504 results about "Management strategy" patented technology

The invention belongs to the technical field of applets, and discloses an applet abnormal behavior management system fused with artificial intelligence. Comprising the steps of collecting a multi-dimensional data stream in real time, performing space-time slicing on the multi-dimensional data stream, extracting a behavior feature vector in each time window, and constructing a dynamic behavior baseline model bound with a user identity; calculating a deviation degree between the behavior feature vector and a dynamic behavior baseline model, and generating a real-time anomaly confidence coefficient in combination with a similarity matching degree with a preset historical anomaly event library; the hidden relevance of a cross-interface calling link is recognized, then an abnormal propagation probability graph is constructed, the weights of the nodes in the abnormal propagation probability graph are dynamically corrected through a real-time abnormal confidence coefficient, and the corrected weights of the nodes in the abnormal propagation probability graph are obtained; and calculating a comprehensive risk entropy value, and when the comprehensive risk entropy value exceeds a risk threshold value, dynamically triggering a multi-level management and control strategy, so that complex applet abnormal behaviors can be comprehensively detected and managed.

The invention discloses a method and system for searching a risk enterprise group based on a knowledge graph, and the method comprises the steps: obtaining the related information of an enterprise industrial chain graph, carrying out the layering of hierarchical labels, selecting the related elements of the labels of the enterprise industrial chain graph according to the basic information of the enterprise industrial chain, forming an enterprise industrial chain key element knowledge base, and carrying out the searching of the risk enterprise group. Performing label keyword extraction and matching, generating an enterprise portrait and enterprise data report information, constructing an enterprise industry chain knowledge graph, performing enterprise risk evaluation according to the enterprise industry chain knowledge graph, and then performing enterprise investment attraction management strategy processing; according to the method, the business feature tag of the enterprise can be formed, and after the business feature tag is accurately matched with the industrial chain graph, the enterprise industrial chain knowledge graph is constructed, and the risk of the enterprise ethnic group is evaluated and analyzed in multiple dimensions, so that the risk evaluation of the enterprise ethnic group is more accurate, efficient and comprehensive compared with a traditional method, and the risk evaluation efficiency of the enterprise ethnic group is improved. And an enterprise investment attraction management strategy is given.

The invention provides an energy consumption simulation method and system for a refrigerating machine room, and is applied to the field of energy consumption data processing. By establishing the real-time monitoring and analysis system, the machine room can effectively integrate dispersed energy consumption data, the peak and valley load time periods are defined, and the energy consumption prediction and management strategy is dynamically adjusted. According to the systematic method, environmental factors and equipment efficiency are combined, energy consumption influences of various load types are deeply analyzed, it is ensured that a machine room can flexibly cope with dynamic changes of equipment, resource waste and operation cost are reduced, meanwhile, key influence factors are recognized, combined load testing is carried out, and the testing efficiency is improved. Management personnel can formulate targeted cooling strategies and optimization schemes, the energy efficiency is improved, decision support of data driving is achieved, and therefore the overall operation efficiency and the sustainable development capacity of the machine room are improved.

The invention provides a financial risk dynamic monitoring and report generation method based on multi-source data, and the method comprises the steps: obtaining the multi-source data of credit default interchange, carrying out the preprocessing and feature extraction of interest difference data of different deadlines, and obtaining a deadline structure feature vector; obtaining a long and short-term interest difference curve based on long and short-term interest difference data obtained in real time, performing anomaly detection on the morphological change of the long and short-term interest difference curve, and triggering an evaluation process of a credit risk spreading effect when distortion anomaly of the interest difference curve is detected; in the risk assessment and monitoring process, market evaluation is continuously collected, information related to key credit default interchange attributes is extracted, the information serves as supplementary input of a risk conduction mechanism analysis and monitoring strategy, and a risk management strategy is dynamically optimized.

The invention discloses an intelligent logistics digital warehouse management method based on deep learning, and relates to the technical field of warehouse management, and the method comprises the steps: calculating the correlation of SKUs through Graph SAGE, constructing an SKU incidence matrix, optimizing an SKU combination based on an SKU co-occurrence frequency, calculating an SKU storage optimization strategy through Transform, analyzing the storage relation between the SKUs through an attention mechanism, and optimizing a storage layout in combination with GNN. A high-frequency SKU can be located in an optimal storage area, in a sorting task scheduling link, a sorting robot is trained by adopting reinforcement learning, an optimal sorting path is autonomously learned in a warehouse environment, optimal distribution of AGVs and manual tasks is calculated in combination with GNN, collaborative operation of automatic equipment and manual operation is balanced, and path conflicts and invalid movement are effectively reduced; and after the task is executed, recording the sorting time and a path optimization result, and applying feedback data to self-supervised learning to optimize a warehouse management strategy, so that SKU storage, a sorting path and task scheduling can be dynamically adjusted.

The invention discloses an intelligent operation and maintenance management system and method, and relates to the technical field of operation and maintenance management. Comprising the steps of 1, deploying a data collection agent in an informatization operation and maintenance environment, and collecting original data through the data collection agent, 2, collecting and processing historical operation and maintenance data according to the original data, and 3, training an operation and maintenance decision big model by using a deep learning framework and utilizing the historical operation and maintenance data, the method comprises the following steps of 1, training a large operation and maintenance decision model and forming a threshold rule and an operation and maintenance alarm processing strategy according to historical operation and maintenance data, 4, integrating the trained large operation and maintenance decision model and the threshold rule into a real-time data analysis engine for real-time analysis and intelligent decision making of real-time data, and 5, establishing a decision generation engine according to output of the large decision model, and automatically generating an operation and maintenance decision suggestion, judging whether a threshold rule is triggered or not according to the change of the real-time data, automatically matching an operation and maintenance alarm processing strategy if the threshold rule is triggered, and executing the operation and maintenance alarm processing strategy.

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, which relates to the technical field of power grid asset monitoring, discloses an intelligent monitoring and management system for a full life stage of power grid assets based on the Internet of Things, comprising a data acquisition module and a dynamic evaluation module. Receiving the parameter data of the data acquisition module by taking T as a period and a monitoring standard rolling calculation equipment state of the corresponding parameter data; the real-time monitoring module is used for monitoring the state of the power grid equipment in real time according to the evaluation value time sequence of the dynamic evaluation module and triggering an early warning signal; the management module is used for managing and adjusting operation and use of the power grid equipment; the full-life stage of the power grid equipment is divided into a newly-added stage, an operation stage and a decommissioning stage, and differentiated monitoring standards and management strategies are formulated according to the characteristics and the equipment state of each stage. In the newly-added stage, scientific grading is carried out according to equipment evaluation values, equipment loads and tasks are reasonably distributed, and it is ensured that the equipment can exert the maximum efficiency in the initial stage.

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 discloses an anti-counterfeiting verification tracking method and system based on an RFID technology, and relates to the technical field of anti-counterfeiting tracking. All unexpected nodes in a second sequence are identified, historical data of the unexpected nodes are analyzed, then a confidence score of each unexpected node is obtained, an influence weight of each unexpected node is obtained according to a correlation index, and the confidence score of each unexpected node is obtained; calculating confidence scores and influence weights of all unexpected nodes through a fusion algorithm, generating a verification coefficient for a product, generating a corresponding management strategy for the product according to a comparison result of the verification coefficient and a verification threshold value, and regularly obtaining an unexpected node identification number of all products in the same series. And analyzing whether the same series of products need to be managed in advance. The tracking system recognizes all unexpected nodes through sequence comparison and performs historical data analysis on the unexpected nodes, so that path abnormity can be effectively found, the recognition capability of the system on abnormal circulation is enhanced, and the tracking precision is improved.

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 relates to the technical field of performance appraisal management, in particular to a mine workshop performance appraisal management method, which comprises data acquisition and preprocessing, dynamic performance evaluation, task difficulty adjustment, an incentive mechanism and performance feedback and optimization. Optimizing factor weights and predicting employee performance based on a multi-factor weighting model in combination with a neural network and reinforcement learning, and calculating performance scores in real time; the task difficulty is quantified by applying an analytic hierarchy process and fuzzy comprehensive evaluation, and real-time adaptive optimization is analyzed in combination with employee ability; a market supply and demand and collaborative filtering algorithm is adopted to adjust an integral value and a customization scheme, and an award standard is analyzed and optimized according to cost effectiveness and performance sensitivity; a report is generated by means of a visual library, suggestions are generated by means of text mining, and through prediction and causal analysis early warning, a management strategy adjustment scheme is recommended according to reinforcement learning, and feedback optimization is performed; according to the invention, the performance of the mine workshop can be comprehensively, accurately and dynamically assessed and managed, the operation efficiency and the enthusiasm of employees are improved, and sustainable development of enterprises is promoted.

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 is suitable for the technical field of energy consumption analysis, and particularly relates to a smart energy consumption analysis method and system, and the method comprises the steps: drawing a technological process PID diagram, marking energy consumption equipment, positioning a control node of the energy consumption equipment, defining a standard working condition, reading a valve position of the control node through a preset DCS central control system, and carrying out the control of the energy consumption equipment. The fluctuation range of the valve position under the standard working condition is configured; and traversing the technological process PID diagram, determining the energy type of each control node, and clustering the fluctuation range based on the energy types. By generating the monitoring tree, the energy analysis result can be quickly output, the abnormal node can be found out, the energy consumption abnormal position can be accurately positioned, the more effective energy consumption management strategy can be formulated, and by drawing the change trend chart, the energy consumption condition can be visually displayed, the energy consumption state can be comprehensively known, and the energy consumption abnormity can be timely found and processed. And stable operation of a production system is ensured.

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 provides an intelligent risk assessment method for enterprise financing businesses, and relates to the technical field of intelligent risk assessment, and the method comprises the steps: carrying out the independent risk assessment according to the business attribute information corresponding to each financing business, and obtaining a plurality of independent risk indexes; performing feature identification on the plurality of financing businesses according to the business attribute information; training a collaborative risk resistance evaluation model according to the financing scale dispersity, the financing channel similarity and the financing type centrality, and outputting a first collaborative risk resistance index; and performing fitting evaluation on the first collaborative risk resistance index according to the plurality of independent risk indexes, and outputting a financing risk index. According to the method and the device, the technical problem that the risk is concentrated into decision-making deviation due to the fact that risk assessment is carried out mostly for single financing in enterprise financing business risk assessment in the prior art can be solved, and the technical target of identifying financing business risk resistance is achieved; the technical effects of improving the risk assessment reliability and formulating the risk management strategy are achieved.

The invention relates to the technical field of machine tool automatic control, in particular to an intelligent multi-station clamping machining control system which comprises an event monitoring module, a state analysis module, a decision logic module and a dynamic scheduling module. According to the method, the monitoring and analysis capability of the production process is improved by capturing and calculating the time point data of the multi-station clamping machining equipment, so that the evaluation of the production efficiency is more accurate and timely, and an instant data processing and evaluation mechanism allows an enterprise to dynamically adjust the production strategy and optimize the scheduling and priority of workpieces, so that the production efficiency is improved. Therefore, waiting time and machining delay in production are reduced, particularly, in multi-station operation, efficient resource allocation and smooth conversion between procedures are guaranteed through real-time adjustment and optimization functions, machining precision is improved, energy consumption is reduced, the production cost is further reduced through an energy consumption management strategy, and the machining efficiency is improved by accurately controlling the machining speed and path. The purpose of saving energy is achieved, the sustainability of enterprises is improved, and the environmental influence is reduced.

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 a conjoint analysis method for waste classification willingness of event activity participants, and the method comprises the steps: observing a plurality of factors affecting the waste classification willingness through latent variable hypothesis, collecting target sample data through a Likert scale, and analyzing and evaluating the reliability and validity of latent variables through verification factors; calculating a path coefficient based on SEM; taking the latent variable with the significant path coefficient and the observation variable corresponding to the latent variable as input features of an XGBoost model, training and optimizing hyper-parameters; sHAP values of the XGBoost model are calculated, key factors of multiple scenes are determined by combining the SEM path coefficients and the importance of the SHAP values, and differentiated waste classification management measures for different activity types are put forward. According to the method, two methods of XGBoost-SHAP and SEM are combined, so that a scientific and practical panoramic analysis framework can be provided for formulating a waste classification management strategy.

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 provides a dynamic reasoning method and a dynamic reasoning system under a large language model, aiming at the problems of diversity of user input and resource consumption difference when the large language model processes complex and simple queries, and the dynamic reasoning method and the dynamic reasoning system under the large language model are provided. And the matching degree of each segmented word is calculated by using two different recognition models, so that a reasoning control factor is calculated. The reasoning control factor is used for guiding a dynamic reasoning process in the large language model, and is specifically realized by adjusting a management strategy of Key-Value cache. When the relative distance between the current token and the context token exceeds the range determined by the inference control factor, the related Key and Value are removed from the cache. According to the method, the use of the storage space and the reasoning efficiency are effectively balanced, so that the large language model can dynamically adjust the internal resource allocation according to different types of user inputs, and the overall response speed and accuracy are improved.

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 discloses a data element management method and device. The method comprises the steps that target subscription theme information pushed by message-oriented middleware of a data transaction system is acquired, and the type of the target subscription theme information comprises at least one of the following information: shelving information of data elements, transaction information and evaluation information; determining a target data element corresponding to the target subscription theme information, and obtaining a plurality of sub-scores of the target data element corresponding to different types of subscription theme information and a comprehensive score obtained by weighting calculation of the plurality of sub-scores from a database; target sub-scores of the corresponding types are updated according to the target subscription theme information, and comprehensive scores of the target data elements are updated according to the target sub-scores; and adjusting a management strategy of the target data element according to the updated comprehensive score. The technical problem that a data transaction system lacks real-time objective and comprehensive evaluation for data elements and is difficult to effectively manage is solved.