270 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 server power consumption management method and device, electronic equipment and a storage medium, and relates to the technical field of computers, the future load change trend of a server can be predicted according to the load data of the server at the current moment, and a power consumption management strategy of the server is generated according to the load data and the load change trend at the current moment. The power consumption management strategy is utilized to adjust the operation parameters of at least one component in the multiple components, and the power consumption management strategy is predicted in advance and adjusted in real time, so that the load fluctuation of the server can be dynamically adapted, and insufficient resource allocation of the server under different load conditions due to a fixed static power consumption management strategy is avoided. Therefore, the technical problems that the resource allocation of the server is unbalanced and the energy efficiency is low due to the fact that the power consumption management strategy cannot be dynamically adjusted in related technologies can be solved, and the purposes of optimizing the resource allocation of the server and improving the energy efficiency of the server are achieved.

The invention relates to an artificial intelligence-based enterprise operation risk assessment method and related equipment, and the method comprises the following steps: collecting operation feature data of a target enterprise; performing time sequence decomposition processing on the operation characteristic data to obtain an enterprise operation trend characteristic sequence; detecting whether the target enterprise has an abnormal operation state based on the enterprise operation trend feature sequence, and if yes, performing risk factor association analysis on the target enterprise based on the enterprise operation trend feature sequence to obtain a risk conduction path map; performing risk propagation node analysis on the target enterprise based on the risk conduction path map to obtain risk propagation key nodes; and comprehensively evaluating the operation risk level of the target enterprise based on the risk propagation key node, and formulating a corresponding management strategy based on the operation risk level, thereby solving the technical problem that an existing enterprise lacks an effective mechanism of systematic decomposition and dynamic monitoring and is difficult to accurately reflect the actual operation state of the enterprise.

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 ecological environment management, and provides a multi-scale ecological environment damage assessment model construction and optimization management system, which comprises a multi-scale data acquisition module used for acquiring various basic data related to an ecological environment under different space, time and other scales; the parameter calibration and analysis module is used for verifying and adjusting parameters required by model calculation and evaluation according to the acquired data to ensure that the parameters are adaptive to the actual ecological environment condition; and the scene simulation calibration module is used for setting different ecological environment change scenes, simulating the development process of the different ecological environment change scenes, calibrating simulation logic in combination with the actual situation, and providing a simulation scene for analyzing the ecological environment damage trend and testing the management strategy effect. Through a multi-scale scene simulation and calibration mechanism, the system can accurately simulate ecological damage trends under different environment change scenes, and a reliable simulation scene is provided for testing a management strategy effect.

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 an industrial equipment health management method and system based on machine learning, and relates to the technical field of industrial equipment health monitoring and predictive maintenance, and the method comprises the steps: collecting operation signals of industrial equipment, and constructing a time sequence feature matrix according to a time sequence through the operation signals; performing time sequence feature learning on the time sequence feature matrix by using a long and short term memory network model, and extracting a first time sequence correlation feature; training a Gaussian process regression model through the first time sequence correlation features, and establishing a health state evaluation model; and inputting a real-time operation signal of to-be-tested industrial equipment into the health state evaluation model, outputting an equipment health management index value, and triggering a hierarchical management strategy according to the equipment health management index value. Through the steps, the maintenance resource allocation is effectively optimized, and the comprehensive effects of reducing the operation and maintenance cost, prolonging the service life of equipment and improving the production efficiency are achieved.

The invention relates to the field of education data analysis and intelligent management, and discloses a tutor student personalized management and evaluation method based on big data, and the method comprises the following steps: firstly constructing a cross-platform heterogeneous data collection module, capturing multi-dimensional data such as scores, attendance, positions and emotions, and generating a dynamic feature vector; secondly, establishing a student relationship intensity matrix through a dynamic graph neural network, and updating classroom interaction topology in real time by adopting a sliding window mechanism; efficient fusion of historical data and newly collected data is realized through truncated singular value decomposition; and finally, in combination with a federated learning framework and a self-adaptive differential privacy mechanism, finishing cross-campus joint modeling under the constraint of privacy budget. According to the method, multi-dimensional data such as scores, attendance, positions and emotions are captured in a cross-platform mode, the student state is modeled through unified vectors, and the problems that a traditional method depends on single score or attendance data, the management strategy deviates from the real situation in the prior art, and the scheme can capture complex association of student behaviors are solved.

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 invention relates to a cash flow and exchange rate management method based on a mathematical model, and belongs to the technical field of data analysis, the cash flow and exchange rate management method based on the mathematical model comprises the following steps: obtaining enterprise historical overall cash flow data, and processing the enterprise historical overall cash flow data through a rolling average and an extreme value range; setting an overall cash flow data early warning line; overall cash flow data are dynamically predicted and updated in a rolling mode through a Prophet model and Bayesian optimization. According to the invention, through combination of the Prophet model and the Bayesian optimization algorithm, the volatility problem in enterprise operational cash flow management and the flexibility and accuracy problems in exchange rate risk management are solved, dynamic smooth cash flow fluctuation is realized, abnormal value early warning is provided, future cash flow and exchange rate trends can be accurately predicted, and the method has good application prospects. And an exchange rate risk management strategy can be dynamically adjusted, and the risk and the income are balanced, so that the full-process intelligence and automation of fund management are realized.

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 discloses a management method and system for outsourcing purchased parts, and relates to the technical field related to data management, and the method comprises the steps: connecting an enterprise ERP system, synchronously screening BOM information and supplier information of purchased parts in real time, and inputting repair data, receiving data and return data, the input mode comprises code scanning input and pc popup window selection input; carrying out full-process tracking analysis, and determining the order state of the purchased parts; and configuring an authority management strategy, and performing control management of accessible data and executable operation on the order state. The technical problems that in the prior art, information transmission is lagged and inaccurate, data entry efficiency is low, errors are prone to occurring, the purchasing process cannot be tracked in the whole process, and the data security risk is high due to the fact that authority management is missing are solved. The technical effects of improving the information processing efficiency and accuracy, realizing the transparency and traceability of the purchasing process and improving the enterprise supply chain collaboration efficiency are achieved.

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 provides a management method for operation of a fire pump house. The method comprises the following steps: at least acquiring regional image information of a fire pump house; processing the fire-fighting water pump room area image information by adopting a fire-fighting water pump monitoring model to generate fire-fighting water pump room area monitoring information; and judging whether the fire-fighting water pump room area monitoring information accords with a preset condition, and generating a corresponding management strategy when the fire-fighting water pump room area monitoring information accords with the preset condition. Therefore, the management method ensures that the large model can correctly provide information through the professional knowledge base and the knowledge graph in the field of maintenance operation of the fire pump house. In addition, the detection rule is defined by using a natural language, so that the utilization rate of physical defense and civil defense can be improved to the greatest extent. And finally, multi-modal identification of a risk scene is realized by using computer vision and a large model, so that known risks can be effectively prevented, left risks can be found through anomaly identification, and a management system is continuously improved.

The invention relates to the technical field of risk identification, in particular to a big data driven financial risk identification system, which comprises a time data synchronization module, a multi-dimensional feature mapping module, a financial behavior analysis module and a risk dynamic identification module. According to the method, the financial data flow is synchronized in real time, multi-dimensional scale transformation is applied, and capturing and processing of financial data are optimized, so that more efficient data flow analysis is realized, subtle changes and key forms of time sequence data can be accurately captured in a complex financial environment, and the sensitivity and accuracy of risk identification are greatly improved; refined similarity calculation and behavior recognition help the system to find potential risks earlier and ensure that financial institutions can respond quickly, so that risk events are effectively prevented and reduced, the market with dynamic changes is dealt with, and the rationality of risk management strategy formulation and resource allocation can be remarkably improved.

The invention belongs to the technical field of digital agricultural management, and particularly relates to a digital agricultural intelligent management system and method based on the Internet of Things. Through real-time data acquisition and multi-dimensional information processing, various strategies can accurately match actual production requirements, waste of resources such as water, fertilizer and pesticide is effectively reduced, the purpose of saving cost is achieved, fertilization and prevention and control schemes are adjusted in time according to real-time crop growth states and environment changes, and therefore the influence of diseases and pests is reduced, and the crop quality is improved. Healthy growth of crops is promoted, the yield and quality are improved, stable operation of various Internet of Things devices is guaranteed through device health monitoring, management interruption or data loss caused by device faults is reduced, efficient operation of the whole intelligent agricultural system is guaranteed, and the intelligent agricultural system is safe and reliable. Spatial data integration enables a management strategy to be optimally configured according to geographical and environmental conditions of different regions, and precise agricultural management according to local conditions is realized.

The invention discloses a supply chain management method, and belongs to the technical field of supply chain management, and the method comprises the steps: collecting a pre-stored supply chain management recognition task or a supply chain management recognition task inputted by a worker through human-computer interaction, enabling the worker to achieve a customized supply chain management strategy, thereby assisting the worker to improve the application flexibility, and improving the management efficiency. Then training a target deep learning model by adopting data specified in the supply chain management identification task, identifying the supply chain real-time data through the trained target deep learning model, and determining a supply chain management tag corresponding to the supply chain real-time data; finally, the automatic management of the supply chain can be realized according to the supply chain management label corresponding to the supply chain real-time data, and workers can be effectively assisted to improve the management efficiency and the management accuracy of the supply chain.