164 results about "Path reconstruction" patented technology

The invention discloses an APT attack path reconstruction method based on time sequence diagram comparison clustering and a medium. A security event standardized data set is obtained; mapping each security event into a multi-modal node through a heterogeneous time sequence diagram set construction method, and generating a directed edge to construct and complete a heterogeneous time sequence diagram set; a stage embedding time sequence diagram set is obtained through the joint attack stage set; outputting a time sequence diagram similarity matrix by adopting a multi-scale diagram similarity algorithm of time alignment perception; generating an event semantic sparse matrix based on the threat intelligence knowledge graph; obtaining an image clustering result set; uncertain samples in the graph clustering result set are obtained and processed, and an APT attack path reconstruction result is obtained. The problem that an APT attack path reconstruction method mainly depends on rule matching, single-dimensional feature comparison and manual analysis of security logs or alarm streams is solved, the interpretability of APT traceability is greatly enhanced, and subjective errors of manual research and judgment are reduced.

The invention relates to the technical field of exception prediction, in particular to a storage cabinet exception trend prediction system based on time series data analysis, which comprises a state monitoring module, an interval sensing module, a path reconstruction module, a symptom activation module and an evolution prediction module. According to the method, the state vectors including the temperature, the voltage, the current and the door lock state are constructed and combined with the timestamp information to form the time sequence data sequence, and the dynamic expression mode of state change is established; a jump characteristic is analyzed by using a ratio of a time interval to a state change amplitude, a short-time disturbance path and a trend evolution path are distinguished by combining a jump rate statistical index, and an evolution activation signal is identified based on trend maintenance and non-fallback characteristics. On the basis, a neural network structure with long-time dependent learning ability is introduced to capture an aperiodic thermal anomaly trend in a state sequence, and the accuracy and timeliness of anomaly recognition are improved through multi-dimensional parameter cooperative processing and path construction logic.

The invention relates to the technical field of communication, in particular to a dynamic priority dual-mode communication fault-tolerant switching method and equipment, and aims to solve the problems of communication interruption and service quality reduction when a main link is degraded in a complex electromagnetic environment. According to the method, a dynamic priority evaluation model and a dual-mode protocol cooperation mechanism are constructed, throughput, bit error rate, delay jitter, task semantic priority and aging constraint data of a main link and a standby link are collected in real time, a priority weight and a communication mode decision matrix are generated through combined modeling, an atomization switching instruction is executed according to the priority weight and the communication mode decision matrix, and a communication mode is switched. And completing path reconstruction and resource redistribution. The system further introduces link health prediction, a resource reservation pool and an online learning feedback loop, and supports fault pre-judgment, key task preemption and model adaptive optimization. According to the method, the limitation of a static threshold is broken through, the task semantics and channel state joint scheduling is realized, and the communication reliability, the service continuity and the intelligent fault-tolerant capability of a distributed system in a high-risk scene are remarkably improved.

The invention discloses a wireless public network communication data transmission system based on multiple central stations, which relates to the technical field of data transmission and comprises a load sensing and balancing module, a path reconstruction module, a self-adaptive coding modulation module and a safety tunnel management module. The load sensing module realizes load balancing and stable transmission by monitoring indexes of a central station in real time and performing dynamic migration connection; the path reconstruction module adjusts and optimizes path parameters by using federal learning, dynamically adjusts a transmission path and optimizes path quality; the adaptive coding modulation module dynamically adjusts a modulation scheme according to channel conditions, generates a pre-coding matrix in combination with environment information, and improves the transmission efficiency; the security tunnel module adopts a plurality of encryption technologies and dynamic port hopping mechanisms to ensure the security of data transmission; according to the invention, the load balancing capability, the transmission efficiency and the security of the system are effectively improved, and the method is suitable for a complex and changeable wireless public network environment.

The invention discloses a distributed model training-oriented routing reconstruction method in a hybrid photoelectric network, and belongs to the field of network communication optimization. The method comprises the following steps: constructing a collaborative routing model based on a current network flow state and topological information, and introducing an adaptive weight mechanism to dynamically adjust the priority of optical channels and electric packet forwarding, thereby realizing efficient collaborative utilization of multi-layer forwarding resources. According to the method, path reconstruction can be rapidly completed according to task characteristics and a network dynamic state, so that the system resource utilization rate is improved, and the maximum task completion time delay is reduced. Compared with a traditional fixed routing and semi-dynamic routing method, the method has the advantages that the average completion time delay and the maximum time delay are improved by more than 45% in the large-flow environment generated by large-scale model training, and the method has good expandability and real-time performance and is particularly suitable for burst flow intensive intelligent computing cluster scenes.

The invention discloses an archive digitization full-life-cycle encryption integrity verification method and system, relates to the field of digital archive safety management, and is used for solving the problem of integrity and safety guarantee in archive digitization management. Logic units are divided through a semantic analysis engine, non-uniform blocks are generated based on sensitivity dynamic partitioning, local hash values are calculated, and a multi-level verification tree is constructed to generate root hash and a hash path mapping table; acquiring visitor identities, terminal environments and timestamps in each stage of an archive life cycle, generating state snapshots in combination with a hash path, and constructing a life cycle state snapshot chain; reconstructing a target logic unit verification path according to the access request, analyzing the structural difference between a current path and a historical snapshot chain through a path reconstruction type difference verification algorithm, and identifying structural offset, node tampering or path loss; and response control is triggered for abnormal verification, including session key updating and permission freezing, so that the security integrity guarantee of the digital archive in the full life cycle is improved.

The invention relates to the technical field of network security, in particular to an intelligent security protection system based on big data, which comprises an access path reconstruction module, an abnormal cross detection module, an instruction stacking monitoring module, a bandwidth resonance judgment module and a risk early warning output module. According to the method, the access abnormal path segment can be quickly positioned by dynamically identifying the jump frequency of the access log, the parameter fluctuation and the node behavior sequence, and the fine screening of the high-risk nodes in the behavior track is realized by combining the calling coincidence characteristics of the node operation resources and the abnormal time density. And further through conjoint analysis of an instruction nesting hierarchy and a parameter structure in a communication link, a stacking expansion trend and parameter abnormal variation in an instruction calling behavior are captured on a time sequence level, and the three-dimensional recognition capability of deep abnormal operation is enhanced. The change trend of the nested instruction and the network bandwidth utilization rate sequence are mapped, and the deep representation form of resource allocation imbalance is effectively captured.

The invention relates to the technical field of collaborative optimization task allocation, in particular to an unmanned aerial vehicle cluster collaborative task allocation method and system based on cluster optimization, and the method comprises the following steps: comparing and calculating the matching degree of a task and an unmanned aerial vehicle based on the task emergency degree, the resource demand and the task time limit in combination with the electric quantity, the flight distance and the current position of the unmanned aerial vehicle; analyzing the relevance of the unmanned aerial vehicle and the task through the matching degree, and generating an initial adaptation degree parameter of the task and the unmanned aerial vehicle; according to the method, task execution and path planning are integrated by matching task resource requirements and unmanned aerial vehicle loading capacity, combining resource sharing and time sequence constraint to optimize resource allocation, analyzing and determining task relevance according to task area coupling strength, optimizing resource competition and dynamically adjusting path time consumption and a task sequence; through path reconstruction and state adjustment, resource waste is reduced, task allocation continuity and resource utilization rate are improved, and adaptability and collaboration of an unmanned aerial vehicle cluster in a complex environment are enhanced.

The invention discloses an additive manufacturing intelligent repairing and surface optimizing method for a complex special-shaped curved surface, and particularly relates to the technical field of additive manufacturing. A three-dimensional digital model is obtained, and a complex special-shaped curved surface defect area is recognized; extracting a local morphology parameter set; constructing a repair path generation model based on a graph neural network, and performing collaborative optimization of forming precision, residual stress and deposition stability on the path through a multi-objective optimization algorithm; additive manufacturing equipment is controlled to perform material deposition according to the optimized path; carrying out online error detection by adopting a structured light system, judging repair quality through a fuzzy decision tree model, and triggering path reconstruction if the repair quality exceeds a threshold value; after repairing is completed, surface micro texture factors are extracted, and functional texture regulation and control are conducted through the laser micromachining technology; according to the method, high-precision intelligent repair of the complex curved surface area and collaborative improvement of surface function performance are achieved, and the method is suitable for the fields of aerospace, energy, high-end equipment manufacturing and the like.

The invention, which relates to the technical field of engineering automation, discloses a BIM-based cable bridge routing autonomous optimization visualization system comprising a heterogeneous professional semantic modeling module, a path identification module, a path generation module, a path conflict evolution module, a path reconstruction module and an output module. The system identifies a cable thermosensitive area by constructing a cross-professional semantic map, constructs a multi-target path evaluation function and generates a high-quality candidate path by adopting a variable weight genetic algorithm; introducing a construction stage dynamic compression prediction mechanism to improve the path robustness; visual expression of the whole process of path design is realized through versioned path reconstruction and a three-dimensional animation evolution function; and finally outputting structured path data and an obstacle avoidance strategy which can be used for construction guidance. According to the invention, the intelligent level of cable bridge path planning, the engineering implementability and the system integration efficiency are significantly improved.

The invention relates to the technical field of liquid cooling energy storage control, in particular to an efficient liquid cooling energy storage system and a control method thereof, and the system comprises a path monitoring module, a path recognition module, an abnormality marking module, a switching execution module and a path reinspection module. According to the method, through combined calculation of the main channel pressure change rate and the outlet pressure deviation value, early recognition of the cooling efficiency attenuation trend is achieved, the channel abnormity sensing precision is improved, the temperature recovery rate is introduced to serve as a heat release accumulation criterion, the dynamic judgment capacity for heat retention is enhanced, and the cooling efficiency is improved through a double abnormity parallel judgment mode. Misjudgment is avoided, early warning accuracy is improved, main and standby channel switching is implemented, an independent cooling path structure is formed, pressure and temperature fluctuation is dynamically rechecked after a standby path is started, stable switching is ensured, the continuous monitoring capacity is achieved, and a parameter trend recognition, path reconstruction and state verification closed-loop mechanism is integrally constructed. And the operation stability and the thermal control reliability of the energy storage system are improved.

The invention relates to the technical field of particle detectors, in particular to a signal acquisition, processing and analysis method and system for a particle detector array, and the method comprises the following steps: segmenting a channel amplitude, marking a stable segment by using a range ratio threshold value, calculating a difference ratio, screening monotonically rising channel marking transition, extracting an earliest excitation channel as a starting point, and carrying out signal processing. And retrieving adjacent channel screening time differences in the window as candidates, and taking the channel with the angle change smaller than a threshold value as a trunk section according to time sequence to generate a path structure. According to the method, the amplitude range of a channel in a fixed period is extracted, a stable signal segment is screened, preliminary signal structuring is completed, a rising trend channel is identified through a difference ratio, the first excitation time is extracted to establish a space starting point index, and path candidates are screened in combination with the time difference of adjacent channels and states. Path trunk segments consistent in direction are extracted through angle change analysis, particle path reconstruction is achieved, multi-dimensional parameters synergistically improve the recognition precision, the analysis capability is enhanced, and the misjudgment rate is reduced.

The invention relates to the technical field of geometric modeling of three-dimensional models, in particular to a three-dimensional assembly path planning method and system for a solar wing structure. The method comprises the following steps: acquiring three-dimensional assembly data of the solar wing, and constructing an assembly reachable atlas according to the three-dimensional assembly data of the solar wing to obtain assembly reachable atlas data; path evasion type behavior planning is carried out according to the assembly reachable atlas data, and assembly path data is obtained; performing optimization scheduling on the assembly path data to obtain assembly path optimization data; performing interference resolution reconstruction on the assembly path optimization data to obtain assembly path reconstruction data; and performing digital twinning scene generation according to the assembly path reconstruction data to obtain assembly path digital twinning data. According to the method, the whole-process digital mapping from action behavior modeling, assembly time sequence control to interactive linkage is realized, and the verifiability, the interactivity and the assessment capability before deployment of the assembly process are remarkably improved.

The invention relates to a multimodal transport path selection method based on multi-agent reinforcement learning. The method comprises the following steps: constructing a multimodal transport network and a system environment; carrying out initial planning by adopting a self-adaptive large neighborhood search algorithm based on a multimodal transport network and a system environment; and when an accident occurs, acquiring accident information, optimizing the initial plan by adopting a multi-agent reinforcement learning algorithm based on the accident information in combination with a cost objective function to obtain an accident action decision, and executing a transportation task based on the accident action decision. By constructing a large neighborhood disturbance response mechanism fused with ALNS, when it is detected that node / path service time offset exceeds a threshold value, path reconstruction is intelligently triggered, a new alternative path is generated, based on an MADDPG multi-agent reinforcement learning framework, all carrier Agents can obtain global information in centralized training and make decisions independently in execution, and therefore the probability that the node / path service time offset exceeds the threshold value is lowered. And the utilization efficiency of transportation resources and the task allocation fairness are effectively improved.

The invention relates to the technical field of big data analysis, and discloses a personalized learning path recommendation method and system based on big data, and the system comprises a data collection processing module, a knowledge graph construction module, a path optimization engine and a dynamic adjustment module. The system can dynamically trigger learning path reconstruction, improve the path matching degree in the middle and later periods of the learning process, set cognitive load constraint terms in a path optimization objective function, avoid continuous arrangement of high-complexity knowledge points, ensure that the learning process conforms to the cognitive psychology law, dynamically update knowledge attribute parameters through a matrix transformation mechanism, and improve the learning efficiency. The learning path is kept in an optimal state all the time, learning feedback data is continuously collected, the system can dynamically trigger learning path reconstruction, the path matching degree in the middle and later periods of the learning process is improved, a cognitive load constraint item is set in a path optimization objective function, and it is guaranteed that the learning process conforms to the cognitive psychology law.

The invention relates to the technical field of edge computing, in particular to an edge computing server information processing system based on deep reinforcement learning, which comprises a node response identification module, an interference node screening module, a node adaptation selection module, a node state aggregation module and an interference avoidance regulation and control module. According to the method, the task behavior category group is constructed by extracting the task allocation rate, the bandwidth proportion and the node energy consumption performance, task dynamic feature recognition and fine classification are achieved in combination with track change and response synchronization information, the nodes distributed abnormally in the path are screened out, and the node screening precision and the path purity are improved; a matching mechanism is established according to the resource demand upper limit and the node state to optimize task allocation and resource use, a node state sequence is aggregated to recognize a track overlapping area to enhance the resource fluctuation trend sensing ability, and the coupling relation between the path interval and the cache stacking period is adjusted to achieve path reconstruction and task load balancing.

The invention discloses an edge computing data transmission and collaborative optimization method based on an ad-hoc network technology, which comprises the following steps: S1, constructing a communication network based on the ad-hoc network technology, and collecting a resource occupancy state and communication link bandwidth information; s2, constructing Fourier neural operator network structure parameters, obtaining optimal structure configuration by using a hunting optimization algorithm, and performing model training; s3, constructing a time sequence feature set based on the collected data, inputting the trained model, and generating an edge network state sequence; s4, scheduling and transmission optimization are carried out based on the state sequence, and an optimal combination scheme is searched by adopting a hunting optimization algorithm; s5, deploying the optimal scheduling and path to an edge ad hoc network, executing multi-hop relay transmission, and supporting path reconstruction and task migration; and S6, collecting actual operation data, comparing the actual operation data with a prediction result, updating model parameters, and executing closed-loop optimization. According to the method, efficient collaboration and dynamic self-adaption of task scheduling and data transmission in an edge computing environment are realized.

The invention belongs to the technical field of path planning, and particularly relates to a three-dimensional dynamic path planning method based on high-density radar point clouds, which comprises the following steps: collecting three-dimensional point cloud data streams of a continuous time sequence, removing ground points after noise filtering, and then dividing the space in a scanning range into voxels with unequal side lengths to form a map set; establishing a multi-dimensional evaluation function about the trafficability of each voxel, screening passable areas, and forming a three-dimensional passing voxel map; setting a cost function, carrying out spatial path search, extracting path key points, and carrying out optimization and curve fitting on a path obtained by search to form a trajectory curve; selecting control input, issuing the control input to an execution unit of the intelligent agent, and performing dynamic tracking control on the trajectory curve; and when an abnormal state occurs, performing path reconstruction by adopting an incremental optimization strategy, and generating a new path until the intelligent agent arrives at the target point. The autonomous navigation task can be efficiently, safely and stably completed in a complex dynamic environment.

The invention relates to the technical field of dredging engineering, in particular to a dredger autonomous path planning method based on reinforcement learning, which comprises the following steps: acquiring a multi-dimensional state space vector in real time; constructing a digital twinborn scene, training a strategy network by adopting a three-stage course learning strategy through increasing environment complexity, and dynamically adjusting a path planning priority through a layered reward function; migrating the trained strategy network to a ship entity; recognizing an obstacle based on a target multi-mode sensing system, evaluating a collision risk through a dynamic probability model, and performing local path reconstruction when the collision risk exists; and in a multi-ship collaborative operation scene, constructing a global optimization model, performing dynamic task redistribution on each ship by adopting a distributed iterative solution and incremental updating strategy until convergence, and outputting a collaborative optimization scheme of each ship. According to the method, the path planning efficiency in a multi-ship collaborative operation scene can be improved, and the training cost is reduced.

The invention discloses a multi-robot path planning method based on multi-heterogeneous population coevolution and parallel search. The method comprises the following steps: firstly, modeling an environment by adopting a two-dimensional grid method, and generating a multi-robot initial path of a total population by utilizing a risk perception A * algorithm; then, dividing the total population into three sub-populations and corresponding external cooperative populations through a hierarchical roulette selection strategy; the three types of heterogeneous algorithms are executed in parallel, namely the collaborative genetic algorithm, the collaborative particle swarm algorithm and the collaborative ant colony algorithm are executed in parallel and synchronously, and a global solution set is generated in combination with a double-solution pool fusion mechanism. And then, a priority-based conflict resolution strategy is adopted, and time-space conflicts are solved by adopting time migration, path detour and local path planning strategies. And finally, path smoothing is realized through a cubic B-spline curve based on path reconstruction. According to the method, heterogeneous population coevolution is combined with parallel calculation, so that the efficiency, safety and cooperation capability of multi-robot path planning are remarkably improved.

The invention relates to the field of information processing, and discloses an underground pipe gallery intelligent inspection robot path planning method, which comprises the steps of judging an environment change risk based on underground pipe gallery structural characteristics, and dynamically adjusting a detection period; triggering local path reconstruction in combination with sensor data change and target conflict conditions; and orderly collaboration of path safety, energy consumption and smoothness is realized through a layered optimization architecture. According to the method, triggering logic is used for replacing a traditional periodic planning mechanism, risks and conflict points in operation can be recognized more accurately, and active adaptation and quick response of path decision are achieved. Through a multi-dimensional decoupling architecture, mutual interference of targets is avoided, and the system stability and execution efficiency of path planning are improved.

The invention discloses a multi-nozzle collaborative printing method, and particularly relates to the technical field of printing path planning. According to the method, a three-dimensional space mesh model is constructed by collecting structural feature information of a target printing area; calculating collaborative coverage parameters of the sub-regions according to the space coverage capability and the region curvature characteristics of the nozzle; constructing a priority map based on nozzle performance and task suitability, and performing task matching by adopting a minimum cross entropy objective function to generate an initial path allocation scheme; constructing a jet printing path conflict graph, extracting conflict feature points, performing local path reconstruction by using a graph convolutional neural network, and generating an optimized jet printing path; and finally, the multiple nozzles are driven to cooperatively execute a jet printing task through time synchronization and instruction scheduling. According to the invention, the conflict rate and waiting time of the nozzles can be obviously reduced, the path planning efficiency and the jet printing precision are improved, and the method has good engineering applicability.

The invention discloses an AGV safety control method for intelligent warehousing, and relates to the technical field of intelligent warehousing, and the method comprises the following steps: constructing a global three-dimensional load distribution diagram of an automatic guided vehicle in real time through the fusion of a laser radar and an inertial measurement unit, and outputting a mass distribution matrix; based on the mass distribution matrix, millimeter wave Doppler tomography is superposed, the barycentric coordinates are dynamically corrected, and a corrected mass distribution matrix is obtained; and inputting the corrected mass distribution matrix into a braking dynamic prediction engine, and generating a prediction curve of an adaptive braking distance and braking response time in combination with online inversion estimation of a ground friction coefficient. Through the digital twin simulation and micro-pulse pre-braking strategy, the risk of inertial collision and cargo overturning is effectively reduced, a storage scheduling system is controlled in parallel, path reconstruction and driving priority dynamic adjustment are achieved, and global safety and efficient scheduling of intelligent storage are guaranteed.

The invention relates to the technical field of network security, in particular to a network security threat assessment and traceability system based on big data, which comprises a node behavior monitoring module, a function offset identification module, a path variation screening module, a behavior trajectory comparison module and a threat traceability judgment module. According to the method, through behavior vector change angles and frequency differences of a plurality of time periods, nodes with violent behavior characteristics fluctuation are identified, communication frequency and interaction direction changes are analyzed, node links with abnormal communication characteristics are screened in combination with time interval differences of abnormal paths, and according to a comparison result of a static access template and a dynamic operation log, the dynamic operation log of the static access template is determined. The deviation item number mapping is calculated, and the suspicious communication starting point is traced, so that the comprehensive evaluation and attack source positioning of node function role change, communication path mutation and operation behavior abnormity are realized, the threat identification precision is effectively improved, and the credibility of attack path reconstruction and attacker judgment is improved.

The invention discloses a finite space communication and security detection method, system and device, and relates to the technical field of image recognition, and the method comprises the steps: building a shielding perception graph, and completing the building of an initial three-dimensional semantic map and a device topological graph through the combination of distance measurement and visual SLAM; a communication Mesh topology is dynamically generated based on sight distance shielding information, path reconstruction is triggered when shielding occurs between nodes, and edge nodes are scheduled to perform image acquisition and transmission on the basis of communication guarantee; and restoring the shielded human body posture track through an optical flow method and a dynamic semantic mask, extracting a behavior tuple, and calibrating an abnormal action. According to the method, the stability and continuity of communication in a limited space are effectively improved, the recognition capability of abnormal behaviors in a shielding environment is enhanced, efficient fusion and intelligent processing of multi-source sensing data are realized, the false alarm rate and missing report rate of safety monitoring are remarkably reduced, and the real-time performance and precision of risk event response are improved.

The invention discloses a network-on-chip fault-tolerant system based on a fault processing router and a dual fault-tolerant distributed route, and relates to the technical field of network-on-chip. The system comprises a fault processing router and a dual fault-tolerant distributed routing mechanism. The fault processing router comprises a port distribution module, a redirection FIFO module, an error correction module, a crossbar switch module, a crossbar switch bypass module and a detection master control module. The modules cooperate to realize fault isolation and path reconstruction. According to the dual fault-tolerant distributed routing mechanism, fault tolerance is realized through single fault isolation, composite fault collaboration and dynamic routing adjustment, and deadlock is avoided and network connectivity under fault topology is maximized by dynamically relaxing and forbidding a steering rule. The problems of single-point failure and composite fault accumulation caused by permanent faults of transistors of the network-on-chip are solved.

The invention provides an urban rail system emergency scheduling method and system combined with big data analysis, and relates to the technical field of urban rail transit, and the method comprises the steps: firstly, converging dynamic monitoring data of an urban rail system, including train real-time working conditions, station region people flow distribution, rail structure vibration, power supply network load and external environment interference data; secondly, constructing an abnormal evolution topology based on the data, taking data fluctuation characteristics as nodes, connecting the nodes into a network by reflecting evolution association strength of a mutual induction relationship, and then identifying key conduction nodes and diffusion boundaries from the abnormal evolution topology; then, key conduction nodes, diffusion boundaries and emergency resource layout data are combined to generate a collaborative scheduling matrix which comprises multiple sequences such as resource pre-scheduling, train path reconstruction, station load evacuation and system function compensation and is linked through time sequence correlation parameters; and finally, converting the cooperative scheduling matrix into executable scheduling instruction sets, and pushing the executable scheduling instruction sets to corresponding control terminals according to execution subject classification, thereby realizing efficient emergency scheduling.

The invention provides a big data anomaly detection method and system based on cloud computing, and relates to the technical field of big data anomaly detection.According to the big data anomaly detection method and system based on cloud computing, a complete time sequence mode is effectively captured through a service cycle self-adaptive sliding window segmentation mechanism, and the regularity and continuity of data representation are remarkably enhanced; multi-dimensional statistical features and graph database-driven entity semantic features are deeply fused to construct a joint fluctuation quantitative model, so that the representation robustness and noise immunity of a high-dimensional feature space are greatly improved; a mutual information dynamic threshold screening strategy is combined with a feature stability collaborative optimization mechanism of a gradient boosting decision tree to form an anti-interference enhanced decision normal form, so that accurate confirmation of abnormal signals and efficient suppression of false alarm behaviors are realized in a complex dynamic environment; and based on a root cause positioning system of decision path reconstruction and weighted contribution accumulation, the problem of offset of characteristic contribution evaluation by a traditional method is solved, and the identification precision and interpretability of key abnormal driving factors are qualitatively improved.

The invention relates to the technical field of image processing, in particular to a cable lead seal defect image denoising and enhancing method and system, and a storage medium, and the method comprises the steps: carrying out the multi-stage denoising processing, employing a U-Net architecture to separate the contour of a lead seal main body from background noise, and reserving defect features; two-way reconstruction processing: performing two-way reconstruction on a denoising result and an original noisy image by using a generative adversarial network, and enhancing defect edge continuity and texture authenticity; feature fusion processing, global structure modeling, local detail convolution extraction, multi-source feature fusion and defect contrast optimization. The system comprises a multi-stage denoising module, a double-path reconstruction module and a feature fusion module. A computer program for implementing the method is stored in the storage medium. According to the method, the problems of low defect contrast and fuzzy details under extreme noise in a traditional method are solved, and the precision and reliability of cable lead seal defect detection are improved.

The invention relates to the technical field of communication network maintenance, and discloses a communication network operation quality monitoring and closed-loop operation and maintenance scheduling management system, which comprises an acquisition unit for acquiring network element telemetry data; the observation module extracts a flow fingerprint, converts a link into a transfer function model, and generates a consistency gradient vector according to singular point offset; the echo prediction module converts the scheduling instruction into an expected gradient offset vector; the module calculates the scheduling residual error of the actually measured offset and the expected offset; the decision-making unit generates a path reconstruction instruction, and the scheduling residual error is utilized to check the execution efficiency and update the operation reference; according to the invention, the flow conduction consistency constraint is anchored, the link degradation characteristic and random fluctuation decoupling is realized, and the transient interference generated by the scheduling action is eliminated.