49 results about "Decision matrix" patented technology

This application discloses a method, equipment, medium, and product for comprehensive evaluation of equalization of basic medical services, relating to the field of medical geography. The method includes: spatial processing of research data to construct a unified geospatial database; calculating multiple evaluation index values ​​representing the spatial allocation level of medical resources and the spatial convenience of residents' access to medical care through spatial analysis to form an original decision matrix; using the entropy weight method, calculating the objective weights of each evaluation index based on the degree of variation of the multiple evaluation index values ​​across different evaluation areas; processing the original decision matrix using the objective weights based on the TOPSIS model to calculate the comprehensive evaluation index of equalization of basic medical services for each evaluation area; and performing spatial hierarchical mapping based on the comprehensive evaluation index of equalization of basic medical services to generate a multidimensional diagnostic report, thereby avoiding evaluation distortion, accurately reflecting residents' comprehensive employment opportunities in accessing medical services, and enhancing public credibility.

The application relates to the technical field of computing power cooperative scheduling management, and specifically discloses a cloud-edge-end heterogeneous computing power cooperative scheduling system and method based on intention driving, which obtains intention semantic vectors, video stream data, buffer occupancy rates and heterogeneous computing power state data, generates an intra-frame intention heat map by fusion when the buffer is overloaded, splits the video stream into core intention and non-core background data streams, and estimates computing power consumption. The computing power consumption is input into a game cycle model, intention retention rate and overflow risk are iteratively calculated, the feature retention range is reduced when the risk is too high, and secondary task computing power is borrowed for compensation when the retention rate is insufficient, until a double-optimal state is reached. The dynamic balance of system load and information value is achieved. Finally, a scheduling decision matrix is generated, the core intention original drawing is transmitted, and redundant background is discarded, effectively solving the problem of key information loss in a resource-limited scene, and significantly improving the response efficiency and business fidelity of the cloud-edge-end cooperative system.

This invention discloses a multimodal interactive interface design method for elderly care robots, relating to the field of intelligent elderly care. The method includes: collecting user facial images, voice signals, and touch operation information to form a raw multimodal data stream; processing the raw multimodal data stream to extract standardized user real-time state feature vectors; inputting the user real-time state feature vectors and historical interaction data into a support vector regression model; obtaining the real-time cognitive load level based on the user's real-time state feature vectors in the current session; and analyzing the rate of change of user touch operation accuracy within a time window using historical interaction data, calculating the acceleration value of capability change and the trend of touch operation accuracy, and fusing them to derive the interaction abstraction level. This invention constructs a joint decision matrix sensitive to the acceleration of capability change, dynamically triggering an adaptive interaction strategy when accelerated capability decline is detected, driving the interface engine to adjust interface rendering and multimodal guidance in real time.

Embodiments of the present disclosure relate to the technical field of medical data processing, and specifically to a nutritional preparation medication information processing method, device, equipment and medium. The main steps of the foregoing method include: in response to an audit request for a medication prescription, retrieving and obtaining clinical diagnosis data corresponding to the medication prescription, extracting a disease keyword from the clinical diagnosis data, matching and analyzing the disease keyword with preset parenteral nutrition indication information to obtain a matching result, when the matching result indicates a matching success, auditing the medication prescription according to a first medication standard, and when the matching result indicates a matching failure, determining a second medication standard according to a nutritional risk score, gastrointestinal disorder information and a preset decision matrix, and auditing the medication prescription according to the second medication standard. The foregoing method can improve the efficiency of prescription auditing, improve the accuracy and rationality of the nutritional preparation medication prescription audit result, and reduce the medication risk of the nutritional preparation medication prescription.

This invention relates to the field of network data security technology, specifically to a method for low-latency secure power transmission via fgOTN. The method involves: acquiring the service-bearing network topology and link transmission parameters to construct a network connection matrix; simulating the traffic redistribution process after a single node failure and calculating the topological betweenness centrality index of substation nodes; at the fgOTN mapping front-end, parsing the payload content of power service data frames, extracting continuous monitoring features and discrete control features, and calculating the real-time service criticality index for each data frame based on the current network frequency deviation and voltage fluctuation status; establishing a two-dimensional decision matrix and generating a defense strategy based on the mapping positions of the topological betweenness centrality index and the criticality index value in the matrix; processing the service data according to the generated defense strategy and mapping it to fine-grained time slots in the fgOTN; embedding security status signaling in the frame overhead and sending it to the peer device via an optical transmission unit.

This invention relates to the field of battery management system technology, and discloses a power battery system and method based on user interaction upgrades. The system includes: S1, constructing a deeply coupled architecture between an interaction management unit and a battery management unit; S2, constructing a dynamic database; S3, dynamically adjusting the liquid cooling pump power and PTC heating threshold based on driving habits and ambient temperature and humidity data; S4, constructing a two-layer architecture of local edge computing nodes and a public cloud; S5, using an LSTM neural network to analyze historical charge-discharge curves and predict cell lifespan; S6, displaying a degradation degree map through an IMU interface, supporting user scanning to replace degraded cells and automatically configuring topology parameters; and S7, establishing a three-dimensional decision matrix of user settings, environmental perception, and battery status to dynamically optimize charging strategies. This application solves the problems of traditional systems such as lag in interaction, insufficient intelligence, maintenance difficulties, and data synchronization bottlenecks, reducing maintenance costs, extending battery pack lifespan, and achieving collaborative full-cycle management.

This invention discloses a method and system for intelligent scheduling and optimization of hospital resources based on multi-source data fusion. The method includes: collecting multi-dimensional data across hospital areas through a multi-source medical data mining and analysis platform and establishing correlation mappings; analyzing resource supply and demand relationships using a dynamic game equilibrium model of medical resources; identifying bottlenecks and temporal coupling relationships at treatment nodes using a treatment process temporal correlation analysis algorithm; establishing a decision matrix by integrating parameters between hospital areas through a cross-hospital resource scheduling collaborative algorithm; constructing a multi-objective optimization model by combining parameters such as resource capacity and treatment timeliness; and outputting cross-hospital resource allocation schemes, treatment process adjustment paths, and dynamic scheduling instructions through a step-by-step refinement process of temporal analysis, cross-hospital scheduling decision-making, and multi-objective solution. This invention achieves precise, collaborative, and intelligent scheduling of hospital resources, improves resource utilization efficiency and the adaptability of treatment services, and is applicable to scenarios involving the overall optimization of cross-hospital medical resources.

The application discloses a method for cooperative jamming based on intelligent optimization algorithm, comprising: constructing a jamming decision model, the jamming decision model comprising a cooperative jamming decision matrix, a gain matrix, a jamming matrix, a jamming gain matrix, a jamming bandwidth ratio factor, a jam-to-signal ratio, and a jamming benefit; establishing an objective function and a constraint condition of the jamming decision model according to the jamming benefit; and using an artificial bee colony algorithm to take the jamming benefit as a fitness function and optimize the cooperative jamming decision matrix A. In different complex electromagnetic spectrum environments such as limited spectrum resources and the same frequency band shared by jamming devices and illegal users, the limited jamming resources are reasonably distributed under the condition that the jamming device of the own side can normally communicate, so that greater jamming benefit is achieved; the algorithm convergence speed and search ability are improved, and the method is helpful for making a decision with higher jamming benefit in a shorter time.

The application discloses an automatic roll changing control method and system of a rewinder, a medium and a product, and relates to the technical field of roll changing control. In the method, roll changing preparation is triggered by monitoring the winding length of a finished product, and the remaining diameter state of a parent roll and the paper path continuity state are determined in the preparation process. Specifically, the state identifier of the parent roll is determined by comparing the remaining diameter of the parent roll with the exhausted diameter threshold value; the paper path continuity identifier is determined by calculating the standard deviation of the paper tension data sequence and comparing it with the tension stability threshold value. A two-dimensional decision matrix is constructed based on the two state identifiers, which is used to determine the optimal roll changing sequence, and the automatic roll changing process from the finished product roll to a new paper core is performed accordingly. The technical solution provided by the application can improve the automatic roll changing control efficiency.

This application provides a method and apparatus for fabricating a micro-angle vibration sensor, relating to the field of micro-angle vibration sensors. The method includes generating a parameter matrix based on multiple sampled values ​​obtained by sampling multiple geometrical parameters of the micro-angle vibration sensor; iteratively solving the parameter matrix using a multi-objective particle swarm optimization algorithm based on time-varying dynamic parameters related to the iteration time to obtain a solution set; determining multiple intermediate solutions from the multiple initial solutions whose crowding distances satisfy preset conditions based on the crowding distance of each initial solution; performing a consistency check on a judgment matrix constructed based on different optimization objectives of the multiple intermediate solutions using the analytic hierarchy process (AHP) to determine the weights of each optimization objective; determining the ideal solution based on the objective normalization decision matrix constructed with the weights and the multiple intermediate solutions when the consistency check passes; and determining the target solution from the multiple intermediate solutions and fabricating the micro-angle vibration sensor based on the parameter values ​​of the multiple geometrical parameters contained in the target solution.

This invention relates to the field of intelligent manufacturing technology, and particularly to an IoT-based system for coding and integrating home furnishing products into an ERP system. The system uses a production sensing module to verify the historical convergence of equipment operating parameters via Mahalanobis distance to obtain equipment status confidence. A coding decision module generates a target coding strategy based on the fusion characteristics of production data and an environmental disturbance index compensation model, and then filters it using a strategy effectiveness prediction model. An edge verification module achieves dynamic error correction and dynamic reconstruction of error-correcting codes through bidirectional verification of code graph semantic integrity and a conflict resolution decision matrix. An ERP synchronization module achieves causal consistency updates of inventory status through topological sorting of the coded data causal graph and transaction compensation. A quality traceability module constructs a product traceability knowledge graph to achieve entity disambiguation and data lineage traceability. This invention solves the technical problems of data silos and broken traceability chains in production coding and ERP systems.

The application discloses an enterprise knowledge graph construction and intelligent decision system based on artificial intelligence, comprising the following modules: an evidence construction module, which is used for collecting enterprise multi-source business data and constructing time-aware evidence anchor points, and generating an enterprise evidence unit set; a relationship extraction module, which is used for performing entity-event-relationship linkage extraction, and generating enterprise candidate relationship edges; a conflict identification module, which is used for constructing a conflict decision matrix and generating a cross-source evidence conflict graph; a credibility generation module, which is used for generating an enterprise evidence credibility vector; a graph pruning module, which is used for generating a credible enterprise knowledge graph; a decision retrieval module, which is used for generating an enterprise decision evidence set based on an improved LightRAG retrieval architecture; and a decision output module, which is used for generating an enterprise intelligent decision result and a traceable evidence chain. The application can improve the credibility of the enterprise knowledge graph, the accuracy of the decision evidence and the traceability of the decision process.

The present application belongs to the field of artificial intelligence and rail transit intelligent operation and maintenance technology, and specifically relates to a subway intelligent maintenance system for collaborative optimization of braking energy consumption, aiming to solve the problems of low braking energy recovery efficiency, equipment perception lag and passive maintenance strategy. The system realizes the determination of regenerative braking energy absorbability and the quantitative evaluation of component health state by constructing a whole-network train dynamic topology and energy-health coupling analysis engine. Combined with a four-quadrant decision matrix, the system generates a hierarchical maintenance instruction and uses a rolling time domain optimization algorithm to coordinate train group operation time to improve energy matching degree. The system integrates a self-adaptive learning hub to continuously calibrate model parameters. The system realizes the collaborative optimization of braking energy efficiency improvement and key component life extension, and reduces the measured resistance energy consumption by more than 28% and the sudden failure rate by 41%.

This disclosure presents an intelligent decision-making method, apparatus, and electronic device based on multi-dimensional profile linkage. One specific implementation of the method includes: collecting multi-dimensional business data, wherein the multi-dimensional business data includes: user datasets, product datasets, sales datasets, organizational resource datasets, and cooperation datasets; utilizing the multi-dimensional business data to construct a five-dimensional profile model, wherein the five-dimensional profile model includes: user profiles, product profiles, sales profiles, organizational resource profiles, and cooperation profiles; constructing a dynamic decision matrix based on the five-dimensional profile model; and generating intelligent decision information based on the dynamic decision matrix. This implementation can improve the decision success rate.

A system (100) for actively predicting road surfaces and proactively activating a vehicle stability system comprises receiving sensor data from multiple sensors (104, 106, 108, 110), a controller (112) configured to determine a weighting matrix (WT) based on driving conditions, and determining the presence of a low-friction surface using a decision matrix (DN), where the decision matrix (DN) is a product of the weighting matrix (WT) and a vector (X) representing the output data from each of the sensors. The system (100) further comprises an electronic stability program (ESP) (114) that is activated based on the detected low-friction surface. ESP activation includes calculating trajectory data, providing warnings to the driver, and activating vehicle control systems (116) if necessary.The system enables improved driving performance and greater passenger comfort through early detection and reaction to low-friction surfaces.

This invention provides a method and system for diagnosing fault conditions in hot strip rolling mills, relating to the field of automated control technology for hot-rolled strip steel. The method includes: collecting a set of operating signals from the hot strip rolling mill to be diagnosed; selecting evaluation indicators from the set of operating signals; determining multiple fault condition types corresponding to the evaluation indicators; establishing a preset scoring system for the equipment fault conditions corresponding to each fault condition type; constructing an original decision matrix regarding the evaluation indicators and equipment fault conditions; performing cloud roughness number processing on the original decision matrix to generate a cloud roughness number decision matrix; calculating the importance ranking of the evaluation indicators using game theory and a comprehensive weighting method; using the grey relational analysis-TOPSIS algorithm to quantitatively score the equipment fault conditions; constructing a fault type evaluation formula; and diagnosing the equipment fault conditions based on the fault type evaluation formula.

The application discloses a target attack priority ranking method and device based on intelligent evaluation, equipment and a medium, and relates to the technical field of data processing.The method comprises the following steps: a target attack priority ranking evaluation index system is constructed; the evaluation index system comprises first-layer indexes and second-layer indexes; the first-layer indexes at least comprise category-type indexes, and the second-layer indexes are used for predicting and evaluating the category-type indexes in the first-layer indexes; a target evaluation decision matrix is constructed based on the first-layer indexes, wherein the values of the category-type indexes in the first-layer indexes are calculated by using an intelligent evaluation method based on a neural network; the weights of the first-layer indexes are calculated by using an entropy weight method; the closeness degrees of the targets are calculated by using a TOPSIS method; the comprehensive correlation degrees of the targets are calculated by using a grey correlation analysis method; the comprehensive evaluation values of the targets are weighted and calculated based on the closeness degrees and the comprehensive correlation degrees; and the targets are ranked according to the comprehensive evaluation values. The application greatly improves the scientificity, objectivity and reliability of the target ranking result.

The application belongs to the technical field of artificial intelligence, and relates to a multi-unmanned aerial vehicle task allocation method in a fuzzy dynamic environment, comprising the following steps: constructing a multi-unmanned aerial vehicle task allocation model; adopting a cloud model to construct cloud representation of each unmanned aerial vehicle attribute and task attribute; constructing an optimization objective function based on the cloud representation of each unmanned aerial vehicle attribute and task attribute; modeling the optimization objective function into a Markov decision process to obtain a state space, an action space and a reward function based on cloud model multi-attribute decision of each unmanned aerial vehicle; and obtaining an optimal task allocation decision by adopting a deep reinforcement learning algorithm based on the state space, the action space and the reward function based on cloud model multi-attribute decision of each unmanned aerial vehicle; the application automatically optimizes attribute weights by constructing a cloud decision matrix, and calculates a relative cloud distance between a task and an ideal task, so as to provide a dense and information-rich reward signal for the unmanned aerial vehicle, and effectively solve the reward sparsity problem.

This invention provides a continuous control method and system for vacuum heat treatment processes based on process phase identification. The method includes: real-time data acquisition; identification of the current process phase based on the data; determination of monitoring data and corresponding thresholds based on the current process phase; calculation of the current warning level based on the deviation between the monitoring data and the thresholds; and calculation of a health index based on the status data of each heating component. A basic decision-making strategy is obtained based on the current process phase, warning level, and decision matrix; the power distribution of each phase heater is dynamically adjusted based on the health index to generate a control command vector; the control command vector is decomposed into specific execution actions and output; and the execution effect is monitored in real-time, feedback data is collected, and the parameters of the decision matrix are updated based on the feedback data. By identifying the current process phase in real-time and using this as a contextual reference to dynamically determine monitoring parameters and thresholds, the shortcomings of existing technologies, such as isolated and flat warning signals and a lack of traceability, are improved.

The application discloses a kind of transport optimization method and system based on intelligent scheduling, it is related to wisdom logistics technical field.The method is first to collect the task information of multiple transport tasks and obtain the resource information of all logistics resources currently available and including unmanned aerial vehicle and rider, then based on this information, construct the man-machine collaborative transport network containing multiple nodes and multiple edges, the transport decision matrix of each transport task and multiple constraint conditions, and with the maximum goal of global logistics resource effective utilization time length ratio, the optimal transport decision matrix of each transport task under multiple constraint conditions is obtained by optimization, finally according to the optimization result, generate and output the transport optimization scheme for scheduling all logistics resources to complete multiple transport tasks, whereby by intelligently coordinating and optimizing scheduling unmanned aerial vehicle and rider two kinds of heterogeneous resources, multiple transport tasks with the same delivery time window requirement can be completed while maximizing the utilization rate of logistics resources.

This invention discloses a method for generating graded administration recommendations for labor analgesia based on pain scores, vital signs, and adverse reactions. The method acquires pain scores, vital signs, adverse reaction grades, and analgesia pump log data. After time alignment, it determines the level of analgesia need and safety risk. Based on a preset decision matrix, it generates recommendations for increasing, decreasing, maintaining, pausing, or requiring manual review. The recommendations are then constrained by time, maximum dosage, step size, and minimum adjustment interval before being output and logged. This method can be implemented independently by a physician.

This invention relates to the field of image recognition technology, specifically to a multimodal visual data fusion processing method and system, comprising the following steps: extracting pixel gradients and temperature matrices, calculating covariance values ​​to generate associated weight vectors, performing dot product and channel splicing to construct a nonlinear reconstruction tensor, extracting classification boundary values ​​to construct a confidence vector, calculating relative entropy to generate penalty term coefficients, performing bias reduction calculations to output a classification decision matrix. In this invention, by weighting heterogeneous data based on the weight vector and reconstructing global confidence distribution features, extracting spatial relative entropy values ​​as correction coefficients and dynamically adjusting the bias of the output probability matrix, this invention suppresses visual frequency band interference caused by light sources, eliminates residual defects caused by feature overlap due to hardware splicing modes, reduces mapping errors while avoiding pixel feature dimension loss and target recognition misjudgment risks, and greatly improves recognition accuracy.

This invention relates to the field of integrated defense and early warning technology for clustered targets, and discloses a clustered target threat early warning method based on intuitionistic fuzzy game theory and consensus decision-making. This method collects raw data from multiple sensors and quantifies it into an intuitionistic fuzzy set to generate a clustered integrated decision matrix; calculates the degree of consensus and hesitation potential energy; when consensus is insufficient, it generates resource focusing instructions to drive sensors to adjust physical parameters and perform feedback correction to form a consensus closed loop; it constructs a strategic conflict model to perform game-theoretic linkage analysis to solve for optimal attribute weights and calculates a dynamic early warning threshold; finally, it calculates the final early warning score based on the MABAC algorithm, compares it with the dynamic threshold to trigger an alarm, and outputs a dynamic interpretation vector. This invention solves the problems of decision oscillation and poor adaptability to fixed thresholds in complex electromagnetic environments by using physical information synergy enhancement and strategic game threshold drift mechanisms, thereby improving the accuracy and interpretability of early warnings.

This application relates to the field of data processing technology and discloses a method and system for adaptive fusion processing of streaming and batch data sources for heterogeneous data sources. The method includes: dividing the input data stream into data blocks; extracting arrival rate, second-order change, and disorder degree, and weighted fusion to obtain a turbulence index; mapping the data blocks to obtain processing intent labels using a decision matrix and attaching them to the data blocks to obtain labeled data blocks; reading the execution mode routing to the corresponding engine, and having a fusion coordinator drive cross-engine collaboration to obtain calculation results and actual execution metric data; and after fine-tuning the time-series prediction model online using a confidence-weighted loss function, updating the gradient contribution ratio inversely to the weighted fusion weight coefficients. This application improves the real-time performance and adaptive accuracy of processing mode decisions in heterogeneous data source scenarios.

This invention discloses an automatic mining and classification method for private data, comprising the following steps: First, receiving private unstructured documents, performing preprocessing and semantic alignment, and using sliding window technology to segment the documents into continuous text blocks; then loading a locally deployed general basic model and an industry-specific model, calculating the perplexity of each text block, and determining the scarcity of the text block by comparing the differences in the model output; next, constructing a statistical model based on the perplexity distribution of the enterprise's historical documents and dynamically setting a threshold, calculating the value score for text blocks exceeding the threshold; finally, projecting the scarcity and value score onto a preset classification decision matrix to automatically determine the secret level of the text block and trigger corresponding security handling strategies. This invention achieves automated and intelligent classification of private data, improving the efficiency and accuracy of data security management.

The application provides a water resource scheduling scheme recommendation method and device to improve the reliability and accuracy of the water resource scheduling scheme recommendation result, comprising: generating an original decision matrix based on a to-be-evaluated scheduling scheme and an evaluation index; converting accurate numerical values in the original decision matrix into spherical fuzzy numbers by using a spherical fuzzy set theory to obtain a spherical fuzzy decision matrix; determining a subjective weight of each element based on an analytic hierarchy process, determining an objective weight of each element based on an entropy weight method of the spherical fuzzy decision matrix, and determining a combined weight of each element based on the subjective weight, the objective weight and a preference factor; constructing a weighted normalized spherical fuzzy decision matrix by using a weighted operation rule of the spherical fuzzy numbers based on the spherical fuzzy decision matrix and the combined weight of each element; and sorting and recommending the scheduling scheme by using a technique for order preference by similarity to ideal solution based on the weighted normalized spherical fuzzy decision matrix.

This invention relates to the field of railway engineering schedule management and intelligent decision-making technology, and in particular to a method and system for ranking railway engineering schedule indicators. The method includes constructing a multi-dimensional schedule indicator system, establishing a hesitant fuzzy decision matrix, introducing a β threshold to construct a covering approximate space, calculating attribute dependencies to reduce redundant indicators, reconstructing the matrix, and calculating a comprehensive evaluation value, ultimately achieving a scientific ranking of schedule indicators. This application can effectively handle uncertainties, incompleteness, and attribute redundancy while preserving the multiple hesitancy and fuzziness of evaluation information, thereby improving the efficiency and accuracy of schedule indicator screening and ranking.

The application discloses a kind of multi-modal anomaly confidence evaluation and hierarchical disposal methods under the scene of power plant inspection, it is related to the technical field of power plant, the method includes: establishing standardization multi-modal dataset;The correlation weight correction and feature fusion are carried out to defect geometric feature and deterioration trend feature;From the corresponding modal branch is separated in multi-modal fusion feature, and the abnormal confidence is obtained in combination with time attenuation factor and preset equipment grade coefficient;Combining preset equipment safety hazard level and fault diffusion spreading risk, a multi-dimensional decision matrix is constructed, and the equipment anomaly is divided into different abnormal risk levels based on the multi-dimensional decision matrix;Match preset hierarchical disposal plan, and execute corresponding disposal measures.The application improves the anti-interference ability and determination accuracy of anomaly confidence evaluation, completes differentiated operation and maintenance disposal, and guarantees the safe, stable and efficient operation of key equipment in power plant.

This invention discloses a method and system for determining design schemes for urban engineering projects. The method involves acquiring design information for each scheme and calculating its information density index; obtaining subjective evaluation indicators and objective performance indicators; determining subjective weights using fuzzy hierarchical analysis and extracting objective weights using CRITIC; and performing a game-theoretic equilibrium fusion of the subjective and objective weights to obtain a weight decision matrix for each design scheme. When the information density index is not less than a preset information density threshold, the method calculates annual photovoltaic capacity indicators and annual building global warming potential, updating the weight decision matrix. Based on the weight decision matrix, the TOPSIS method is used to rank the design schemes. Using this invention, adaptive adjustments to decisions can be achieved during the design phase evolution, enabling an automatic switch from a subjective-objective integrated decision structure to a life-cycle objective performance-driven decision structure.

This invention discloses a preventive maintenance scheduling method for an integrated energy system in a park, comprising: collecting operating parameters of multiple devices in the HVAC system and energy storage system within the park, the operating parameters including at least heterogeneous characteristic parameters reflecting mechanical degradation, electrical degradation, and thermal degradation of the devices; based on the collected heterogeneous characteristic parameters, obtaining a health index through a health assessment model, and predicting the remaining service life of each device based on a time-series prediction model; triggering maintenance decisions based on the obtained health index and remaining service life, and sorting the devices requiring maintenance based on a multi-dimensional decision matrix including urgency, criticality, and impact to generate a maintenance priority list; obtaining production plan information for a future preset period, and for each maintenance task in the maintenance priority list, optimizing it based on the remaining service life of the corresponding device and the production plan information, and recommending a maintenance window.