132 results about "Path reconstruction" patented technology

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 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 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 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 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 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-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 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.

The invention relates to the technical field of chip design, and provides a clock constraint generation method, electronic equipment and a storage medium, and the method comprises the steps: building a relational database based on hierarchical design information of a chip, the relational database comprising an instantiated module relationship in the chip and a cross-module port connection relationship; determining a clock source from the relational database, and traversing a propagation path of the clock source in the relational database by taking the clock source as a starting point; and generating a clock constraint file of the chip based on the propagation path of the clock source. According to the method, the electronic equipment and the storage medium provided by the invention, the hierarchical design information is converted into the dynamic, queriable and traversal relational database, so that the clock propagation path is reconstructed, the clock constraint file is driven to be automatically generated, and on the premise that the complete hierarchical design information is reserved, the automatic generation of the clock constraint file is realized. The efficient, accurate and complete propagation path reconstruction is realized, and the clock constraint generation efficiency and accuracy are improved.

The invention relates to the technical field of electronic manufacturing, and discloses a multi-patch suction nozzle synchronous flexible mounting method and device, and the method comprises the steps: configuring two sets of physically independent XYZ movement mechanisms and suction nozzles, and executing the synchronous operation of different mounting regions; the mounting points are divided into a plurality of sub-regions based on a spatial clustering method, and tasks are dynamically allocated to corresponding suction nozzles; the mounting path is optimized in real time based on a heuristic algorithm, so that the motion path of the suction nozzle is shortest; whether path crossing or potential interference exists between the suction nozzles is detected in real time, if yes, an evasion mechanism is triggered, and the evasion mechanism comprises a priority scheduling strategy, a path reconstruction strategy and a speed limiting adjustment strategy. The double-station synchronous surface mounting system is suitable for improving the parallel operation efficiency of multiple devices in surface mounting production.

The invention relates to an intelligent study and judgment method for abnormal behaviors based on a data flow scene, and aims to solve the technical problem that the data flow scene of data security of an existing digital power grid lacks artificial intelligence technology deep fusion management for physical natural problems under the large background of multi-source heterogeneous energy fusion. The method is characterized in that a unified operation interface of the method is provided with an abnormal state model, and rapid fault diagnosis, data credibility evaluation and automatic data path reconstruction are realized through the abnormal state model so as to store and recover power grid security data; the model covers links of data acquisition, analysis decision, execution recovery and closed-loop feedback, realizes data fusion, anomaly detection and repair by means of digital twinning, edge intelligence and an LSTM neural network, forms a trusted data sequence, supports research and judgment and outputs a recovery instruction; and the decision instruction is issued to the SDN / NFV controller, the data toughness is improved through path switching, and normal operation of the power grid is quickly recovered.

The invention provides a routing communication control method and system for end-to-end delay dynamic optimization. The method comprises the following steps: performing multi-dimensional acquisition and analysis on real-time operation states of candidate paths in a network, performing comprehensive evaluation, and dynamically selecting an optimal path with the minimum end-to-end delay at the current moment for data forwarding; after the optimal path is started, monitoring the time delay change trend of the optimal path, constructing a multi-dimensional dynamic feature, performing similarity and correlation analysis, and extracting a feature input vector; and inputting the vector into a prediction model, generating a short-term prediction result of future time delay, comparing the short-term prediction result with a set threshold, and when the prediction result shows that an over-limit risk exists, triggering a path switching or alarm mechanism. Nonlinear time sequence characteristic analysis and probability prediction judgment are introduced into path evaluation, so that active early warning and path reconstruction before time delay abnormity occurs are realized, and dynamic balance and stable transmission effect of communication time delay can be kept under the condition of link load fluctuation or node performance change.

The invention discloses a visual AI workflow arrangement method and system, and the method comprises the steps: building a task dependence flow graph through extracting a manufacturing industry application scene demand and combining with a cue word template library, and determining a low-code arrangement standard; the AI node type is identified, the coupling strength between the nodes is detected, and a dynamic workflow topological graph is generated; multi-model interface configuration parameters are obtained, capacity matching is carried out, a scheduling opportunity atlas is formed through difference compensation factors, and multi-model collaborative scheduling is achieved; determining a dynamic analysis trigger condition based on the execution bottleneck position, identifying a failure node, performing path reconstruction, and generating a self-adaptive execution sequence; node execution efficiency analysis is carried out through manufacturing industry application verification, a graphical node library is constructed, and drag-and-drop arrangement interface data is generated. According to the method, the arrangement threshold of the AI workflow is reduced, the multi-model cooperation efficiency is improved, and the adaptive optimization capability of the workflow is enhanced.

The invention discloses a micro-grid multi-objective optimization configuration method and system based on double-layer modeling, and relates to the technical field of micro-grid planning optimization, and the method comprises the following steps: obtaining equipment operation parameters, branch connection relations and typical daily multi-source power time sequence data of a micro-grid; constructing a first configuration space and constructing a configuration layer model according to the first configuration space; generating a physical topology based on the configuration layer model and performing time sequence simulation to obtain a power flow path reconstruction sequence; extracting loss time sequence distribution of each branch and constructing a loss migration feature set; constructing a branch loss fluctuation constraint based on the loss migration feature set, and feeding back the branch loss fluctuation constraint to the configuration layer model as a feasibility constraint to obtain a corrected configuration layer model; the optimal configuration scheme meeting the feasibility constraint is searched in the first configuration space by adopting the multi-objective optimization algorithm, so that the problem of frequent reconstruction of a power flow path caused by dynamic change of power injection of a distributed power supply and an energy storage device along with time in the operation process of the micro-grid is solved.

This application discloses a dynamic path control method, device, medium, and product for cross-domain AI computing power networks, comprising: acquiring the status information of computing power nodes to be connected and generating an access description; constructing a hierarchical topology diagram including a node layer, a cluster layer, and a regional layer based on the access description; determining the AI ​​communication mode corresponding to the data stream to be transmitted; performing constraint pruning on the hierarchical topology diagram based on cross-domain compliance constraints and hardware compatibility constraints; generating corresponding candidate path structures based on the AI ​​communication mode and the pruned hierarchical topology diagram; scoring the candidate path structures based on network status parameters and task context parameters to determine the target path and corresponding path forwarding control of the business data stream; collecting telemetry indicators during the operation of the target path for joint judgment, and performing local path reconstruction or global path reconstruction based on the joint judgment result, thereby realizing a dynamic path control mechanism for cross-domain AI computing power networks.

The invention relates to the technical field of data processing, in particular to a data information storage method and system for an online Chinese language learning platform. The method comprises the following steps: obtaining platform user interaction data, and carrying out pinyin input track analysis and Chinese character component structure coding to obtain a pinyin alignment input frame; extracting a pinyin-Chinese character mapping relation in the pinyin alignment input frame, and performing Chinese character structure path reconstruction to obtain a user input word structure path set; learning content data are called, multi-modal content node embedding is carried out, and a content linkage fragment cluster is obtained; constructing a single session learning topology track according to the content linkage fragment cluster, and performing bidirectional topology sorting to obtain a learning track topological graph; and establishing a multi-dimensional primary key index and behavior triggering mechanism for the learning track topological graph to obtain a dynamic index distribution block, and transmitting the dynamic index distribution block to an online Chinese language learning platform. According to the invention, the stability and expansibility of the learning platform are obviously improved.

The invention relates to the technical field of crossing of power electronics and industrial automation control, and discloses a digital power supply networked control method and system based on electrical parameters. The system comprises a partition monitoring module, an electrical parameter acquisition module, a data analysis module, a self-adaptive model prediction module, a control module, an event trigger communication module, a fault self-healing module and a global optimization module, the partition monitoring module tracks the operation state of a power supply node in each sub-region in real time, and the electrical parameter acquisition module acquires multi-dimensional electrical parameters; the data analysis module carries out calculation according to the multi-dimensional electrical parameters, the prediction module constructs a dynamic model based on historical data, the control module formulates a dynamic adjustment strategy based on the historical data and a calculation result, and the event triggers the communication module to monitor abnormity and start a communication mechanism. The fault self-healing module realizes self-repairing through standby node switching, load redistribution or path reconstruction, and the global optimization module improves the reliability and dynamic response performance of the whole network.

The application belongs to the technical field of network security, and particularly relates to a CPS attack path reconstruction method and system based on a kill chain model clustering. CPS information layer and physical layer operation data are collected, a feature vector is generated through time window fusion, a feature subspace is divided based on the four stages of the kill chain, candidate clusters are obtained by clustering the data of each stage, high-confidence clusters are screened in combination with time concentration and behavior intensity, a causal diagram is constructed according to time proximity and cross-layer correlation, and complete reconstruction of the attack path is realized; the application adapts to the cross-layer coupling characteristics of CPS, improves the accuracy of attack stage division and path reconstruction, and can provide reliable support for attack tracing and defense strategy formulation.

The application relates to the field of intelligent warehouse management, and discloses a safe operation management platform system for intelligent warehousing, which comprises a sensing and modeling module, a path scheduling module, a fault prediction module and a visual interaction module. The sensing and modeling module constructs a real-time space model, which is used by the path scheduling and visual interaction modules. The path scheduling module plans a path based on the space model, and executes event-driven path reconstruction when an emergency obstacle occurs. Meanwhile, the scheduling strategy is optimized in combination with the equipment state evaluation of the fault prediction module and the user control instruction of the visual interaction module. The fault prediction module analyzes equipment operation data and feeds back risk information in advance. The visual interaction module integrates system state display and artificial intervention instruction transmission, and forms an intelligent safety management system with multi-module linkage.

The invention relates to the technical field of high-definition positioning, and discloses an intelligent path planning method and system for an unmanned fast working boat, and the method comprises the steps: obtaining a traffic flow quantitative index, carrying out the periodic extraction and trend prediction, and obtaining the density prediction distribution. And if the predicted density exceeds a set threshold value, carrying out dynamic analysis on a spatial region, and generating a dynamic sea area division map. Path reconstruction and flow feature extraction are carried out based on the division graph, and a preliminary path set is obtained; and then track overlapping between the paths is identified, a passing time sequence is adjusted, and a path adjusting scheme is generated. And if sudden change occurs in operation, identifying abnormity and correcting the path. And finally, generating a navigation sequence based on the corrected path and the division graph, and completing safety scoring in combination with feedback information to obtain a safety verification result. The method can solve the problem of route conflict in path planning.

The invention discloses a full-process automatic tracking method and system for property work order processing, particularly relates to the technical field of service scheduling, and is used for solving the problem of unreasonable property work order task allocation. According to the method, a standardized work order feature set and a service resource set are constructed, and an adaptation relation matrix is constructed in combination with task skill integrating degree, arrival accessibility, time window overlapping degree and conflict indexes, so that structured matching and path screening of work orders and personnel are realized, and resource binding conflicts and path overlapping are effectively avoided; in the execution process, node changes are dynamically tracked through a state mapping graph, abnormity is recognized in real time, task path reconstruction is executed, the self-healing capacity of a scheduling system is improved, behavior tracks and feedback information are further collected, a behavior feature data set is constructed, strategy deviation groups are recognized through a clustering algorithm, scheduling parameters are updated, and a closed-loop optimization mechanism is formed; therefore, the property work order processing efficiency and the user satisfaction degree are improved.

The invention discloses a hypergraph propagation source positioning method based on interaction enhancement. A traditional traceability method mainly depends on a pairwise interaction hypothesis of a simple graph structure, and the positioning precision under a complex topology is limited. The method comprises the following steps: firstly, constructing a hypergraph propagation model and obtaining a complete observation snapshot of a network node; then, extracting the dynamic state and global spectrum features of the nodes, and constructing a basic feature vector; an improved double-flow interaction hypergraph neural network is utilized, hyperedge internal key information is aggregated through local attention flow, and multi-scale topology diffusion flow is utilized in parallel to capture overall long-range dependence; and finally, introducing a gating residual fusion mechanism to adaptively integrate the double-flow features, and accurately outputting the propagation source probability based on a weighted cross entropy loss function. According to the method, the path reconstruction deviation of a traditional method can be effectively overcome and the robustness can be improved in the aspect of processing high-order interaction and long-distance dependence. According to the method, the performance bottleneck based on simple graph traceability is broken through, and powerful technical support is provided for propagation source positioning.