97 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 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 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 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 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 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 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.

The invention discloses an orchard multi-robot task allocation optimization method based on hierarchical path reconstruction, and the method comprises the steps: S1, building a multi-target mathematical model, optimizing the maximum completion time and total energy consumption, including robot load, speed, energy consumption and battery capacity constraints, and employing VLDIM to generate an initial solution population; s2, optimizing the task sequence of each path by using DRRM in the microscopic layer; s3, robot-level DRRM optimization, TRRM task redistribution, CRRM charging path reconstruction and environment selection are executed iteratively; and S4, splitting the longest path by using the SRRM, redistributing the longest path through the MILP1, and outputting a non-dominated solution set. Through hierarchical coding, multi-stage collaborative optimization and a special reconstruction mechanism for core constraints, the task allocation efficiency and scheme quality of a multi-robot system under complex constraints are significantly improved, and the method is suitable for agricultural automation and warehouse logistics scenes.

The application 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 module. In the application, a task behavior category group is constructed by extracting a task allocation rate, a bandwidth ratio and a node energy consumption performance, task dynamic feature identification and fine classification are realized by combining trajectory changes and response synchronization information, abnormal distribution nodes in a path are screened out to improve node screening precision and path purity, a matching mechanism is established according to a resource demand upper limit and a node state to optimize task allocation and resource use, a node state sequence is aggregated to identify a trajectory overlap area and enhance resource fluctuation trend perception capacity, and the coupling relationship between a path interval and a buffer accumulation period is adjusted to realize path reconstruction and task load balancing.

The present application relates to the technical field of intelligent logistics scheduling and transportation path optimization, and discloses a truck dynamic path and time window optimization method, which is used for solving the problems of instruction failure caused by cloud reconstruction delay and vehicle continuous displacement asynchronization in traditional methods, and the problems of global rearrangement range expansion and calculation efficiency reduction caused by the fact that local delay is not effectively isolated; the method firstly establishes a distribution session, uniformly processes vehicle operation data, task data and road network state data, finds future execution position and execution starting node after disturbance occurs, checks time sequence of subsequent distribution nodes, finds abnormal nodes, and sequentially carries out time extension absorption, node reorganization, path reconstruction and terminal confirmation within the local path boundary, completes dynamic path adjustment and version update, and realizes normal operation of truck distribution scheduling.

The invention relates to the field of network-on-chip, and particularly provides a network-on-chip hybrid routing design method and device, and the method comprises the following steps: S1, carrying out the pre-calculation and coding of multi-path deterministic information; pre-calculating deterministic information of a shortest path and 1-2 non-shortest paths, and encoding the information to a data packet header flit; s2, carrying out two-hop congestion sensing and multi-path cooperative switching; receiving a self VC state and a next hop node VC state through an extended credit signal, and calculating a joint congestion index; s3, fault detection and dynamic remapping are coordinated; comprising a fault monitoring unit and a path reconstruction unit, the fault monitoring unit detects faults of the fault monitoring unit and adjacent nodes and links, and the path reconstruction unit generates deterministic information of a local alternative path; s4, data packet arrival and resource release; and the collaborative decision module instructs to output the data packet from the local port. Compared with the prior art, flexible path switching, accurate congestion avoidance and dynamic fault bypassing can be realized.

The invention relates to a dynamic update path planning method and system based on Voronoi error measurement. The method comprises the following steps: acquiring input data and preprocessing the input data; generating a seed set according to the preprocessed data, and constructing a Voronoi diagram; extracting seed points according to the Voronoi diagram, and generating a candidate point set in the neighborhood of the seed points; comprehensive error calculation is carried out on candidate points in the candidate point set, and position updating is carried out on the seed points according to comprehensive errors; when the position of the seed point is updated or the local environment is changed, performing local reconstruction on the Voronoi diagram; and constructing a navigation map according to the reconstructed Voronoi map, obtaining a path with the minimum comprehensive cost according to the navigation map, generating a new local optimal path fragment, and obtaining a path planning result. According to the method, only the affected area is updated when the environment is locally changed, so that real-time and low-overhead path reconstruction and re-planning are realized.

The invention discloses a traceability report generation method and device, electronic equipment and a storage medium, and the method comprises the steps: determining a target task for a research problem according to the research problem input by a user; creating a traceability knowledge base having an association relationship with the research problem; obtaining original text information of a to-be-executed step for executing the target task; metadata used for representing the logic relation between the original text information and the execution result of the to-be-executed step is determined; storing the original text information, the execution result and the metadata into a traceability knowledge base; and in response to a received traceability request of a user for the task report of the target task, extracting the original text information, the execution result and the metadata from the traceability knowledge base to generate a traceability report. According to the method, the original information, the intermediate execution result and the logic association metadata are synchronously stored in the task generation process, a complete mapping system from a fact manuscript to a final conclusion is constructed, and logic trace reserving and evidence path restoration of artificial intelligence generation content are realized.

The invention provides an AIS intra-cluster local path self-adaptive reconstruction method and system, and the method comprises the steps: generating a global route based on an off-line constructed maritime traffic network connected graph plan according to a starting and ending point and an expected navigational speed inputted by a user, and carrying out the local path reconstruction of a cluster where a route turning point is located, global course information of the global course is obtained; according to the global course information and the expected navigational speed, obtaining an optimal sub-cluster in the cluster where the course turning point is located; and obtaining a cluster entrance reference point and a cluster exit reference point in the optimal sub-cluster through an adaptive algorithm, reconstructing a local path in the cluster according to the cluster entrance reference point and the cluster exit reference point, and updating global course information of the global course. According to the method, the global course information is utilized and the AIS track points in the clusters are combined to guide the adaptive reconstruction of the local paths in the clusters, so that the centroids of the clusters are not directly connected any more, the route planning is more consistent with the logic, and the authenticity and availability of the route planning are improved.

The invention discloses a low-interference taint path restoration system and method based on static instrumentation, and mainly solves the problems of stiff analysis strategy, high operation overhead and result semantic deficiency faced by existing vulnerability mining and security assessment. The system comprises a target data acquisition end and an analysis engine which run independently and are connected through a shared memory channel; lightweight static instrumentation is performed on a source code in a target program compiling stage, and a shared memory channel with an analysis engine is established; by constructing a "producer-consumer" asynchronous decoupling architecture, time-consuming taint rule management and path restoration logic are stripped from a service main thread; a lockless annular buffer area is utilized to efficiently capture a runtime data stream, and a source code level taint propagation path is restored in an independent analysis engine in combination with debugging information and a call stack Hash algorithm. According to the method, a high-precision source code level analysis result can be obtained while low performance loss is kept, and the efficiency and flexibility of vulnerability mining are greatly improved.

The invention belongs to the technical field of network security, and particularly relates to a CPS attack path reconstruction method and system based on killer chain model clustering. The method comprises the steps of collecting operation data of a CPS information layer and a physical layer, generating feature vectors through time window fusion, dividing feature subspaces based on four stages of a killing chain, clustering data of each stage to obtain candidate clusters, screening high-credibility clusters in combination with time concentration and behavior intensity, and constructing a causal graph according to time proximity and cross-layer correlation. Complete reconstruction of an attack path is realized; the method adapts to CPS cross-layer coupling characteristics, improves the accuracy of attack stage division and path reconstruction, and can provide reliable support for attack traceability and defense strategy formulation.

The invention provides a communication guarantee method and device for an open-pit mine field, electronic equipment and a storage medium, and relates to the technical field of communication, a physical double-route Ethernet ring network architecture which contains main and standby routes and keeps a preset distance is constructed, and a single-board-level redundancy protection mechanism is configured for a baseband processing unit and a core network server in the architecture, so that the communication guarantee capability of the open-pit mine field is improved. In addition, fault detection and path reconstruction are executed based on a preset protection protocol and a software defined network controller, and meanwhile, a base station dynamic migration operation which triggers a migration process based on environment monitoring data and realizes base station configuration deployment and service switching through a network function virtualization technology is executed. The problems that in the prior art, due to the fact that single routing topology is adopted, key equipment lacks single-board-level redundancy, the fault detection and path recovery efficiency is low, and a base station depends on manual migration, the single-point fault risk is high, the fault recovery time is long, and the mining pit propelling dynamic coverage requirement is difficult to adapt can be solved.

The present application relates to a kind of intelligent carbon accounting method and system based on large language model, for the automatic analysis of carbon accounting rule in multi-source heterogeneous text, logic conflict resolution and path self-consistent reconstruction problem, a kind of method based on large language model is extracted, accounting path atlas construction and dynamic weight applicability adjustment of logic anchor point.It includes heterogeneous text semantic deconstruction and accounting logic anchor point solidification, accounting dimension scene configuration, topological accounting path automatic generation, logic constraint direction annotation and bidirectional consistency check, conflict intelligent negotiation and path reconstruction and compensation correction based on weight, finally realize the data consistency of whole-process carbon accounting, traceability integrity and compliance delivery.The method effectively improves the degree of carbon accounting automation and result reliability, can dynamically respond to the rule conflict in actual scene of enterprise, guarantee the internal logic of multi-dimensional carbon accounting result Self-consistent and reliability.

The application discloses a kind of big data dynamic path intelligent planning system and method for instant distribution, its system includes multi-source real-time sensing module, big data fusion processing module, distribution demand prediction module, multi-objective dynamic optimization scheduling module, path reconstruction and adaptive updating module and feedback learning module.The application realizes the quick identification and path dynamic reconstruction to traffic congestion, order change and emergent event by constructing multi-source real-time sensing and feature deviation trigger mechanism, avoid distribution personnel to follow invalid path, simultaneously, to traffic, meteorology, road risk and driver load multi-source heterogeneous data are unified feature modeling and weight adaptive fusion, and combined with local path repair strategy, only abnormal section is re-planned, significantly reduce computational complexity and improve system stability in large-scale concurrent scene.

The invention discloses a logistics supply chain dynamic management method and platform based on the Internet of Things, and belongs to the technical field of logistics network state awareness, and the method comprises the steps: obtaining the real-time state parameters of each logistics node, including the number of tasks, the queuing length, the processing speed and the node connection frequency; a dynamic weighted directed graph is constructed based on the parameters, nodes in the graph represent processing units, and edge weights reflect transportation efficiency and delay; performing coupling analysis on the graph structure, extracting a transmission incidence matrix, and calculating a node coupling degree; constructing a fragile node set based on the coupling degree, and calculating the node fault-tolerant capability in combination with the task flow pressure and the abnormal frequency; task transfer, path reconstruction and temporary resource adjustment are carried out on the nodes with the fault-tolerant capability lower than a set threshold value, and real-time identification and optimization of logistics network bottlenecks are achieved; according to the invention, the scheduling stability and anti-pressure capability of the logistics network can be improved, and the method is suitable for intelligent logistics management in a multi-source order and high-concurrency scene.

The invention discloses a multi-source heterogeneous data quasi-real-time synchronization method and system based on a lake-warehouse integrated architecture, and relates to the technical field of power system automation and data communication, and the method comprises the steps that a transmitting end collects multi-source heterogeneous data and stores the multi-source heterogeneous data in the lake-warehouse integrated architecture in a hierarchical manner; converting a data format according to the channel memory margin, and injecting the data into a corresponding channel according to a data level; calculating the priority of each transmission branch at the transmission node, and selecting the branch with the highest priority to form a priority transmission path; performing performance detection on the priority transmission path, and dynamically reconstructing the transmission path according to a detection result; and the receiving end performs consistency verification and timeliness evaluation on the data, and injects the data into a data lake or a data warehouse if the verification is passed, otherwise, requests retransmission and executes data completion and time sequence reforming. Through data grading, dynamic routing and path reconstruction mechanisms, the real-time performance, reliability and resource utilization efficiency of cross-station data transmission are improved, and stable operation of key services of a power grid is guaranteed.

The application relates to the technical field of anomaly prediction, in particular to a storage cabinet abnormal 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.In the application, a state vector containing temperature, voltage, current and door lock state is constructed, time stamp information is combined to form a time series data sequence, a dynamic expression mode of state change is established, the ratio of time interval to state change amplitude is used to analyze the jump characteristic, and a jump rate statistical index is combined to distinguish a short-time disturbance and a trend evolution path, an evolution activation signal is identified based on trend retention and non-falling characteristics, a neural network structure with long-time dependent learning ability is introduced to capture the aperiodic thermal abnormal trend in the state sequence, and the accuracy and timeliness of abnormal identification are improved through multi-dimensional parameter collaborative processing and path construction logic.

The application discloses an agricultural equipment intelligent operation quality evaluation method and system, and relates to the technical field of quality evaluation. The method comprises the following steps: dividing a buffer area into edge grid units; performing segmented coding processing on original operation track data to form a set of fragmented path units, and extracting the spatial interaction relationship between each path unit and the corresponding edge grid unit; establishing a quality linkage matrix based on the calculation results of multiple parameters; calling the path unit set of the historical high-scoring area of the edge grid unit to reconstruct the trajectory path according to a preset operation specification template track, and generating a set of offset coding vectors according to the spatial offset vectors between the original operation path and the reconstructed trajectory; performing multidimensional quality index clustering analysis on the consistency score and the set of offset coding vectors to generate a fragmented quality level label and form a linkage quality evaluation layer. The application combines historical high-quality path reconstruction and multidimensional clustering mechanism, and effectively improves the accuracy and adaptability of quality evaluation.