103 results about "Decision management" patented technology

The invention relates to the technical field of health information monitoring and management, and discloses a health information monitoring and management system based on multi-source data fusion analysis, which comprises a physiological data acquisition unit, a fusion analysis engine, an intelligent decision management module and the like. The physiological data acquisition unit acquires a multi-source heterogeneous data stream, and a multi-layer fusion topology is constructed through preprocessing; the fusion analysis engine realizes data feature association and anomaly detection through feature association and mode recognition; and the intelligent decision management module generates a health state reference strategy and dynamically allocates data source weights. The real-time calibration module calibrates a signal time domain and adapts to an analysis frequency, the data weight optimization module evaluates an optimization strategy based on credibility, and the fault-tolerant processing module completes data verification and recovery in combination with the distributed cache unit. The system realizes efficient fusion, dynamic decision and reliable management of multi-source data, improves the accuracy of health monitoring and the robustness of the system, and is suitable for intelligent health management scenes.

The invention relates to the technical field of semantic decision management and control, in particular to a multi-modal semantic alignment intelligent decision and risk management and control system, which comprehensively and accurately captures cross-modal semantic association through multi-modal semantic alignment processing so as to generate a plurality of possible reasoning chains with reliability and interpretability. And combining dimensions such as knowledge conflicts and historical risks, calculating reasoning overlapping values to evaluate inter-chain association, realizing multi-dimensional and multi-angle analysis of potential risks, bringing risk fingerprint values and the reasoning overlapping values into a quantitative calculation framework of decision response values, dynamically setting a decision response threshold value, and realizing quantitative calculation of the risk fingerprint values and the reasoning overlapping values. The system can flexibly trigger emergency, early warning or monitoring response according to different risk levels, and refinement and differentiation of risk management and control are realized. The mechanism not only ensures timely disposal in a high-risk scene, but also avoids resource waste in a low-risk scene, and effectively balances risk prevention and control and execution efficiency.

The invention discloses a host USB structure verification management system based on model fusion, and particularly relates to the field of computer security. The system comprises a data acquisition layer for acquiring a protocol data packet, an electrical signal waveform and equipment logic state information of a USB interface in real time, and generating a standardized multi-modal data stream through time alignment processing; the feature extraction layer is used for extracting modeling features from the standardized multi-modal data stream and encoding the modeling features into a structured matrix; the model fusion layer is used for fusing and outputting joint verification confidence through a protocol rule model, an anomaly detection model and a simulation model; the decision management layer executes a three-level verification strategy in sequence; the user interaction layer provides a visual monitoring and manual feedback interface and supports dynamic model replacement. According to the method, the structural legality of the USB equipment is verified through multi-model cooperation, real-time monitoring and risk level-to-level management of the USB equipment are achieved, the safety and compliance of the host terminal are improved, and the method is suitable for enterprise-level USB equipment protection scenes.

The invention relates to the technical field of industrial data management, in particular to an unmanned aerial vehicle task scheduling intelligent method for low-altitude economy. Obtaining business demand data, equipment state data and environment monitoring data; establishing a business priority dynamic adjustment scheme, evaluating the urgency, potential hazard and environmental complexity of the tasks according to the business risk evaluation matrix, dynamically adjusting the corresponding priorities, and constructing a multi-task cooperation sequence; a multi-target business decision optimization method is adopted to analyze the multi-task cooperation sequence and the equipment state data, load matching clustering is utilized to carry out task preliminary grouping, and a multi-target business decision optimization function is solved to carry out task distribution and business decision management; and carrying out predictive analysis on the river and lake environment risk by adopting a prospective service scheduling scheme, and taking a prediction result as the prospective input of the multi-target service decision optimization method to realize service pre-distribution and decision management. According to the invention, adaptive task scheduling can be realized.

The invention discloses an intelligent asset dynamic monitoring method and system based on full life cycle management, and relates to the technical field of asset full life cycle management, and the method comprises the steps: carrying out the twinborn modeling based on the structure attribute information of a target asset, generating an asset virtual twinborn body, recognizing a key part, deploying a sensor, and constructing an asset multi-dimensional sensing network; acquiring asset operation data flow through the network, and mapping the asset operation data flow to twin monitoring and prediction to determine current operation state parameters of assets; constructing an asset full life cycle strategy according to asset management requirements, and matching a current state to obtain a current-stage management strategy of the assets; and determining management parameters based on stage strategy analysis, and performing asset decision management. The technical problems that in the prior art, it is difficult to accurately obtain the state data of all stages of the full life cycle of the assets, and accurate management and decision making of the assets cannot be met are solved, and the technical effects that the state data of all stages of the full life cycle of the assets are accurately obtained, and the accurate management and decision making requirements of the assets are met are achieved.

A join decision manager (JDM) generates a data processing pipeline. The data processing pipeline includes at least one join operation associated with build-side row data and probe-side row data. The JDM determines the maximum cardinality associated with the probe-side row data. The JDM determines size of the build-side row data at a decision node of the at least one join operation. The JDM configures execution of the at least one join operation as one of a broadcast join or a hash-hash join based on the size of the build-side row data and the maximum cardinality.

The invention provides a micro-grid main grid cooperative switching intelligent regulation and control method and system, and relates to the technical field of power grid regulation and control. According to the method, the historical operation data of the micro-grid and the main grid are collected in real time, and the future operation state parameter set is generated, so that the operation states of the micro-grid and the main grid are accurately predicted. The dynamic stability index of the micro-grid and the power grid health index of the main grid are calculated, the two indexes serve as input, a switching decision management model is applied to generate a switching tendency score, and the switching tendency score is compared with a dynamic decision threshold value in real time to judge whether switching conditions are met or not. According to the method, the collaborative switching control instruction is automatically generated according to the correlation index and the switching mode rule, and the micro-grid is finely pre-adjusted in advance, so that smooth seamless switching between the micro-grid and the main grid is realized when the physical switching action is executed, and the safety, the reliability and the stability of the switching operation between the micro-grid and the main grid are improved.

The invention discloses a photovoltaic base ecological environment effect evaluation system, and relates to the technical field of effect evaluation, the photovoltaic base ecological environment effect evaluation system comprises a data acquisition module, a data processing module, an effect analysis module and a decision management module, and the effect analysis module comprises an ecological sensitive submodule, an environment bearing submodule and an effect synthesis submodule. According to the invention, multiple factors are integrated through the ecological sensitivity sub-module to quantify ecological sensitivity, surface soil crust shear strength is introduced to fill the blank of traditional evaluation, multi-dimensional evaluation is realized, the collaborative level of ecological protection and energy efficiency is quantified through the output result, and the environment bearing sub-module is used to introduce the overground wind direction and underground hydrology, so that the comprehensive evaluation of the ecological protection and energy efficiency is realized. According to the method, the influence of ecological sensitivity and installed capacity can be accurately adjusted to improve the matching degree of photovoltaic development and ecological capacity, calculation results and factors of multiple sub-modules are integrated through the effect integration sub-module, the problem that existing evaluation is inaccurate is solved, and the whole system is based on the artificial intelligence technology and has an excellent intelligent effect.

The invention discloses a digital project management method and system based on artificial intelligence, and relates to the technical field of project management, and the method comprises the steps: constructing a project digital portrait and multi-dimensional feature analysis framework, determining an analysis domain boundary of intelligent auditing, and automatically generating an intelligent analysis grid adaptive to multi-source heterogeneous data; and performing grid division on project progress data according to an analysis domain boundary, performing adaptive partition on a project grid through an unsupervised clustering algorithm, dynamically determining a critical threshold value of risk early warning based on a project digital portrait and intelligent analysis grid and comprehensive risk stress analysis, and outputting a risk early warning decision management conclusion containing the critical threshold value. According to the management system, an intelligent analysis grid adaptive to multi-source heterogeneous data is generated by constructing a project digital portrait and a multi-dimensional feature analysis framework, and a risk early warning critical threshold value is dynamically determined, so that real-time perception, accurate analysis and intelligent decision support of the full life cycle state of a project are realized.

The invention is suitable for the field of power station equipment management, and provides a power station equipment health management system based on multi-modal perception and hybrid reasoning, and the system comprises a data perception layer which is used for obtaining and preprocessing multi-modal perception data of power station target equipment, and obtaining multi-modal feature data; the hybrid reasoning layer is used for taking the multi-modal characteristic data as input of a pre-constructed hybrid reasoning model to obtain an equipment fault diagnosis result; the decision management layer is used for generating an equipment health management decision according to the diagnosis result and outputting the decision; the hybrid reasoning model comprises a rule reasoning sub-layer, a deep learning sub-layer and a case reasoning sub-layer, the deep learning sub-layer adopts a lightweight Transform model, and the lightweight Transform model comprises a cross-modal attention enhancement module and a fault feature intensified training module. In conclusion, the hybrid reasoning architecture is constructed, so that the fault diagnosis accuracy of the power station equipment is obviously improved, the false alarm rate is reduced, and the technical problems of few unconventional fault samples and difficulty in diagnosis of the power station are solved.

The invention relates to the technical field of artificial intelligence and city updating, in particular to an artificial intelligence assisted city updating decision management system, which comprises an information acquisition module, a city physical examination module and a scheme generation module, the information acquisition module acquires data input by a multi-role user, processes and verifies the data and transmits the data to the city physical examination module; the city physical examination module performs multi-dimensional potential evaluation on the received data based on a machine learning model, generates a visual evaluation result and transmits the visual evaluation result to the scheme generation module; and the scheme generation module generates a structured update strategy and an effect picture according to the evaluation result in combination with the knowledge base, and feeds back the effect picture to the information acquisition module to form a data closed loop. According to the system, a multi-module technology is fused, information collection ensures efficient collaboration of multi-role data, urban physical examination accurately evaluates the land potential, scheme generation gives consideration to compliance and innovation, the system is ensured to be safe, stable and elastically expanded, urban update decision intelligence is realized, and decision scientificity is improved.

The invention relates to the technical field of intelligent manufacturing and supply chain management, in particular to an intelligent management system for titanium plate inventory and production plan collaboration. Comprises: a multi-source data fusion center for a supply chain management system to obtain operation data and generate a collaborative situation original data set; the situation quantitative analysis unit is used for receiving the collaborative situation original data set and generating a market agility demand score and an inventory liquidity current situation score; the collaborative conflict evaluation unit is used for calculating a collaborative conflict index and determining a collaborative conflict situation level; the evolution trend prediction unit is used for generating an evolution sequence of future collaborative conflict indexes; and a dynamic decision management unit for generating an immediate conversion policy in response to the collaborative conflict context level and generating a predictive inventory structure pre-configuration decision. According to the method, the accuracy and objectivity of decision making are remarkably improved, and enterprises can deeply insight into operation core contradictions.

The invention discloses a medical oncology specimen collection and management system, and relates to the technical field of specimen management, and the system comprises a collection and detection unit which is used for collecting biopsy specimens and corresponding specimen data; the tracking quality control unit is used for storing full-process information of the biopsy specimens by utilizing a block chain and dynamically evaluating and tracing quality information of the biopsy specimens; the integrated learning unit is used for constructing a multi-omics database, generating a tumor multi-omics knowledge graph and carrying out dynamic knowledge updating through data mining; and the decision management unit is used for sharing resources and monitoring authorized use records of the biopsy specimens. By integrating the block chain, federal learning and process reconstruction, full-chain upgrading of medical oncology specimens from'collection 'to'application' is realized, tumor diagnosis and treatment are promoted to step forward from'experience-driven 'to'data-driven', digitization, standardization and traceable management of biopsy specimen full-process information are realized, and the efficiency of medical oncology diagnosis and treatment is improved. And data support and decision support are provided for clinical practice and scientific research.

The application discloses a kind of based on Copula function's urban water, energy and environmental system optimization decision management method, comprising: S1. the Copula joint distribution function of water environment capacity and available energy amount in urban water is constructed;S2. the resource optimization allocation and joint risk management model of urban water, energy and environmental system is established;S3. the resource optimization allocation and joint risk management model of urban water, energy and environmental system is solved;The method realizes the optimization allocation and joint risk management of urban water-energy-environment system water and energy under uncertain conditions.Provide urban water-energy-environment system resource optimization allocation scheme and long-term planning scheme of sewage treatment process under different risk scenario combinations on the basis of maximum economic benefit, and help decision makers better realize the trade-off between joint default risk and system benefit, to realize urban risk controllable and water-energy-environment collaborative development provides scientific method.

The invention relates to the technical field of business and financial management, in particular to a business and financial fusion analysis decision management system and method based on multi-modal AI, and the method comprises the steps: configuring deep learning and testing according to financial use and formation information, and obtaining a multi-source contact analysis record; according to the multi-source connection analysis record, feature line and non-feature line difference is configured, and a fusion decision result is obtained; according to the fusion decision result, configuring information-driven machine learning and clustering analysis to obtain a decision value decision record; configuring real-time information analysis according to the real-time decision plan to obtain a real-time financial analysis record; configuring flow analysis according to the real-time financial analysis record to obtain a flow decision result; and configuring real-time information flow analysis and personalized management according to a process decision result to obtain a real-time management result. According to the method, the capability of effectively integrating different information sources is achieved, and the real-time decision-making and long-term financial demand evaluation effects are good.

The invention relates to the technical field of production decision management, and discloses a factory production decision management method and system based on an MES (Manufacturing Execution System), and the method comprises the steps: obtaining monitoring data through a sensing module, carrying out the analysis and classification, generating a production monitoring information flow, calling a production decision record, and carrying out the multi-dimensional analysis with an overall production target, so as to carry out the prediction simulation; the method comprises the steps of obtaining a reference decision chain, carrying out preliminary material scheduling and task allocation, analyzing decision computing power requirements, identifying idle computing power time periods, positioning abnormal feedback information, carrying out bidirectional equilibrium analysis, generating an expansion decision chain, constructing an expected decision tree and optimizing production scheduling and task allocation. According to the invention, resource allocation is optimized, the capability of dealing with abnormities of factories is improved, intelligent production management is promoted, and the problem of low efficiency of production decision making in the prior art is solved.

The invention discloses a power grid operation decision management system based on artificial intelligence, and the system comprises an application service layer which is used for receiving a power grid operation financial problem text transmitted by a user through a user side, and transmitting the power grid operation financial problem text to a semantic analysis module; the semantic analysis module is used for carrying out natural language understanding on the power grid operation financial problem text to obtain a standardized problem text; the knowledge retrieval module is used for performing vector similarity retrieval in the data resource layer based on the standardized problem text and recalling candidate fragments; the intelligent question and answer module is used for obtaining a dynamic recall threshold value, filtering the candidate segments according to the dynamic recall threshold value, generating decision answers corresponding to the power grid operation financial question text according to the reserved candidate segments through a pre-trained AI model, and returning the decision answers to the user side; the data resource layer is used for dynamically constructing a power grid exclusive knowledge base; and the model optimization layer is used for optimizing the AI model based on the feedback of the decision answer.

The invention relates to the technical field of data visualization, and provides an operation and maintenance data visualization method and system based on a smart power grid, which are used for improving the efficiency and the intelligent degree of power grid operation and maintenance visualization so as to improve the timeliness of power grid operation and maintenance decision management, and the method comprises the following steps: collecting a multi-source heterogeneous operation and maintenance data set in the smart power grid in real time; performing cross-modal cleaning and time alignment processing on the operation and maintenance data set to generate a preprocessed data set; executing dynamic feature fusion and correlation analysis on the preprocessed data set to generate an equipment state feature set and an equipment-environment interaction feature set; based on the equipment state feature set and the equipment-environment interaction feature set, generating an interactive visualization element set through a multi-dimensional visualization strategy; performing dynamic rendering and layout optimization on the interactive visual element set, and outputting a power grid operation and maintenance visual interface updated in real time; the power grid operation and maintenance visual interface is configured with a user operation priority response mechanism used for processing interaction conflicts.

The invention discloses a factory production scheduling decision management system with combination of multi-factor intelligent analysis, which belongs to the technical field of intelligent production decision and specifically comprises an order obtaining and sorting unit, a task decomposition unit, a capacity resource matching unit and a production execution unit. According to the method, a priority evaluation coefficient is obtained based on order information and is used as dynamic priority division guidance to construct a priority initial sequence, and production work orders matched with priority attributes are created according to the priority initial sequence from high to low. And matching and scheduling process tasks of production work orders and productivity resources through reverse reasoning and an intelligent algorithm on the basis of the sub-time windows, integrating synchronous influence factors of'changing orders' in real time in the production process, rejecting and cancelling order products, determining priority attributes based on priority evaluation coefficients of inserted order products, and completing the production process. A local dynamic adjustment method of'front maintenance 'and'rear insertion' is adopted, and a production scheduling mode which is driven by multi-source data, intelligently optimized and dynamically and circularly adjusted in real time is achieved.

The invention relates to the technical field of decision management, in particular to an intelligent delivery decision management method and system for ROI dynamic optimization. Firstly, user behavior data, market bidding data, content feature data and time sequence data are obtained and preprocessed; then, constructing a multi-dimensional ROI prediction model based on a space-time attention mechanism, capturing time sequence dependence by using a time attention head, mining spatial relevance through a spatial relation graph, and outputting ROI prediction values of different putting strategies; thirdly, constructing a budget allocation module by adopting a deep Q network algorithm, modeling a delivery decision into a Markov decision process, and generating an optimal delivery strategy; and a multi-level feedback closed-loop optimization mechanism is established and comprises a real-time feedback layer, a short-term adjustment layer and a long-term optimization layer, so that strategy dynamic adjustment of different time scales is realized. The method can accurately predict the ROI, intelligently distribute the budget, quickly respond to the market change, and remarkably improve the advertisement putting effect and the resource utilization efficiency.

The invention discloses a nuclear power plant production command center management system, which relates to the technical field of nuclear power and comprises a data storage device, an equipment management platform, a decision management platform and an operation management platform. Wherein the equipment management platform comprises an equipment remote monitoring module and an intelligent early warning module; the decision management platform comprises an intelligent decision center module; the operation management platform comprises an intelligent dispatching module; the equipment remote monitoring module realizes real-time acquisition of equipment operation parameters through an intelligent sensor network, and the intelligent early warning module performs anomaly detection based on a dynamic threshold model, so that the problem of lagging of traditional fixed threshold early warning is solved; the intelligent decision-making center module responds to the early warning information, carries out deep knowledge reasoning by combining historical operation data and a nuclear power plant group reactor knowledge graph, generates a scientific decision-making scheme, and solves the limitation of artificial experience decision-making; and the intelligent dispatching module automatically generates the operation task based on the decision scheme and matches the operation team, so that the operation efficiency is improved.

The invention provides a palm oil plantation multi-modal decision management method, system and device and a medium, and belongs to the technical field of Internet of Things in the planting industry, and the method comprises the steps: building a heterogeneous federated learning framework for a palm oil plantation; the edge layer constructs a lightweight model, a planting industry heterogeneous terminal device is used for collecting data for local training, and asynchronous federal aggregation is performed in combination with the fog computing layer until requirements are met; the edge layer is used for extracting local features from data collected by heterogeneous terminal equipment in the planting industry; the cloud computing layer aligns local features of the edge layer by using a cross-modal space-time encoder, constructs a causal graph in combination with a Bayesian network, determines intervention measures, quantifies the influence of the intervention measures by using anti-factual reasoning, generates and executes a decision scheme, and solves layered Nash equilibrium through a distributed Actor-Critic algorithm, so as to improve the performance of the cloud computing layer. And task allocation and path planning are carried out on the planting industry heterogeneous terminal equipment. According to the invention, the utilization rate of planting management data, decision reasonability and equipment collaboration are improved.

The invention relates to the technical field of computers, discloses a panoramic situation intelligent emergency decision management and control method and system based on cloud computing, and aims to solve the problems of serious information island, response lag, weak intelligent decision capability, disjunction of resource scheduling and emergency strategies, man-machine interaction separation and the like in an existing emergency management system. The method comprises the steps that multi-source heterogeneous data are collected through a global data access engine, and a unified data lake is formed through data fusion and cleaning; constructing a dynamic three-dimensional digital twinborn model to realize panoramic situation real-time modeling; and carrying out risk identification and graded early warning by using a deep neural network. According to the technical scheme, global situation second-level perception, risk active prediction, scientific auxiliary decision making and integrated collaborative management and control are realized, and the emergency response efficiency and the decision making intelligence level are remarkably improved.

The present invention relates to a method for constructing digital ship intelligent manufacturing. The construction method comprises the following steps: digital dock construction; digital weight and center of gravity control; hull response monitoring system construction; operation and management integrated digital management platform construction: building a digital overall framework, mainly including a basic layer, an equipment resource layer, an execution basic layer, a coordination business layer, an operation management layer and a decision management layer; digital inspection management platform construction. The advantages of the present invention are: with the help of digital transformation, the components are based on engineering decomposition, with planning management as the command, and an integrated solution for overall design, materials, production, quality and cost management is constructed. Through the construction of the industrial Internet and the integrated application of the information system, a digital shipyard based on digital twins is constructed, facilities, equipment, resources, etc. are interconnected, early warning and analysis are carried out from multiple angles, a big data operation system is explored, a digital operation model is constructed, and finally a new digital production model is formed.

The invention discloses an expressway maintenance decision management method based on multi-source data analysis, and the method comprises the following steps: 1, road region pre-segmentation and feature extraction: carrying out the preprocessing of an expressway monitoring video through employing a MobileNetV3 + DeepLabV3 model, carrying out the pre-segmentation of a road region, and only carrying out the feature extraction of the segmented road region; step 2, image splicing and complete image generation: splicing the segmented road area images; 3, highway maintenance demand analysis: designing a cascade network architecture, adopting a YOLOv8 model for detecting large-scale damage in the first layer, adopting a-Net + + model for finely segmenting micro-cracks in the second layer, adopting a Vision Transform model for analyzing the damage depth in the third layer, and combining the output of the three layers of networks to accurately evaluate the maintenance demand of the highway; 4, performing maintenance engineering analysis and duration prediction; 5, analyzing historical traffic flow; and step 6, maintenance time decision and influence evaluation.

The invention discloses a charging and discharging control system for a power battery of a new energy tourism and sightseeing vehicle, and relates to the technical field of battery charging and discharging. Comprising a power generation prediction module, a demand analysis module, a decision management module and a dynamic supervision module, wherein the power generation prediction module is used for extracting future travel and future weather data from a sightseeing bus management system and a meteorological platform; the technical key points are as follows: a standardized API (Application Program Interface) cooperates with an existing sightseeing vehicle management system to collect travel data such as departure time, routes, loads and driving styles of the sightseeing vehicle, travel segmentation and weather data association are carried out by combining meteorological data, and a charging and discharging regulation and control strategy is generated after power generation prediction, demand analysis and decision management and is fed back to the sightseeing vehicle management system. The edge network is used for monitoring execution data in real time to form a closed loop, energy flow and operation scheduling dynamic matching is achieved, the energy utilization efficiency and the operation economy are improved, the use effect is good, and the method has good use prospects.

This invention belongs to the field of port operation and management technology. It provides a port project full-process decision management system based on big data analysis, comprising: verifying and analyzing multiple historical estimated arrival times using a hypothetical moving window; determining the optimal moving window using the moving window averaging method and Euclidean algorithm; obtaining a model correction factor based on historical estimates and actual times; obtaining the estimated arrival time; comparing it with the waiting-to-departure time; if a mismatch is found, removing the original stable connecting vessels; reselecting vessels based on connection matching degree and re-evaluating; until a vessel with a time-dimensional connection match is found within the connecting vessel sequence. By optimizing the moving window and introducing correction factors, the accuracy of arrival time prediction is improved, avoiding delays due to time mismatches and effectively improving port vessel scheduling efficiency. This invention solves the problem of how to accurately predict the arrival time of stable connecting vessels and determine whether it matches their waiting-to-departure time.

The invention relates to the technical field of power conversion cabinet management, in particular to a power conversion cabinet intelligent analysis decision management method and platform and electronic equipment, and the method comprises the steps: collecting the historical power conversion data of a user, and carrying out the seasonal classification of the historical power conversion data of the user; performing habitual analysis on the user to construct a user activeness index and a demand stability index; an environment interference index of the user is constructed, a user state factor is constructed, and portrait construction is carried out on the user according to the user state factor; dividing a power conversion area of the user based on a historical power conversion position of the user, and constructing a load index of each power conversion cabinet placement point according to a portrait construction result and a power conversion area division result of the user; and establishing a configuration adjustment scheme of each power conversion cabinet placement point according to the load index of each power conversion cabinet placement point. According to the invention, the decision analysis efficiency of the power conversion cabinet is effectively improved.

The application relates to the technical field of decision management, and provides a phosphorus mine industrial internet intelligent decision method and system based on a knowledge graph. Production data in a phosphorus mine industry is collected through intelligent sensors, and decision information is generated by performing edge processing on the production data through an edge computing node; knowledge tuples are generated based on the production data and the decision information; entity devices and their relationship information are mapped into device nodes and edges of a Bayesian network, a knowledge state code and a hidden state at a set time are generated based on a confidence weight of the device nodes; the knowledge state and probability of the device nodes at a next time are predicted based on the hidden state at the set time; and a dynamically evolved knowledge graph is generated based on the hidden state at the set time and the knowledge state and probability at the next time, so that the phosphorus mine industrial production is decided through the knowledge graph. The dynamically evolved knowledge graph generated by the scheme can assist decision making, and the efficiency and accuracy of dynamic decision making in the phosphorus mine industrial production are improved.

The invention provides an online dynamic filtering method for fan lubricating oil, and relates to the technical field of data processing.The online dynamic filtering method comprises the steps that performance denaturation analysis is conducted on target lubricating oil, a filtering trigger threshold value is determined, an online filtering unit is trained in combination with the filtering trigger threshold value, real-time state information is transmitted to the online filtering unit, and an online filtering scheme is determined; and lubricating oil online filtering treatment based on the target filter is executed, and feedback decision management and feedback control management of target lubricating oil filtering are carried out in combination with the filtering record. The technical problems that in the prior art, the filtering control precision of online filtering of fan lubricating oil is low, and the effectiveness of fan operation stability and fan service life maintenance is poor are solved. The technical effects that the filtering effectiveness of online dynamic filtering of the fan lubricating oil is improved, the operation stability of a wind turbine generator is guaranteed, and the risk that the service life of the generator is shortened due to the fact that lubricating oil impurities are not filtered in time is avoided are achieved.