65 results about "Decision management" patented technology

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 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 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 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 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.

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.

A decision management technique comprises obtaining, for a set of items, data indicative of a previous percentage division between a first item type and a second item type for each item in the set of items. The technique further comprises classifying each item in the set of items into one of a plurality of clusters, based on the obtained data, wherein each cluster represents a different percentage division range between the first item type and the second item type. The technique further comprises identifying any items in each cluster that deviate from the percentage division range for the cluster, and then applying weights to any identified items based on the deviation from the percentage division range. The technique further comprises re-classifying any identified items to another cluster in the plurality of clusters based on the applied weights.

This invention provides an intelligent power distribution system and its intelligent control method based on operational decision-making and control. The method includes: S1. Standardized partitioned deployment of modular intelligent power distribution cabinets and mapping configuration of sensing-control-communication full-link units; S2. Full data entry and structured parsing of business rules and safety control boundary constraints for electricity consumption scenarios; S3. Full-link multi-source heterogeneous operational data processing based on an improved grey relational analysis preprocessing algorithm with dynamic time curvature; S4. Dynamic operational decision generation based on an improved multi-objective antlion optimization algorithm; S5. Hierarchical execution of control commands adapted to multiple scenarios and full-process status traceability. This invention realizes standardized modular deployment, full-link safety control, intelligent energy and water conservation, and closed-loop optimization of data-decision-rules in power distribution systems. It is suitable for the power distribution and energy consumption control needs of various industries such as KTVs, hotels, and restaurants, improving the intelligence, safety, and energy-saving level of power distribution systems.

The invention relates to the technical field of agent collaboration systems, in particular to a power station equipment anomaly detection decision management method based on Agent collaboration and related equipment, the system constructs four functional modules of a detection Agent, a diagnosis Agent, a knowledge Agent and a scheduling Agent, and real-time collection and standardized processing of multi-source heterogeneous detection data are achieved. The detection Agent monitors the standardized data in real time, and triggers the scheduling Agent to generate a diagnosis task tree when the standardized data is abnormal; and the scheduling Agent issues the subtasks to the diagnosis Agent for root cause analysis, or calls the knowledge Agent to obtain auxiliary diagnosis knowledge. The diagnosis Agent generates a preliminary conclusion, the knowledge Agent provides domain knowledge support, and the scheduling Agent fuses the information of the diagnosis Agent and the knowledge Agent to form a final decision containing an abnormal level, a risk level and an operation and maintenance strategy. According to the system, through data sharing and task collaboration between Agents, accurate identification, rapid diagnosis and scientific decision making of power station anomalies are realized, and the operation and maintenance efficiency and safety are improved.

The invention relates to the technical field of intelligent equipment and intelligent warehouse management crossing, and particularly discloses an intelligent equipment application warehouse management container number identification system. The intelligent equipment application warehouse management container number identification system comprises an intelligent identification module, a cooperative control module, a warehouse decision management module, a data storage and sharing module and a self-inspection and optimization module. The accuracy and legality of the recognition result are further guaranteed, the integrity and reliability of container information collection are improved through the multi-level recognition mechanism, high-quality data support is provided for subsequent equipment scheduling and inventory management links, operation errors caused by information errors are reduced from the source, and the operation efficiency is improved. And an accurate data foundation is laid for warehouse management.

This invention relates to the field of data visualization technology, and provides a method and system for visualizing operation and maintenance data based on smart grids. This method aims to improve the efficiency and intelligence of power grid operation and maintenance visualization, thereby enhancing the timeliness of power grid operation and maintenance decision-making and management. The method includes: real-time acquisition of multi-source heterogeneous operation and maintenance data sets from the smart grid; cross-modal cleaning and time alignment processing of the operation and maintenance data sets to generate a preprocessed data set; dynamic feature fusion and correlation analysis of the preprocessed data set to generate a set of equipment status features and a set of equipment-environment interaction features; generation of an interactive visualization element set based on the set of equipment status features and the set of equipment-environment interaction features through a multi-dimensional visualization strategy; dynamic rendering and layout optimization of the interactive visualization element set to output a real-time updated power grid operation and maintenance visualization interface; and configuration of a user operation priority response mechanism for handling interaction conflicts within the power grid operation and maintenance visualization interface.

The invention relates to the technical field of marketing, in particular to a scheme making decision management system adaptive to a marketing activity page, which comprises a real-time dynamic data acquisition layer used for accessing multi-source heterogeneous data, acquiring dynamic data of a user side and dynamic data of a promotion channel, the data source acquisition frequency is improved according to a data fluctuation threshold value or a scene event triggering mechanism; and the multi-dimensional data governance layer is used for constructing a dynamic anomaly recognition model based on the real-time data distribution characteristics to filter invalid data and extreme values, and dynamically associating the real-time data with historical same-scene data. Multi-source heterogeneous data are accessed through the real-time dynamic data acquisition layer, the acquisition frequency is improved according to a data fluctuation threshold value or a scene event, and the problem of data lag of a traditional system is solved; the multi-dimensional data governance layer constructs a dynamic anomaly recognition model to filter invalid data and extreme values, and correlates real-time data with historical same-scene data to calibrate decision basis, so that dependence on fixed historical data is avoided.

The invention relates to the technical field of human resource intelligent management, in particular to a five-dimensional dynamic talent portrait intelligent decision management system fused with a large model. The system comprises a five-dimensional data acquisition module, a portrait construction module, an intelligent decision module and a management optimization module. Wherein the five-dimensional data acquisition module is used for acquiring a multi-source data set, and the multi-source data set comprises human feature data, scientific research and innovation feature data, ecological cooperation feature data, organization operation feature data and system execution feature data; and the portrait construction module is used for performing normalization processing and weight distribution on the multi-source data set to generate a fusion evaluation result. According to the invention, based on a human resource intelligent management technology, talent portrait accurate construction, breeding management whole-process collaborative optimization and dynamic short board identification are realized, and the precision, efficiency and suitability of talent management are greatly improved.

This invention discloses a charging and discharging control system for a new energy sightseeing vehicle's power battery, relating to the field of battery charging and discharging technology. It includes a power generation prediction module, a demand analysis module, a decision management module, and a dynamic monitoring module. The power generation prediction module extracts future travel and weather data from the sightseeing vehicle management system and a meteorological platform. Its key technical points are: through a standardized API interface, it collaborates with the existing sightseeing vehicle management system to collect travel data such as departure time, route, load, and driving style; it combines this with meteorological data to segment the travel and correlate weather data; after power generation prediction, demand analysis, and decision management, it generates a charging and discharging control strategy and feeds it back to the sightseeing vehicle management system; it utilizes an edge network to monitor execution data in real time to form a closed loop, achieving dynamic matching of energy flow and operational scheduling, improving energy utilization efficiency and operational economy, demonstrating good performance and promising application prospects.

The invention relates to the technical field of sewage management, and discloses a sewage toxicity joint effect prediction and discharge traceability decision management system, which comprises a data acquisition module, a logic mapping module, a residual decision module, an active verification module and a decision instruction generation module, collecting a terminal comprehensive biological inhibition rate sequence and an upstream working condition feature vector, and establishing a logic time delay transfer model from each emission source to the terminal; calculating a dynamic residual error of the actually measured inhibition ratio relative to the sum of the independent contribution expectation values of the emission sources, and distributing a collaborative toxicity contribution weight according to a working condition activeness weight; and when the weight is in an uncertain interval, a pseudo-random flow perturbation instruction is issued, and the collaborative toxicity contribution weight is corrected by using the correlation between the terminal response waveform and the instruction. The synergistic effect is quantified into a marginal contribution weight with exclusive causal evidence efficacy, and an anti-distinguishing blind area in administrative responsibility determination is eliminated.

The invention relates to the technical field of artificial intelligence, solves the technical problems that in the prior art, limitation of physical laws is prone to being neglected, complex coupling effects cannot be captured, large-scale strategy exploration is difficult to support, and geothermal energy overpressure mining occurs due to local optimum, and particularly relates to a development decision management method and system for geothermal energy. The method comprises the steps that geothermal multi-source data in a geothermal system is collected, a geothermal associated database is obtained through preprocessing and integration, a high-fidelity data set covering multiple scenes is generated through parameter uncertainty sampling, and a built physical sensing simulator can achieve the multi-scene high-fidelity data set while keeping the coupling physical law. The time consumption of single simulation is compressed from several hours to a second level, dynamic optimization of a development strategy under physical constraints is achieved through connection interaction of the DQN and the physical perception simulator, and the problems that the model falls into local optimum or violates the physical constraints to cause overpressure mining and the like are avoided.

The application discloses a network security decision management system based on multi-source data processing analysis, relates to the technical field of network security decision, and aims to solve the technical problem that in the prior art, communication behavior security detection cannot be accurately realized according to two types of core metadata, namely, a packet length sequence and a packet arrival time interval sequence. Specifically, a full-dimension security protection system covering single-point communication detection, group device cooperative monitoring and protection effect self-checking is constructed through multi-unit cooperative linkage. Multi-dimension data acquisition and analysis units lay a foundation for accurate detection, device cooperative sensing and detection units fill the gap of IoT device group security monitoring, a network risk self-checking and evaluating unit realizes continuous optimization of the protection effect, and a network security decision management platform guarantees efficient linkage and centralized control of each link, and effectively improves the comprehensiveness of network security detection, the accuracy of abnormality identification, the pertinence of protection measures and the efficiency of system operation.

The invention particularly relates to an enterprise intelligent decision management system based on big data, which relates to the technical field of big data processing, and comprises the following steps: initiating and executing a secure multi-party computing process to realize secure model aggregation and malicious update detection; and recording the global model weight version of each round of training, and distributing the updated global model weight version to all enterprise clients. According to the invention, multiple modules cooperatively construct a four-level privacy protection system, so that the risk of commercial secret leakage is effectively avoided and the data compliance requirement is met; the local model is updated through homomorphic encryption or secret sharing encryption, and whole-course ciphertext transmission and aggregation are ensured; the privacy protection decision-making module deploys the global model locally, and sensitive data is prevented from being transmitted outwards during decision making; according to the system, commercial confidentials such as purchase cost and core sales data are prevented from being reversely deduced through model updating, the requirements of laws and regulations such as GDPR are completely met, meanwhile, the data training value is guaranteed, and the privacy concern of enterprises participating in cross-collaboration is eliminated.

Disclosed herein are system, method, and computer program product embodiments for a transaction management system. The transaction management system may receive a request, e.g., transaction data, from a merchant system. The transaction management system may also receive metadata associated with a corresponding transaction card holder. The transaction management system may analyze the transaction using a system employing machine learning model(s) to perform functions such as pattern recognition and anomaly detection. Transaction management system may also use machine learning systems to manage approval and payment for transactions, especially in the context of new and unforeseen transactions. Transactions may be classified so that the transaction may be automatically approved, in need of additional review, or denied. Each of these classifications may be decided without input or oversight from users.

The embodiment of the invention provides a pasture decision management system and method, and belongs to the technical field of pasture management. The system comprises an equipment end used for collecting multi-modal data and sending the multi-modal data to an edge end; the edge end is used for performing behavior recognition by adopting a behavior recognition model based on the dairy cow image data to obtain dairy cow behavior category data; based on the cow audio data, performing sound classification by adopting a sound classification model to obtain cow barking category data; performing abnormal value filtering and aggregation processing based on the sensor data to obtain aggregated data; the multi-modal data, the dairy cow behavior category data, the dairy cow sound category data and the aggregated data are sent to a cloud end; the cloud is used for storing the knowledge graph; updating the knowledge graph based on the data sent by the edge end; and generating a management decision based on the updated knowledge graph. The system is used for overcoming the defects in existing pasture intelligent decision making.

The application relates to the technical field of semantic decision management and control, in particular to an intelligent decision and risk management and control system based on multi-modal semantic alignment. The system comprehensively and accurately captures cross-modal semantic correlation through multi-modal semantic alignment processing, and then generates multiple possible reasoning chains with reliability and interpretability. In combination with dimensions such as knowledge conflict and historical risk, the system simultaneously calculates reasoning overlap values to evaluate the correlation between chains, realizes multi-dimensional and multi-angle analysis of potential risks, and incorporates risk fingerprint values and reasoning overlap values into a quantitative calculation framework of decision response values. By dynamically setting a decision response threshold, the system can flexibly trigger emergency, early warning or monitoring responses according to different risk levels, thereby realizing the refinement and differentiation of risk management and control. This mechanism not only ensures timely disposal in high-risk scenarios, but also avoids resource waste in low-risk scenarios, effectively balancing risk prevention and control and execution efficiency.

The application provides a system and method for multi-angle examination and evaluation of engineering contractors and employees, which can instantly manage and record multi-aspect data related to the examination of the contractors and employees, determine examination results by using corresponding examination models according to examination requirements, provide data display services and examination result output services matched with instructions in response to the operation of users, identify data attributes and decide storage paths based on received data, decide management action signals based on received instructions, distribute and store multiple data, support real-time data state updating in response to management action instructions, and form prompt instructions according to set rules on data state information. The system can overcome the defects of the prior art, such as complicated data entry, limited application range of evaluation and analysis, and insufficient reliability, form a comprehensive business data examination and management system for engineering contractors and employees, and provide business monitoring and credit repair and update services based on examination data.