123 results about "Path network" patented technology

The invention provides a multi-path cooperation method, device and equipment for a cluster quadruped inspection robot, and the method comprises the steps: obtaining gait dynamics data and topographic data, and recognizing a topographic adaptive rhythm based on the spectrum decomposition of a foot end contact force sequence; converting the topographic data into a potential energy gradient field to construct a dynamic potential energy diagram, and generating a natural path network; a cooperative trigger point is set based on the stable support moment, a path ripple field is formed, and a multi-machine cooperative potential well is constructed; decomposing the path network into a dominant task chain and an auxiliary task chain, and weaving to form an initial collaborative path; rigid nodes and flexible sections are identified through topological transformation, and random disturbance is injected to reconstruct a self-adaptive collaborative path; gait phase information is extracted to construct a synchronous network, a potential energy flow vector is used for phase alignment to generate a group resonance frequency, and a resonance cooperation path is formed; and finally, an execution instruction is generated based on the resonance cooperation path and the terrain adaptability rhythm, and multi-path cooperation of the cluster quadruped inspection robot in the complex terrain environment is achieved.

The invention discloses a crack segmentation method based on condition debugging and a hierarchical dual-path network, and relates to the technical field of computer vision, and the method comprises the steps: 1, collecting and marking a crack image, and generating a training data set; 2, constructing a condition debugging and hierarchical dual-path network model, and training by using the training data set to obtain a crack segmentation model; the conditional debugging and hierarchical dual-path network model comprises a ResNet50 branch network, a CMT branch network and a double-flow cascade segmentation header network, the CMT branch network comprises a conditional modulation gating convolution module, and the double-flow cascade segmentation header network comprises a hierarchical adaptive feature modulation module; and 3, acquiring a to-be-detected image, and inputting the to-be-detected image into the crack segmentation model to obtain a crack segmentation result. According to the method, global context and local feature extraction are effectively combined, and the most advanced accuracy and robustness are achieved in various crack scenes.

The invention discloses a digital twinning-based flow machine operation intelligent collaborative management system, and the system comprises an operation data collection module which is used for obtaining multi-source data in a target scene and carrying out the preprocessing of the multi-source data; the twinning modeling module is used for constructing a flow machine operation digital twinning model and generating a twinning operation initial situation; the space-time path analysis module is used for constructing a time hierarchical path network and generating a path conflict cone set and a feasible space-time path set; the operation load calculation and scheduling module is used for establishing task link representation, generating a storage yard congestion potential field and forming a multi-stream-machine cooperative scheduling candidate scheme; and the virtual-real correction module is used for issuing execution and collecting operation feedback, correcting the twin model and generating an optimized target collaborative scheduling scheme. According to the method, the space-time-behavior-link coupled digital twinborn model is constructed to realize flow machine operation predictive collaborative scheduling, and the method has the advantages of conflict recognition in advance, congestion trend suppression and remarkable improvement of scheduling efficiency.

The invention discloses a surface mine transportation real-time intelligent scheduling method and system based on multi-objective optimization. The method comprises the steps that S1, an initial data loading module initializes the system; s2, the algorithm module verifies the vehicle state after receiving the scheduling request; s3, executing a path generation strategy according to states, wherein a normal vehicle generates all feasible routes based on the global path network; when a user appoints a starting point and a terminal point, a point-to-point optimal route is intelligently generated; s4, performing dynamic optimization scheduling based on the multi-source data; optimal task allocation is solved through a multi-objective optimization model, and a vehicle and route mapping relation is output; and S5, a scheduling result is issued to the vehicle-mounted terminal through the service layer, and a terminal interface displays a recommended route and operation guidance. According to the invention, a dynamic event-driven mechanism and a multi-source data collaborative optimization intelligent scheduling framework are fused, so that efficient distribution of vehicle tasks, real-time generation of paths and quick response to abnormal conditions are realized.

The invention relates to the technical field of logistics collaborative optimization, in particular to an urban logistics distribution range collaborative optimization method and system, and the method comprises the following steps: obtaining a path network node and order coordinate time sequence, constructing a path mapping number, extracting a direction vector and an included angle difference value, and recognizing a path sudden change node and a density change node. Dividing boundaries, calculating density ratio differences, screening unbalanced sections, extracting direction and distance abrupt change nodes, reconstructing a path structure and outputting a boundary graph. According to the method, the corresponding relation between the order and the path node is enhanced by constructing the path mapping number sequence, the adaptive capacity of the boundary to the path trend is improved, the density ratio analysis is combined with the spatial distribution difference to identify the non-uniform region, the responsiveness of the boundary to the order aggregation situation is enhanced, and the boundary node is jointly judged through the included angle and the centroid distance. And target judgment and structural stability control of boundary adjustment are realized, connection coherence is kept in path structure reconstruction, and a boundary path shape with dynamic adaptation capability is output.

The invention provides a low-altitude networking of intelligence multipath data transmission system based on MPQUIC, and belongs to the technical field of novel network protocols, and the system comprises a network state analysis module which is used for obtaining the network state indexes of all communication paths of an unmanned aerial vehicle under different position coordinates; the flow scheduling module is used for predicting the track of the unmanned aerial vehicle, scoring the communication paths by combining the network state indexes of the communication paths, and obtaining the communication paths corresponding to the data flows with different priorities of the unmanned aerial vehicle according to the priorities of the data flows with the scores of the communication paths; and the path selection optimization module is used for dynamically adjusting weight parameters in communication path scores based on deep reinforcement learning by taking network throughput maximization and time delay minimization as targets to obtain optimal communication paths corresponding to data streams with different priorities. According to the invention, the problem that the prior art is difficult to have high reliability and low time delay due to the dynamic change of the network environment caused by the position change of the unmanned aerial vehicle in the low-altitude Internet of Things is solved.

Devices, systems, methods, and processes for enabling path monitoring in multi-path networks are described herein. Load balancing of packets is often performed based on outer encapsulating headers and inner packet headers. However, inner packet headers of probe and data packets can be different. Thus, the probe packet can traverse a different path as compared to the data packet, causing inconsistent hashing. To address this, a device is provided with a routing logic that encapsulates a packet, having a packet header, with an outer header. The outer header has a control bit, source and destination address fields, and a flow label field. When a value of the control bit is set to a predetermined value, load balancing is performed exclusively based on the outer header, exempting a utilization of the packet header. Therefore, load balancing for probe and data packets performed exclusively based on the outer header is consistent.

The invention belongs to the field of medical image classification, and discloses a novel dual-path network architecture combining Mama and a convolutional neural network, and the novel dual-path network architecture comprises a Patch Embedding module, a plurality of Conv-OSS layers, a Patch Merging layer, and a final feature classifier. An OSS module is developed, local features are extracted through parallel convolution branch, overall representation is enriched, and global features are effectively captured; an OSSM module is designed, spatial features are modeled from multiple directions, and the extraction capability of global features is enhanced; an MSAConv network is designed, and the overall feature representation quality is improved by aggregating eight diagonal features reserved after 8DScan; an FFM module is developed, Mama output is further optimized, and fine features related to pneumonia in a medical image are emphasized. Experimental results of a pneumonia data set show that the method is superior to an existing computer-aided pneumonia diagnosis method in multiple evaluation indexes, and the huge potential of the method in actual clinical application is highlighted.

The invention relates to the field of thermal flow guide path optimization, and discloses an electronic communication equipment heat dissipation method based on thermal flow guide path optimization, which comprises the following steps: arranging a plurality of thermal paths between a chip and a radiator, combining the thermal paths into a network structure in parallel and series modes, and constructing a selectable thermal flow path network structure; collecting real-time temperature and load data of each module, and constructing a multi-dimensional thermal state database in combination with historical operation records and environmental parameters; thermal resistance and module temperature of each thermal path are analyzed, priority is set according to module types and power loads, key modules correspond to specific thermal flow paths, non-key modules distribute thermal flow through remaining paths, and a thermal flow planning scheme is generated; the thermal resistance of each path is adjusted through the phase change material, and a target thermal resistance value is generated; in the chip operation process, the temperature, load and thermal resistance state of each thermal path are continuously collected, and a thermal resistance adjusting instruction is generated; the method has the advantage that the thermal management efficiency and the temperature control precision are improved.

The invention discloses an apple leaf disease segmentation method based on a lightweight dual-path network, an apple leaf disease segmentation device based on the lightweight dual-path network, an apple leaf disease segmentation device and a computer readable storage medium. The problems that an existing disease segmentation method is high in model complexity and insufficient in multi-scale recognition capability are effectively solved. The lightweight encoder adopts a depth separable convolution and channel recombination technology, so that the parameter quantity and the calculation complexity are greatly reduced while the feature extraction capability is maintained, and the model can be deployed on edge equipment such as an unmanned aerial vehicle and a field robot. The enhanced cavity space pyramid pooling module constructs abundant multi-scale receptive fields through multi-branch parallel cavity convolution with different expansion rates, and can capture feature information of initial tiny disease spots and later fused disease spots at the same time.

The invention discloses a multi-machine collaborative mobile measurement robot path control method, which belongs to the technical field of path planning, and specifically comprises the following steps: collecting surface height information of a target area, and binding the surface height information with geographic coordinates to generate a simplified three-dimensional model; based on the sun moving trajectory data in the current season, calculating an accumulated time length distribution diagram of receiving direct sunlight by each coordinate point on the ground in the current time period; dividing a high sunlight intensity region and a continuous shadow region according to the cumulative duration distribution diagram, and carrying out topological association on the coordinates of the target point to be measured and the continuous shadow region; establishing a dynamic path distribution protocol in a multi-robot communication network, enabling each robot to select a non-overlapping measurement path in a path network formed by continuous shadow areas according to a real-time position and a task queue, and updating a path occupation state through periodic broadcast; according to the invention, the path planning of the mobile measurement robot in the urban high-temperature environment is optimized.

The invention provides a remote control method and device for intelligent multimedia interaction equipment, equipment and a medium, which are used for improving the real-time performance and the execution success rate of remote control and enabling complex multimedia interaction to be reliably operated on a heterogeneous network and heterogeneous equipment. The method comprises the following steps: carrying out hierarchical analysis processing on interaction data input by a user to generate perception command data, and carrying out execution planning and resource allocation on the perception command data according to equipment performance characteristics to generate an executable operation sequence, and performing transmission quality evaluation and path selection on the propagation path network according to the executable operation sequence to obtain a target transmission path, and performing protocol encapsulation on the executable operation sequence according to the target transmission path to obtain a remote control instruction data stream.

The present invention discloses a deep learning-based image semantic segmentation optimization method and system, which belongs to the field of image processing technology. The method and system are used to solve the technical problem of poor boundary perception ability in existing solutions. By constructing a dual-path network architecture, the collaborative learning of semantic features and boundary features is realized, and a closed-loop optimization is formed from input preprocessing to feature fusion, which directly addresses the core problem of poor boundary perception ability. The dynamic attention fusion module realizes the closed-loop optimization of boundary area enhancement, key channel screening, and dynamic noise suppression. The designed weighted cross entropy can solve category imbalance, the designed improved boundary distance loss can enhance boundary positioning accuracy, and the designed multi-scale feature consistency loss can ensure semantic alignment of deep and shallow features. The dynamic weight adjustment mechanism can adaptively balance the contribution of each loss according to the training progress, which can significantly improve the model's segmentation robustness to complex boundaries.

The invention discloses a traffic scheduling method and electronic equipment, and relates to the technical field of traffic scheduling, and the method comprises the steps: responding to a condition that a to-be-sent data stream accords with a transport layer protocol and the traffic size of the to-be-sent data stream is greater than a preset threshold value, and generating a plurality of candidate port values used for identifying the to-be-sent data stream at a data sending end; creating a plurality of detection data packets, wherein source port fields of the detection data packets are set as different candidate port values; sending the detection data packets to a data receiving end, and obtaining performance indexes of candidate network paths corresponding to the plurality of detection data packets based on the response of the data receiving end; determining a target source port value from the plurality of candidate port values based on the performance index; according to the embodiment of the invention, the source port field of the data packet corresponding to the to-be-sent data stream is modified into the target source port value, and the modified data packet corresponding to the to-be-sent data stream is sent to the data receiving end, so that the technical problem of load imbalance of a multi-path network in related technologies is solved, and the reliability of traffic scheduling is improved.

The invention discloses a video behavior recognition method based on multi-scale time feature fusion, and the method comprises the steps: taking a dual-path network as a basic structure, carrying out the normalization and uniform frame extraction of an input video, and respectively inputting a Slow path and a Fast path for feature extraction. The Slow path focuses on low-frequency background semantic modeling, the Fast path integrates a dynamic Token sparse module (VDTS) constructed based on one-dimensional convolution, and a key action area is adaptively selected through a Gumbel Top-K mechanism. Furthermore, according to the method, a gating bidirectional cross attention module (Bi-CAM) is designed between a fast path and a slow path, dynamic complementary fusion of bidirectional information streams is achieved by introducing a Query-Key-Value fusion structure and a channel attention gating mechanism, and the coupling efficiency and expression ability between multi-scale features are effectively improved. And finally, accurate recognition of behavior categories is completed through a classifier. The method gives consideration to the recognition precision and the calculation efficiency, is suitable for scenes such as behavior monitoring, man-machine interaction and intelligent security and protection, and has a good application prospect.

The invention discloses a construction unit safety responsibility tracing method based on an HSE map, and the method comprises the following steps: S1, collecting multi-source HSE data of a construction site, and constructing a structured data set; s2, constructing an HSE atlas containing multiple types of nodes and relationships based on the data set; s3, inputting the HSE atlas into the logic attention path network model, extracting a causal path and embedding nodes; s4, performing fuzzy responsibility scoring on the nodes in the extraction path to generate an attribution score; s5, adopting a cuckoo search algorithm to perform joint optimization on the model structure and the scoring parameters to improve the recognition effect; and S6, outputting a construction unit responsibility node and grade result based on the optimized model for responsibility tracing and decision making. According to the method, the recognition efficiency and the decision transparency are remarkably improved while the construction responsibility is accurately traced, and the method is suitable for various types of safety management scenes.

The invention relates to the technical field of intelligent manufacturing, and discloses an intelligent control system for a battery shell aluminum plate stamping production line. A data cleaning module of the system receives original sensing data of a stamping production line and executes fluctuation smoothing processing and abnormal point removing operation to generate clean data flow; the feature sequence generation module detects starting and ending event points of a stamping process based on the clean data flow, and performs splicing according to a time sequence to form a process chain and marks key process nodes; the path network creation module maps process nodes into network vertexes, and adds directed connection edges based on a node precedence relationship to construct a path network map; an intersection analysis module traverses the atlas to search for a shared vertex group, calculates an aggregation degree index and screens a core intersection; the attribute grouping module is used for summarizing association path attribute data sets according to the core intersections and executing similarity grouping operation to distribute group labels; and the control topology generation module maps the path groups to a control unit output control topology structure table according to the group labels.

The invention discloses an image semantic segmentation optimization method and system based on deep learning, and belongs to the technical field of image processing. The method is used for solving the technical problem of poor boundary sensing ability in the existing scheme. By constructing a dual-path network architecture, collaborative learning of semantic features and boundary features is realized, closed-loop optimization is formed from input preprocessing to feature fusion, and the core problem of poor boundary perception capability is directly solved; the dynamic attention fusion module realizes closed-loop optimization of boundary region enhancement-key channel screening-noise dynamic suppression; the designed weighted cross entropy can solve class imbalance, the designed improved boundary distance loss can enhance the boundary positioning accuracy, and the designed multi-scale feature consistency loss can ensure semantic alignment of deep and shallow layer features; through a dynamic weight adjustment mechanism, each loss contribution can be adaptively balanced according to the training progress, and the segmentation robustness of the model to a complex boundary can be significantly improved.

The invention relates to a multi-modal emotion recognition fusion and communication method oriented to real-time human-computer interaction, and aims to solve the problems of asynchronous modal time sequence, non-uniform feature dimension, single fusion mode, high communication delay and the like of an existing emotion recognition system and improve the recognition precision and real-time performance of the system. According to the method, voice, images and physiological signals are synchronously collected through a multi-modal input module, feature alignment is achieved through time sequence interpolation, dynamic time warping and space coordinate mapping, double-domain feature fusion is conducted in combination with a time path network and a space path network, emotional state judgment is completed through a lightweight neural network, and the emotional state recognition accuracy is improved. And outputting six types of basic emotions and confidence coefficients. Meanwhile, a low-delay communication protocol based on UDP clipping extension realizes rapid feedback of emotion data, and in combination with modal priority scheduling, data compression and bandwidth sensing mechanisms, high-efficiency and low-delay transmission is ensured, and the real-time interaction requirement in a weak network environment is met. The method has the advantages of high accuracy, low power consumption, low time delay and flexible deployment, is suitable for various real-time interaction application scenes such as voice assistants, virtual customer service, emotion accompanying and telemedicine, and has wide application value and market prospect.

The invention discloses a phi-OTDR (Optical Time Domain Reflectometer) vibration signal classification method based on multi-scale residual convolution and a bidirectional long-short-term memory network, relates to the technical field of phase-sensitive optical time domain reflectometer vibration sensing, and solves the problems of insufficient feature extraction, single model fusion mechanism, poor system real-time performance and the like of an existing phi-OTDR vibration signal classification method. The method is realized through the steps of a multi-scale deep convolutional network, a bidirectional long-short term memory network, a cross attention fusion module, a learnable gating mechanism and the like. A cross attention fusion module is adopted, association between spatial features and time sequence features is dynamically established in a double-path network, feature interaction and information coupling are deepened, and the sensing ability of the model to key information is remarkably improved. Meanwhile, the fusion proportion of the multi-path features is adaptively adjusted according to the actual distribution of the input features, background noise interference is effectively suppressed, and the robustness and discrimination performance of the model are enhanced. Accurate identification and classification of external vibration signals are realized.

Systems and methods for integrating multi-orbit and multi-path networks to provide unified wide area network (WAN) connectivity for user devices, based on combining data streams from satellite orbits and cellular networks, are provided. A system includes first wireless network connection equipment with satellite-based multi-path transport protocol, involving indoor and first outdoor device. Additionally, second wireless network connection equipment incorporates non-terrestrial-based and satellite-based multi-path transport protocol, with indoor and second outdoor device. Multi-path radio modem receives data stream, assesses network parameters to determine appropriate data transmission path, dynamically switches between networks, and modifies unified data stream accordingly. Modified data streams are provided as WAN connectivity to user device through indoor device of determined transmission path. First wireless network connection equipment includes D2D LEO modem, which includes NTN D2D antenna unit for LEO satellite communication. The connectivity between first and second outdoor device is established through IFL connection protocol comprising coaxial cable(s).

The invention discloses an LED lamp energy consumption optimization control method and system adopting an ant colony algorithm, and particularly relates to the field of LED lamp energy consumption optimization control, and the method comprises the steps: constructing a path network model of a multi-node illumination region, obtaining the movement track information of a user in the illumination region in real time through a collection module of LED illumination equipment, and generating path use frequency data; and performing pheromone optimization enhancement processing on the path use frequency based on an ant colony algorithm, and driving an existing illumination control module to select an LED lamp corresponding to the current high-frequency path area to perform activation response. By introducing pheromone attenuation control, a negative pheromone suppression mechanism and a virtual individual tentative activation strategy into a path frequency atlas, outdated paths are identified and suppressed, and new user behavior paths are detected and enhanced, so that time sequence sensitive scheduling of lighting resources and real-time optimal updating of energy consumption configuration are realized, and the optimal scheduling efficiency is improved. And the response capability and the energy-saving efficiency of the system to behavior transition are improved.

The invention discloses an information fusion and reasoning method and system based on multi-agent collaborative networking search, and relates to the technical field of network information, and the method comprises the steps: a deep research strategy scheduling agent deconstructs an unstructured request through a task formalization mechanism, generates a high-dimensional instruction code, and dynamically optimizes an execution path. And the network intelligence index generation agent extracts and screens the trusted URL by using a rule engine and a semantic evaluator based on the code, and generates a structured link instruction stream. The structured analysis intelligent agent implements multi-level semantic distillation and conversion on an original document to obtain key value pair representations, and the key value pair representations are packaged into machine readable data. And generating a comprehensive intelligence abstract with consistent logic through the information fusion analysis agent. And the natural language generation engine converts the information into a professional report, and returns the professional report to the scheduling module through a feedback interface to realize cognitive closed-loop iterative optimization. The technical problem that in the prior art, it is difficult for geologists to obtain accurate, comprehensive and credible professional conclusions is solved.

The invention discloses a cloud guarding customer behavior identification method based on deep learning, and the method comprises the following steps: S1, collecting and processing a monitoring video stream, and forming a continuous frame sequence; s2, inputting an action proposal network, and obtaining candidate time periods and time masks; s3, constructing a local attention map, and highlighting a key frame region; s4, fusing the attention map and the original frame to generate an enhanced frame sequence; s5, inputting the enhanced frame into an improved SlowFast dual-path network, and extracting short-time and long-time features; s6, fusing the features and the mask information to generate customer behavior representation vectors; and S7, outputting a behavior category and a time positioning result through a behavior classification module. According to the invention, the customer behavior identification accuracy in an unattended environment can be improved.

The invention discloses a knowledge graph-based online service recommendation method in a micro-service system. The method comprises the following steps: constructing a micro-service knowledge graph according to historical dependency records of micro-services in a workflow storage library; obtaining a micro-service anchor point combination path set according to the micro-service knowledge graph based on a path generation strategy; constructing a combined path network model for micro-service prediction, and performing model training by using the micro-service anchor point combined path set to obtain an optimized combined path network model; and obtaining the micro-service prediction probability that each candidate micro-service is used as an original workflow to execute the next service by optimizing the combined path network model, carrying out descending sorting on the micro-service prediction probabilities, and selecting the micro-service with the top k in the sorting result as the online recommended target micro-service. The problems that an existing micro-service recommendation method lacks the capacity of mining information in a micro-service dependency graph, and a traditional auxiliary decision support system is often difficult to deal with complexity and variability of micro-services and cannot flexibly adapt to new business requirements and scenes are solved.

The invention provides a multipath P2P file reliable transmission method based on intelligent grading, which comprises the following steps: detecting available port resources and multipath network quality required by P2P transmission in a multipath P2P environment so as to construct a multipath resource pool, and performing targeted matching of an intelligent grading transmission mode on the multipath resource pool in combination with network quality information; calling the configured intelligent blocking model to block a to-be-transmitted file to obtain file blocks and generate a block state table, performing multi-path parallel cooperative transmission on the file blocks in cooperation with the matched intelligent hierarchical transmission mode, and synchronously monitoring the transmission state of each path on the file blocks to generate a transmission breakpoint record; and based on the configured abnormal degree intelligent grading model, determining an abnormal grading grade according to a transmission breakpoint record, and according to the abnormal grading grade, triggering the intelligent blocking model to adjust blocking parameters and synchronously adjust a multi-path resource pool to optimize matching of an intelligent grading transmission mode so as to implement file transmission based on intelligent grading control in a multi-path P2P (Peer-to-Peer) environment.

A cyber security system includes an importance node module to compute and use graphs to compute an importance of a node based on factors including a hierarchy and a job title of the user, aggregated account privileges from network domains and a level of shared resource access for the user. The graphs are supplied into an attack path modeling component to understand an importance of the network nodes and determine key pathways within the network that a cyber-attack would use, via a modeling the cyber-attack on a simulated and a virtual device version of the network. The cyber security system provides an intelligent prioritization of remediation action to a remediation suggester module to analyze results of the modeling the cyber-attack for each node and suggest how to perform intelligent prioritization of remediation action on a network node in one of a report and an autonomous remediation action.

Embodiments herein relate to a method performed by a network node (110) for handling signal quality variations for a UE (120). The network node (110) predicts a future signal quality variation indicative of a path of movement for a second UE (120b) being served by the network node (110). The predicting is based on historical data of previous signal quality variations for one or more first UEs (120a) having been served by the network node (110). The historical data of previous signal quality variations is indicative of a path of movement of the one or more first UEs (120a). The network node (110) determines to change a network configuration for the second UE (120b) based on the predicted signal quality variation. The network node (110) initiates the change of the network configuration for the second UE (120b). Embodiments herein further relate to a method performed by a UE (120a, 120b) for handling signal quality variations for the UE (120a, 120b).

The invention relates to the technical field of network transmission, in particular to a parallel coding and decoding method based on a multi-channel network TCP-UDP (Transmission Control Protocol-User Datagram Protocol). According to the invention, by constructing a multi-path parallel transmission mechanism of a transmission control protocol and a user datagram protocol, the transmission performance of each path can be dynamically evaluated and quantified according to the round-trip time and packet loss rate of the path at the beginning of session establishment, and the initial data distribution proportion is scientifically calculated, so that the initial optimization utilization of network bandwidth resources is realized; in the data transmission process, the actual sending rhythm and the bandwidth utilization condition of each link are continuously monitored, and the evolution trend of the bandwidth load is accurately identified through a time sequence prediction model, so that the path weight is dynamically modulated, the distribution proportion of the data packet is corrected in real time, and the data stream can be continuously and uniformly transmitted on the optimal path. The problems of transmission bottleneck and delay caused by performance fluctuation of a single path are effectively solved, and the reliability, stability and efficiency of data transmission are greatly improved.

A device, method, and system for flight path network data reporting. A request for an unmanned aerial vehicle (UAV) flight path network data report indicating at least one waypoint is received. In response to the request for the UAV flight path network data report, a request for data analysis information regarding the at least one UAV corresponding to the at least one waypoint is transmitted. In response to the request for data analysis information, data analysis information regarding the at least one UAV corresponding to the at least one waypoint is received. A UAV flight path network data report is transmitted based on the data analysis information. In some implementations, a network device implements a crewless air system network function (UAS-NF). In some implementations, the network device receives a request for a UAV flight path network data report from a UAS service supplier (USS).