116 results about "Management strategy" patented technology

The invention discloses an urban ecosystem service tradeoff and cooperative relationship analysis method and device. The method comprises the following steps: dividing a target region according to different spatial scales to obtain sample sets corresponding to the spatial scales respectively; acquiring basic geographic information data of the target area in different time periods; according to the basic geographic information data, calculating service evaluation results of each ecological system service in the target area and each sample in different time periods; according to a service evaluation result, calculating a pairwise trade-off cooperative relationship of each ecological system service under a plurality of spatial scales and time sequences; the samples are clustered according to the service evaluation results of the samples to obtain a plurality of service clusters, and the service clusters are used for representing overall tradeoff cooperation features of multiple ecological system services; and determining a management strategy of each ecological system service according to the pairwise trade-off cooperation relationship and the overall trade-off cooperation characteristics. According to the invention, tradeoff and collaboration characteristics of each ecological system service can be identified under multiple spatial scales and multiple time sequences.

Embodiments of the application disclose a dynamic route management method, device and equipment and a storage medium, wherein the method comprises: in response to receiving an access request of a user, obtaining an interface request log recorded by a gateway; wherein the interface request log is used to represent request operation information of the user on all resources in the application system; the request operation information is a standard HTTP request method; determining a key resource in the application system within a preset time period; the processing priority of the key resource is higher than that of other resources in the application system; dynamically managing route information in the gateway according to a preset management strategy; the gateway at least includes dynamic route information generated for providing access to the key resource. Thus, the access speed to the key resource is accelerated, and the problem of service unavailability caused by the collapse of downstream microservices is reduced.

The invention discloses a multi-period multi-weight electric energy quality real-time optimization decision-making method, device and medium, through dynamically generating a multi-period weight matrix and carrying out optimization calculation, voltage deviation, harmonic pollution and three-phase imbalance of a transformer area can be effectively treated, and based on a double-label classification system of power utilization characteristics and topological positions, the real-time optimization decision-making method of the multi-period multi-weight electric energy quality can be realized. According to the method, the first classification label of the power utilization characteristics of the transformer area and the second classification label of the topological position are obtained, multi-period and multi-target refined modeling of the running state of the transformer area is achieved, the strategy of combining multi-period optimization and rolling optimization is adopted, and the bus voltage is used as a decision variable to conduct multi-period optimization. And the optimal bus voltage curve and the reactive plan in the future time period are output, so that the network loss is reduced, the voltage qualification rate is improved, a rolling optimization algorithm is adopted, the gear shifting action can be dynamically adjusted according to real-time data, the change of the running state of the power distribution network can be responded in time, and the timeliness and the effectiveness of a treatment strategy are ensured.

The invention discloses a real estate business financial business risk control management method based on a big data model, and relates to the technical field of financial science and technology and risk management, and the method comprises the steps: collecting multi-dimensional original data, carrying out the fusion cleaning, and generating a structured risk control basic data set; extracting three feature sequences of application subject static portraits, real estate dynamic values and associated enterprise risks from the application subject static portraits and the real estate dynamic values; inputting the multi-source risk control feature fusion model into a pre-trained multi-source risk control feature fusion model for interactive modeling, and generating a comprehensive risk feature map; constructing a hierarchical risk decision network by using the map, and outputting a multi-dimensional vector containing credit and collateral risks and associated party conduction risk scores; and based on the vector, generating an adaptive credit line, a guarantee rate floating and a post-loan monitoring scheme through a dynamic strategy matching engine, and converting the adaptive credit line, the guarantee rate floating and the post-loan monitoring scheme into an executable instruction set to be issued and executed. According to the invention, the deep correlation analysis of the multi-source risk information and the automatic and accurate generation of the risk management strategy are realized.

The present application relates to the technical field of equipment life cycle management, and specifically includes an aviation equipment full life cycle management method and system based on digital threads, the method comprising: acquiring process parameters of each stage, determining first digital thread master data and a first layer of associated test sequences, second digital thread master data and a second layer of associated test sequences, mapping the deviation of the physical equipment state and the virtual simulation state, and identifying the performance difference point under the cross-stage data correlation link; setting a management strategy for collaborative optimization. The technical problem of being unable to identify the cross-stage performance difference point equipment performance fluctuation root cause positioning in real time, causing the reliability of aviation equipment to be limited is solved, the first and second digital thread master data and the associated test sequences are constructed, the deviation of the physical equipment and the virtual simulation state is accurately mapped, the management strategy is set in combination with the double thread data and the performance difference point, and full life cycle collaborative optimization is performed, and the technical effect of effectively improving the equipment reliability is achieved.

The invention discloses a bidding margin intelligent management method and system based on a dynamic risk portrait, and the method comprises the steps: building a dynamic risk portrait through obtaining project attribute data and supplier multi-source heterogeneous behavior data, and inputting a pre-trained bidding risk prediction model to calculate a dynamic risk index; and synchronously executing cash deposit payment state rule verification and dynamic risk index evaluation at the bid opening verification node, generating a comprehensive verification report, calculating a real-time commercial credit score through a credit evaluation engine, generating a differentiated cash deposit management strategy according to the score, and outputting a final state scheme. According to the invention, the conversion of bidding margin management from formal review to risk intelligent early warning is realized, the bidding risk is effectively identified, the risk prevention and control accuracy is improved, and the safety and intelligent level of government purchase are significantly improved based on a differentiation strategy of real-time credit scoring.

The application discloses a financial institution customer value stratified management system and method based on big data, and particularly relates to the field of financial customer management, and is used for solving the problems of static stratification results, difficulty in reflecting structural correlation between customers and multi-period behavior changes in the existing customer value grading management process. The application maps network structure risks to the customer level to obtain customer network value gains by constructing a customer correlation network with time sequence weights between customers, identifying customer potential communities and evaluating community structure vulnerability. Meanwhile, the application decomposes the trend of multi-period transaction behavior of customers and constructs an behavior stability index. On this basis, a two-dimensional customer value stratification matrix is constructed to realize dynamic stratification of customer value, and targeted management strategies are generated according to the historical management benefits of adjacent stratified customer groups, so as to improve the accuracy of customer value identification and the scientificity of management decisions of the financial institution.

The invention discloses a detention-free lease service management method and system based on credit evaluation, and relates to the technical field of service management. The method comprises the following steps: traversing a user set in a detention-free lease service, and constructing a detention-free lease multi-source behavior data set; constructing a detention-free leasing business relation graph; constructing a credit homogamete graph and a risk heterogamete graph; performing feature analysis on the credit homogamete graph and the risk heterogamete graph to obtain user stable credit features and user risk anomaly features; performing feature fusion based on the user stable credit feature and the user risk anomaly feature to obtain a user comprehensive credit assessment result; and executing detention-free lease service management decision analysis on the target user to obtain a detention-free lease management strategy. The technical problem of insufficient service management decision reliability caused by inaccurate user credit evaluation in the detention-free leasing service in the prior art is solved, and the technical effects of improving the credit evaluation precision and enhancing the service management decision reliability through multi-source behavior data association analysis are achieved.

This invention discloses a train control and management system based on multi-bus fusion in rail transit, relating to the field of onboard network control technology. Specifically, it includes: a multi-path acquisition module, a confidence assessment module, a cross-validation module, and a simulation prediction module; deploying three independent sensing paths, which are uploaded in parallel to the fusion processor via different onboard multi-buses at 100ms intervals; calculating confidence scores for the estimated data output by each sensing path based on Bayesian methods to determine the path's reliability and mapping it to discrete quality levels; performing three-way voting through weighted K-fold cross-validation to calculate a consistency index and compare threshold judgment instructions; constructing a digital twin model, and in case of conflict, performing Monte Carlo simulation for the next cycle, outputting the probability of the result to switch the bus fusion management strategy, effectively avoiding system blind spots caused by single sensor or bus failures, and improving the energy utilization efficiency of trains under high-speed sudden changes, severe load fluctuations, or strong interference scenarios.

The embodiment of the application belongs to the technical field of financial science and technology, is applied to a financial data warehouse management scene, and relates to a data warehouse management method, device, equipment and storage medium thereof, comprising the following steps: obtaining characteristic indexes of all data source tables in a target data warehouse by analyzing metadata of the target data warehouse; identifying a target data source table according to the characteristic indexes and a preset evaluation strategy, and managing the target data source table according to a preset management strategy; and managing the target data warehouse in a timing task mode. According to the characteristic indexes and the preset evaluation strategy, the target data source table is identified, and the target data source table is managed according to the preset management strategy; a timing task is adopted, the target data warehouse can be managed regularly, the automatic and regular processing of the target data warehouse is ensured, the situation that a large amount of CPU resources are wasted due to chimney processing is avoided, and the standardization and scientific arrangement of financial business data in the data warehouse are facilitated.

The present application relates to the technical field of insurance claim settlement, and discloses a property insurance claim settlement risk management method and system based on multi-source spatio-temporal precipitation data, which comprises the following steps: obtaining a precipitation data set of a target region in a target period; determining a precipitation time series stability index based on the continuity change characteristics of the precipitation data set in the time series dimension, and constructing a regional difference dispersion degree based on the distribution discrete characteristics of the precipitation data set in the spatial dimension; obtaining a risk spatial correlation degree according to the historical claim case region and the current precipitation grid, in combination with the intensity evolution mode of the precipitation data set; generating a claim settlement threshold dynamic correction coefficient by fusing the precipitation time series stability index, the regional difference dispersion degree and the risk spatial correlation degree; and dynamically adjusting a preset claim settlement trigger threshold based on the claim settlement threshold dynamic correction coefficient to form a claim settlement risk control management strategy, which significantly optimizes the survey resource allocation efficiency and the disaster prevention and loss reduction action efficiency of the insurance company under the premise of improving the claim settlement risk identification accuracy.

This invention discloses a comprehensive management system for cadres' daily work, comprising: a basic configuration module for configuring user roles and their respective departments; a task management module, including: a task decomposition unit, a task execution unit, a task feedback unit, a task query unit, and a task summary unit; the task decomposition unit is used to horizontally and / or vertically decompose tasks to be executed to the corresponding cadres; the task execution unit is used to record task execution status; the task feedback unit is used to record feedback on task execution status; the task query unit is used to query detailed information and completion status of tasks; the task summary unit is used to summarize all task information; and an evaluation management module, which uses evaluation management strategies to evaluate cadres' performance in completing tasks. This system constructs a closed-loop management system for the entire task lifecycle, standardizes the task flow process, and significantly improves the efficiency of government work.

The application relates to the technical field of industrial data processing, and specifically provides an industrial time series data management method and system, a terminal and a medium, which comprise the following steps: using a large language model to analyze user management requirements and obtaining error and delay weight coefficients; extracting noise and dynamic change rate features of industrial time series data in real time; generating dynamic error and delay constraint targets through harmonic scaling based on the weight coefficients and the dynamic features; based on a noise propagation and variable correlation physical mechanism model, taking the dynamic constraint as a target, using an improved A * algorithm to plan a data management path constrained by physical laws; calling a management tool according to the path to execute management and monitor the state; and dynamically adjusting the strategy and parameters according to the deviation of the execution result from the target. The application improves the adaptive capacity for the time-varying characteristics of industrial time series data, realizes accurate mapping of user intentions and management strategies, guarantees that the data after management conforms to the industrial mechanism, and improves the precision and reliability of data management.

The invention provides a dynamic hierarchical management method and device based on steam turbine adaptive indexes, and the method comprises the steps: collecting monitoring data reflecting the running state of a steam turbine in a gas-steam combined cycle combined heat and power generation unit in real time, the monitoring data at least comprising oil quality parameters and oil temperature parameters of lubricating oil; determining a current self-adaptive health index of the steam turbine based on the monitoring data; determining a current state level corresponding to the current self-adaptive health index based on the configured mapping relationship between different steam turbine self-adaptive health indexes and different state levels; different state levels correspond to different management strategies; and based on the current state grade, a control instruction of a corresponding management strategy is generated and executed so as to dynamically adjust the operation mode of the SSS clutch. According to the method, intelligent optimization and fault advanced early warning of the running mode of the SSS clutch are achieved.

The present invention relates to an apparatus and a method for establishing an asset management strategy, the apparatus comprising: a memory including at least one instruction; and at least one processor for executing the at least one instruction stored in the memory, wherein the processor selects a target facility for which an asset management strategy is to be established, on the basis of an asset health index (AHI) for each item according to at least one power facility, and identifies a plurality of action levels sorted according to individual probability of failure (PoF) grades for each item of the selected target facility. Other embodiments are also applicable.

This invention relates to the fields of artificial intelligence and carbon emission management technology, specifically to a multi-agent reinforcement learning carbon emission management decision-making method and system based on capability boundary recognition. The method includes: acquiring carbon emission environmental state data; inputting the carbon emission environmental state data into a trained variational autoencoder to calculate the reconstruction error between the carbon emission environmental state data and the reconstructed environmental state; determining whether the reconstruction error exceeds a preset threshold; when the reconstruction error does not exceed the preset threshold, inputting the carbon emission environmental state data into an optimal carbon emission management strategy pool, and dynamically selecting the optimal reinforcement learning agent from the optimal carbon emission management strategy pool to execute carbon emission management decisions; when the reconstruction error exceeds the preset threshold, triggering a conservative carbon management strategy. This invention achieves out-of-distribution detection of carbon emission environmental state and dynamic strategy routing based on risk perception, significantly improving the stability, robustness, and security of carbon emission management decisions.

The invention discloses a multi-source data management method and device based on a federation mechanism, and belongs to the technical field of computer data processing, and the method comprises the steps: collecting local data of each data source node, extracting a semantic summary, and forming a distributed data source registry through consensus verification; receiving a cross-source collaborative analysis request, and analyzing the cross-source collaborative analysis request into a non-cyclic directed task graph; query is executed locally at each node according to the task graph, cooperative calculation is completed by encrypting and sharing an intermediate result, and a final analysis result and a task execution log are generated; analyzing the task execution log to generate a dynamic reputation point for each node; and updating the node weight according to the reputation integral so as to adaptively optimize the node selection of the subsequent task. According to the method, the multi-source heterogeneous data is subjected to semantic abstraction, the collaborative analysis request is analyzed into the execution task graph, the dynamic reputation integral is generated in combination with the task execution log, and a closed-loop management strategy of node selection is adaptively optimized according to the dynamic reputation integral, so that safe, efficient and intelligent collaborative analysis and management of the cross-domain data can be realized.

The invention relates to the technical field of smart city data management, and discloses a smart city-oriented data index management method and system. According to the method, management tasks with hierarchical relations are defined in a platform, and data index instances meeting task targets are extracted from heterogeneous data sources to form an initial set. And associating a predefined business constraint rule for each instance, and screening out an abnormal index instance set violating the rule. This set of anomalies is input into a dynamic threshold-based evaluator, and a dynamic confidence score is calculated for each anomalous instance. And grading the anomaly set according to the score, and generating a hierarchical result containing at least two anomaly levels. And according to a layering result, assigning a management strategy template preset with a processing instruction sequence for each exception level. And finally, the instruction sequence is issued to a data execution terminal, and correction of the abnormal index instance is completed. According to the invention, automatic and precise identification and grading processing of the data index abnormity of the smart city are realized.

The invention discloses a communication method and device, which are favorable for reducing resource overhead caused by model training and management and improving model management efficiency. The method comprises the following steps: a second communication device performs statistical analysis on model information of at least one first model associated with a second model, generates first information containing the model information of the at least one first model and / or statistical information corresponding to the model information, and sends the first information to a first communication device, and the first communication device determines a management strategy of the maintained second model by taking the model information of the at least one first model or statistical information corresponding to the model information as reference experience according to the received first information.

A large language model driven forest management data processing method belongs to the cross technical field of forest resource management and artificial intelligence data processing. Multi-source data collection and preprocessing, forest management knowledge graph construction, large language model fine tuning and retrieval enhancement generation, data semantic fusion and understanding, management strategy reasoning: based on the fusion data, management intention and industry knowledge, the large language model automatically reasons to form the management strategy conforming to the forest growth law and the multi-objective balance, management scheme text generation: on the basis of completing the strategy and space matching, the forest management scheme text conforming to the forestry industry specification and management requirement is compiled, human-computer interaction and continuous optimization: through the human-computer interaction interface, the feedback information of the forest management scheme of the forestry workers is collected, and the generated management scheme is systematically evaluated from the forest resource sustainability, ecological function improvement, management target achievement degree, risk controllability and policy compliance.

This invention relates to the field of hovering management technology and discloses a UAV simulation adaptive management system based on a multimodal neural network, comprising: a data fusion unit, a hovering analysis unit, a pose planning unit, and a pose management unit. This solution generates an accurate sea surface deformation map by fusing flight, power, and wave modal data, and understands in real time the dynamic changes in sea surface morphology caused by rotor downdraft and rescue vessel wake. The generated dynamic hovering tolerance domain can effectively offset wave-induced displacement, generate multi-step optimal pose trajectories and adaptive management commands, reduce simulation distortion, improve the stability and targeting of UAV hovering, ensure the effective implementation of management strategies, and enhance the reliability of maritime rescue.

The application discloses a virtual enterprise collaborative decision-making method and system for team heterogeneity identification and management strategy recommendation, and the method comprises the following steps: collecting team member basic information, dynamic cooperation behavior and periodic psychological measurement data; constructing task and relationship heterogeneity indexes based on the collected data; establishing and training a machine learning model; deploying the model, predicting future high-intensity conflict probability based on the heterogeneity indexes; and generating conflict management strategy suggestions in combination with the heterogeneity indexes and the predicted probability. The application integrates multiple data sources, constructs quantitative task and relationship heterogeneity indexes, and accurately portraits the team state. On this basis, the trained machine learning model is used to dynamically predict the future high-intensity conflict probability. This changes the passive situation of the existing team management system in responding after the event, improves the collaborative decision-making efficiency and overall management efficiency of the virtual team, and realizes the scientization and foresight of team management.

This invention discloses an adaptive running exercise management method and system based on the Internet of Things (IoT), relating to the field of sports service management strategy technology. An IoT-based adaptive running exercise management system includes: a request acquisition module, a strategy generation module, a structure adjustment module, a strategy pruning module, a parameter adjustment module, and a strategy update module. This invention constructs a segmented processing and sub-strategy pruning mechanism for candidate scheduling strategies. Under multi-dimensional constraints such as comprehensive sports mode switching costs, trend information, persistence characteristics, and historical path information, it structurally decomposes, filters, and reorganizes scheduling strategies, achieving controllable optimization and dynamic reconstruction of complex scheduling strategies.

The invention relates to the technical field of warehouse management, in particular to an intelligent warehouse management method and system. Comprising the steps of setting various spare part categories according to storage equipment requirements of a warehouse; generating a first-level management strategy according to all spare part categories, and setting a plurality of monitoring sub-areas according to the first-level management strategy; and obtaining a feedback data packet of each monitoring sub-region, and judging whether an early warning instruction of each monitoring sub-region is generated or not according to the feedback data packet. And dynamically adjusting the storage layout in the warehouse based on each spare part set storage evaluation value, thereby performing centralized storage on the spare parts with scheduling association, improving the in-out warehouse scheduling efficiency of each type of spare parts in the warehouse, and improving the management efficiency of the new energy station warehouse. The corresponding monitoring sub-model is constructed according to the storage characteristics of each spare part set, storage monitoring of different types of spare parts is realized, early warning of potential risks in a warehouse is performed in time, and the storage safety of the new energy power plant spare parts is ensured.

The invention relates to a construction management system and method for a complex construction scene, and relates to the technical field of construction management, and the system comprises a monitoring module which obtains the construction progress and construction monitoring information of the construction scene in real time, and a digital twinborn model recognition module which builds a three-dimensional digital twinborn model and determines the risk recognition result of equipment and personnel; the decision module determines a plurality of initial management strategies based on the risk identification result and a pre-constructed construction safety knowledge graph; and based on the construction progress, the construction plan, the environment information and a preset multi-objective optimization algorithm, optimizing the plurality of initial management strategies, determining an optimal management strategy, and sending the optimal management strategy to a corresponding management terminal in the optimal management strategy. The method has the effects of effectively identifying the construction risk, optimizing the management strategy, guaranteeing the construction safety and improving the construction efficiency.

Embodiments of the present application provide a kind of server management method based on load prediction, program product and equipment, method includes: based on the load data periodically uploaded from server, predict the predicted load in future time;Based on predicted load and mapping relationship, determine the first operating parameter of slave server;Mapping relationship indicates corresponding operating parameter in different load interval;If predicted load is in the boundary range of load interval, based on load deviation adjustment first operating parameter, obtain second operating parameter;Load deviation characterizes the difference between predicted load and the boundary value of load interval;Control slave server runs under target operating parameter;Target operating parameter is first operating parameter or second operating parameter.Main server adjusts the operating parameter of slave server in advance according to predicted load, so that operation management is more prospective and initiative. By load deviation adjustment first operating parameter, it can effectively prevent the jump of operating parameter, and the operation management strategy is optimized.

The invention relates to the technical field of safety education training management, and discloses a safety education training management method and system. The method comprises the steps of generating a core data seed containing initial parameter definition and parameter evolution rules, establishing a corresponding question bank, and performing capability detection and comprehensive evaluation. A plurality of parameter evolution interaction paths are constructed with the seeds as starting points, and observation sentinel points and behavior signal monitors are arranged on the paths and used for capturing state switching, behavior triggering and other original signals in the training process. When a signal is responded, dynamic mapping of the signal to a specific whistle point on a specific path is established, and the signal is analyzed into an adjusting or resetting instruction for a specific parameter according to the dynamic mapping. According to the method, automatic evolution of management parameters according to preset rules is realized, discrete events in training can be accurately converted into control instructions for driving self-optimization of the system, and closed-loop and self-adaptive regulation and control of a safety management strategy are completed.

The invention discloses an EAM-based asset full-life-cycle management method and system, and the method comprises the steps: obtaining key performance indexes of assets in purchasing, operation, maintenance and scrapping stages, carrying out the dynamic smoothing of the data of each stage based on an EAM algorithm, constructing an asset state trend curve, extracting feature change points of each stage according to the asset state trend curve, and carrying out the calculation of the feature change points. And calculating a weight coefficient of each stage based on the feature change points, generating a comprehensive score of the full life cycle of the assets based on the weight coefficients, dividing an optimization priority of the assets according to the score, comparing the optimization priority with a preset threshold, and judging whether an operation strategy of the assets needs to be adjusted or a maintenance stage needs to be entered. Therefore, the management model is trained through the historical data, the asset management strategy is verified according to the comprehensive score and the optimization priority, the model parameters are adjusted when the strategy is abnormal, and the overall efficiency and precision of asset management are improved.

The invention relates to the technical field of knowledge management and artificial intelligence, and discloses a knowledge management system based on artificial intelligence big data. The system comprises knowledge flow capture, symbol reasoning, neural embedding, strategy synthesis and strategy deployment components. The system generates structured knowledge through multi-modal data acquisition and cleaning; performing symbolic reasoning by utilizing predicate logic and a rule base, and constructing a symbolic knowledge graph; a deep learning model is adopted to generate low-dimensional vector representation; fusing the symbolized knowledge graph and the vector representation, and generating a knowledge management strategy through a strategy gradient algorithm; and asynchronously executing the strategy in the cloud environment. According to the scheme, through combination of symbol reasoning and a neural network technology, interpretability and logic preciseness of a knowledge processing process are ensured, and meanwhile, the dynamic adaptive capacity and decision accuracy of system strategy generation are improved.

The application provides a long-distance water conveying pipeline leakage rapid positioning method, and relates to the technical field of pipeline leakage detection, and the specific steps comprise: the method first divides pipeline regions and sections according to geographical environment data, forms a continuous section sequence according to the water flow direction, collects historical monitoring data and leakage types of each section, generates fluid transmission abnormality coefficients, structure thermal deformation coefficients, and flow difference values and water pressure ratio values of the current section and the previous section, constructs a leakage type identification model for different regions, trains the model with historical time period data as input and corresponding leakage types as labels, inputs the data of the current time period, and rapidly and accurately predicts the current leakage type and position of the water conveying pipeline. The application adopts a hierarchical management strategy of first zoning and then sectioning, and constructs a leakage type identification model for different regions, so that efficient positioning and accurate judgment of long-distance water conveying pipeline leakage are realized.