378 results about "Resource Management System" patented technology

The invention relates to a digital resource sharing method and system based on a pedigree mapping relation, and belongs to the technical field of enterprise digital resource management, and the resource sharing method comprises the steps: obtaining multi-source heterogeneous data from an internal distributed system of an enterprise through a preset interface protocol; performing standardization processing on the multi-source heterogeneous data to generate a structured resource pool; a graph dictionary module is constructed, the resources in the structured resource pool are subjected to association topology analysis, and a resource relation topological graph is output; establishing a pedigree relationship between resources and business scenes based on the three-dimensional mapping model, respectively generating and fusing a business process demand map, a production stage demand map and a product capability matching map, and constructing a visual digital resource capability platform; and outputting the target resource identifier and the associated path in response to a resource calling instruction input by a user. Integration and sharing of internal and external multi-source heterogeneous data of an enterprise can be realized, the problem of data islands is solved, and meanwhile, the intelligent degree of a resource management system is improved.

The invention provides a natural resource analysis method and system combined with multi-source data, and the method comprises the steps: firstly obtaining a multi-source data set of remote sensing images, geographical monitoring, environment monitoring and the like of a target region, and then carrying out the spatial-temporal feature extraction processing of the multi-source data set, thereby obtaining a resource spatial distribution feature set and a time change feature set; according to the method, features of resources under different time-space dimensions are accurately described, then, based on a preset dynamic association rule set, dynamic association processing is performed on a resource space distribution feature set and a resource time change feature set, a resource dynamic association relationship set is generated, and interaction and evolution rules among the resources are revealed. According to the resource dynamic association relationship set, a resource state evaluation strategy is generated, a resource management optimization direction is determined, finally, the resource management optimization direction is fed back to a resource management system, resource scheduling operation is triggered, scientific, accurate and dynamic management of natural resources is achieved, and resource management efficiency and sustainability are improved.

The invention relates to the technical field of resource scheduling, in particular to a cloud computing resource management system. In the cloud computing resource management process, the execution data of the computing tasks are analyzed, the task combinations with relevance are screened, and the computing path task set is established, so that the scheduling of the computing tasks is more intelligent, and the resource utilization rate is improved. And resource allocation is performed according to the matching degree of the calculation demand and the computing power pool, the adaptability of the calculation resources is dynamically adjusted, and accurate scheduling of the calculation task is realized. And in combination with the load state of the calculation node, the CPU utilization rate, the memory occupancy rate, the I / O load and other key indexes are monitored in real time, so that the resource allocation is more flexible, the calculation pressure of the high-load node is reduced, and the stability of the whole system is ensured. The load conditions of the computing power pools are evaluated, the task allocation strategy is adjusted, the computing tasks are reasonably distributed among the multiple computing power pools, the task migration and resource expansion strategy is optimized, and the balanced utilization rate of computing resources is improved.

The invention relates to the technical field of production resource management, and discloses a production resource management system based on an AI neural network. The system comprises a multi-source data acquisition module for acquiring multi-source heterogeneous data of a production environment; the multi-dimensional feature extraction module is used for extracting feature vectors of each data source; the heterogeneous data fusion module is used for generating a resource state semantic representation based on a cross-modal attention mechanism; the resource topological graph construction module is used for constructing a double-layer topological library; and the intelligent optimization decision module is used for generating a real-time resource scheduling strategy and a task allocation optimization scheme based on the result. In addition, the system also has the functions of anomaly detection, path re-planning, resource aging prediction and the like. The system can comprehensively process production data, optimize resource scheduling and task allocation, improve the resource utilization rate, shorten the task completion time, enhance the stability of the production process and the scientificity of equipment maintenance, and improve the production management level of enterprises.

The invention discloses a building industry human resource management system based on a block chain technology, and aims to solve the problems that a current building industry human resource management system is slow in processing speed and difficult to meet flexible employment and rapid recruitment requirements of the building industry, so that transaction processing is delayed, sensitive information of employees may be leaked, a decentralized architecture needs multi-party coordination, and the efficiency is high. The system comprises a block chain module which is used for data storage, security and automatic execution of a consensus mechanism; and the business management module is used for carrying out precise management on complex scenes in the building industry through characteristics of decentralization, non-tampering and the like. According to the method, a decentralized distributed architecture is adopted, the data is stored on a plurality of nodes, the security of the data is ensured, each piece of data is recorded on the block chain and cannot be tampered by any person, the authenticity and integrity of the data are ensured, intermediate links can be reduced, and the management cost and risk are reduced.

The invention belongs to the technical field of online educational resource management, and particularly relates to an online resource management system and method based on an AI large model. According to the method, spatial and temporal features in an online resource scene are analyzed, a basic framework containing a time axis feature vector and a spatial resource association degree matrix can be constructed, the resource conflict probability and the conflict coverage range in a demand interval are predicted on the basis of the basic framework, and when the resource conflict probability exceeds a preset evaluation threshold value, the resource conflict coverage range is predicted. A dynamic decoupling mechanism is triggered, a resource allocation strategy corresponding to the service emergency degree and the space-time adaptation degree is generated through decoupling calculation, accurate matching of resources is achieved, in the process of executing the resource allocation strategy, resource conflict events can be monitored in real time, and when resource conflicts are found, standby resource scheduling and a cross-regional coordination mechanism are started immediately. And through continuous feedback and optimization, a closed-loop optimization mechanism can be formed step by step, and the efficiency and accuracy of resource management are continuously improved.

The invention provides an AI reasoning optimization method and system for a dynamic model switching framework for edge equipment, and relates to the technical field of AI optimization, and the method comprises the steps: training a quantization compensation model through federated learning to correct a quantization error; constructing a hierarchical heterogeneous resource management system to realize multi-core collaborative scheduling; the compensation parameters are integrated to an FPGA acceleration circuit, and an intelligent power consumption equalization technology is adopted; and constructing a dynamic decision engine by using the graph neural network to trigger model switching. According to the invention, the balance between high-precision AI reasoning and low-power consumption requirements on the edge equipment is realized, the utilization efficiency of computing resources is improved, and the energy consumption is reduced.

The invention discloses a cross-platform job resource integrated management system and method based on the Internet, and belongs to the technical field of job resource management. A baseline model is constructed, and whether resource consumption data is abnormal or not is judged; extracting an abnormal resource type, obtaining a corresponding resource demand, identifying tasks having resource competition with the current job, and obtaining an abnormal competition task set; defining job attributes, determining a resource association relationship between tasks, and constructing an abnormal competition task relationship graph; determining the latest execution time of each competition task; analyzing the maximum bearing capacity of the current resource meeting all task execution under the conflict-free condition; calculating the quantity of resources needing to be added; subtracting the actually added resource quantity from the resource quantity needing to be added to obtain a vacant resource quantity; when personnel resources are vacant, the competition tasks are sorted, and the competition tasks are preferentially allocated to high-priority exclusive jobs in combination with the maximum bearing capacity; when the device resources are vacant, idle special devices are preferentially allocated for exclusive jobs.

The invention provides a special education resource management system. The special education resource management system comprises a data analysis and evaluation module, a learning support tool module, a teacher and assistant management module, a resource allocation and scheduling module, a student demand evaluation module, a resource database and a user interface module. The data analysis and evaluation module is responsible for monitoring and evaluating the use effect of resources in real time and providing accurate data support for decision makers, and the data analysis and evaluation module has a data acquisition and monitoring function, a data processing and cleaning function and a data analysis and evaluation function. Wherein a data acquisition part in the data acquisition and monitoring function is used for integrating a plurality of data sources. The special education resource management system provided by the invention has the advantages that various resources can be monitored and scheduled in real time, accurate resource configuration and evaluation analysis are provided, and then the problems existing in current special education resource management can be effectively solved.

The invention discloses a multi-objective optimization-based enterprise resource coordination and distribution method, which comprises the following steps of: S1, acquiring multi-dimensional description information of enterprise task requirements, and constructing a requirement diagram; s2, based on the demand graph structure, performing clustering division on task nodes by adopting a self-organizing clustering algorithm, generating task clusters, generating a candidate resource demand set, and constructing a resource node set; s3, constructing a resource scheduling candidate topological graph; s4, defining a scheduling adaptability function containing a multi-target index, wherein the multi-target index comprises cost, period, resource utilization rate and resource scheduling distance; s5, extracting an optimal scheduling sub-graph from the candidate scheduling topology by adopting an improved heuristic aggregation algorithm; and S6, outputting a corresponding resource allocation scheme to complete resource allocation. According to the method, task structure modeling and multi-target scheduling optimization are fused, the precision and adaptability of resource allocation are improved, and the method is suitable for an enterprise resource management system in a complex scheduling scene.

The invention discloses a data resource management system and method based on artificial intelligence, and belongs to the technical field of artificial intelligence. According to the method, multi-source data is collected, and data cleaning and preprocessing are carried out; selecting meteorological indexes and soil indexes, and calculating an influence factor of each index in the meteorological indexes and the soil indexes; determining the weight of each influence factor, and calculating to obtain an agricultural product external environment influence factor; historical data are collected, meteorological data and soil data are matched with agricultural product planting areas and planting periods, and a multi-dimensional data set is constructed; performing feature engineering on the multi-dimensional data set to obtain a feature set; training a long short-term memory network model, and performing model evaluation and optimization; after training is completed, meteorological data and soil data are input into the model, and agricultural product yield prediction data are output; and performing data alignment, comparing the predicted agricultural product yield value with the average value of the market demand, and analyzing a confidence interval to obtain a decision suggestion.

The invention discloses a database resource management system and method based on authority control, and belongs to the technical field of database resource management.The method comprises the steps that database resource information is collected and classified, and a resource category identification system is constructed; user access operation information is collected, and high-frequency access resources are screened out through statistical analysis; extracting features related to user permission access, and constructing a permission prediction model based on user behaviors; capturing a user operation request in real time, extracting real-time features related to the authority in combination with context information, inputting the real-time features into the trained authority prediction model, predicting whether the user will access the high-frequency resources, and verifying role authority; if it is predicted that the high-frequency resources are about to be accessed and the permission exists, the user access permission is hierarchically controlled according to the user role and the use condition of the current data resources; through the method, the security of database resource access is effectively improved, meanwhile, the risk of data leakage caused by permission abuse is reduced, and resource allocation is optimized.

A request associated with accessing resources of the second party may be received at a first agent installed on a device for a first party, the being received from a second agent installed on the device for a second party. Based on the request, a signed access request that includes a refresh token previously obtained from a service of the resource management system (RMS) may be sent to the service of the RMS. The signed access request may be signed by a private key of the device. Also, a public key of the device corresponding to the private key may be registered with the service of the RMS. In response to sending the signed access request, a session token associated with accessing the resources of the second through the second agent may be received by the first agent from the service of the RMS.

According to the task scheduling and resource management system and method provided by the invention, full-link automatic management from peripheral event triggering to resource dynamic allocation and recovery is realized through close cooperation of the peripheral collaborative network, the dynamic power management module, the task scheduling module, the self-adaptive clock module and the exception monitoring unit. Compared with the prior art, the system not only can perform hardware-level scheduling in real time according to task priorities and system loads, but also can improve energy efficiency through regionalized power supply and clock frequency adjustment, supports rapid recovery and storage of task contexts, has a perfect fault monitoring and isolation mechanism, and is high in practicability. Therefore, the real-time performance, the energy consumption efficiency and the operation reliability of the system are obviously improved.

The invention relates to the technical field of educational resource management, and discloses an educational resource management method and system based on multi-modal semantic analysis, and the method comprises the steps: obtaining educational resource data and user-related data, and inputting the data into a preset text feature model, an image feature model, an audio feature model and a video feature model respectively, text features, image features, audio features and video features are obtained, then text feature vectors, image feature vectors, audio feature vectors and video feature vectors are obtained through calculation according to the text features, the image features, the audio feature vectors and the video feature vectors, and attention weights among the text feature vectors, the image feature vectors, the audio feature vectors and the video feature vectors are calculated based on a multi-modal semantic fusion model; and according to the attention weights, performing weighted averaging on the attention weights to obtain a resource representation vector, inputting the resource representation vector into a clustering library, then scoring the educational resources retrieved by the user, and outputting the educational resources of which the scores are greater than an optimal score threshold. The method has the following effect that the educational resource management system can perform accurate resource search and recommendation.

The invention discloses an unmanned aerial vehicle real-time task scheduling and resource management system, which belongs to the technical field of unmanned aerial vehicle computing resource management, and comprises a task load prediction module, a priority dynamic evaluation module, a resource elastic distribution module and a closed-loop feedback optimization module, the task load prediction module adopts an LSTM-GRU hybrid neural network to predict a resource demand peak value in advance and triggers a pre-scheduling signal; the priority dynamic evaluation module dynamically adjusts the task priority based on the multi-dimensional evaluation matrix; the resource elastic allocation module realizes elastic allocation through a reserved resource pool and a multi-level preemption mechanism; the closed-loop feedback optimization module monitors performance deviation and reversely adjusts parameters of each module, the four modules are deeply coupled to form a closed-loop cooperation mechanism, and intelligent task scheduling and dynamic resource optimization are achieved.

A resource management system based on cloud computing relates to the technical field of resource management and comprises a cloud server, and the cloud server is in communication connection with a data acquisition module, a data storage module, a flow prediction module, a resource extension module, a resource activation module, a real-time flow monitoring module and a data aggregation module. The data acquisition module is used for acquiring data information; the data storage module is used for storing data information; the traffic prediction module is used for acquiring predicted data traffic of each edge equipment terminal; the resource extension module is used for presetting to-be-activated extension elastic computing nodes; the resource activation module calculates a pre-estimated activation time period of the node according to the determined expansion elasticity; the real-time flow monitoring module is used for judging whether a to-be-aggregated terminal is generated or not and determining to-be-aggregated flow of the to-be-aggregated terminal; the data aggregation module is used for generating an aggregated data packet and a verification list; and the cloud server is used for carrying out integrity verification on the aggregated data packet to ensure that the cloud server provides stable, safe and efficient cloud services.

The invention provides an equipment spot inspection platform and method, and relates to the technical field of data processing. An enterprise resource management system generates a spot inspection work order based on the operation time and state of equipment; the data system receives the spot inspection work order, distributes the spot inspection work order to a spot inspector processing the work order, responds to maintenance data of a measuring point corresponding to the spot inspection work order uploaded by the mobile module, changes the work order state from the to-be-processed state to the completed state if the spot inspection work order is not processed overtime, and changes the work order state to the to-be-explained state if the spot inspection work order is processed overtime; in response to the explanation submitted by the spot inspector, changing the work order state to be completed; and in response to the confirmation of the completed spot inspection work order by the supervisor, feeding back the completed work order state of the spot inspection work order to the enterprise resource management system. Therefore, the maintenance data can be uploaded to the data system in time, the situations of omission and recording errors are reduced, and later problem tracing is also facilitated. In addition, the data system interacts with an enterprise resource management system to promote a point inspection plan of the next equipment.

An LCS processing feature access control system includes a resource management system coupled to a processing system including a plurality of processing subsystems. The resource management system receives a first LCS request to provide a first LCS, and provides the first LCS using a first subset of the processing subsystems. The resource management system also receives a second LCS request to provide a second LCS, and provides the second LCS using a second subset of the processing subsystems that is different than the first subset of the processing subsystems, The resource management system then receives a first LCS feature access request to provide access for the first LCS to a processing feature that is not available via the first subset of the processing subsystems, and provides the first LCS using a third subset of the processing subsystems that is different than the first subset and that provides the processing feature.

The invention discloses an intelligent scheduling and resource management system for a microbiological detection laboratory, and relates to the technical field of intelligent management, and the system comprises a resource distribution module which carries out the space-time resource distribution of biological safety constraints through a quantum computing adaptive modeling method, generates a task scheduling optimization problem, carries out the optimal solution search through a quantum annealing algorithm, and carries out the optimization of the optimal solution; obtaining a reference resource scheduling scheme; the scheduling module is used for analyzing through a preset public health risk monitoring threshold value, and adjusting resource application and allocation by utilizing a dynamic resource recombination algorithm to obtain a resource scheduling instruction; the collaboration module is used for predicting a laboratory reagent consumption trend by adopting a metabonomics data analysis method, constructing a cross-laboratory emergency resource sharing path through a GAN network and outputting an emergency resource sharing scheme; according to the method, the biosecurity isolation rule is converted into the quantum bit coupling relation through quantum computing adaptive modeling, and the resource scheduling efficiency and the instrument utilization rate of high-grade experiments are improved.

The invention relates to the technical field of cable resource management, in particular to a cable resource management system based on big data analysis. The system comprises a laying path terrain simulation module, a laying environment analysis module and an abnormal laying area positioning management module. According to the invention, external influence condition analysis is carried out on a cable laying path, a corresponding monitoring value is acquired in real time, abnormal response information is acquired through an abnormal laying area positioning management module in combination with real-time monitoring information on a current laying path, a corresponding influence type is matched, and the corresponding influence type is acquired according to information change. According to the terrain influence project and the environment influence project of the laying path, the abnormal sections are predicted, the influence weights of different abnormal sections are obtained, and the on-site maintenance sequence is divided according to the influence weights, so that the maintenance sequence can be automatically planned for maintenance personnel, the maintenance and troubleshooting efficiency is improved, and the cable monitoring and management resources are utilized to the maximum extent.

The disclosed technology includes systems and methods for addressing data gaps in fragmented data collections generated by discontiguous networks comprising multiple systems distributed across digital and physical locations. They include composing a record of a resource by matching identifiers across one or more data sources. A model is used to identify data gaps in the record by comparing first values of first attributes against a reference comprising second attributes and second values retrieved from the data sources. Each data gap includes a nonconforming attribute with an invalid value and a fault type. The system generates a request to resolve the data gap by correcting the invalid value of the nonconforming attribute according to the identified fault type.

Mechanisms for resource management are described. A trust anchor for a device may be obtained. A trust anchor for a user may be obtained. A public key of the device that corresponds to a private key stored in a cryptographic component internal to the device may be generated. The public key of the device may be registered, with a service operated by a resource management system, to a combination of the device and user. Registering the public key of the device may include sending, to the service operated by the resource management system, a registration message that indicates the trust anchor for the device, the trust anchor for the user, and the public key of the device.

The invention is suitable for the technical field of resource management, and provides a resource management system based on an artificial intelligence platform, which comprises a resource demand end, a resource scheduling center and a resource supply end, the resource demand end comprises a resource demand identification module used for identifying the resource type of the demand; the resource scheduling center comprises a resource demand receiving module, a resource supply feedback module, a resource state analysis module and a resource scheduling module; the resource demand receiving module is used for receiving demand information sent by the resource demand end; the resource supply feedback module is used for receiving supply information fed back by the demand supply end; the resource state analysis module is used for analyzing the state change of resource demand and supply; the resource scheduling module is used for performing resource scheduling according to the analyzed resource state change; the resource supply end comprises a resource checking module which is used for counting available resource information. According to the invention, dynamic resource scheduling can be carried out based on state change, and the resource utilization rate is effectively improved.

The invention relates to a data processing method and device, computer equipment and a readable storage medium. The method is applied to an application programming interface (API) management layer of a main node in the cloud computing resource management system, and the API management layer is connected with candidate controller partitions in the main node. The method comprises the steps of obtaining target ETCD data when it is detected that data update exists in a distributed storage system ETCD, determining a target controller partition corresponding to the target ETCD data from candidate controller partitions according to data information of the target ETCD data, sending the target ETCD data to the target controller partition, and sending the target controller partition to the target controller partition. And processing the target ETCD data in the target controller partition. By adopting the method, the processing efficiency of cloud computing resources can be improved.

The invention relates to the technical field of computing power management, in particular to an intelligent network computing power resource management system which comprises a node response load combing module, a task computing power grading module, a computing power scheduling adaptation module, a link load blocking detection module and a computing power flow direction reprogramming module. In the invention, by tracking the continuous change and inversion frequency of the node response direction, combining the computing power use state and the cache occupation ratio, dynamically evaluating the node stability, extracting the task logic depth and the cache interaction deviation to divide the computing power level, the hierarchical matching of the task demand and the node capability is realized; nodes with high cache load and unstable response are screened out, the resource scheduling accuracy and the integrating degree of task allocation are improved, the link abnormal growth trend and the queuing backlog proportion are monitored, the transmission blocking risk is recognized in time, and the flow direction is reconstructed by preferably selecting replaceable nodes based on the idle computing power and the bearing ratio; and the agility of resource calling and the continuity of task execution in a complex network are improved.

The invention discloses a dumb resource management system automatic test method, system, device, medium and product, and belongs to the technical field of optical network communication test.The method comprises the steps that configuration information of passive dumb resources is obtained according to a passive dumb resource management system, and a relation graph G = (V, E) of the passive dumb resources is constructed; based on a predefined state machine model, controlling a test process to be automatically switched among a plurality of preset states so as to execute a full-process test from passive dummy resource data preparation to optical path verification; wherein after at least one non-terminated preset state is experienced in the test process, automatic verification is carried out based on the relation graph, and an automatic verification result is used as a trigger condition of state transition of the state machine model and a judgment basis of a whole-process test result. According to the invention, efficient, comprehensive and accurate automatic testing of the optical network passive dumb resource management system with extremely complex service logic is realized.

A dynamic independent SDS resource adjustment system includes a resource management system coupled to compute nodes and storage system(s) including storage devices. The resource management system monitors a first subset of resources the compute node(s) that are being used to provide an SDS system using the storage devices that are assigned to the first subset of resources, determines that first subset of resources require adjustment, and identifies a second subset of resources in the compute node(s) to provide the SDS system, with the second subset of resources different than the first subset of resources. The resource management system then reassigns at least one of the storage devices from at least one of the first subset of resources to at least one of the second subset of resources, and provides the SDS system using the at least one second subset of resources.

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 distributed intelligent calculation center coupled with land wind power generation and an energy consumption control method thereof, and aims to solve the problem that land wind power generation resources are not matched with high-performance calculation requirements and realize effective utilization of power resources and optimal configuration of calculation resources. The distributed intelligent calculation center comprising the wind power generation system, the electric power allocation system, the calculation resource management system and the energy consumption monitoring control system is designed, and electric power supply is dynamically adjusted through the electric power allocation system according to the wind power generation power and the electricity demand of the intelligent calculation center. The computing resource management system reasonably allocates computing resources according to task load and computing requirements, and the energy consumption monitoring control system monitors energy consumption in real time and optimizes a strategy; the utilization rate of land wind power generation is improved, the energy consumption cost of an intelligent calculation center is reduced, and the overall efficiency of calculation resources is improved; in addition, diversified development of land economy is promoted, stable technical services and employment opportunities are provided for the local area, and deep fusion and application of new energy and information technologies are further promoted.