209 results about "Management strategy" patented technology

The invention provides a server management method, device and equipment and a storage medium, and belongs to the technical field of computer system architecture.The server management method comprises the steps that first equipment information of PCIe link equipment and second equipment information of SMBus link equipment are obtained; determining a device supporting a management component transport protocol (MCTP) based on the first device information and the second device information; constructing a heterogeneous equipment topological graph in the server based on the equipment supporting the MCTP protocol; determining a global unique logic identifier (EID) of each device in the heterogeneous device topological graph based on the first device information and the second device information, and constructing a global unique logic identifier mapping table based on the EIDs; and managing the internal equipment of the server according to the global unique logic identifier mapping table. According to the method, the unified management strategy of the heterogeneous bus equipment is integrated in the firmware system of the BMC, additional conversion equipment is not needed, and the design and maintenance cost of hardware is reduced.

The invention discloses a determination method, an application method and a related system of a grain yield prediction model, and relates to the field of grain yield prediction, and the determination method comprises the steps: obtaining time-space dynamic data; preprocessing and normalizing climate data, management data and soil data in the spatial-temporal dynamic data to obtain a spatial-temporal data set; respectively inputting the spatio-temporal data set into a plurality of machine learning models to obtain outputs of the plurality of machine learning models; respectively calculating a root-mean-square error and a decision coefficient of each machine learning model based on grain annual output data corresponding to the spatio-temporal data set and the output of each machine learning model; abandoning the machine learning model with the determination coefficient smaller than a preset value to obtain a screened machine learning model; and based on the screened machine learning model and the corresponding root-mean-square error, constructing a weighted average grain yield prediction model. According to the invention, a scientific basis can be provided for grain production under different climate scenes and management strategies.

The invention discloses an RISC-V vector register pressure-oriented dynamic management method and system, and relates to the technical field of computer processor architecture and compiler optimizing.The method comprises the steps that in the program compiling or executing process, the use state of an RISC-V vector register is monitored in real time, and when it is monitored that the vector register pressure exceeds the limit, the RISC-V vector register pressure is monitored; if yes, selecting a target register and triggering a management strategy; identifying a source instruction of a vector value in the target register, executing heavy materialization validity verification, and dividing the vector value into a heavy materialization type and a non-heavy materialization type according to a verification result; according to a division result, dynamically estimating the heavy materialization cost and the register overflow cost; comparing the two types of costs, and if the heavy material cost is higher, deciding to adopt a register overflow strategy and executing the strategy; and if the overflow cost of the register is higher, a decision is made to adopt a heavy matter strategy and is executed. The computing performance of the processor can be improved, and the problem of performance loss caused by high register pressure is solved.

The invention provides a digital intelligent base service achievement management method and system, and relates to the technical field of digital intelligent base service management.The method comprises the steps that firstly, a basic feature set containing attributes, sources and process features of service achievements in a digital intelligent base is collected, then associated features of the basic feature set are mined, and a service achievement associated network is formed; then, a multi-level management index containing a transverse attribute dimension and a longitudinal flow node dimension is constructed based on the service achievement association network, and a management strategy covering storage, updating and sharing features is generated in combination with index features and service achievement real-time state features; and finally, performing dynamic management and control on the business achievements in the digital intelligent base according to the management strategy, thereby deeply mining the relationship between the business achievements, realizing multi-dimensional fine management, dynamically adapting to business changes, and improving the management efficiency.

The invention discloses a forest management data processing method driven by a large language model, and belongs to the technical field of forest resource management and artificial intelligence data processing crossing. The method comprises the steps of multi-source data acquisition and preprocessing, forest management knowledge graph construction, large language model fine adjustment and retrieval enhancement generation, data semantic fusion and understanding and management strategy reasoning, wherein the large language model performs automatic reasoning based on fused data, management intention and industry knowledge to form a management strategy conforming to a forest growth law and multi-target balance; operation plan text generation: on the basis of completing strategy and space matching, compiling a forest operation plan text meeting forestry industry specifications and management requirements, and man-machine interaction and continuous optimization: collecting feedback information of forestry workers on forest operation plans through a man-machine interaction interface, and systematic evaluation is carried out on the generated operation scheme from forest resource sustainability, ecological function improvement, operation goal achievement degree, risk controllability and policy compliance.

The invention discloses a business risk management method and device, a computer program product and electronic equipment. The method relates to the field of artificial intelligence and big data, and comprises the following steps: obtaining business data, and encoding the business data to obtain a business vector; the business vector is input into a target model to obtain a risk prediction level, the target model comprises an input layer, a hidden layer, an attention mechanism layer and an output layer, the input layer is used for extracting a state vector from the business vector, the hidden layer is used for extracting hidden features from the state vector, the attention mechanism layer is used for calculating an attention weight, and the output layer is used for outputting the attention weight; the target feature is determined through the hidden feature and the attention weight, the attention weight is determined by the similarity between the business risk index and the hidden feature and the position code of the hidden feature, and the output layer outputs a risk prediction level based on the target feature; and determining a target risk management strategy through the risk prediction level. Through the method and the device, the problem of low business risk management efficiency in related technologies is solved.

The invention provides an urban internal lake water pollution treatment method and system. Real-time water quality data are collected from a lake; processing the collected data; using the prediction model to predict a water quality trend and identify a pollution risk; according to the analysis result of the machine learning algorithm and the water quality model, the decision support module generates a targeted treatment strategy; according to the treatment strategy provided by the decision support module, specific treatment measures are implemented; and the intelligent management and control module is used for monitoring and managing the whole treatment process in real time. All the modules are linked to construct a whole-process and intelligent treatment system, the treatment efficiency of urban internal lakes is effectively improved, and continuous improvement of water quality and long-acting restoration of an ecological system are achieved.

The invention relates to the technical field of data life cycle management, and discloses a dynamic self-adaptive data life cycle management system and method.The dynamic self-adaptive data life cycle management method comprises the steps that a data object and a business lineage log thereof are logically bound, and a lineage entropy value is calculated in a non-semantic counting mode based on the log; according to the method, a data management path which does not depend on an external complex rule engine to carry out event analysis is established, so that a management strategy can be generated by business process records of data in an endogenous and deterministic manner; and meanwhile, for the data which is subjected to high-level control due to accidental high-risk events, after the risk of the data is actually dissipated, automatic falling of the control level is realized, and the coupling degree and the maintenance complexity of the whole management system are reduced.

The invention provides a mobile game resource dynamic loading optimization method, terminal equipment and a mobile game resource dynamic loading optimization system. According to the method, player behavior characteristics and the space-time correlation degree between player behaviors are analyzed in real time through a double-engine analysis network, and then the player behaviors are predicted from different time scales through a lightweight micro neural network so as to obtain game resources needing to be loaded under the different time scales. And finally, different management strategies are adopted to carry out elastic loading and time-sharing management on the game resources in combination with game resource types, so that under the condition of occupying a small amount of memory resources, the loading speed of the mobile phone game resources is greatly improved, and the smoothness of game operation is ensured. In conclusion, the technical problem of game lagging caused by too long loading time can be solved without loading game resources in a large range and occupying memory resources.

The invention discloses a teaching information management method based on big data, and particularly relates to the field of teaching, comprising data acquisition, behavior characteristic analysis, knowledge mastering evaluation, dynamic feedback adjustment, comprehensive evaluation and management strategy. The teaching behavior analysis depth is improved through multi-dimensional data fusion and non-linear feature analysis, real-time adjustment of an evaluation model along with knowledge mastery is achieved through a dynamic feedback mechanism, limitation of traditional static analysis is broken through, theoretical test and practice project data are integrated through an innovatively constructed three-dimensional evaluation system, and the teaching behavior evaluation efficiency is improved. A comprehensive evaluation model covering knowledge application and innovation ability development is formed, a dynamic path selection mechanism synchronously supports basic ability enhancement and high-order skill development requirements, a resource allocation strategy has instant intervention and long-term optimization characteristics, and adaptability and decision scientificity of a teaching management system are remarkably enhanced.

The invention relates to the related technical field of equipment life cycle management, in particular to an aviation equipment full life cycle management method and system based on a digital thread. Determining main data of a first digital thread and a first-layer associated test sequence and main data of a second digital thread and a second-layer associated test sequence, mapping the deviation between a physical equipment state and a virtual simulation state, and identifying a performance difference point under a cross-stage data association link; and setting a management strategy for collaborative optimization. The technical problem that the reliability of aeronautical equipment is limited due to the fact that a cross-stage performance difference point equipment performance fluctuation root positioning fuzzy cannot be recognized in real time is solved, first and second digital thread main data and an associated test sequence are constructed, the deviation between physical equipment and a virtual simulation state is accurately mapped, and the reliability of the aeronautical equipment is improved. And a management strategy is set in combination with dual-thread data and performance difference points, and full-life-cycle collaborative optimization is performed, so that the technical effect of equipment reliability is effectively improved.

The invention relates to the technical field of industrial data management, in particular to an energy network dynamic game optimization method under a carbon quota constraint, and the method is suitable for strategy linkage and response relationships generated by different management subjects due to carbon resource allocation and scheduling policy adjustment under a carbon quota system. According to the method, various local policy adjustment events are simulated, the response influence of the events on other management subjects is analyzed, and the possible management conflict risk is recognized; on the basis, a strategy buffer area and a time sequence transition mechanism are introduced, and transition path planning from a local strategy to global coordination is achieved; and the management strategy is dynamically adjusted in combination with feedback information in the execution process. According to the method, the potential instability region can be dynamically identified in the operation process, local reconstruction and iterative optimization are implemented in real time, and the compliance and stability of an energy system in a complex carbon quota management environment are guaranteed.

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.

The invention discloses an enterprise asset management strategy generation method and apparatus, an electronic device and a medium. The method comprises the steps of obtaining asset flow data of a target enterprise, customer behavior data of the target enterprise and market data of a target industry; analyzing the asset flow data of the target enterprise through an asset flow prediction model, and outputting a first analysis result by the asset flow prediction model; analyzing the market data of the target industry through a market fluctuation prediction model, and outputting a second analysis result by the market fluctuation prediction model; analyzing the customer behavior data of the target enterprise through a customer behavior analysis model, and outputting a third analysis result by the customer behavior analysis model; and generating an asset management strategy of the target enterprise based on the first analysis result, the second analysis result and the third analysis result. The method can be applied to the business fields of financial science and technology, medical health, old-age care and the like, and the rationality of an asset management strategy is improved through multi-dimensional data analysis and model prediction.

The present invention discloses an enterprise human resources comprehensive management platform, which relates to the field of data management technology and includes: a resource management system determination module for determining a resource management system; a management logic chain determination module for determining a management logic chain; a task management strategy determination module for determining a task management strategy; a sensitive management mode setting module for setting a sensitive management mode for sensitive data classes; a sensitive management strategy determination module for determining a sensitive management strategy; and a pre-management task control module for executing and controlling pre-management tasks. The platform solves the technical problems of the existing enterprise human resources management, such as the difficulty in coping with complex and changing enterprise management needs, the lack of integration of multiple enterprise application systems, and the risks of sensitive data management, which lead to the inaccuracy and security of enterprise human resources management. The platform intelligently realizes the comprehensive integration and efficient use of human resources information, achieving the technical effect of improving the accuracy and security of enterprise management.

The invention discloses a resource management method and electronic equipment, and relates to the technical field of cloud platforms, and the method comprises the steps: receiving a business intention instruction of a user; converting the business intention of the user into a corresponding management strategy type through an intention analysis model; obtaining a hardware strategy parameter according to the management strategy type, obtaining a container scheduling strategy parameter according to the management strategy type, and constructing a unified objective function according to the hardware strategy parameter and the container scheduling strategy parameter; generating a controller configuration command and a container scheduling strategy request through an optimization solution result of the unified objective function; and performing resource configuration between the controller and the container arrangement platform according to the controller configuration command and the container scheduling strategy request. According to the method, bidirectional state synchronization and event linkage between the controller and the containerized platform are realized, and an active migration mechanism of intention-driven uniform resource arrangement and hardware health prediction is fused, so that the collaborative optimization capability of the system among energy efficiency, performance and reliability is remarkably improved.

The invention provides an energy management method based on an AI algorithm and a large model, and belongs to the field of energy management. The problem of low energy management efficiency is solved; the method specifically comprises the following steps: calculating expected power consumption rates of rigid loads and adjustable loads of each municipal district, and designing a greedy algorithm to adjust power supply power of a power supply end to each municipal district; calculating the equivalent receiving voltage and the expected power transmission loss of each municipal district, introducing distributed new energy power generation equipment for the target region by combining the actual power transmission loss, and adjusting the operation state of the power grid; continuously updating the expected power consumption rate of the rigid load and the adjustable load of each municipal district, and adjusting the operation states of the power supply end and the power grid; according to the invention, fitting and mathematical modeling are carried out on the load of each municipal district of the target region, a management strategy of energy scheduling is designed, and the stability of energy supply of the target region is improved.

The present invention relates to the field of risk identification technology, specifically a big data-driven financial risk identification system, which includes a time data synchronization module, a multidimensional feature mapping module, a financial behavior analysis module, and a risk dynamic identification module. In the present invention, by synchronizing financial data streams in real time and applying multidimensional scaling transformations, the capture and processing of financial data are optimized, and more efficient data stream analysis is achieved. It is possible to accurately capture subtle changes and key forms of time series data in complex financial environments, greatly improving the sensitivity and accuracy of risk identification. Refined similarity calculations and behavior identification help the system discover potential risks earlier, ensuring that financial institutions can respond quickly, thereby effectively preventing and mitigating risk events, and then responding to dynamically changing markets, which can significantly improve the formulation of risk management strategies and the rationality of resource allocation.

The invention relates to the technical field of data processing, and discloses an engineering construction whole process digital management method and system. The method comprises the following steps: collecting and standardizing engineering construction full-period multi-source data; constructing a stacked integrated prediction system by using the standardized data to obtain a three-dimensional index prediction value; allocating resources based on the predicted value in combination with the ergonomics risk index to obtain a configuration scheme; uncertainty factors are quantified for the configuration scheme, an optimal scheme set is calculated through a multi-target symbiotic algorithm, and a management strategy is formed. According to the method, high-precision prediction of the engineering three-dimensional indexes is realized through the stacking integration algorithm, ergonomics risk assessment and resource allocation optimization are combined, and the technical defect that human factor engineering is neglected in a traditional method is overcome.

The invention discloses a cross-platform member unified management and service optimization system. A data fusion processing module breaks a data island, fuses multi-platform member data, and constructs a complete member portrait; the member value evaluation module comprehensively and accurately evaluates the member value by using a member value evaluation formula based on the comprehensive data, insights the contribution and potential of the member value, and provides a basis for a marketing strategy; the member layering module divides levels according to values and dynamic thresholds, enterprises can formulate personalized management strategies accordingly, resources are reasonably distributed, and the management efficiency and the member satisfaction degree are improved; the service recommendation module calculates and recommends personalized services according to a matching degree formula in combination with the hierarchy and historical behavior data; all the modules are tightly matched, a complete closed loop is formed from data integration, value evaluation, hierarchical division and service recommendation, the member experience is effectively improved, and the recognition sense and stickiness of members to enterprises are enhanced.

The invention provides an enterprise management system intelligent optimization method and system based on digital twinning, and the method comprises the following steps: constructing an organization structure diagram with a path weight by taking a post as a node, taking a superior-subordinate relationship as a directed edge, and taking a KPI (Key Performance Indicator) target vector as a node attribute; constructing an instruction conduction path model through breadth-first traversal based on the organization structure chart to generate an effective conduction path set, and calculating a target propagation vector of each path; a post is regarded as a reinforcement learning agent, an optimal post behavior strategy is recovered through inverse reinforcement learning, and a target vector matrix of each post adjusted according to the strategy is obtained; constructing an anti-fact management strategy optimization instruction conduction path, and generating an optimized target matrix and path set; and comparing the optimized target matrix with actual execution data, and calculating a target deviation as an execution effect evaluation value. According to the method, the defects of an existing system in the aspects of multi-level management collaboration, behavior deviation modeling and strategic execution consistency guarantee are overcome.

The invention discloses a consumable management optimization method and system, and belongs to the technical field of material management. Comprising the following steps: S1, in a total warehouse, carrying out statistics on all consumables to obtain the name, the category, the model, the existing quantity and the purchase price of each consumable; s2, in the sub-warehouses, performing statistics on each department to obtain a practical affair type, a name, a category, a model, an existing quantity, a consumption quantity and a use score; s3, integrating and processing the data of S1 and S2 to obtain a stock index, a consumption index, a price index and an evaluation index; and S4, performing calculation according to the indexes in the S3 to obtain a management strategy and a current purchase list, and adjusting the number of consumables in the total warehouse and the sub-warehouses according to the management strategy.

The invention relates to the technical field of ocean sample management, and discloses an ocean sample full-life-cycle digital management method, which comprises the following steps of: firstly, collecting characteristic data such as sample types, source positions and chemical component information of ocean samples, and calculating a classification scheme and storage parameters according to the characteristic data; evaluating a management demand and generating a target management strategy by combining the real-time management data of the sample library; making and optimizing a preliminary management plan by using algorithms such as k-means clustering and a decision tree in combination with historical management data and sample use frequency data, and generating a personalized management scheme; and finally, implementing the scheme, monitoring a sample preservation rate index through a sensor, dynamically adjusting a temperature threshold value and a storage position of classified storage equipment, introducing a real-time feedback mechanism to iteratively correct and adjust parameters, and generating a full-life-cycle management scheme. According to the method, full-process digital management of the ocean samples is realized, and the management efficiency and the sample storage quality are improved.

The application discloses a kind of air pump unit fault management systems, it is related to air pump fault management technical field, real-time acquisition and air pump safety operation related multi-source data are obtained by sensing equipment, processing module is preprocessed to the multi-source data collected, including data cleaning, denoising, etc. Operation is to ensure data quality and accuracy. Feature data is extracted from the data after preprocessing, analysis module will be obtained into air pump prediction model and comprehensively analyzed after feature data, air pump prediction model is air pump and outputs an operation assignment, regional division module will air pump be divided into corresponding area according to the comparison result of operation assignment and gradient threshold value, and according to the regional division result of air pump, corresponding management strategy is generated, and management strategy is sent to administrator. The management system can effectively predict the fault of air pump in the operation process, so as to carry out early warning and management before air pump failure, guarantee the safety and stability of air pump operation.

The embodiment of the invention relates to the technical field of water quality monitoring and equipment management, and provides a remote management method and system for a multi-terminal water quality analyzer cluster, and the method comprises the steps: constructing a mutual influence degree matrix according to a first historical monitoring data set and a first historical operation parameter information set corresponding to water quality analyzers in the water quality analyzer cluster, obtaining a first influence degree matrix; performing three-dimensional reconstruction on the first influence degree matrix according to first historical environment information corresponding to water quality analyzers in the water quality analyzer cluster to obtain a second influence degree matrix; constructing a management strategy corresponding to the water quality analyzers in the water quality analyzer cluster according to the second influence degree matrix to obtain a first management strategy; managing the water quality analyzers in the water quality analyzer cluster according to the first management strategy; and the accuracy of remote management of the multi-terminal water quality analyzer cluster is improved.

The present invention relates to the field of information sharing and management technology. It provides an agricultural information sharing and management method based on multi-source data fusion, spatially calibrates multi-source data, and constructs an agricultural health spatial dataset. It mines the correlation between microbial communities and soil health through spatial correlation network analysis and constructs a spatial correlation model. It optimizes the allocation of agricultural resources. It also generates a personalized agricultural resource allocation plan for each region through a spatial optimization algorithm. After the agricultural resource allocation plan is implemented, a correction plan is automatically generated based on spatial feedback from microbial communities and soil health, and an agricultural health assessment report is generated. The present invention establishes a closed-loop feedback system through an adaptive optimization mechanism, dynamically adjusts management measures based on real-time monitoring data, and ensures that the management strategy is always in an optimal state.

The invention discloses a collaborative management method and system for port engineering full life cycle data, and relates to the technical field of port engineering digital management, and the method comprises the steps: connecting a port management platform with a multi-source data interface, and updating a layer library and a database; developing a management engine array, acquiring a task, interpreting, matching and reconstructing a layer, and writing into a database to determine a task scene model; and visualizing the task scene model, and determining a management strategy based on a multi-thread sub-order of task decomposition and an extended task greedy decision. According to the invention, the technical problems of incomplete data integration, inaccurate task response and difficulty in supporting efficient decision and accurate management caused by difficulty in realizing synchronous updating and deep collaboration of full-process data in the traditional data management mode in port engineering life cycle management are solved, and efficient collaborative management of port engineering full-life cycle data is achieved. The technical effects of ensuring synchronous updating and deep integration of data, accurately responding to tasks, supporting scientific decisions and improving management quality and efficiency are achieved.

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 an intelligent contract-driven APP automatic account division system and method, and particularly relates to the field of intelligent contracts, and the system comprises a data collection module, an account division rule modeling and contract generation module, an intelligent contract execution module, a risk level determination module, and a response optimization module. According to the method, APP multi-source service data is collected and transmitted through a distributed message queue, the service data is subjected to hash calculation and then synchronized to an alliance chain for evidence storage, a contract template is selected according to a configuration strategy, parameters are injected to generate and deploy an intelligent contract, the evidence storage data on the chain is input into an intelligent contract execution module to trigger account division calculation, and the service data are stored in the alliance chain. According to the method and the system, the deserved amount and the contract rationality index of each account division role are obtained, double-track system risk assessment is performed on an intelligent contract execution result, a risk disposal strategy is updated based on a verification result, and a final disposal scheme is determined after a compliance coefficient of risk disposal is calculated, so that the accuracy and compliance guarantee capability of account division risk identification are improved.

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.