571 results about "Optimal matching" patented technology

The invention provides an intelligent short message scheduling method and device based on multi-dimensional dynamic optimization, and the method comprises the steps: obtaining the performance data of a plurality of short message channels, and calculating a channel health score based on a weight dynamic adjustment model; determining a scheduling strategy according to the priority identifier of the to-be-sent message, and performing channel screening and optimal matching; executing message sending and monitoring a sending state; terminal state detection is carried out on the failure message through operator base station signaling, and a decision tree model is applied to determine a retry strategy; and performing Huffman coding compression processing on the P2-level marketing messages which fail in retry, and performing batch sending in an idle window. According to the method, a comprehensive performance evaluation index and reward function model is also constructed, and parameter optimization is performed by applying a reinforcement learning algorithm. According to the invention, multi-dimensional dynamic channel scoring, intelligent retry decision making based on terminal state perception, batch processing with balanced cost-time efficiency and a closed-loop self-optimization system are realized, the short message delivery rate is obviously improved, and the invalid retry rate and the sending cost are reduced.

The invention discloses an AI virtual coach training system based on standard action matching and deviation feedback, which relates to the technical field of AI virtual coach training systems and comprises a user modeling module, an action acquisition module, a template matching module, a deviation calculation module, a feedback generation module, an interactive presentation module and a learning optimization module. The user modeling module is used for modeling a registered user by adopting a body parameter acquisition and health data analysis method to obtain a user personalized feature vector; the action acquisition module is used for capturing actions executed by a user in real time by adopting a multi-source sensor fusion method to obtain a time sequence containing key point coordinates; and the template matching module is used for comparing the time sequence of the key point coordinates with corresponding actions in a preset standard action template library by adopting an improved dynamic time warping (DTW) algorithm to obtain an optimal matching path and a corresponding minimum matching cost.

The invention discloses a cost optimization method for resource scheduling management of a cloud data center, and relates to the technical field of cloud computing, and the method comprises the following steps: S1, collecting and modeling a multi-dimensional resource state of the cloud data center, and generating a resource change trend based on a sliding time window and a prediction model; and S2, constructing a multi-target game scheduling model taking calculation, storage, bandwidth and energy consumption as participants, outputting a scheduling game solution in combination with task modal adaptability parameters, and forming task-resource optimal matching. According to the method, through multi-dimensional resource state collection, a sliding time window and an advanced prediction model, resource dynamic changes and future trends can be captured more accurately, more reliable input is provided for scheduling decisions, resource waste or performance bottlenecks caused by information lag are avoided, calculation, storage, bandwidth and energy consumption are modeled as multi-party game participants, and the game efficiency is improved. Nash equilibrium is solved in combination with task modal adaptability parameters, and an optimal scheduling scheme giving consideration to resource utilization rate, performance and cost can be found.

The invention relates to the technical field of data processing, and discloses a cold and hot data exchange method and system based on optical storage and a storage medium. The method comprises the following steps: performing access feature acquisition and analysis on data in the optomagnetic hybrid storage system, calculating an access life cycle factor, generating a logic interval heat evaluation result, performing temperature grading, formulating a cold and hot data placement strategy, generating a data placement mapping table, executing batch migration according to priority, and updating a data position record. According to the method, the problem of how to realize logic interval hot degree accurate identification, temperature grade dynamic grading, storage mapping optimal matching and batch migration task efficient organization under the optomagnetic hybrid architecture is solved.

The invention discloses an order-driven cross-factory collaborative production system, and relates to the technical field of intelligent manufacturing and supply chain collaboration, and the system obtains the productivity data, logistics cost and tax policies of a plurality of production bases such as Ningbo, Thailand and America in real time, and carries out the intelligent splitting and distribution of orders through a multi-base productivity game algorithm. And dynamic optimal matching of the order and the productivity is realized. Meanwhile, the system integrates WMS inventory data and third-party logistics real-time quotation, a transportation scheme with the lowest total cost is generated by adopting a genetic algorithm, and cross-border logistics and tax expenditure are remarkably reduced. The system overcomes the problems of information isolated island, response lag, extensive cost control and the like in traditional multi-factory production, realizes global productivity collaborative optimization and supply chain integrated intelligent decision, and improves the enterprise order performance efficiency and the overall resource utilization rate.

The invention relates to the technical field of smart community data management, in particular to a smart community metadata interaction method and system based on an edge computing framework, and the method comprises the steps: carrying out the dynamic storage of multi-dimensional data through a hierarchical rolling cache; generating a uniform resource vector through sequential progressive mapping; community semantic tags are added for the uniform resource vectors through extensible tag slots, connection is established according to association rules, and a resource semantic graph oriented to community services is constructed; receiving a resident task request, constructing a multi-factor weighted scoring engine based on the resource semantic graph, and generating a task data packet with a priority label; a two-channel hybrid scheduling network is constructed, a first channel outputs resource availability and node association degree, and a second channel outputs time sequence characteristics and load prediction parameters; and fusing dual-channel output to obtain task-node correlation and generate an optimal matching strategy. According to the method, task collaboration and resource adaptive management are realized through semantic resource modeling and hybrid scheduling.

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention relates to the technical field of remote sensing data ocean application, and provides an ocean ship association identification method based on AIS and satellite-borne SAR data and electronic equipment, and the method constructs a full-process technical route from data acquisition, preprocessing, ship target detection to trace point association. The method runs through the whole process from SAR data and AIS trajectory preprocessing, intelligent identification of ship targets in SAR images and optimal matching of plots of the SAR data and the AIS trajectories, and a set of uniform, cooperative and efficient technical links is formed. According to the SAR-AIS fusion monitoring method, accurate alignment of AIS data and SAR imaging time is realized by introducing cubic spline interpolation, a ship target in an SAR image is automatically extracted in combination with a deep learning model, finally, global optimal association between trace points is completed by means of a Munkres algorithm, links from data preprocessing to result output are seamlessly connected, and the precision and efficiency of SAR-AIS fusion monitoring are remarkably improved.

The invention discloses a network construction type energy storage capacity optimal matching method based on a frequency response model, and belongs to the technical field of gas turbine power plant financial supervision and artificial intelligence, and the method comprises the steps: S1, quantifying the power vacancy of a typical station under an extreme short-circuit fault, determining a virtual inertia constant and a virtual droop coefficient of network construction type energy storage, and carrying out the calculation of the virtual inertia constant and the virtual droop coefficient; setting an initial output coefficient and adjusting a step length; s2, constructing and integrating a system frequency response model based on the inertia and frequency modulation characteristics of the conventional unit, the wind turbine generator and the network construction type energy storage; s3, applying power disturbance to the system, gradually increasing an initial output coefficient and performing simulation, and recording the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation; and S4, when the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation are respectively smaller than or equal to set threshold values, the energy storage capacity proportion corresponding to the current initial output coefficient is the optimal proportion. According to the method, the problem of accurately measuring the optimal configuration proportion of the network construction type energy storage in the new energy station is solved.

The invention relates to the technical field of information processing, and discloses a big data-based information matching management system, which comprises an acquisition module, an attribute analysis module, a multi-dimensional modeling module and a dynamic matching module. The acquisition module acquires a user behavior data set and extracts active information demand features; the attribute analysis module analyzes the real-time updating frequency, the semantic association degree, the historical matching success rate and the data topological structure characteristics of the target information resources; a multi-dimensional modeling module performs heterogeneous modeling on each feature to generate timeliness, semantic association, matching probability feature vectors and topological structure vectors; and the dynamic matching module generates an adaptation degree score and determines an optimal matching object through heterogeneous space projection and feature coupling operation. The system improves the accuracy, real-time performance and efficiency of information matching through multi-dimensional feature processing, topological structure analysis and a dynamic matching algorithm, and is suitable for an accurate information matching scene in a big data environment.

The invention discloses a task allocation method, device and equipment, a storage medium and a computer program product, and relates to the technical field of computers.The method comprises the steps that reasoning task information in a first preset time period and computing power information of all computing cards in a heterogeneous computing system are obtained; predicting a predicted number of the reasoning requests received in a second preset time period based on the reasoning task information; and according to the reasoning task information, the computing power information of each computing card and the predicted number, allocating a pre-filling task or a decoding task to each computing card, and determining the number of tasks allocated to each computing card, so that the time for the heterogeneous computing system to respond to the predicted number of reasoning requests is shortest. According to the method, the optimal matching of the heterogeneous resources and the reasoning tasks is realized, the utilization rate of the heterogeneous resources is improved, and the execution efficiency of the reasoning tasks is improved.

The invention relates to the technical field of intelligent recommendation, and discloses an AI intelligent matching method based on a knowledge graph, and the method comprises the steps: constructing a quaternary knowledge graph containing a time dimension; identifying legal entities and semantic relationships by adopting an entity identification and relationship extraction technology; multi-level semantic features are extracted through a two-layer progressive semantic matching algorithm of a grammar layer, a semantic layer and a reasoning layer; constructing lawyer ability portraits based on a heterogeneous graph neural network and a time sequence perception graph convolution technology; progressive matching calculation is adopted, the optimal matching weight is learned through a multi-layer attention mechanism, and dynamically optimized intelligent matching is achieved. The technical problems of cold start, insufficient semantic understanding ability and poor timeliness processing ability in the existing legal consultation matching system can be solved, and the matching precision and the user satisfaction are improved.

The invention discloses a remote sensing image searching and matching method and device based on feature fusion, and relates to the technical field of remote sensing, a test image of an unmanned aerial vehicle or a heterogenous satellite is acquired, a feature library is established after preprocessing, and feature extraction and index establishment are performed on each base map in the base map library. And selecting a retrieval strategy according to the size of the test image, retrieving the name of the base image and cutting a related grid image. The method comprises the following steps: converting a test and grid image into gray scale and zooming, extracting feature points to form a set, estimating an affine transformation matrix by using a random sampling consensus algorithm, and preliminarily evaluating the number of matched inner points; if the first preset threshold value is exceeded, executing a fine matching step; and if the number of points in the fine matching step exceeds a second preset threshold value, returning to match the name of the base map and the longitude and latitude of the angular points. And if not, iterating precise matching until an optimal matching result and the longitude and latitude of the angular point are returned. The problem of how to efficiently and accurately find an image of a specific area for massive and multi-source remote sensing image data is solved.

The invention relates to the technical field of electric digital data processing, and discloses a multi-dimensional data logic processing method based on an artificial intelligence algorithm, which comprises the following steps: receiving a target discrete data packet, extracting metadata, and calculating to generate a dimension entropy feature vector representing data logic complexity; inputting the vector into a preset topological mapping model, and outputting an initial adjacent matrix; calling a feedback suppression mask matrix generated based on a historical operator utility state, and executing bitwise logic AND operation with the initial adjacent matrix to generate a corrected effective topological matrix; the matrix is analyzed, a logic operator function pointer is dynamically indexed in an instruction cache, and a directed acyclic execution linked list is constructed; according to the method, redundant logic nodes in AI prediction are definitely eliminated through a bit operation mask mechanism based on historical feedback, and deterministic convergence of processing delay and optimal matching of computing power resources are achieved.

The invention provides an elevator multi-mode crowd feature perception and intelligent advertisement putting method and system, and relates to the technical field of intelligent advertisement putting, and the method comprises the steps: collecting multi-mode perception data in an elevator through an edge computing terminal; performing feature decoupling on the data, performing cross-modal semantic alignment, establishing a directed association relationship between modals, and constructing a scene feature map; calculating the topology importance degree of map nodes, screening feature nodes, and extracting context information for semantic coding; mapping the scene semantic code and the advertisement audience semantic code to a two-dimensional coordinate system to construct a semantic matching graph, and extracting an optimal matching path to form a candidate set; predicting a scene evolution trend based on the scene characteristic spectrum evolution trajectory, and calculating an advertisement adaptive score to generate a playing sequence; putting and collecting user interaction data feedback according to the sequence to update the graph structure. According to the invention, accurate crowd feature recognition and advertisement dynamic matching are realized, and the advertisement putting efficiency and the user experience are improved.

The invention provides an object grabbing method and system based on point cloud deep learning, and the method comprises the steps: obtaining a target object point cloud of a target object, carrying out the point cloud matching of the target object point cloud through a point cloud registration model based on a deep learning network, obtaining an initial matching position, optimizing the matching position through a nearest iteration algorithm, and obtaining an optimal matching position; obtaining an accurate transformation matrix of the target object relative to the template under a camera coordinate system; based on a pre-obtained hand-eye transformation matrix, calculating a transformation relation of the point cloud attitude of the target object relative to a tool coordinate system through teaching; the hand-eye transformation matrix is used for indicating a transformation relation between a sensor coordinate system and a mechanical arm tail end coordinate system; and according to the conversion relation and the accurate transformation matrix, the grabbing position coordinates of the target object under the mechanical arm base coordinate system are solved. According to the invention, point cloud registration is carried out through the deep learning network, and the position of the target object can be determined more accurately.

The invention provides a user credit scoring method and system based on multi-source behavior data, and the method comprises the steps: obtaining user behavior data, so as to construct a standardized multi-module behavior event record; taking a user as a unit, using the behavior event record as a node to construct a behavior graph, and constructing a directed edge according to a timestamp of event occurrence; based on the behavior map, constructing a user behavior path by using a constrained maximum edge weight path search algorithm; performing structure matching on the high-credit user behavior path template set and each user behavior path, calculating a path deviation degree of each user behavior path, and obtaining a minimum deviation score; and in combination with the minimum deviation score and the matched optimal matching path, predicting a risk tag of the user through a credit scoring model based on dichotomy learning, and outputting a customer credit score.

The invention provides a code reuse method and device based on AI drive, equipment and a medium. When a code submission event is detected, a code scanning process is triggered, a code analysis module driven by AI carries out multi-dimensional analysis on submitted codes, quantitative evaluation is carried out on the basis of a preset reusability evaluation model, and code snippets meeting a reuse standard are stored in a code knowledge base with a semantic index structure; analyzing annotation semantics based on a natural language processing technology through an AI retrieval engine, calculating semantic similarity between demand description and code snippets in a knowledge base in combination with a deep learning model, and positioning the code snippets which are optimally matched; after code multiplexing execution, performing incremental modification on the multiplexing code snippets and automatically generating change records; and carrying out value layering on the code snippets based on the Pareto analysis principle by using frequency trend prediction. Through combination of the AI technology and code reuse, the development efficiency can be remarkably improved.

The invention discloses a battery capacity prediction and state evaluation method and system based on multi-model collaborative learning, and belongs to the technical field of battery management. The method comprises the following steps: constructing a database containing multiple lithium ion battery long-term cycle data, and classifying according to a capacity attenuation trend; cleaning and preprocessing short-term cycle data of the to-be-tested battery; matching the to-be-tested data with the long-term attenuation trend in the database by using a clustering algorithm, and determining an optimal matching trend; distributing weights for the data in the matching trend by adopting a correlation algorithm, and generating initial capacity attenuation prediction; performing sequence correction on the preliminary prediction in combination with meta-learning and a related model, and generating a smooth future attenuation trend conforming to a physical law; and outputting a capacity prediction and health state evaluation result, and evaluating the prediction precision through a root-mean-square error and an average absolute percentage error. The method significantly improves the precision and generalization ability of long-term capacity prediction, and is suitable for various scenes such as electric vehicles, energy storage systems, consumer electronics and the like.

The invention discloses a dynamic learning path planning method and system based on a learner portrait. The method comprises the steps of collecting learning behavior data, learning result data and background attribute data of a target scholar based on a plurality of data sources, and obtaining knowledge point mastery degree vector representation, learning style preference and cognitive ability level to construct a multi-dimensional dynamic portrait of the target scholar; generating an initial personalized learning path of the target scholar based on a preset learning target and the multi-dimensional dynamic portrait of the target scholar; learning process data of a target scholar for related learning resources is monitored in real time, a learning effect evaluation report is generated according to the learning process data, a multi-dimensional dynamic portrait is adjusted to trigger a path replanning mechanism to generate a brand new personalized learning path, and the brand new personalized learning path is pushed. The learning path and the current state of the learner are kept optimally matched all the time, and personalized static planning is upgraded to dynamic syndrome guidance.

The invention discloses a time-frequency analysis method for impact signal positioning based on transient scale extraction transformation, and belongs to the technical field of mechanical vibration signal processing. The method comprises the following steps: collecting a rotating machine fault vibration signal, and reconstructing a signal model through Hilbert transform and a Dirac function; a transition matrix is generated by using a Gaussian window function traversal model, and matching and Fourier transform are carried out in combination with a discretized scale basis function; solving a frequency partial derivative of a transformation result to generate a time redistribution operator, and redefining by a Dirac function to obtain a transient scale extraction operator; based on the sub-time-frequency representation of scale-based rotation discretization, screening optimal matching results of each time center through a maximum kurtosis value, and integrating the optimal matching results into a complete time-frequency representation; and finally, redistributing a time-frequency coefficient by using a transient extraction operator to realize accurate positioning of the impact component. According to the method, the problems of serious impact energy diffusion and insufficient positioning precision in existing time-frequency analysis are solved, and the time-frequency representation readability and the impact positioning reliability are remarkably improved.

The invention discloses a coarse-to-fine point cloud registration method, and the method comprises the steps: carrying out the preprocessing of a to-be-registered source point cloud and a to-be-registered target point cloud, and obtaining a first point cloud and a second point cloud after preprocessing; performing coarse registration on the first point cloud and the second point to obtain an initial matching coefficient; performing iterative solution on the target function and the threshold function based on the initial matching coefficient, the source point cloud and the target point cloud until an optimal matching coefficient meeting a convergence threshold condition is obtained; and carrying out rotation and translation operation on the source point cloud based on the optimal matching coefficient, converting the source point cloud into a coordinate system where the target point cloud is located, enabling the source point cloud and the target point cloud to be aligned in the same coordinate system, and completing registration operation. According to the method, the point cloud is preprocessed and coarsely registered, and then the optimal matching coefficient is solved step by step through iteration, so that the defects of a traditional method can be effectively overcome, and the point cloud registration efficiency and precision are improved.

The invention relates to the technical field of computer vision, and discloses a vehicle paint make-up color matching method based on computer vision, and the method comprises the steps: collecting a vehicle paint image in real time through an industrial camera, constructing a multi-dimensional color feature extraction system after preprocessing, and building a color feature database through a color space conversion technology; and positioning a target color card by combining a feature matching algorithm. Meanwhile, a historical paint make-up case information base is constructed, a multi-factor weight distribution model is established, an optimal matching scheme is solved by adopting an improved particle swarm optimization algorithm, and finally a result is output through an interactive verification platform and tracked and fed back. According to the method, multiple factors are comprehensively considered, the precision and efficiency of color matching are improved, and the method is suitable for paint make-up color matching of vehicles of different vehicle types, years and paint surface types and has high practicability and popularization value.

A system and method for matching multimodal user-query input at an information handling system includes storing capabilities associated with a plurality of AI productivity tool-enablable software applications in a hierarchical capabilities decision tree with each node including natural language textual and non-textual modality descriptions of a capability and multimodal capability intent values generated from the same. Executing code instruction to receive a multimodal user-query input in any of text, audio, or image and generate a multimodal query input intent value for matching to a best match capability for a responsive action to be taken by one of the plurality of AI productivity tool-enablable software applications executing on the information handling system via a semantic similarity search comparing the multimodal query input intent value to the multimodal capability intent values in the hierarchical capabilities decision tree based on a highest cosine semantic similarity search score.

The invention discloses a defect segmentation loss evaluation method based on space consistency optimization, and relates to the field of computer vision and defect segmentation. The problem that an existing loss calculation method is difficult to give consideration to defect space consistency and fair modeling of defects of different scales, and consequently loss evaluation accuracy is poor is solved. According to the method, a prediction result and a defect area in a real label are subjected to instantiation modeling, a prediction and real defect set is constructed, and one-to-one optimal matching of the prediction result and the real defect set is realized through a Hungary matching algorithm; in order to enhance the modeling capability for defects of different sizes, a Wasserstein distance is introduced to measure the spatial similarity of matched defect pairs, defect segmentation loss is calculated for successfully matched defect pairs based on the distance, a fixed loss value is directly applied to predicted defects which are not successfully matched, and the sum of the defect segmentation loss and the fixed loss value is used as a total loss value of a sample. The method is mainly applied to an industrial product surface defect segmentation task.

The invention relates to the technical field of three-dimensional image data processing, in particular to a priori constraint-based tower point cloud and model multi-scale registration method and system. The method comprises the following steps: S1, performing standardization processing on multi-source data to enable to-be-registered point cloud data to be matched with a point cloud representation standard of a prior knowledge base of a candidate model; s2, preliminary screening; s3, performing coarse registration, and performing candidate model rapid screening and key point priority sampling strategies; s4, fine registration is carried out, soft constraint optimization is adopted, priori knowledge is quantized into weights, and weighted ICP and nonlinear least square are combined for solving; and S5, performing matching scoring according to the matching parameters, and obtaining an optimal matching result of the to-be-registered point cloud data and the candidate model of coarse registration. A self-learning priori knowledge base is constructed, a high-confidence matching result is fed back to update the priori base, a dynamic weight adjustment strategy and a candidate screening rule are realized, and the adaptability of the system to different pole tower models is improved.

The invention provides a coal ash composite material goaf closed filling parameter intelligent matching method, and belongs to the technical field of deep learning and mining engineering crossing. According to the method, dynamic optimization and accurate matching of the filling parameters are realized through combination of data driving and an intelligent algorithm. The method comprises four core links: multi-source data perception and fusion, a material performance prediction model, key parameter identification and boundary constraint, and intelligent matching and optimization decision. According to the method, the modeling capability of the model for the complex coupling relationship is improved through the attention mechanism and the feature cross network; the performance evolution trend of the material under different proportions and process conditions is accurately predicted, key regulation and control parameters are automatically identified, and efficient search and optimal matching of a parameter space are achieved through a strategy gradient method. According to the method, multi-source data can be fused, key parameters can be dynamically identified, and the intelligent matching and optimizing capability is achieved.

The invention provides a multi-merchant hardware equipment supply chain demand intelligent matching method, and relates to the technical field of supply chain management, and the method comprises the steps: obtaining a demand order and supply capability data through a supply chain platform, and enabling the supply capability data to obtain production line test data in real time through an Internet of Things interface; performing preliminary screening based on equipment types and necessary authentication standards; calculating the comprehensive reliability of the optical fiber sensing system by adopting a comprehensive evaluation model fused with nonlinear transformation; calculating the multi-dimensional demand integrating degree of the supplier and the demander through multi-dimensional difference analysis and normalization processing; intelligent sorting is carried out based on weighted scores of configurable weight factors; implementing global productivity monitoring and conflict resolution optimization, and outputting an optimal matching pair; and continuously iteratively optimizing the evaluation model parameters according to the performance feedback data by adopting an incremental learning mechanism. According to the method, accurate matching and dynamic optimization under multi-target constraints are realized, and the supply chain resource configuration efficiency and the system autonomy capability are effectively improved.

The invention relates to the technical field of data management, and discloses a labor market supply and demand prediction and intelligent matching system driven by real-time big data. The system comprises a feature separation module, a spatial-temporal feature matrix acquisition module, a supply and demand gap prediction module, a dynamic knowledge graph generation module, an optimal matching path acquisition module and a labor resource allocation module, and is characterized in that dynamic features of original data streams are extracted and separated to obtain a standardized feature vector set; performing space-time alignment on the standardized feature vector set to obtain a space-time feature matrix; capturing time dependence, aggregating spatial neighborhood information to obtain a regional supply and demand gap predicted value to construct a basic graph skeleton, and mapping entity dynamic attributes in the original data stream to the basic graph skeleton to obtain a dynamic knowledge graph; generating an optimal matching path set; performing desensitization processing on the optimal matching path set to obtain a labor resource allocation scheme; according to the invention, the rationality of labor resource allocation can be improved.

The invention relates to the technical field of server resource scheduling, in particular to a server virtualization integration system and method. The method comprises the following steps: executing a multi-dimensional reference pressure test on each NUMA node in a physical server cluster to generate physical node performance data; monitoring a virtual machine cache event of a tenant virtual machine in real time by utilizing the virtualization management platform, and determining tenant load snapshot data; evaluating the matching degree between the remote memory access demand of the virtual machine and the node localization supply capability according to the tenant load snapshot data, determining an optimal host migration target, and starting real-time migration of the virtual machine by the virtualization management platform to obtain a server migration integration process; and monitoring an abnormal state after migration in real time according to a server migration integration process so as to realize secondary forced scheduling integration request processing. According to the method, the optimal matching between the virtual machine and the physical node in the server is realized, the resource utilization efficiency is improved, and the cross-NUMA access overhead is reduced.