289 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 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 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 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.

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 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.

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 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.

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 relates to the technical field of big data, and discloses a teaching big data resource management system based on a cloud database, the system integrates a data servicing unit, a cloud base unit and an intelligent center unit through a layered distributed architecture, a closed-loop service system is constructed, and the system depends on the elastic storage capability of the cloud database to realize resource management. A teaching scene load is sensed in real time through an elastic scheduling module, database computing resources and storage space are dynamically adjusted by using a storage resource expansion model and a resource dynamic allocation function, automatic capacity expansion in a high-load period and accurate capacity reduction in a low-load period are realized, the problems of performance bottleneck and resource idleness of a traditional scheme are avoided, and the teaching efficiency is improved. And meanwhile, data security is guaranteed through cross-regional synchronization, encrypted storage and disaster recovery backup, intelligent analysis and recommendation of teaching big data are realized in combination with a knowledge graph and machine learning, elastic and efficient cloud database service and big data resource service management capability are provided for a teaching scene, and resource utilization efficiency and teaching service quality are improved.

The invention relates to the technical field of resource management systems, in particular to a virtualized resource management platform which comprises an infrastructure layer, and the infrastructure layer converts scattered hardware equipment into a resource pool managed in a unified mode and is composed of a physical server cluster, network equipment and storage equipment. According to the virtualization resource management platform, accurate mapping from physical resources to business services is achieved through perception, decision making, execution and optimization of full-link automation, each link from bottom layer hardware perception to top layer business guarantee of the virtualization resource management platform comprises precise automatic control and is also provided with a flexible manual intervention interface, and the automation degree of the whole link is improved. Finally, double improvement of the resource utilization rate and the service quality is realized, the operation and maintenance cost is reduced, the resource delivery efficiency is improved, rapid business innovation is supported, the level-of-guarantee three-level compliance requirement is met, and the key operation can support audit tracing.

A resource / control plane / LCS authentication system includes a resource system coupled to a resource management system. The resource system includes an SCP device that authenticates with the resource management system to provide an authenticated LCS control plane. An operating system in the resource system signs operating system authentication information and transmits the signed operating system authentication information to the resource management system to provide an authenticated resource system. The operating system creates an LCS vTPM for an LCS that was composed by the resource management system, and stores an LCS vTPM payload from the resource management system in the LCS vTPM. The operating system then provides the LCS using the LCS vTPM payload, with the LCS authenticating itself and the operating system to provide an authenticated LCS. The operating system then performs a workload using the authenticated LCS, the authenticated resource system, and the authenticated LCS control plane.

The invention discloses a large model deployment and service method and device for realizing intelligent customer service based on container arrangement, equipment and a medium, and relates to the field of artificial intelligence, and the method comprises the steps: constructing a standardized container mirror image which comprises an inference engine, a model acceleration library and a model encryption and decryption assembly and is adaptive to an intelligent customer service scene; establishing a graphic processing unit resource management system in the container arrangement cluster; storing model data of the intelligent customer service model to a target storage system, and determining a mounting mechanism corresponding to the model data; generating a container arrangement and deployment file based on a graphic processing unit resource management system, the operation demand of the standardized container mirror image and a mounting mechanism corresponding to the model data; and submitting the container arrangement deployment file to a container arrangement platform, so that the container arrangement platform carries out intelligent customer service model deployment, and the deployed intelligent customer service model is utilized to receive a user request and return a corresponding target response. According to the method, the deployment complexity is reduced, and the model deployment efficiency and stability are improved.

The invention discloses an Internet-based intelligent human resource management method and system, and relates to the technical field of intelligent human resource management, and the system comprises an automatic recruitment subsystem, a performance management subsystem, an employee service management platform, an intelligent optimization adjustment module, and a personnel data management module. The automatic recruitment subsystem is used for selecting and collecting data information of applicants meeting company post requirements, screening the data information and then automatically arranging interviews, inputting the applicants according to interview results, and arranging corresponding skill training according to personal skill information of the applicants. According to the intelligent human resource management system, datamation and intelligentization of various businesses of human resource management are realized, the operation process is simplified, and the management efficiency is improved; through real-time management of employee information, the employee condition can be mastered more accurately, human resources are reasonably allocated, the overall competitiveness of an enterprise is improved, the diversified requirements of modern enterprises in the aspect of human resource management are met, and the management efficiency and the decision quality are improved.

According to the Bloom filter-based data resource management system and method provided by the invention, the processing pressure of a back-end server can be reduced, the overall load of the system is reduced, and the stability and response speed of the system are improved. The data resource management system based on the Bloom filter comprises a Bloom filter module used for creating, initializing and maintaining at least one Bloom filter; the data resource query cache module is used for judging whether to-be-queried data resources exist in a cache or not in the data resource query process; the data resource content auditing module is used for auditing whether violation information exists in the to-be-audited data resources or not; and the data resource recommendation module is used for filtering out the recommended data resources when the recommendation list is generated.

Systems and methods are disclosed comprising instructions to receive real-time operation logs for at least one allocable resource unit corresponding to a target unit value, determine a set of key performance indicators (KPIs) for the at least one allocable resource unit using a set of operational attributes of the real-time operation logs, generate a forecasting time-series dataset using the real-time operation logs in response to at least one KPI failing to satisfy a stability threshold, generate a set of adjustment values for the forecasting time-series dataset using the set of profiling attributes for a receiving end user, determine a modified target unit value for the at least one allocable resource unit based on the forecasting time-series dataset and the set of adjustment values, and display a notification alert indicating deviation of the at least one KPI and recommendation for adjusting the target unit value.

The invention relates to the technical field of computer processors, and discloses a graphics processor resource management system and method and a server, the resource management system comprises an inference engine module and an inference service module, a graphics processor resource pool comprises a plurality of graphics processors, and the graphics processors are configured with first identifiers; the reasoning service module is used for generating a message request for indicating starting of a target reasoning instance and sending the message request to the reasoning engine module, and the message request comprises a second identifier and a third identifier; the reasoning engine module is used for determining a target graphics processor from the plurality of graphics processors according to the first identifier, the second identifier, the third identifier and the occupancy state information of the plurality of graphics processors, and is used for sending a response message to the reasoning service module; and the reasoning service module is also used for mounting the target reasoning instance to the target graphics processor according to the response message. According to the method, multiple reasoning instances can occupy resources of the same GPU more reasonably, and the utilization rate of the GPU is increased.

The embodiments of the present application relate to the technical field of computers. Provided are a distributed resource management method, apparatus and system, and a device and a nonvolatile readable storage medium. The method comprises: when a power-on instruction has been received, controlling a switch, a target device and a target calculation unit to synchronously power on; when a reset instruction has been received, executing a reset operation on a device to be reset that is indicated by the reset instruction, wherein the device to be reset comprises at least one of the target device, the target calculation unit and the switch; and performing resource scheduling on target resources in a plurality of resource pools on the basis of a resource scheduling request, wherein the resource scheduling comprises resource resetting and resource allocation. In this way, the distributed resource management system can reset the whole system or device, and supports resource resetting and resource reallocation during resource scheduling, thereby providing a more efficient and more flexible resource management architecture, realizing life cycle management of pooled resources, and improving the practicability and flexibility of resource management.

The invention discloses a public data fusion development platform which comprises a platform portal website, a business center, a capability support plate and an infrastructure layer. The platform web portal serves as a user layer to provide an external service window; the business center serves as a business layer and comprises an operation management system, a second-level development system and a competition management system which serve a first-level development subject, a second-level development subject and the public respectively. The capability supporting plate serves as a middle table layer and comprises a data management system, a resource management system, a privacy computing system and an operation and maintenance management system which are used for providing data management, resource scheduling, security computing and full-life-cycle operation and maintenance support for the service center; the infrastructure layer provides one or more infrastructures including a cloud resource pool, a privacy computing environment, a computing engine, containerization, a scheduling engine, intrusion protection, threat detection, a firewall, and a bastion host for the capability support section.

A resource management system (39) manages a supply of resource material and / or resource items to a plurality of at least two additive manufacturing machines (3) for additive manufacturing. The resource management system (39) includes a control unit (41) that is configured to receive a signal indicative of a demand for resource material and / or resource items of any of the additive manufacturing machines (3). The control unit (41) is configured to schedule the supply of demanded resource material and / or resource items according to a predetermined prioritization scheme for resolving demand conflicts.

The present invention provides a resource management system for a stateless microservice architecture, which is applied to a machine cluster based on a Linux operating system and a Kubernetes container orchestration platform. The system includes: a Linux kernel for specific system calls, which is installed in the cluster; a microservice resource manager, which corresponds one-to-one to a microservice and is installed on a control node of the cluster. The microservice resource manager is provided with a plurality of sub-managers, which are used to drive the corresponding sub-managers to generate buoys of the number of Pod copies in the corresponding state based on statistical data; a coordinator, which corresponds one-to-one to a microservice and is installed on a control node of the cluster. The coordinator is used to control the state transfer, creation, and deletion of the Pod of the corresponding microservice based on the buoys generated by the sub-managers; and a Pod resource manager is installed on each computing node of the cluster. The Pod resource manager is used to monitor the state changes of each Pod and perform corresponding Pod resource management operations. The system of the present invention can significantly improve the utilization of CPU and memory resources while ensuring the end-to-end delay service level.

The invention provides an on-line resource management system and method oriented to a requirement of integration of communication, inductance and calculation, and the method comprises the steps: carrying out the correlation scheduling constraint between a sensing target and edge intelligent equipment under the constraint of transmitting power resources, the constraint of calculation resources of a base station end, the constraint of long-term queue backlog and the constraint of average power; constructing a non-convex optimization problem of the common inductance calculation integrated system under a long-term optimization framework; and solving the non-convex optimization problem to obtain an optimal perception scheduling decision, an optimal transmitting beam forming vector of perception and communication, an optimal receiving beam forming variable of perception echoes and calculation resource allocation of a base station end. The online resource management strategy provided by the invention aims to maximize the long-term weighted average rate, meet the requirements of queue stability, average power constraint and quality of service (QoS) at the same time, and explore the cooperative gain of the communication, inductance and calculation integrated system from a long-term perspective.