50 results about "Operational decision" patented technology

The invention discloses an intelligent enterprise financial data analysis system and method based on artificial intelligence, and belongs to the technical field of enterprise informatization management and intelligent finance. The objective of the invention is to solve the technical problems of low processing efficiency, insufficient data insight, lagging risk identification, weak intelligent decision-making assistance capability and the like in the existing enterprise financial data analysis process. The system generally integrates a data automatic acquisition and integration module, a financial data deep analysis and modeling engine based on artificial intelligence, a key financial index and operation performance intelligent calculation module, a multi-dimensional financial risk intelligent identification and dynamic early warning module and an interactive visual analysis report and decision support module. The core of the method is that internal and external multi-source heterogeneous financial and operation data of an enterprise are automatically collected and fused, deep processing and intelligent analysis are performed on the data by applying artificial intelligence technologies such as machine learning and natural language processing, and accurate portrait of the financial condition of the enterprise, dynamic evaluation of operation performance and real-time monitoring and prediction of financial risks are realized. And analysis reports and optimization suggestions with insight are generated, so that enterprise managers can make efficient and scientific operation decisions. According to the method, the automation and intelligence level of enterprise financial data analysis can be remarkably improved, the financial information quality and decision support efficiency are improved, the risk prevention capability of enterprises is enhanced, and the enterprises are assisted to realize refined operation and value creation.

The invention discloses a data processing method and system based on a business management platform, particularly relates to the technical field of advertisement putting business management, and is used for solving the problems of difficulty in abnormal attribution and low decision-making efficiency caused by dimension data isolation of an existing system. The method comprises the following steps: synchronously acquiring a channel dimension click rate and a contact dimension blacklist interception rate; when the channel click rate is abnormally reduced, extracting a blacklist interception log associated with the corresponding channel and a product dimension conversion rate; user behavior path topology distortion intensity and decision manifold compactness caused by a blacklist strategy are verified based on an interception log; when the distortion intensity exceeds a threshold value or the compactness is lower than a threshold value, backtracking an advertisement exposure user path of a corresponding channel product; analyzing time sequence association of blacklist interception nodes and product exposure nodes in the path, and generating a candidate root cause set; and detecting the product conversion rate fluctuation direction in the abnormal time period, thereby determining the root cause from the candidate set, and remarkably improving the operation decision-making efficiency and accuracy.

A multi-tiered optimization and machine learning framework is provided in which operational decisions as far as driver capacity allocation are taken at three different levels or stages, namely, long-term, short-term, and real-time. While decisions at each stage have individual importance for the operations at different time horizons, this framework utilizes decisions upstream to inform better results downstream. This synergy helps improve allocations when compared to solutions that assign driver capacity for each stage individually.

Examples relate to an adaptive AI coworker system for enhancing organizational operations. The system generates personalized AI coworkers based on role requirements, employing adaptive learning to understand unique organizational practices. It utilizes multi-agent coordination for complex task execution, automatically generating, prioritizing, and allocating tasks based on organizational context. The system integrates data from various sources, implementing data governance measures. Customized large language model instances are created for specific roles and organizations, incorporating organization-specific data and operational practices. The system provides explainable AI features for operational decision-making, ensuring transparency in critical tasks. Security measures and access controls are implemented to maintain data integrity and compliance with relevant regulations and standards.

The invention discloses a heat supply unit deep peak regulation and heat supply optimization operation method based on multi-mode collaboration, and particularly relates to the technical field of thermal power generation and heat supply, and the method comprises the steps of S1, system modeling and parameter identification, S2, thermoelectric load prediction, S3, multi-objective optimization solution, S4, operation decision and feedback, and S5, data agent mapping acceleration. According to the method, the thermoelectric load is accurately predicted through mechanism and data dual-drive modeling, and based on the target of maximizing the economic benefits of the whole plant, the optimal cooperation scheme of multiple operation modes such as heat supply, power supply and deep peak regulation is dynamically solved by utilizing the multi-target optimization algorithm on the premise of ensuring the safety of the unit, so that the power supply efficiency is improved. Finally, production is guided through an online decision-making and closed-loop feedback system, and safe, economical and flexible operation of the unit under the working condition of deep peak regulation is achieved.

An adaptive air intake control system and method for cooling towers, coupled with airflow diversion for energy saving, belongs to the field of adaptive control system technology. The system includes an information acquisition unit, an execution unit, and an adaptive control unit. The information acquisition unit collects environmental wind parameters and key operating parameters in real time. The execution unit includes multiple independent fan blade groups with circumferential partitioning and vertical layering for precise execution of commands. The adaptive control unit executes operational decisions, selecting the dominant operating mode from airflow diversion energy saving, anti-freezing energy saving, and anti-drip energy saving modes; it then identifies and outputs wind condition characteristics and thermal anomaly indicators through key parameter status identification, and generates control commands based on the adaptive control strategy and command generation. The system forms a closed loop, capable of adaptively adjusting the identification model and control strategy based on feedback. A corresponding control method is also disclosed, enabling intelligent and precise control of air intake in cooling towers under complex environments, improving cooling efficiency and energy saving while ensuring anti-freezing safety.

The invention discloses a thermal power enterprise operation decision result generation method, device and equipment, and relates to the technical field of energy optimization, and the method comprises the steps: collecting production operation data, fuel purchase data, inventory management data and other multi-mode operation data, and carrying out the preprocessing to obtain standardized data; and inputting the data into a preset management and control model, generating a prediction result and a scheme, and generating an operation decision result through comprehensive analysis in combination with a preset warning threshold. According to the method, the multi-modal data is input into the management and control model formed by combining multiple models, the multi-dimensional result is generated, and the final operation decision result is obtained through comprehensive analysis in combination with the threshold value, so that deep integration and analysis of the multi-modal data of the thermal power enterprise can be realized, the data utilization efficiency is improved, and the scientificity and the accuracy of operation decision are improved.

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 discloses an intelligent benchmarking analysis method and system based on multi-dimensional data fusion. The method comprises the following steps: firstly, determining various operation data involved in the white feather broiler breeding process of a target enterprise, carrying out standardized definition, and storing the data in a unified data storage layer; secondly, detecting and cleaning data quality problems through a rule engine and a machine learning algorithm; collecting historical data of all monitored enterprises in the industry, and establishing a multi-dimensional benchmarking analysis system; processing the cleaned data based on the benchmarking analysis system, and generating improvement suggestions for the target enterprise; and finally, real-time data acquisition and analysis are carried out to realize rapid processing and analysis of real-time data. According to the invention, through intelligent data processing and analysis means, accurate operation decision support and risk early warning are provided for enterprises.

This invention discloses a method, system, equipment, and medium for predicting railway freight volume, relating to the field of railway freight technology. The method includes: acquiring target data, which includes target historical railway freight volume data and target historical railway freight-related data. The target historical railway freight-related data consists of target historical data corresponding to multiple target influencing factors related to railway freight volume, and the target influencing factors are factors that have a direct or indirect impact on railway freight volume; inputting the target data into a pre-constructed target railway freight volume prediction model to obtain the predicted railway freight volume value corresponding to the target data. This invention solves the problem that existing railway freight volume prediction methods do not consider the impact of influencing factors related to railway freight volume on railway freight volume and the relationships between these factors, resulting in low prediction accuracy and limiting and lagging operational decisions for railway freight enterprises.

The invention discloses a private domain traffic competition relationship quantitative generation method, belongs to the technical field of business intelligent analysis, and aims to solve the problem that the competition relationship in a private domain scene is difficult to accurately quantify. Through an intelligent contract deployed in a block chain, user operation behaviors of each private domain scene are monitored in real time, and standardized tuple data and an operation data set are constructed. When new data is accessed, operation type codes are analyzed, key operations are identified, associated behaviors are retrieved by using a time window, and a causal association map of the key operations is constructed in combination with service priorities and time decay factors. And extracting map feature vectors, marking scene codes, dividing clustering categories, and triggering an updating mechanism to obtain a feature causal association map. And constructing a standardized behavior matrix based on the updated graph, quantifying the behavior mode similarity between scenes through a graph editing distance algorithm, generating a keyword vector in combination with semantic classification mapping, forming a private domain traffic competition relationship quantitative report, and providing data support for enterprise operation decision making.

Techniques for improving operational decisions of an autonomous vehicle are discussed herein. In some cases, a system may generate a lane change score based on one or more lateral metrics and one or more longitudinal metrics associated with the lane change trajectory. The system may then determine if the lane change operation is safe based on the lane change score.

The invention discloses an enterprise AI workstation system based on a private large model, and the system comprises a hardware base layer which is used for providing computing power, storing data, and providing a plurality of hardware interfaces to be connected with intelligent robot terminals of different manufacturers; the enterprise private large model layer is used for carrying out exclusive model inspection, multi-modal interaction and safety auditing under the support of the hardware base layer; the AI digital employee and robot cooperation layer is used for generating digital employees under the support of the enterprise private large model layer, performing intelligent cooperative scheduling and adapting to an intelligent robot terminal; and the operation decision support layer is used for carrying out total data fusion, intelligent analysis and generation of decision suggestions. According to the method, the integration capability, the large model adaptability, the cooperation efficiency and the decision-making efficiency are improved.

This application provides an intelligent management system for integrated sports fields. By organically combining user terminals, sensing and execution devices, edge computing servers, and a cloud management platform, it constructs a full-link intelligent management system covering "user interaction - data collection - local real-time processing - cloud-based in-depth analysis." Addressing pain points in traditional sports field management such as cumbersome reservations, delayed status perception, and insufficient data support, this system utilizes edge computing and local AI model deployment to achieve real-time response to local events, avoiding response delays caused by cloud transmission latency. Simultaneously, it leverages the big data analytics capabilities of the cloud platform to provide data support for operational decisions. This combination satisfies both the immediate needs of on-site management and the decision-making requirements of macro-operation, thereby comprehensively improving the management efficiency and service quality of integrated sports fields and realizing intelligent collaboration among "people, equipment, venues, and data" in sports scenarios.

The invention relates to the technical field of bulk commodity operation, in particular to a data sharing and analysis method and system based on multi-dimensional association, and the method comprises the steps: collecting multi-source heterogeneous data through a mixed mode of interface synchronization and manual reporting, and quantifying an unstructured text into a standardized index through a bidirectional LSTM semantic analysis algorithm; a cosine similarity and grey relational degree fusion algorithm and an LSTM dynamic weight optimization model are adopted, association rules are mined in combination with an improved Apriori algorithm, and market pre-judgment is achieved through three sub-models of supply and demand, price and risk; and finally, establishing a closed-loop mechanism, and outputting an inventory adjustment scheme and a futures hedging strategy. The system correspondingly comprises four functional modules, full-process data processing and decision support are achieved, and scientificity and operability of operation decision are improved.

The invention discloses a reservoir operation data suspicious value identification method and system fusing a sliding window 3 sigma statistical criterion and physical mechanism verification. The method comprises the steps that a reservoir operation data distribution rule is deduced; trying and determining the length of a sliding window for screening suspicious values; performing suspicious value primary screening based on a sliding window; carrying out secondary screening on suspicious values based on differential characteristics; multi-source data collaborative verification based on a physical mechanism; finally confirming a suspicious value; the system comprises an acquisition module, a trial calculation module, a primary screening module, a secondary screening module, a judgment module and a confirmation module. According to the method, the quantitative relation among the reservoir inlet flow, the reservoir water level and the reservoir outlet flow is verified through the water balance principle law, so that whether the statistical suspicious data really violates the physical law or not is distinguished, the misjudgment risk caused by a pure statistical method is avoided, and the suspicious data screening accuracy and reliability are further improved; and a solid foundation is laid for subsequent data cleaning and operation decision making.

The present application relates to the technical field of low-altitude aircraft scheduling, and particularly relates to a low-altitude aircraft cluster task scheduling method and system based on big data, which comprises the following steps: generating a predicted delivery time length through a time sequence feature hybrid model based on historical data of a low-altitude aircraft distribution big data platform, wherein the time sequence feature hybrid model is constructed based on LSTM and Transformer network architecture; constructing an operational decision model with the optimization objective of minimizing the average of multiple predicted delivery time lengths and the decision variable of order allocation mode; and solving the operational decision model based on a multi-objective search strategy to determine the order allocation mode of the low-altitude aircraft cluster. The present application realizes the balance between instant response and global efficiency in low-altitude aircraft cluster scheduling, improves the adaptability of dynamic delay scheduling strategy, and improves the delivery efficiency and reduces the overtime rate.

This invention discloses a mechanism- and data-driven root cause diagnosis method and system for industrial production lines. The method includes: acquiring and preprocessing multi-source heterogeneous industrial data from the production line; performing anomaly detection on the preprocessed multi-source heterogeneous industrial data, identifying abnormal time windows, and extracting multimodal anomaly data; constructing a three-level hybrid diagnostic model and generating a preliminary root cause candidate set based on the model's diagnostic output for the multimodal anomaly data; performing source tracing analysis on the preliminary root cause candidate set and generating a structured natural language diagnostic report based on the source tracing analysis results. This invention significantly improves the accuracy and generalization ability of root cause diagnosis across a wide range of scenarios by constructing a three-level hybrid model architecture. Furthermore, through source tracing analysis and natural language extension, it provides a structured report supported by clear physical logic and data evidence, thereby improving operational decision-making efficiency, reducing reliance on scarce domain experts, and significantly reducing unplanned downtime.

This application provides a drilling simulation system and method based on a desert scenario. The system establishes a data-driven relationship between environmental parameters and equipment performance models through a coupling processing module. This allows dynamic environmental data to influence the values ​​of virtual equipment performance parameters through mathematical rules, thereby simulating the continuous impact of the environment on equipment status. Addressing the issue that macroscopic environmental simulation output data cannot be used for real-time operational simulation, the data generated by the environmental simulation module is real-time and can be directly used as calculation input for the coupling processing module and the effect simulation module, achieving compatibility with operational simulation. Furthermore, through equipment performance status, the environmental impact is simultaneously transmitted to the physical interaction level and the operational decision-making level. This transforms environmental factors from isolated external conditions into core variables that drive the simulation process and shape training difficulty. Therefore, it can significantly improve the simulation effect of drilling operations in extreme desert environments.

The invention discloses a dynamic and static combined evaluation method and device for the operating efficiency of a power grid enterprise. The method comprises the following steps: constructing a power grid enterprise operating efficiency evaluation index system; constructing a power grid enterprise operation efficiency static evaluation model based on the power grid enterprise operation efficiency evaluation index system; the static evaluation model is a power grid enterprise total factor production efficiency static evaluation model based on a DEA-BCC model; calculating a comprehensive technical efficiency value of each decision-making unit in a certain period through a static evaluation model, and reflecting the resource utilization efficiency of a power grid enterprise in a given period; based on the power grid enterprise operation efficiency evaluation index system, constructing a power grid enterprise operation efficiency dynamic evaluation model; the dynamic evaluation model is a power grid enterprise total factor production efficiency dynamic evaluation model based on a Malmquist index, and the dynamic evaluation model measures the dynamic change of the power grid enterprise total factor production efficiency; the method can make up the defects of current research, and provides reference for the operation decision of a power grid enterprise.

The invention provides a multi-type energy storage planning method and device considering a continuous low output scene, and belongs to the field of power system planning. The method comprises the steps that a stochastic programming framework considering the new energy continuous low-output Markov process is established, the framework comprises a programming stage and multiple operation decision stages, and the multiple operation decision stages are obtained by dividing the operation year of a power system; based on the Markov process planning framework, a multi-type energy storage planning model considering a continuous low output scene is established, and the planning model comprises a planning stage sub-model and a multi-stage operation sub-model considering a Markov conversion process; and solving the multi-type energy storage planning model to obtain a multi-type energy storage planning scheme. According to the method, the evolution dynamic state of the continuous low output process of the new energy can be described more truly, the robustness and rationality of energy storage planning are improved, and the load loss risk of the system is reduced.

The invention provides a supply chain toughness evaluation method and device based on an agent complex network, and the method comprises the steps: enabling each company node in a conventional supply chain network model to serve as an agent, enabling each agent to be provided with a perception sub-agent, a decision sub-agent and an execution sub-agent, enabling each node to be provided with the capability of autonomously perceiving, making a decision, and then carrying out the execution, company entities in a supply chain network are abstracted into intelligent agent models with perception, decision and execution capabilities, business exchange of the company entities is abstracted into connecting edges in a complex network, and through an interaction mechanism between intelligent agents, each company entity node in the supply chain network model has a dynamic characteristic; and the change of the operation decision of the company entity can be made according to the dynamic characteristics, so that a balance can be quickly achieved after disturbance, and the toughness evaluation can be quickly and accurately performed on the state of the supply chain network.

1. The name of the design product: graphical user interface of electronic device management decision. 2. The use of the design product: the design product is used for electronic device. 3. The design points of the design product: the graphical user interface content in the screen, the display screen panel is the existing design; the "XX" area in the interface is the editable text content, and the single color block in the interface is the variable content picture. 4. The picture or photo that best shows the design points: front view. 5. The electronic device is a common design, and the rear view, left view, right view, top view and bottom view are omitted. 6. The use of the graphical user interface: the design product is used for information display. 7. The human-computer interaction mode of the graphical user interface: in the front view, clicking any one row of specific index name can enter the interface change state diagram.

This invention provides a method and equipment for park operation decision analysis, relating to the field of smart park operation management technology. The invention constructs a candidate solution scenario library through structured definitions of covariates and intervention variables, covering various operational intervention scenarios to be evaluated; relying on generative causal Bayesian networks to integrate causal inference and generative modeling capabilities, it achieves reliable causal prediction of counterfactual solutions without historical observation data; it outputs predicted effect values ​​and uncertainty indicators to quantify prediction credibility and decision risk; in high-uncertainty hypothetical scenarios, through proactive surveys and incremental updates, it achieves effect prediction of counterfactual and hypothetical solutions to assist decision analysis, reduce investment risk and decision costs, and improve the robustness and efficiency of operational decisions. This invention addresses the problem of unreliable prediction and difficulty in proactive decision-making for counterfactual and hypothetical solutions without historical data in park operations, achieving a closed-loop, end-to-end park operation decision analysis.

The invention discloses a financial institution customer value hierarchical operation system and method based on big data, particularly relates to the field of financial customer operation management, and is used for solving the problem that structural association and multi-cycle behavior change among customers are difficult to reflect due to static hierarchical results in the existing customer value hierarchical management process. According to the method, the client association network with the time sequence weight among the clients is constructed, the potential communities of the clients are identified, the community structure vulnerability is evaluated, and the network structure risk is mapped to the client level to obtain the client network value gain; meanwhile, trend decomposition is carried out on the multi-cycle transaction behaviors of the clients, and behavior stability indexes are constructed. On this basis, a two-dimensional customer value layering matrix is constructed, dynamic layering of customer values is realized, and a targeted operation strategy is generated according to historical operation benefits of adjacent layered customer groups, so that the accuracy of financial institution customer value identification and the scientificity of operation decision are improved.

The invention discloses an operation decision-making method, device and system for refrigeration equipment and the refrigeration equipment, and belongs to the technical field of intelligent household appliances. The method comprises the steps of obtaining to-be-processed data of the refrigeration equipment; encoding the data to be processed to generate encoded data; generating clue information based on the coded data and the to-be-processed data; based on the clue information, decoding the coded data to obtain decoded data; and based on the decoded data, making a decision on the operation of the refrigeration equipment, and generating a decision result of the refrigeration equipment. According to the method, the operation decision precision of the refrigeration equipment is improved.

The invention relates to the technical field of big data analysis and supply chain capacity management, in particular to an enterprise intelligent operation decision support system based on big data, which is characterized in that a supply chain data acquisition unit acquires a one-week capacity decrease value and a fluctuation period identifier caused by raw material delay in real time; a fluctuation characteristic analysis unit generates a fluctuation amplitude grade identifier through continuous interval threshold matching, analyzes a period identifier to obtain a continuous period characteristic, and packages the continuous period characteristic into structured data; the dynamic fusion processing unit fuses real-time productivity, same-period historical fluctuation and global historical productivity mean data according to two dimensions of grades and periods, and distributes differentiation weights; and the reference capacity generation unit is based on real-time data, superposes historical capacity change trend coefficients, and outputs a dynamic reference capacity value retaining instantaneous fluctuation characteristics through global mean smooth calibration, so that reliable support is provided for an enterprise to formulate a distribution plan matched with the actual capacity of the current week, and the operation decision scientificity is improved.

An Enterprise-Oriented Cloud Control System is disclosed for providing time-sensitive, vehicle-specific guidance and control instructions to autonomous vehicles (AVs) in long-tail corner cases. The system operates through an enterprise-oriented cloud intelligent unit (E-CIU) that integrates service, management, communication, sensing, prediction, planning, control, and support subsystems. Automated driving scenarios are classified by a traffic fence based on demand and supply characteristics. The E-CIU receives multi-source event and non-event data from vehicles, roads, clouds, and centers, offers various “as-a-service” functions, and employs event models to convert macroscopic event data into microscopic operational data. These models generate cooperative sensing, prediction, planning, and control solutions and produce customized, real-time instructions for longitudinal / lateral motion and operational decisions. An onboard unit exchanges information with the E-CIU and executes cloud-generated commands, providing effective management of AVs in rare and complex scenarios.

The invention discloses a merging unit error self-monitoring method, system and device, a storage medium and a program product, and relates to the technical field of data processing, and the merging unit error self-monitoring method comprises the steps: obtaining high-frequency sampling data; performing error related feature extraction based on the high-frequency sampling data to obtain error evaluation features; and performing error classification and trend prediction based on the error evaluation features to obtain an error type and an error trend. According to the method, error related feature extraction is carried out by acquiring high-frequency sampling data, so that inherent calculation errors caused by insufficient sampling are reduced, and meanwhile, the problem of low recognition precision caused by simple threshold comparison is avoided; through prediction of the error trend, advanced risk prediction of the error condition is realized, and richer and more predictive information support is provided for operation and maintenance decisions. By constructing a progressive perception decision-making system, high-precision and real-time self-monitoring of various errors is realized in a merging unit, and the use cost of the system is reduced.

This invention relates to a substation comprehensive situational awareness and fault simulation system and method, belonging to the field of substation safe operation technology. The technical solution involves: first, constructing a refined digital twin of the substation connected to real-time / historical data streams; second, importing the twin model into the GE GridOS platform to form a data-simulation hybrid closed loop synchronized with the physical station; and finally, based on this hybrid closed-loop system, achieving real-time, dynamic, and cross-scale panoramic awareness of the substation's operating status, as well as immersive simulation, intelligent diagnosis, and recovery plan verification for pre-set or sudden faults. This invention moves from post-event presentation to pre-event prediction, enhancing the initiative and depth of situational awareness, constructing a simulation environment with primary-secondary-grid panoramic interaction, achieving realistic reproduction and in-depth diagnosis of fault processes, optimizing the scientific nature of operational decisions and emergency response, and establishing a dynamic calibration mechanism for models and data to ensure the long-term accuracy of the digital twin.