62 results about "Server farm" patented technology

The invention relates to the field of data processing, in particular to a server fault diagnosis method and system for a server group, and the method comprises the steps: obtaining and preprocessing server operation state data; constructing a running state isolated tree by using an isolated forest algorithm, analyzing over-limit and low-limit conditions of data of each dimension, determining a neighbor monitoring sequence and constructing a threshold offset index; based on the index, analyzing association characteristics among the dimension data to construct a fault association index; further analyzing data anomaly differences between leaf nodes and child nodes in the isolated tree, and constructing an intra-layer isolated index; and calculating an abnormal isolated weight and a significant abnormal weight, improving an isolated forest algorithm, and realizing accurate diagnosis of server faults. According to the method, the significant abnormal weight is introduced to improve the isolated forest algorithm, and the threshold offset index and the fault correlation index are constructed, so that the server fault diagnosis precision is significantly improved, false alarm and missing alarm are reduced, operation and maintenance personnel are helped to accurately position faults, and stable operation of the server is guaranteed.

A method includes receiving parameters of each server of a plurality of servers. The parameters of each server are converted from classical to quantum bits. A final quantum state is determined based on a first converted parameter of each server. First intermediate weights of the servers are determined based on the final quantum state. (a) A subsequent final quantum state is determined based on current intermediate weights and a subsequent converted parameter of each server. (b) Subsequent intermediate weights of the servers are determined based on the subsequent final quantum state. Operations (a) and (b) are repeated until all converted parameters of each server are considered and final weights of the servers are determined. The final weights are converted from quantum to classical bits to determine converted weights. Statuses of the servers are determined based on the converted weights. The servers are grouped into server clusters based on the statuses.

The invention relates to the technical field of databases, and discloses a structural data synchronization method and system of a relational database. Data users are distributed to candidate data center servers deployed with the relational database; acquiring deployment state information and communication state information of a plurality of data center servers in a target structure data synchronization period, and solving an optimal relational database synchronization strategy of a server group by taking a synchronization target demand, a synchronization data volume demand and a synchronization frequency demand of each data user in different minimum service periods as a drive; therefore, the structure data synchronization between the relational databases is controlled. Therefore, by realizing the structure data synchronization between the relational databases under the scene of uneven regional communication load, the period consistency of the metadata of the databases is kept, the selection for the source data center server and the rationality and scientificity of the structure data synchronization path planning are improved, and the reliability of the structure data synchronization path planning is improved. The stability of the structure data synchronization period is ensured as much as possible.

The invention provides a teaching evaluation method and device, a storage medium and a program product, and relates to the technical field of teaching quality evaluation.The method comprises the steps that a first issuing instruction sent by a first user in a teaching classroom through a first server is received, and a first classroom questionnaire set is sent to a second user group and a second server group in the teaching classroom; acquiring a second classroom questionnaire set uploaded by a second server group; a second issuing instruction sent by the first user through the first server is received, and the first extracurricular questionnaire set is sent to a second user group and a third server group of the non-teaching classroom; obtaining a second extracurricular questionnaire set uploaded by a third server group; performing data fusion on the second classroom questionnaire set and the second extracurricular questionnaire set to obtain a target archive data set; and the target archive data set performs teaching evaluation on the target user group. The technical problem of low reliability of teaching evaluation in related technologies is solved, and the technical effect of improving the reliability of teaching evaluation is achieved.

The invention relates to the technical field of servers, and discloses a server group which comprises one or more layers of server units, and the multiple layers of server units are sequentially arranged in the first direction. The server unit comprises a shell and two supporting assemblies, and the two supporting assemblies are arranged on the two sides of the shell in the second direction respectively and detachably connected with the shell; the supporting assemblies extend in the first direction, and every two adjacent layers of supporting assemblies are connected in an inserted mode. The two adjacent layers of shells are connected in a sliding manner along a third direction; the first direction is a vertical direction, and the first direction, the second direction and the third direction are vertical pairwise, so that modular flexible combination of multiple layers of server units is realized, and the maintenance efficiency of single-layer server units is improved.

The invention belongs to the technical field of data processing systems, and particularly relates to a cloud side online data system and a verification method thereof. Comprising a cloud platform service module, an edge service module and a terminal service module. The cloud platform service module is composed of server group equipment, data isolation equipment, security gateway equipment and data storage equipment; the system comprises a service collaboration component, a service management component and a system monitoring component. The edge service module is based on a power distribution network edge internet-of-things proxy structure and is composed of near-end edge data and network service equipment; comprising a service registration component, a service discovery component and a service updating component. The terminal service module is composed of data acquisition terminals of a power distribution network user side and a management and control side, and data transmission terminal operation data and environment data sensing ports or devices; comprising a terminal monitoring component and a user generation component. The invention provides a system scheme for improving the validity of data service under the situation of larger data concurrency.

The invention relates to the technical field of computer load balancing, in particular to a large model reasoning load balancing method and system based on a dynamic resource grading threshold value. The method comprises the following steps: performing resource consumption estimation on a user reasoning request to obtain a request score value; the servers are divided into two server groups with different computing capabilities; differentiated scheduling is carried out based on the request score value and the current dynamic resource grading threshold value; and collecting the load state of the server cluster, calculating a load difference value, and dynamically adjusting the dynamic resource grading threshold value through a feedback control algorithm to realize load balancing. According to the method, the resource consumption of the reasoning request of the user is accurately estimated, and the efficient load balancing of the large-model reasoning task is realized in combination with the hierarchical management of the server, the real-time load state evaluation of the server cluster and the dynamic threshold adjustment mechanism.

The invention discloses a data center dynamic energy-saving scheduling method and system based on AI prediction, electronic equipment and a storage medium, and the method comprises the steps: collecting load data of a data center server and external service flow data in real time to construct an original data set, and processing the original data set through employing a long short-term memory network to obtain a load change trend; according to the load change trend, determining distribution characteristics of the thermal load trend in a future time window, extracting idle computing resource data from the server group based on the distribution characteristics, and computing a computing resource distribution proportion needing to be adjusted; carrying out iterative optimization on the calculation resource distribution proportion by adopting a genetic algorithm to obtain an optimized server load distribution scheme; through a server load distribution scheme, obtaining associated refrigeration system parameter data, determining an adjustment vector of a refrigeration strategy, fusing real-time energy consumption data of a power supply module, and judging a correction demand of power supply scheduling; and obtaining the operation configuration of the overall energy efficiency optimization according to the correction demand.

The embodiment of the application discloses a data synchronization management system, method, device, medium and product, the system comprises: at least two server groups; each of the server groups comprises a data synchronization process and at least one business server; each of the business servers is associated with the data synchronization process; for each server group, the business server is used for generating a data synchronization message containing to-be-synchronized business data when having a data cross-group synchronization demand, and sending the data synchronization message to the associated data synchronization process; the data synchronization process is used for determining a target server group to be cross-group synchronized according to the received data synchronization message, and transmitting the to-be-synchronized business data to a target business server of the target server group through a generated synchronization data packet. The flexibility and effectiveness of application business data synchronization under a large-scale Internet architecture are better ensured, and the generality of data synchronization under the large-scale Internet architecture is better embodied.

The application discloses a kind of server fault diagnosis method and system for server group, it is related to server operation and maintenance technical field, present and propose the following scheme, including data acquisition unit, data preprocessing unit, data training unit, data diagnostic analysis unit, scheme generation unit, scheme verification trial unit, data storage unit and data early warning unit, each unit is connected by communication bus or network link and forms closed loop diagnosis system.The application constructs full-link fault diagnosis system, through multidimensional collection hardware, software, network data, improves data quality by scientific pretreatment, adopts rule matching+machine learning hybrid model, combined with self-learning mechanism, improve diagnostic accuracy and coverage, generate personalized repair scheme and be verified by simulation / real machine, avoid secondary failure, full-process data safety storage and double early warning mechanism, realize fault tracing and prevention and control in advance, greatly improve server group operation and maintenance efficiency, guarantee business continuity.

A server farm with at least one hybrid computing module operating at clock speed optimally matching intrinsic clock speed of a related semiconductor die related thereto.

The invention discloses a control method of refrigeration equipment and related equipment, and the method comprises the following steps: determining a heating information set according to a first operation information set of a server cluster and current weather forecast information according to the control method and system of the refrigeration equipment, electronic equipment, a storage medium and a program product; determining a target refrigeration server according to a preset threshold set and the heating information set; according to the target refrigeration server, the current weather forecast information and the heating rule model, target refrigeration equipment and a refrigeration parameter set are determined; and performing directional refrigeration on the target refrigeration server according to the target refrigeration equipment and the refrigeration parameter set. According to the embodiment of the invention, the target server can be determined according to the heating information of the server cluster, and the target refrigeration equipment is determined and called to perform directional refrigeration in combination with the heating rule model, the current weather forecast information and the target server, so that the refrigeration energy consumption is reduced, and the refrigeration precision is improved. The method can be widely applied to the technical field of data center heat management.

Systems, methods, software and a framework are disclosed for an improved, adaptable, intelligent, real time, machine learning, AI / Robot integrated, large scale, cloud computing based synchronous communications / computing network enabled with adaptive intelligent ‘4D spherical scaling’ of databases, servers, and / or virtual servers and / or server clusters and elastically with their directly associated computer caches / storage and linked and ‘intra-scalable microservices’ and with external resources and with ‘tech stack awareness’, and ‘tech stack integration’ functionality.

Systems, apparatus, articles of manufacture, and methods to distribute workloads in server farms based on temperature are disclosed. An example first compute device includes at least one programmable circuit to at least one of instantiate or execute machine readable instructions to: analyze temperature data indicative of a first temperature of a first compute device and a second temperature of a second compute device; and cause an adjustment in at least one of a first workload executed by the first compute device or a second workload executed by the second compute device based on the temperature data, the adjustment to reduce a difference between the first and second temperatures.

Systems, methods, software and a framework are disclosed for an improved, adaptable, intelligent, real time, machine learning, AI / Robot integrated, large scale, cloud computing based synchronous communications / computing network enabled with adaptive intelligent ‘4D spherical scaling’ of databases, servers, and / or virtual servers and / or server clusters and elastically with their directly associated computer caches / storage and linked and ‘intra-scalable microservices’ and with external resources and with ‘tech stack awareness’, and ‘tech stack integration’ functionality.

This invention discloses an automatic inspection method and system for server security self-testing. The method includes the following steps: S1, acquiring distributed node information and initial security configuration parameters of a server group, and establishing a global security inspection topology; S2, deploying an adaptive inspection agent on each server node, wherein the agent initializes a local inspection strategy based on the global security configuration parameters; S3, through a distributed computing architecture, each adaptive inspection agent independently performs local security checks and generates node security status data. This invention relates to the field of server security technology. This automatic inspection method and system for server security self-testing, through collaborative communication between agents, enables the system to share threat information, vulnerability remediation suggestions, and system status data in real time, constructing a global security data pool, and deriving a global security posture value based on weighted calculation. This allows the system to promptly understand the security status of the entire server group and provide accurate security posture awareness.

The invention discloses a privacy protection smart medical service recommendation method based on weight matching. The method comprises the steps that a medical management center generates materials needed by a security key and sends the materials to a patient user and a server cluster; the medical management center carries out doctor registration service and patient user registration service through copy secret sharing and the server group; after registration is completed, each server in the server cluster obtains a pair of shared values; screening doctors by calculating a similarity vector between the user demand vector and the doctor attribute vector; and after the doctor service is completed, calculating the weight of each dimension of the score vector of the user, and updating the score of the doctor based on each dimension value of the score vector and the corresponding weight. According to the method, the most suitable doctor is recommended by calculating the similarity among the patient demand, the doctor attribute and the score vector; multi-party calculation of data is realized through a function secret sharing technology, and the data security is enhanced. Calculation and auxiliary servers are introduced, so that the online calculation cost is reduced, and malicious scoring is effectively resisted.

A multiphase cooling system for electronics racks, server farms, and data centers is described. An inlet of a three-way flow control valve (FCV) is coupled to a primary coolant source. A first outlet of the FCV is coupled to a single-phase cooling system, and a second outlet of the FCV is coupled to a two-phase cooling system. The FCV is configured to adjust the amount of coolant between the single-phase cooling system and the two-phase cooling system. Upon detecting a rise in vapor pressure in a return line of the two-phase cooling system, the FCV can be adjusted to direct more coolant to the two-phase cooling system and less coolant to the single-phase system. The FCV can continuously monitor the vapor pressure and adjust the amount of coolant to each cooling system accordingly.

The present invention relates to the technical field of liquid cooling system management, and particularly relates to an energy consumption data management platform for a liquid cooling system based on artificial intelligence. The energy consumption data management platform for the liquid cooling system includes a data acquisition module for acquiring the actual energy consumption data of each server in a server group under each historical liquid cooling cycle; a first partitioning module for obtaining initial clustering clusters; a second partitioning module for reassigning the energy consumption data points in the initial clustering clusters to obtain respective target clustering clusters; and a liquid cooling system management module for reassigning and managing the servers controlled by the CDU of the liquid cooling system according to the servers corresponding to the energy consumption data points in the target clustering clusters. Moreover, the present invention can reduce the energy consumption waste during the cooling of a centralized liquid cooling system, improve the energy consumption utilization rate and energy utilization rate of the centralized liquid cooling system, and realize the balanced management of the energy consumption and resources of the centralized liquid cooling system.

Server farm power management system.

The present disclosure relates to a method for edge level training of a model by one or more processors (202) The method includes adding one or more edge servers (104) to a cluster of centralized servers. Further, the method includes receiving, at the one or more edge servers (104), data from one or more edge devices (102). Further, the method includes training, at the one or more edge servers (104), a model with the data. Further, the method includes deploying the trained model onto at least one of, the one or more edge devices (102) or the one or more edge servers (104). Further, the method includes synchronizing the trained model with the cluster of centralized servers.

The embodiment of the invention provides a server state monitoring system and method, and relates to the technical field of computers. The system comprises a monitoring center and at least one server group, the server group comprises a plurality of servers, each server comprises a central server and at least one edge server, and each server automatically detects own operating environment information, registers matched resource acquisition services according to the operating environment information, and transmits the matched resource acquisition services to the monitoring center. The edge servers perform multi-source heterogeneous data acquisition on monitoring objects of the edge servers to obtain monitoring data, the monitoring data are packaged into structured message bodies, the edge servers report the structured message bodies to the central server, and the central server receives the structured message bodies from the edge servers and sends the structured message bodies to the server. And locally storing the structured message bodies of the edge servers and the structured message bodies of the edge servers and reporting the structured message bodies to a monitoring center. Therefore, the problem that a traditional monitoring scheme is poor in adaptability in a heterogeneous environment is effectively solved.

The application relates to the technical field of databases, and discloses a relational database structure data synchronization method and system, wherein data users are distributed to candidate data center servers in which relational databases are deployed, the deployment state information and the communication state information of a plurality of data center servers in a target structure data synchronization period are acquired, the synchronization target demand, the synchronization data volume demand and the synchronization frequency demand of each data user in different minimum service time periods are taken as driving, the optimal relational database synchronization strategy of a server group is solved, and the structure data synchronization between relational databases is controlled. Therefore, the structure data synchronization between relational databases in a regional communication load uneven scene is realized, the periodic consistency of database metadata is maintained, the rationality and the scientificity of the selection of a source data center server and the structure data synchronization path planning are improved, and the stability of the structure data synchronization period is ensured as much as possible.

The invention belongs to the technical field of oil field exploration, and particularly relates to an oil field exploration, development and production C / S (Client / Server) architecture system which comprises a data access terminal, a data transmission unit and a background data center, the data transmission unit is composed of a common server cluster, a standby server cluster and a control cabinet, a plurality of common server unit bodies are integrated in the common server cluster, and a plurality of standby server unit bodies are integrated in the standby server cluster. According to the invention, by setting the common server cluster and the standby server cluster, when the common server unit body breaks down, replacement work can be carried out through the standby server unit body, so that the situation that one common server unit body breaks down to influence the operation of the whole system is avoided; the problem that a C / S architecture system is poor in operation stability at the present stage is solved.

A data processing system implements obtaining a set of input parameters associated with an update to be deployed to a plurality of server farms of a cloud-based service, wherein each server farm includes a primary replica configured to handle user traffic and a disaster recovery replica configured to handle user traffic responsive to a failure of the primary replica; determining temperature information for each of the server farms, ranking the server farms based on the temperature information to determine an order in which an update is to be deployed to the server farms; iteratively deploying the updates to the primary replicas of the server farms according to the ranking until an deployment threshold has been satisfied; and iteratively deploying the updates to the primary replicas of server farms for which the primary replicas have not yet been updated and to the disaster recovery replicas of the server farms.

Methods, media, and systems are provided for automated discovery schedules for telecommunication networks associated with a particular provider. The methods, media, and systems identify telecommunication networks corresponding to the provider. Additionally, location data for each of the telecommunication networks is received. The location data may be associated with one or more IP addresses for each telecommunication network. Servers or server clusters are identified along with associated location data for the servers or server clusters. The servers or server clusters are also associated with the provider. The location data for each of the telecommunication networks and the location data for each of the servers or server clusters are mapped. A distance between at least one of the telecommunication networks and at least one of the servers or server clusters is determined. Based on the distance determined, a telecommunication network is assigned a discovery schedule.

The present invention relates to a CAD and BIM file remote online browsing collaborative system. It includes a server cluster, a Web / mobile intelligent terminal, a model parsing module, a model processing module, a model graphics rendering module and a model Web / mobile intelligent terminal application, as well as a model original system data entity, a component library and a model own system data geometry library for storing converted geometric bodies. The present invention can compress the size of the model information file to about 10%-25% of the size of the original model file while maintaining most of the information of the original model, so as to render through a browser, and facilitate engineering personnel to use the Web or mobile intelligent terminal to perform remote browsing, annotation, review and other collaborative work of CAD and BIM files.

The invention provides a data processing method and system based on edge cloud and a storage medium, and belongs to the technical field of data processing.The method specifically comprises the steps that standby servers in servers are determined on the basis of the distance between the servers and different common access sites, the standby servers are freely combined to obtain a server group, and the server group is stored in the server group; determining a matched server group in the server groups according to access processing delays and residual storage spaces of standby servers in different server groups on different dates, and determining the distance data between the different standby servers in the matched server group and a common access place and the access data of the matched access user according to the distance data between the different standby servers in the matched server group and the common access place and the access data of the matched access user. According to the method and the device, the data processing sequence of the standby servers corresponding to the different common access sites is determined, and when the access processing delay of the standby server does not meet the requirement, the next standby server in the data processing sequence is automatically switched, so that the data processing efficiency is improved.

The application belongs to the technical field of information matching and data evaluation, and proposes a full asset wealth protection and inheritance risk evaluation method and system, specifically: first, a full asset network identification benchmark server and a redistribution server are identified, which are respectively recorded as a benchmark object and an evaluation object, a benchmark word adaptive portrait is constructed through an asset database of each evaluation object, then inheritance execution quality time sequence analysis is performed by using the benchmark word adaptive portrait to obtain an inheritance execution score of each evaluation object, and finally, inheritance risk evaluation is performed in combination with the inheritance execution score of each evaluation object. The coordination between the redistribution server and the related database of the full asset wealth needing to perform inheritance processing and the benchmark server can be effectively quantified, and the risk of data tampering, loss or individual asset data read / write failure of the server group in the inheritance task is reduced.

The invention relates to a big data driven network security situation monitoring and visualization method, and relates to the field of network security, and the method comprises the steps: employing a big data network element to carry out each training operation on a feedforward neural network to obtain a network security situation monitoring model; based on the duration of each time interval, the network security situation monitoring model is used for setting various configuration data of the server cluster and setting the number of the past attack virus of the server cluster to intelligently judge the number of the attack virus corresponding to the set server cluster in the target time interval. According to the invention, the method can depend on the big data network element resources in a distributed design, and employs the network security situation monitoring model after the corresponding training scale to predictively predict, monitor and cope with the network security situation of the server cluster in the future time interval for the server cluster composed of a plurality of servers. Therefore, the probability of successful virus attack is reduced, and the network crisis of data leakage or system paralysis is avoided.