurement positioning device for a heading machine, and belongs to the technical field of heading positioning. An elastic base station network comprises a plurality of groups of miniature base stations arranged on the side wall and the top plate of a roadway; the multi-source sensing module fuses multi-source data through the data acquisition and processing device to realize continuous dynamic positioning in the working process of the heading machine, the target card provides an absolute distance reference, the inertial navigation unit calculates the pose of the heading machine in real time through a built-in MEMS gyroscope and an accelerometer, the geological radar scans the stratum structure in the heading direction in real time, and the real-time positioning of the heading machine is realized. And the laser radar scans a roadway contour by emitting a laser beam to perform point cloud matching with a preset three-dimensional digital map. The probability of tunneling deviation caused by settlement or displacement is reduced by arranging the vertical crossing positioning network and dynamically correcting the positioning reference, the working environment and self state information of the tunneling machine can be comprehensively sensed from different dimensions, and the actual distance and position relation between the tunneling machine and objects around a roadway can be judged in an auxiliary mode.

The invention relates to the technical field of parking resource scheduling, in particular to an intelligent parking resource scheduling system based on multi-source data analysis, which realizes full-scale digital mapping from vehicle individual behaviors and parking lot flow fields to a regional traffic network by constructing microscopic-mesoscopic-macroscopic three-level parking twin bodies and fusing dynamic and static multi-source data. Through scale conversion and data interaction among the three-level model, dynamic characteristics of parking resources in space and time dimensions are accurately captured, a high-fidelity and fine-grained digital twinborn base is provided for scheduling decision making, splitting of local optimization and global collaboration in traditional scheduling is broken through, the accuracy and foresight of resource allocation are supported, and the scheduling efficiency is improved. Based on dynamic construction of an algorithm strategy library and twinborn simulation evaluation, the scheme takes a multi-dimensional scheduling value as a core to realize closed-loop optimization of algorithm combination-effect rehearsal-strategy optimization-dynamic adjustment.

The invention discloses an operation and maintenance prediction and self-repairing method, system and device and a storage medium, and the method comprises the following steps: collecting structured, semi-structured and non-structured data, and carrying out the analysis and format conversion of the collected data, and obtaining the preprocessed data; based on the preprocessed data, constructing a dynamic knowledge graph containing entities, relationships, attributes and timestamps; a depth map neural network model is applied to analyze the dynamic knowledge graph, node features are aggregated, key fault nodes are identified, and potential faults are predicted; when the potential fault is predicted, a reinforcement learning algorithm is applied, and a repair strategy is generated according to the operation and maintenance cost, the service influence and the repair duration; and executing the restoration strategy, and feeding back an execution result and performance index data in the restoration process to update the dynamic knowledge graph and depth graph neural network model. Panoramic digital mapping of the operation and maintenance system is realized, and the fault prediction accuracy and the self-repairing success rate are improved.

The invention relates to the technical field of digital twinning, in particular to a distributed photovoltaic power generation system optimization control method based on digital twinning, and the method comprises the steps: constructing a photovoltaic module-level digital twinning body through multi-source data fusion, generating a digital mapping body through the training of historical fault data, and achieving the simulation of the operation state of a photovoltaic module; a multi-agent cluster is introduced, a multi-target optimization architecture framework coordinated with the multi-agent cluster is constructed, and balance and collaborative optimization among multiple targets are achieved through power generation efficiency optimization, equipment life management, power distribution coordination and other agents; and based on the candidate strategy after multi-target collaborative optimization, the improved grey wolf algorithm is used for further optimization processing, an optimal scheme is obtained, system optimization operation is realized, and power generation benefits and stability are improved.

The invention relates to an automatic printing calibration method and system combining vision and AI and a storage medium, a multi-mode sensing unit integrated on a printing head assembly synchronously collects two-dimensional images, three-dimensional shapes and working spacing data of a printing area in real time, and data fusion is carried out by using a model based on a neural radiation field; generating a high-fidelity multi-dimensional digital mapping; carrying out defect identification and positioning by adopting a pre-trained convolutional neural network CNN model, and predicting an optimal adjustment parameter according to the defect information and the historical sequence through a long short-term memory network LSTM model; the edge calculation and control unit is combined with a material characteristic database to generate a hierarchical calibration instruction in a millisecond level, adopts fuzzy PID self-optimization control to dynamically set PID control parameters, and is combined with feed-forward compensation of the inertial measurement unit to perform accurate, smooth and robust control on an execution mechanism, so that the accuracy of calibration is improved. And full-automatic, high-precision and real-time online calibration of the printing process in a complex industrial environment is realized.

The invention discloses an intelligent monitoring method and system for a roundabout cableway, and relates to the technical field of cableway engineering, and the method comprises the steps: configuring a regional sensing network, and generating cableway digital mapping data; executing space-time sequence characteristic analysis on the cableway digital mapping data, extracting key component state parameters, and outputting cableway system operation characteristics; integrating the expert rule base and the depth Q network in a distributed calculation mode, and generating a cableway key component health degree matrix; candidate circuitous paths are calculated, and a fault-tolerant circuitous scheme is output in combination with dynamic constraints and a path optimization strategy; reading a fault-tolerant roundabout scheme, configuring parameters of a fuzzy controller, adjusting operation parameters of a cableway system, and updating a digital mapping state based on edge nodes. According to the invention, the operation state of the cableway system can be accurately monitored in real time by arranging a regional sensing network constructed by the optical fiber strain sensor, the inertial measurement unit and the acoustic emission sensor and combining temperature-strain compensation and edge computing technologies.

The invention relates to the technical field of power equipment management, in particular to a digital low-voltage cabinet full life cycle data management method and system, and the method comprises the steps: distributing a global unique digital identity identifier for a low-voltage cabinet and key components, and constructing a standardized digital mapping model based on an OPCUA framework; gathering full-process multi-source heterogeneous data to a cloud edge collaboration platform by taking the digital identity as an index to form a data consanguinity chain; creating a digital twinborn body, dynamically updating the model through real-time data and iteratively calibrating parameters; carrying out abnormity identification, health assessment and residual life prediction by using an AI algorithm, and generating an operation and maintenance instruction; and feeding back the instruction to an upstream system. According to the method, a data island is broken, predictive maintenance is changed into full-period optimization, and the equipment safety and the operation efficiency are improved.

The invention discloses a heavy crane multi-task intelligent collaborative scheduling and path optimization control system, which is characterized in that by collecting operation state data, environment perception data and task queue data of a plurality of heterogeneous cranes, a time-space unified modeling module constructs and updates a collaborative operation digital map in real time; the collaborative operation digital map and the task queue are input into a multi-task dynamic collaborative scheduling module, a task-equipment allocation mapping relation and a plan time window are output, and then the collaborative operation digital map is combined to be input into a layered collaborative path planning module to generate a real-time motion control instruction sequence; and finally, issuing to a crane bottom layer control system through a protocol adapter to drive collaborative operation. According to the method, the problems of insufficient global optimization and high conflict risk when a plurality of heterogeneous cranes cooperatively work in a dynamic complex environment are solved, intelligent scheduling and safe planning are realized, and the working efficiency and safety are improved.

The invention relates to the technical field of irrigation water, and discloses an irrigation district water optimization method and system based on dynamic data management, and the method comprises the steps: collecting the basic data of an irrigation district project and a monitoring station, the data of the irrigation district monitoring station, the monitoring data of the water volume of each branch water port in an access irrigation district, and the geographic information topological data of an integrated channel system and a land parcel; constructing a digital archive library; obtaining a water demand forecast and a recommended water distribution scheme according to the data in the digital archive, and making an actual water consumption scheme by referring to the water distribution scheme; calculating a canal system utilization coefficient and an irrigation water utilization coefficient based on the canal head water diversion amount, the water consumption data of each tail end water diversion port and the actual water consumption data of the field plot, and evaluating the water utilization efficiency; and when optimization is determined, optimizing water consumption of the irrigation area according to the canal environment data, the canal system utilization coefficient and the irrigation water utilization coefficient. According to the invention, by establishing a total-factor digital mapping relation, accurate metering and dynamic optimization of irrigation water are realized, and dynamic planning of water saving of an irrigation area is realized.

The invention discloses a soil organic matter digital mapping method and device based on crop rotation historical change, and the method comprises the steps: bringing crop rotation interannual change and crop rotation change frequency into an environment variable set, and carrying out the screening and combination with other environment covariables; the historical crop rotation change and the long-term frequency thereof are brought into the environment variable set, and the environment variable set is enriched, so that the influence of human activities on soil spatial-temporal heterogeneity is considered in soil organic matter prediction, and the precision of model prediction is improved. By utilizing the method, the influence of historical agricultural practice can be reflected, and the spatial heterogeneity of the farmland soil organic matters can be better described, so that the performance of a soil organic matter spatial prediction model is improved.

The invention discloses a fire scene area rapid estimation method based on passive positioning. Relates to the technical field of image processing, in particular to a method for calibrating laser optical axis deviation through target positioning in the air. According to the method, a digital map and a digital elevation model file of an actually measured image of a target area are loaded, orthographic transformation is carried out on the image in combination with pose data corresponding to a single-frame image, and the work of foreground extraction and fire scene area estimation is completed on the basis. The method comprises the following steps: making a digital elevation model data set of an area to be measured; shooting an actual measurement image of the to-be-measured area; resolving the central position of the field of view; finding out a digital elevation model file corresponding to the actually measured image according to the central position of the field of view; performing orthographic transformation to obtain an image of a right downward view angle; calculating the area of the to-be-measured region based on the digital elevation model file according to the image of the right downward view angle; and extracting an image foreground to estimate the fire scene area.

The invention provides an artificial intelligence-based polar region unmanned warehouse cargo identification and inventory method and system, and relates to the field of artificial intelligence and computer vision, and the identification and inventory method specifically comprises the steps that at least one unmanned vehicle carries out the data collection of cargos in an unloading area, obtaining video data and point cloud data of the goods; at least one unmanned aerial vehicle serves as a data relay, and video data and point cloud data collected by the unmanned aerial vehicle are transmitted to a cloud system; performing multi-modal fusion based on the pure cargo surface point cloud and the features of the video data to realize cargo instance segmentation, and identifying the content category of each cargo instance in combination with the image information in the region of interest; generating a three-dimensional digital map of the unloading area; and based on the three-dimensional digital map, performing multi-dimensional dynamic warehousing planning to generate a dynamic task queue for transporting goods from the unloading area to the unmanned storage room.

The invention belongs to the field of industrial control, particularly relates to a workshop production line whole-process data processing and intelligent monitoring method and system, and aims to solve the problem that the whole production process is difficult to effectively monitor in related technologies. The method comprises the following steps: collecting asynchronous data streams from different sources in the whole production process; mapping each data point in the asynchronous data stream to a unified space-time reference framework; constructing a knowledge graph; inputting the knowledge graph into the space-time diagram convolutional network model to obtain an abnormal probability score of each node in the knowledge graph; and determining an abnormal probability node, carrying out reverse backtracking along a relation edge in the knowledge graph, positioning a source abnormal node, carrying out feature attribution analysis on state data of the source abnormal node, and generating a root cause interpretation report of an abnormal working condition. According to the method, the knowledge graph with the work order as the entity and the process flow as the relation chain is constructed, digital mapping and unified representation of the whole production flow are achieved, and the whole production flow can be effectively monitored.

A system is provided for localizing an autonomous work vehicle within mapped agricultural environments when GPS may be unavailable. Range sensors capture LiDAR images that are segmented by a trained model into features such as trunks and canopy. Trunk points are clustered to compute centroids, which are used to estimate left and right tree lines and a lane centerline relative to the vehicle. This sensor-derived centerline is compared with georeferenced lane and row geometry stored in a digital map. A particle filter maintains multiple pose hypotheses, propagates them using vehicle odometry, and weights them according to alignment between the lane centerline relative to the vehicle and the mapped lane geometry for the particle. Resampling and averaging of high-weight hypotheses produces an adjusted pose that corrects for lateral and heading errors. The adjusted pose serves as input to a navigation controller that issues steering and speed commands, enabling accurate autonomous operation.

The invention discloses a distributed waterlogging and ponding digital map prediction system based on edge calculation, and relates to the technical field of urban flood prevention intelligent decision making. The system comprises a distributed intelligent acquisition module, an edge integrated processing module, a multi-stage collaborative analysis module and a visualization and decision output module. Collecting multi-source heterogeneous data through multiple types of sensing equipment; calling a local digital elevation map slice and a lightweight hydrological model, generating local waterlogging state information and self-calibrating model parameters; key information is screened based on double threshold values of information entropy change and accumulated water depth change rate, multi-monitoring-point data and a drainage pipe network topological relation are fused, and regional-level inland inundation situation analysis and accumulated water and recessive water trend prediction are achieved; and generating and updating a whole-region distributed ponding digital map, and outputting graded early warning and auxiliary decision information. According to the method, distributed acquisition and edge calculation are combined, the monitoring real-time performance and precision are improved, and the problems that traditional monitoring is high in delay, low in precision, fuzzy in decision and the like are effectively solved.

The invention discloses a cage rearing broiler chicken multi-level epidemic situation early warning and prevention and control optimization method, which comprises the following steps: S1, constructing a data support system; S2, constructing a cross-level epidemic situation propagation simulation system, and realizing multi-scale digital mapping from a coop to a region; s3, according to the epidemic situation simulation result in the S2, early warning grades are divided; and S4, according to the early warning level in the S3, an association rule base addition and deletion measure is called, a candidate prevention and control measure combination is generated, and dual-target optimal measure combination screening with the maximum control effect and the minimum total cost is realized. According to the method, multi-scale accurate simulation of epidemic situation propagation and quantitative optimization of prevention and control measures can be realized, the control effect and cost are balanced, the scientificity and operability of prevention and control decisions are improved, and the method is adaptive to different early warning levels and breeding scenes and is high in robustness and practicability.

The invention provides a riding navigation blind area track correction method and system based on stopwatch inertial navigation, electronic equipment and a storage medium, and the method comprises the steps: obtaining the angular velocity and acceleration information of a stopwatch and the direction information of an earth magnetic field, and carrying out the calculation to obtain a riding course angle and a real-time riding speed; when it is detected that a satellite positioning signal is interrupted, the last credible satellite positioning coordinate before interruption serves as an estimated coordinate point of a first operation period, and in each operation period, a relative displacement vector is calculated by combining the course angle of the current period, the real-time riding speed and the time interval of the operation period; iteratively determining the estimated coordinate point of the current period by taking the estimated coordinate point of the previous period as a reference, and forming a continuous extension track; and finally, matching the extended continuous coordinate points with a road network of a preset digital map, calculating and accumulating the distance between adjacent coordinate points based on the matched real road coordinates, and completing accurate correction of the track and the distance.

The invention discloses a digital twin intelligent water-saving system based on a distributed optical fiber sensing network, which belongs to the technical field of water-saving management and specifically comprises the distributed optical fiber sensing network laid along a water delivery pipeline in a tree topology; the water use event recognition module collects optical fiber vibration signals, converts the optical fiber vibration signals into digital waveforms and recognizes trigger points through an amplitude threshold value; the water supply topology construction module constructs a water supply dependency topological graph containing nodes and directed edges according to the spatial position and time delay of the trigger point; a hydraulic model generation module superposes the topological graph and the digital map of the water delivery pipeline to generate a dynamic hydraulic balance model containing node water intensity weight coefficients; the intelligent water-saving control module sends a periodic intermittent closing instruction to the intelligent water valve of the low-weight node according to the node weight change, and the duration of the instruction is in direct proportion to the weight difference value of the adjacent node; according to the invention, refined intelligent water-saving management of a centralized water consumption scene is realized.

The invention relates to the technical field of multi-motor cooperative control, and discloses a multi-motor cooperative control system and method based on digital twinning. The multi-motor cooperative control method based on digital twinning comprises the following steps: acquiring real-time operation data of each motor in a multi-motor system, and generating a physical parameter data set containing time sequence information; based on the physical parameter data set, calculating digital twinborn model parameters of each motor, and generating a motor digital twinborn body; analyzing a topological connection relationship between the motors, and generating a cooperative control constraint condition; and inputting the real-time state data of the motor digital twin and the cooperative control constraint condition into a cooperative optimization algorithm, and outputting a multi-motor cooperative control instruction. According to the method, the problem that accurate digital mapping between a physical motor and a control system is lacked in a traditional method is solved, and the technical problem that a cooperative control strategy cannot be optimized in real time is solved.

The invention relates to a robot control technology, and discloses a robot cluster control method and system and a storage medium, and the method comprises the steps: receiving motion state data, network link state data and service type information of each robot; predicting a motion track of each robot in a future preset time period based on the motion state data in combination with a digital map, and predicting a communication quality change trend of a current network link of each robot in the future preset time period based on the network link state data; determining a target network link of each robot based on the motion trail, the communication quality change trend, the digital map, the service type information and the received node operation state information of each edge relay node, and generating a network switching instruction containing the target network link and the switching opportunity, and the corresponding robot executes network link switching according to the network switching instruction. According to the invention, global prediction and active switching of robot cluster communication can be realized.