198 results about "Spectral clustering" patented technology

The invention discloses an electricity larceny identification method based on graph calculation, and particularly relates to the technical field of electricity utilization anomaly detection of an electric power system. Historical power consumption data and a power supply topological relation of power consumers are collected, and a multi-dimensional behavior graph model fusing behavior characteristics and structural information is constructed; performing structure disturbance analysis on each node in the graph, calculating information entropy change before and after node removal, performing attention fusion on a time sequence behavior feature of the node and a structure disturbance vector, constructing a joint feature vector, and enhancing feature expression through spectral clustering and linear reconstruction; a behavior propagation field and a disturbance adjustment mechanism are introduced into the graph to form a disturbance response graph, and an abnormal gathering area is identified through path energy analysis and focusing area fitting; calculating confidence scores of the nodes and outputting a suspicious user list; the method can realize efficient identification of electricity stealing behaviors with strong concealment and complex transmissibility, and has the advantages of high precision, strong interpretability and wide application scene adaptability.

The invention discloses a high-precision distributed photovoltaic output prediction method and system based on micrometeorology and feature hierarchical clustering, and relates to the technical field of distributed photovoltaic output prediction.The method comprises the steps that numerical weather forecast data are collected, an initial micrometeorological field is generated through space-time alignment and self-adaptive KNN interpolation, and the initial micrometeorological field is subjected to feature clustering; a WRF-LES system and a bidirectional LSTM are combined to establish cross-scale mapping, a dynamic residual correction field is fused to generate hectometer-level high-resolution micrometeorological data, and the problem of insufficient resolution of traditional numerical forecasting is solved. MIC and PA-DTW are used for jointly analyzing the characteristics of the power station, and dynamic clustering is achieved through a sliding time window and incremental spectral clustering. According to the method, a physical information graph network and causal expansion convolution are coupled to extract features, federal learning cross-power-station cooperative training is combined, the distributed photovoltaic output prediction precision and robustness are improved, and privacy security is considered.

The invention discloses a high and cold arid region slope soil stability safety risk evaluation system and method, and relates to the technical field of slope engineering. The state of each side slope under multi-dimensional indexes such as freeze-thaw cycle frequency, dry-wet alternation index, wind erosion strength, shear strength, water content, porosity and fracture density is quantified, the feature similarity between any two side slopes is calculated, and then a side slope feature similarity matrix is formed. Based on the matrix, an unsupervised clustering algorithm (such as spectral clustering, similarity propagation and the like) can be adopted to divide a plurality of side slopes into similar subsets with structural characteristics similar to environmental response, and category attribution of risks is achieved. On the basis, structural variation analysis, historical instability statistics and central risk difference extraction are performed on similar slope samples, so that the internal instability tendency of the slope can be identified, and a risk prediction model suitable for the type of slope can be constructed through feature training.

The invention discloses a method for constructing an evaluation model for the influence of climate change on biodiversity, and particularly relates to the technical field of ecological modeling and climate response analysis. The method comprises the following steps: generating a high-resolution characteristic grid based on microclimate disturbance data and a remote sensing image, extracting ecological plaques and establishing a heterogeneity spatial index model, constructing a connectivity map and a coupling response path library, extracting a typical species response mode by using nonlinear dimension reduction and spectral clustering, and performing dynamic fitting in combination with biological survey data. And finally, generating a diversity attenuation trend prediction curve and a risk thermodynamic diagram, identifying a biodiversity collapse critical point and outputting an intervention priority, and the method can be widely applied to regional ecological early warning and protection planning.

The invention discloses a decision scheme generation method and device based on complex multi-modal management data, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: receiving enterprise management data of texts, numerical values and images, extracting a unified feature vector, carrying out the clustering analysis through the dynamic updating of torque clustering and spectral clustering, and obtaining a clustering result; and a causal knowledge graph is constructed according to the result, and a decision scheme is generated by using a fourth-order inference engine, so that efficient processing and real-time response of an enterprise to dynamic multi-modal management data are realized, the cross-modal data analysis capability is enhanced, and the decision accuracy is improved.

The invention discloses an intelligent substation virtual loop online transmission verification method based on GOOSE / SV closed loop test, and relates to the technical field of intelligent substation operation and maintenance. Through precise clock synchronization and an improved cross-correlation algorithm, in combination with wavelet noise reduction and spectral clustering analysis, nanosecond synchronization quality evaluation of GOOSE / SV signals is realized, and the hidden transmission risk discovery time is shortened from regular maintenance to real-time monitoring; a hierarchical hidden Markov model is adopted to analyze equipment-level to system-level behavior modes, real-time probabilistic reasoning is realized in combination with a dynamic Bayesian network and particle filtering, a multi-dimensional evaluation system is constructed, the reliability evaluation capability under complex working conditions is remarkably improved, an optimization scheme is generated based on network path characteristic analysis and bottleneck identification, and the reliability of the system is improved. Multi-scene closed-loop verification is carried out by means of a digital twin technology, safety and reliability of parameter optimization are ensured, and full-process intelligent operation and maintenance of the virtual circuit of the intelligent substation from state perception to optimization verification are realized.

The invention relates to the technical field of air tightness detection, in particular to a diaphragm type energy accumulator air tightness detection method which is used for solving the problems that in the prior art, equipment operation characteristics cannot be accurately described, typical defect modes cannot be recognized in combination with spectral clustering, and a quantitative basis cannot be provided for equipment fault trend analysis and intelligent maintenance. The method comprises the following steps: constructing a low-dimensional state map to describe equipment operation characteristics, identifying typical defect modes in combination with spectral clustering, establishing a nonlinear correlation model to reveal a defect evolution relationship, optimizing classification model parameters by adopting an evolutionary algorithm, improving the identification accuracy, mining a most probable defect evolution path based on a transition probability matrix, and improving the identification efficiency. And a quantitative basis is provided for equipment fault trend analysis and intelligent maintenance.

The invention discloses a multi-view clustering method based on tensor feature extraction, and the method comprises the steps: inputting a multi-view data matrix, constructing a similarity matrix of each view through a K-NN algorithm and a Gaussian kernel function, and carrying out the spectral clustering to obtain a sample embedding matrix; performing singular value decomposition on an original data matrix of each view, taking first c left singular vectors to construct a feature embedding matrix, applying 2, 1 norm group sparse constraint on the feature embedding matrix, and connecting a sample embedding matrix through a bigraph to extract features; and normalizing the sample embedded matrix, and reconstructing a block diagonal matrix into a third-order tensor. And integrating the sample embedding matrix, the feature embedding matrix and global tensor learning to construct a target function, and optimizing through an alternating direction multiplier method until convergence. And finally, the normalized samples are embedded into the matrix to form block diagonals to form a consistent similarity graph, and a clustering result is obtained by using an N-Cut or k-means algorithm.

The invention discloses an active fluctuation collaborative stabilizing method and system for a high-proportion distributed new energy power grid, and relates to the technical field of power grid dispatching. According to the method, a physically consistent weather prediction model is established through multi-source meteorological data fusion, and a regional fluctuation propagation rule is accurately captured; identifying a high-risk fluctuation cluster based on dynamic time warping and spectral clustering, simulating a fluctuation propagation path by using a digital twin platform, and quantifying resource requirements; energy storage resource configuration is optimized by adopting mixed integer programming and a column generation algorithm, and multi-dimensional stability verification is carried out through a digital twin environment; a self-adaptive optimization mechanism based on reinforcement learning is established, continuous evolution of the system is realized, the technical bottlenecks of a traditional method in the aspects of fluctuation perception, resource allocation, system self-adaption and the like are solved, a collaborative stabilization mechanism with accurate prediction, intelligent recognition and decision optimization is formed, and a complete solution is provided for safe and stable operation of a high-proportion new energy power grid.

The invention provides a product full life cycle management method based on digital twinning, a medium and a product full life cycle management system based on digital twinning, and belongs to the technical field of digital twinning. Based on a super-sparse pre-training model of a liquid neural network architecture, in combination with spectral clustering and an attention mechanism, precise prediction of product performance degradation is realized, and intelligent maintenance suggestion generation and dynamic optimization of operation parameters are realized through a Hilbert matrix state evaluation and decision adjustment mechanism. The balance between calculation efficiency and prediction precision is realized by using a multi-precision simulation analysis and incremental updating technology, a backtracking decision matrix is established to support the optimal decision of product decommissioning and recovery, and the technical problem that the real-time perception and dynamic prediction optimization management of the full-life-cycle multi-dimensional state of the product cannot be realized in the prior art is solved.

The invention belongs to the technical field of computer big data, and discloses a relation graph construction and layout method, device and system based on spectral clustering and a storage medium, a clustering center is initialized through a genetic algorithm, the clustering center serves as genetic information and is coded into a character string, the operation time can be shortened, and the classification precision can be improved; furthermore, a weighted Euclidean distance is constructed as a distance function of a K-means algorithm, mutual relation weighting between the features can be reflected, features of different weights are counted into the distance, the classification precision can be effectively improved, the loss is reduced, and the classification efficiency is improved. According to the method, an initial similarity matrix, obtained through a traditional similarity calculation method, between XML documents is corrected through an affinity propagation algorithm, the similarity between the hidden similar XML documents can be reflected, on the basis, the correct clustering number and the correct clustering result are obtained by applying a multi-path spectral clustering method NJW, the method is irrelevant to the sequence of the XML documents, and the method has the advantages of being high in practicability and easy to popularize. The method is suitable for clustering the retrieval results of the XML documents arranged in any sequence.

The invention provides a non-technical line loss state evaluation method based on multi-scale spatial-temporal feature fusion in a low-voltage distribution network environment, and relates to the technical field of electric power big data analysis and intelligent operation and maintenance of a distribution network. According to the invention, a fusion architecture based on a multi-scale time convolution network and a long short-term memory network is constructed; extracting multi-scale spatio-temporal characteristics of a user from instantaneous electricity utilization abrupt change to a periodic load rule through an MSTBlock unit; designing a cluster balance constraint mechanism to ensure that rare and key non-technical line loss abnormal early warning signals are not covered by mass normal power utilization data; according to the data scale, adaptively selecting a graph segmentation or spectral clustering integration strategy to output a clustering label, and mapping the clustering label into a user power consumption behavior evolution track; according to the method, the power utilization abnormal level can be identified from the original load signal with random fluctuation interference, and the troubleshooting priority is calculated in combination with the transformer area correlation analysis, so that the accuracy and interpretability of the non-technical line loss unsupervised evaluation decision of the power distribution network are remarkably improved.

The application relates to the technical field of signal processing, data representation and classification, and discloses a radar signal spectrum clustering sorting method based on SOM anchor point extraction and graph fusion, which comprises the following steps: configuring a radar signal sorting cluster number and a radar pulse parameter, obtaining a normalized radar pulse data set, taking each normalized radar pulse in the radar pulse data set as a node, and constructing a KNN graph of the radar pulse; extracting an anchor point of the normalized radar pulse data set based on SOM, calculating the similarity between the extracted SOM anchor point and all nodes, obtaining a similarity matrix, and constructing an anchor graph adjacency matrix based on the similarity matrix, namely an adaptive anchor graph; weightedly fusing the KNN graph and the adaptive anchor graph to obtain a fusion graph; and performing spectrum clustering sorting based on the radar signal sorting cluster number and the fusion graph to obtain a sorting result. The application breaks through the limitation that a classical radar sorting clustering algorithm can only utilize distance information and density information, and improves the sorting performance under complex radar pulse distribution conditions.

The invention provides a fine-grained sentiment analysis-oriented sentiment data automatic labeling method and system, and belongs to the field of artificial intelligence and sentiment analysis. According to the method, coarse-grained emotion pre-classification and confidence weighted fusion are carried out by extracting text, voice and visual features; fine-grained emotion recognition is realized by combining large language model reasoning and spectral clustering; optimizing a result by utilizing a conflict resolution mechanism, and generating a final label through multi-level voting integration; and finally, through multiple dimensions of multi-modal consistency, feature space outlier degree and conflict resolution decision effect, evaluating the credibility of the final label, and identifying a low-credibility sample. According to the method, progressive analysis from coarse granularity to fine granularity is realized, the problems of modal isomerism, information conflict and low labeling credibility are effectively solved, the manual labeling cost is reduced, and the accuracy and reliability of sentiment analysis are improved.

The invention discloses a virtual power plant multi-dimensional resource dynamic aggregation and optimization regulation and control method based on cloud edge collaboration. The method comprises the steps of collecting multi-dimensional data and encrypting the multi-dimensional data to generate a data stream with a timestamp; extracting a global feature vector through cloud edge-end federal learning; dividing an aggregation unit by using Nystrom accelerated spectral clustering based on features, and constructing a virtual power plant model capable of being refreshed along with feature drift; designing a three-time-scale control closed loop and combining a digital twinning synchronization state; and realizing regulation and control result chaining, automatic settlement and model reverse optimization based on the alliance chain. According to the virtual power plant multi-dimensional resource dynamic aggregation and optimization regulation and control method based on cloud edge collaboration, data privacy is guaranteed, resource aggregation dynamics and regulation and control precision are improved, cross-subject credible collaboration is achieved, and the method is suitable for efficient operation of a virtual power plant.

The invention relates to a consistent semantic enhanced single-step parameter-free multi-view clustering method, which comprises the following steps of: 1) automatically learning anchor points, and avoiding the problem that the quality of the anchor points is reduced due to the randomness of an anchor point selection strategy; (2) spectral clustering is converted into decoupling decomposition of a representation matrix, and single-step data processing is completed without depending on a subsequent clustering method; and 3) the method does not contain any parameter, so that the problem that the clustering quality depends on the parameter is eliminated. According to the method provided by the invention, the accuracy which can be achieved on a Dermatology data set is greatly improved on the same data set compared with the accuracy which can be achieved on the same data set through traditional anchor point-based multi-view subspace clustering. According to the method provided by the invention, the problem that parameters such as multi-view clustering anchor point selection are difficult to adjust can be effectively solved, the problem of optimizing flow splitting is solved, and the method does not need to depend on a subsequent clustering method, so that the multi-view data clustering precision is improved.

The invention provides a distribution network dynamic partition power recovery optimization method considering uncertainty resource support under a typhoon extreme disaster, and belongs to the technical field of distribution network partition power recovery. A representative fault scene is generated by adopting a Latin hypercube sampling and spectral clustering method, the output characteristics and the damage degree of distributed power supplies such as photovoltaic wind power energy storage and the like under a typhoon condition are evaluated, and a centralizable power recovery region and an incentralizable power recovery region are dynamically divided by applying a depth-first search algorithm. A multi-target optimization model of fault reconstruction and island power recovery is constructed for different areas, a dynamic feedback adjustment mechanism based on a load recovery rate is established, and multi-period dynamic power recovery scheduling is implemented to realize multi-source coordinated dynamic power supply recovery in the whole process of typhoon disasters. The technical problem that the power restoration method of the power distribution network under typhoon disasters lacks dynamic self-adaptive ability and a multi-source coordinated scheduling mechanism is solved.

The invention discloses a main earthquake group intelligent identification method considering a micro-earthquake contour coefficient and spectrogram clustering. The method comprises the following steps: 1, acquiring micro-earthquake monitoring data; 2, acquiring a spatial similarity matrix, an energy similarity matrix and a time similarity matrix, and weighting to form a comprehensive similarity matrix; 3, forming a feature matrix based on the comprehensive similarity matrix, and mapping data points in the feature matrix into two-dimensional space coordinates; 4, clustering data points in the two-dimensional space coordinates by adopting a DBSCAN algorithm, and obtaining a neighborhood radius and a minimum sample number optimization combination based on a micro-seismic contour coefficient; and 5, under the optimization combination of the neighborhood radius and the minimum sample number, clustering data points in the two-dimensional space coordinates by adopting a DBSCAN algorithm to obtain a plurality of micro-seismic event optimization clusters. And 6, screening out a micro-seismic event cluster with the highest comprehensive feature score as a main seismic group. The method is reasonable in design, and the main earthquake group is selected by fusing evaluation indexes of space density, energy characteristics and time characteristics.

A composite material damage terahertz imaging method based on two-stage unsupervised learning relates to the field of composite material nondestructive testing, and comprises the following steps: preparing composite material laminated plate samples with different damage thicknesses, collecting time domain terahertz signals, and constructing a label-free single sample damage terahertz data set; constructing a data alignment layer, and carrying out alignment processing on the time domain terahertz signals; a two-stage unsupervised learning framework is adopted, damage features are extracted through a stack auto-encoder, and a pseudo-label data set is generated through spectral clustering; training a convolutional neural network classifier by using the pseudo-label data set, and classifying non-label data; and carrying out terahertz category coding imaging on a classification result to obtain a high-resolution damage two-dimensional image. The method does not depend on any manual intervention and data labels, and intelligent recognition and high-resolution imaging of the internal damage of the composite material in different application scenes are achieved.

The invention relates to the technical field of intelligent group collaboration, and discloses a cloud dynamic load-driven group intelligent collaboration processing system and method, and the method comprises the steps: carrying out the feature extraction, coupling degree matrix construction, spectral clustering initialization, load sensing clustering, and high coupling node optimization operation based on the network topology and task demands of an intelligent agent. Dynamically dividing intelligent agents into cooperative subgroups, and outputting a subgroup division structure and a resource capability matrix on the premise of ensuring load balance and minimizing communication overhead; and according to the task resource demand and the subgroup capability matrix, constructing a multi-objective optimization model. According to the method, efficient self-adaptive processing in a dynamic environment is realized through deep fusion of cloud computing elastic resources and a swarm intelligence collaboration mechanism, topology mutation and resource fluctuation caused by frequent joining / quitting of the agents can be autonomously dealt with, the delay sensitivity of multi-agent interaction is reduced through optimization of communication efficiency, and the service life of the multi-agent interaction is prolonged. And real-time cooperative computing of large-scale groups on a cloud platform is supported.

The invention discloses an image-text retrieval method and system based on multi-modal fusion and depth spectral clustering, and relates to the technical field of data retrieval, and the method comprises the steps: extracting image training features and text training features; splicing to obtain a global feature; constructing local image training features and local text training features; performing alignment processing on the local image training features and the local text training features; fusing the global features and the aligned local image training features and local text training features; performing clustering analysis on the normalized features after fusion feature normalization to obtain a plurality of clustering centers; and extracting query features of the query sample, and determining a retrieval result according to the cosine similarity. According to the method, the characterization capability and robustness of the model to the multi-modal data are improved through data-level fusion and local structure constraint, self-supervised alignment of the image and text modal data is realized through the positive sample pair and the negative sample pair, the clustering center of the image-text data is learned through depth spectral clustering, and new sample data are quickly adapted.

The invention discloses a data insight report generation method and system based on knowledge enhancement and fact verification. Building a domain index relation knowledge graph by training a large language model; mapping the insights to a map, constructing a weighted insights map, carrying out spectral clustering, and dividing insights theme areas; a viewpoint unit is generated through the writing agent, and consistency verification and correction are carried out through the fact verification agent; the user intention is analyzed, candidate topics are screened, and a report outline is generated through heuristic search arrangement based on SRCI scoring, semantic progression and logic entropy calculation; finally, the verified viewpoint units are integrated according to the outline, and a final report is output. According to the method, the full-automatic and high-credibility report generation is realized, and the accuracy, logicality and interpretability are improved.

The invention discloses a power consumer group intelligent identification method, system and device based on a PSO-KMeans algorithm, and a medium, and belongs to the technical field of power big data analysis, and the method comprises the steps: collecting original load data of power consumers, carrying out the preprocessing of the original load data, obtaining standardized load data, extracting multi-dimensional features, and constructing a weighted feature matrix based on an entropy weight method; performing coarse clustering by utilizing spectral clustering to generate an initial clustering center set, taking the initial clustering center set as an initial particle position of an improved adaptive inertia weight PSO algorithm, optimizing a K-Means clustering center, and outputting a global optimal clustering center; and performing clustering by taking the result as a K-Means initial center to obtain a final user group, and identifying abnormal power utilization suspected users in combination with a contour coefficient and a local outlier factor. According to the method, high-quality and high-stability user grouping and accurate anomaly detection are realized, and efficient technical support is provided for power user management and demand side response.

The invention relates to an intelligent calculation center optical network optical path survivability-oriented air-ground cooperative unmanned aerial vehicle distributed optimization deployment method and system, and belongs to the technical field of optical communication network protection. The invention aims to solve the technical problems of long switching time delay, limited coverage and poor stability of a traditional single ground or air protection mechanism. According to the technical scheme, an air-ground cooperative protection architecture is constructed, and firstly, an integer linear programming model with the aim of minimizing the total cost of a system is established to carry out precise deployment optimization of unmanned aerial vehicle nodes; in order to improve the solving efficiency, a spectral clustering and K-means clustering-based heuristic algorithm is designed to realize rapid intelligent division of the unmanned aerial vehicle service domain; the deployed unmanned aerial vehicle cluster adopts a master-slave architecture and has a failover mechanism, and a temporary relay network can be quickly established when a ground optical path fails. According to the invention, the survivability and fault recovery efficiency of the optical network are effectively improved, and good balance between deployment cost and protection performance is realized.

The invention discloses a power transmission system pre-disaster preventive island division method and system based on constraint spectral clustering. The power transmission system pre-disaster preventive island division method comprises the following steps: acquiring power grid data including grid data, unit parameters, load data and typhoon path parameters; calculating a typhoon maximum wind speed radius and a Holland B parameter by adopting a Holland wind field model based on the acquired power grid data, and then calculating to obtain the wind speed of each node of the power grid; calculating the fault probability of the power transmission line and the tower according to the obtained typhoon maximum wind speed radius and the wind speed of each node of the power grid; simulating cascading faults caused by single-line faults, and generating a high-probability fault scene set; calculating a risk index RI, performing fuzzy classification, and outputting a risk level; and when the risk level exceeds a set threshold value, performing preventive islanding. Through data-driven evaluation-dynamic risk decision-constraint spectral clustering isolation three-level joint control, the technical pain points of pre-disaster defense deficiency and extensive island division in extreme weather are overcome, and a core support is provided for an elastic power grid.

A method for node cluster assignment in a graph includes initializing a plurality of wavefunctions, each one of the plurality of wavefunctions corresponding to nodes of the graph, constructing a plurality of quantum circuits, each corresponding to a graph Laplacian of the graph, evolving the plurality of wavefunctions at the plurality of quantum circuits, each one of the plurality of wavefunctions being evolved to a different time than other ones of the plurality of wavefunctions, measuring evolved states of the plurality of wavefunctions to generate a time-evolved wavefunction vector, and identifying a cluster assignment of a node of the graph based on the time-evolved wavefunction vector.

Liquid chromatography coupled with tandem mass spectrometry (LC-MS / MS) is commonly adopted in large-scale glycoproteomic studies involving hundreds of disease and control samples. Current methods for glycopeptide identification in such data analyze the individual datasets and do not exploit redundant spectra of glycopeptides present in related datasets. A concurrent approach is provided for glycopeptide identification in multiple related glycoproteomic datasets by using spectral clustering and spectral library searching.

The application discloses an image clustering method based on a composite subspace, and is implemented according to the following steps: step 1, pre-training a convolutional autoencoder; step 2, training a composite subspace image clustering network; and step 3, obtaining a clustering result by applying spectral clustering. The application realizes image clustering in a manner of unsupervised learning, obtains an explicit nonlinear mapping by simultaneously learning self-expression coefficients in an input space and a latent space, embeds input samples into corresponding deep representations, and thus better captures a subspace structure and improves clustering precision.

The application provides a kind of unsupervised hyperspectral image classification method based on mixed space spectrum information, comprising the following steps: S1, obtains binary segmentation graph by entropy rate superpixel segmentation algorithm, applies binary segmentation graph on original hyperspectral image to obtain segmented superpixel block, converts input hyperspectral image into multiple homogeneous regions based on superpixel segmentation, removes redundant information and guides data purification;S2, optimize the redundant information in principal component domain by two-dimensional singular spectrum analysis method, enhance spatial spectral feature;S3, realize the unsupervised classification of large-scale hyperspectral image by anchor point graph clustering unsupervised classification method.The application is closer to actual engineering application compared with existing supervised classification method, can process larger image scale compared with existing unsupervised classification method, has the advantages of not needing prior information reference, high classification precision, fast classification speed and the like.

This invention relates to the field of task scheduling technology, and discloses a task scheduling method and system based on spectral clustering and auction algorithms. The method includes: acquiring registered resource information and task information to be scheduled; performing spectral clustering on the resource information and task information respectively to form resource group sets and task group sets; based on the auction algorithm, performing inter-group matching and task allocation on the resource group sets and task group sets to generate an initial allocation scheme; performing global balance adjustment on the initial allocation scheme to obtain a final allocation scheme, and performing task scheduling according to the final allocation scheme. This invention solves the problems of complex process scheduling easily getting trapped in local optima and insufficient dynamic response and cross-departmental collaboration in large-scale continuous task input scenarios, improving the global balance of resource allocation, real-time accuracy of scheduling, and business continuity assurance capabilities.