1795 results about "Resource information" patented technology

The invention belongs to the technical field of fishery resource informatization management and ecological prediction, and particularly relates to a dynamic prediction method of a resource habitat dynamic prediction system based on multi-source heterogeneous data fusion, and the method comprises the steps: a multi-source heterogeneous data collaborative collection and standardization processing module synchronously collects cross-regional data, and generates a time-space aligned standardized data set; the multi-modal habitat adaptability evaluation and prediction model is used for receiving the standardized data set as input, constructing environmental, biological and social modalities based on multi-source fusion data, and generating a habitat adaptability prediction result; the three-dimensional dynamic visualization and decision support platform is used for receiving the habitat suitability prediction result and generating a habitat thermodynamic diagram, a resource abundance gradient and an environmental parameter dynamic visualization display and decision under multiple spatial and temporal scales; according to the method, an international data collaboration mechanism, an ecological niche model optimization algorithm and a lightweight visualization engine are subjected to system-level integration, and an engineering solution is provided for biological resource protection of a sea area.

The invention discloses an intelligent storage multi-AGV scheduling method and system, and relates to the technical field of storage scheduling. A graph optimization algorithm is combined with a multi-target scheduling tradeoff factor model to solve a task path, output a task allocation and path planning scheme, load the task allocation and path planning scheme to a time axis, and perform time sequence simulation on the path; potential congestion points or resource conflict areas in the paths are identified, part of AGV paths are optimized according to identification results, time passing pressure indexes and actual AGV operation states are used as input, scheduling verification is performed through multi-round offline simulation, a multi-AGV scheduling holographic data set is integrated, and a task state driven multi-AGV closed-loop scheduling scheme is constructed. According to the scheduling method, a multi-dimensional scheduling model fusing tasks, paths and resource information is constructed, and graph optimization, dynamic simulation and closed-loop control are combined, so that efficient collaboration and real-time response of multiple AGV systems are realized.

The embodiment of the invention provides a cloud side-end cooperative control method and system based on remote communication. The method comprises the following steps: generating a task scheduling input data set according to real-time operation state data generated by terminal equipment in a remote communication process, current computing resource information of an edge computing node and global optimization target parameters issued by a cloud management system; calling a preset task scheduling decision model to carry out structured splitting on the cloud side-end cooperative control task to obtain original task structure information; task structure labeling information is generated for sub-task labeling priorities in combination with multi-aspect information, and cyclic matching deduction processing is performed on the task structure labeling information to obtain a hierarchical resource allocation result; and distributing the sub-tasks to corresponding cloud edge end nodes (including a first node corresponding to the terminal device, a second node corresponding to the edge computing node and a third node corresponding to the cloud management system) for execution, and configuring a task execution process adjustment mechanism based on real-time resource change to adapt to resource fluctuation.

The invention relates to an intelligent robot feeding and discharging method and system, and the method comprises the steps: obtaining the image information of a target material, carrying out the recognition and analysis of the image information of the target material, and obtaining an analysis result which comprises the position information and posture information of the target material; inputting the analysis result into a pre-trained operation decision model for analysis, and generating an action planning instruction corresponding to the target material; task scheduling operation is executed based on the current task state and the resource information, a task scheduling result is obtained, and the scheduling operation comprises task distribution optimization and path generation control; according to the task scheduling result and the action planning instruction, the target robot is scheduled to complete feeding and discharging operation; and in the loading and unloading execution process, the operation state is monitored in real time, the operation state is detected and analyzed, an interference detection result is obtained, and the action planning instruction is dynamically adjusted based on the interference detection result. The production line has the effect of improving the efficiency of the production line.

The invention discloses a heterogeneous resource scheduling method and device of a cloud data center, a medium and a product, and relates to the technical field of cloud computing, and the method comprises the following steps: receiving a scheduling task of an application instance containing source architecture resource information; screening the processing nodes according to the computing power demand of the application instance and the specification reference values of the processing nodes, and determining candidate nodes according to the screening result; constructing an application performance portrait of the application instance according to the performance data of the application instance; according to the application performance portrait and the scheduling task, equivalent resource configuration corresponding to deployment of the application instance on the candidate node is determined, a score is determined based on the equivalent resource configuration and the specification reference value, and a target node is determined based on the score; and modifying the resource request of the application instance based on the equivalent resource configuration to obtain a modified resource request, and deploying the application instance to the target node, so that the target node performs resource configuration based on the modified resource request to complete heterogeneous resource scheduling. And equivalence quantization and intelligent scheduling of heterogeneous resources are realized.

The invention belongs to the technical field of cooperative control, and particularly relates to a development experiment box multi-device cooperative control method based on an open-source gap system. According to the method, access, identity authentication, automatic discovery and dynamic connection of experimental box equipment are realized through a distributed equipment management platform of an open-source Gao Mongolia system; a uniform resource scheduling mechanism is utilized to complete resource information collection, strategy matching and dynamic allocation; task allocation is carried out by adopting an intelligent task scheduling algorithm and combining equipment states and task requirements; real-time data transmission between devices is realized by means of a low-delay communication protocol; and finally, automatically summarizing and analyzing experimental data and generating a report. According to the method, the problems of low equipment identification efficiency, low resource utilization rate, low task allocation efficiency and the like in the existing development experiment box are solved, the intelligent level and the system stability of equipment cooperative control are improved, and the method is suitable for a multi-equipment cooperative control scene of the development experiment box.

The invention relates to a TSN network configuration automatic generation method and system based on an anti-Agent. The system is composed of a network state perception and intention reflection module, a YANG model configuration parameter and structure decision module and a YANG model intelligent generation module. The network state sensing and intention reflecting module is responsible for structurally sensing the network state and resource information, the YANG model configuration parameter and structure decision module fuses multi-source knowledge and context information, intelligently generates deployable TSN scheduling parameters and a YANG configuration structure for service requirements, and sends the TSN scheduling parameters and the YANG configuration structure to the network state sensing and intention reflecting module. And the YANG model intelligent generation module automatically generates a corresponding YANG configuration model according to an optimized result, and issues the YANG configuration model to target equipment after completing grammar and semantic verification, so that automatic deployment of network configuration is realized. According to the method, the intellectualization level of TSN network configuration and the deployment automation capability are remarkably improved by introducing the reflection reasoning and feedback optimization technology.

The embodiment of the invention relates to the field of large language models and multi-agents, and provides a multi-agent collaborative query response method based on a state dependency graph and a related device, and the method comprises the following steps: receiving a to-be-queried text input by a user; performing query semantic analysis on the to-be-queried text to obtain the analyzed to-be-queried text and query complexity; instructing the intention recognition agent to generate a domain index; calling a preference matching agent to generate preference information; calling a resource retrieval agent to query to obtain query use resource information corresponding to the to-be-queried text according to the query complexity; the query response information is generated at least according to the historical context of the to-be-queried text, the to-be-queried text, the analyzed to-be-queried text, the field structure information, the field index, the preference information and the query use resource information, and the personalized service corresponding to the to-be-queried text currently input by the user can be automatically adapted according to the actual interaction scene; and response type and high-efficiency intelligent assistance is realized.

The invention relates to the technical field of virtual power plants, and provides a partition virtual power plant optimization scheduling method and device based on reinforcement learning, and a medium, and the method comprises the steps: determining a power partition set based on the resource information of a virtual power plant; constructing a global optimization scheduling model, and establishing global constraint conditions about the global optimization scheduling model; constructing a partition optimization scheduling model, and establishing a partition constraint condition set; determining a global optimization scheduling strategy based on a near-end strategy optimization algorithm, the global optimization scheduling model and a global constraint condition; determining a partition optimization scheduling strategy based on a heterogeneous proxy near-end strategy optimization algorithm, the partition optimization scheduling model and the partition constraint condition set; and performing joint training, generating a cross-region optimal scheduling strategy, and determining a target scheduling method of each partition. According to the embodiment, the scheduling efficiency and flexibility of the virtual power plant in the multi-partition power system are improved by combining the global and partition optimization scheduling strategy, the resource configuration is optimized, and the energy consumption and the cost are reduced.

The embodiment of the invention discloses a distributed computing power resource scheduling method and system, a storage medium and a program product. The method comprises the following steps: decomposing a composite computing task into a plurality of sub-tasks and constructing a dependency graph; obtaining demand resource information of each subtask and constructing a demand resource matrix according to the demand resource information; obtaining performance data of the global computing power node and constructing a global dynamic resource portrait according to the performance data; dividing global computing power nodes into different areas based on the global dynamic resource portrait; based on the demand resource matrix and the performance data, determining an adaptive area of each sub-task from all the areas, and then determining adaptive computing power nodes of the sub-tasks from the adaptive areas of the sub-tasks; and sequentially executing each subtask based on the dependency graph and the adaptive computing power node of each subtask. According to the method, the problems of low cross-data center resource utilization rate, high burst task response delay and poor heterogeneous environment scheduling efficiency can be solved, the overall computing power resource utilization rate is improved, and the task average delay is reduced.

The invention belongs to the technical field of informatization management, and provides a unified authority management method and system, electronic equipment and a medium, and the method comprises the steps that an application management module generates an application system ID based on application system information; the resource management module generates a resource identifier based on the resource information; the role management module creates roles; establishing an association relationship between the role and the role permission range; the user management module creates a user account; the authorization management module receives an authorization request carrying the user account and the corresponding role, and establishes an association relationship among the user account, the corresponding role and the role permission range by using the association relationship; the authorization management module accesses the request, and pushes an application to the user account by using the access address; and according to the association relationship among the user account, the corresponding role and the role permission range, authorizing the corresponding operation permission to the user. By defining the relationship among applications, roles, users and resources, centralized and refined authority management of a plurality of background systems of an enterprise can be realized.

The invention relates to the technical field of data transmission optimization, in particular to a low-power-consumption data transmission optimization system and method for a smart watch. According to the method, the data transmission strategy is dynamically adjusted by combining the user behavior state, the data content characteristics and the system operation parameters. The system firstly senses the wearing state, interaction frequency and equipment resource information of a user, evaluates the priority of to-be-transmitted data, predicts the short-time attention probability of the user based on the behavior trend, and calculates the cognitive perception index of the data. And further comprehensively considering energy consumption, cognitive risk and resource occupation through a scheduling cost function, selecting an optimal transmission mode, and executing instant uploading, delayed caching, compression processing or communication protocol switching operation at an equipment end. According to the scheme, the data redundancy transmission frequency can be effectively reduced, the timeliness of key data is ensured, the overall energy efficiency of the system is remarkably improved, and the method is suitable for the smart watches in various dynamic use scenes.

The present application discloses a production line resource scheduling method, a scheduling apparatus, an electronic device, and a storage medium. The method comprises: generating an initial scheduling population on the basis of production line resource information, the initial scheduling population comprising a plurality of pieces of first scheduling information; on the basis of the production line resource information, determining a first scheduling time of each piece of first scheduling information; performing a crossover operation on a plurality of pieces of target first scheduling information to obtain a crossover scheduling population, the crossover scheduling population comprising a plurality of pieces of second scheduling information; obtaining third scheduling information on the basis of the crossover scheduling population, a third scheduling time of the third scheduling information being less than a second scheduling time of the second scheduling information; and outputting fourth scheduling information on the basis of the first scheduling time and the third scheduling time. According to the method, in an automatic production process, the crossover operation is performed on the initial scheduling population that is generated on the basis of the production line resource information, so that a scheduling scheme of the production line resources is automatically generated, thereby improving the production efficiency of the automatic production process.

The invention relates to the technical field of artificial intelligence chips, and provides a register resource management method and device, equipment and a storage medium, and the method comprises the steps: obtaining register resource information needed by an operator; based on the register resource information, target register addresses are searched from register resource pools of all the thread bundles, and the target register addresses are register addresses which are the same in address and are not used in the register resource pools of all the thread bundles; and allocating the target register address to the operator, so that each thread bundle shares the target register address when executing the operator. According to the method, through an intelligent register resource searching and distributing mechanism, independent application in the processing logic of each thread bundle is not needed, and only one-time application outside the logic of the thread bundle is needed, so that the requirements of different logic among multiple thread bundles and application of same address register resources can be met, and the expense of register resource application is reduced.

The invention relates to the technical field of tourism resource information processing, in particular to a tourism hotel dynamic scoring and recommending method and system based on multi-factor fusion, and the method comprises the steps: constructing a dynamic game portrait containing a user space-time behavior mark and hotel multi-modal attributes, and carrying out the dynamic scoring and recommending of a hotel based on the dynamic game portrait; the method comprises the following steps: analyzing a recessive preference vector of a user and an adaptive strategy vector of a hotel by simulating an interactive game process of the user and hotel attributes, performing dynamic equalization matching on the recessive preference vector and the adaptive strategy vector to generate a hotel situation score, generating an anti-consensus recommendation sequence according to the hotel situation score, and performing recommendation on the hotel situation according to the anti-consensus recommendation sequence. The anti-consensus recommendation sequence is subjected to interpretable packaging, and a final recommendation result with a decision guide clue is output to the user, so that the user can obtain a personalized hotel recommendation list and can also understand logic and diversity values behind recommendation, and the user experience is improved. Therefore, the transparency, the user trust degree and the exploration satisfaction degree of the recommendation system are improved.

The present application discloses a low-orbit satellite beam scheduling and resource allocation method, device, equipment and storage medium, which relates to the field of communication technology. The method includes: determining the time slot priority of the user to be scheduled; dividing the users to be scheduled into different user groups and determining the wave position interference matrix between the user groups; allocating time slots based on the time slot priority of the user to be scheduled and the wave position interference matrix between the user groups, and generating a beam hopping spectrum; within the update period of the beam hopping spectrum, determining the target user to be scheduled corresponding to the available beam in each time slot; allocating resources to each time slot based on the service priority of the target user to be scheduled for the available beam in each time slot. Through the above method, interference isolation and time slot allocation calculation are completed based on user priority and user grouping, multi-user resource allocation scheduling is performed in a single time slot, air interface resource information is fully utilized, and spectrum utilization and system throughput are maximized.

The embodiment of the invention relates to the technical field of task scheduling, and discloses an AI-based Kubernetes task priority scheduling method and device, and the method comprises the steps: obtaining the historical task information of at least one task in a Kubernetes cluster, the historical task information comprises the historical task execution information of each task, business importance levels and business timeliness information of businesses to which the tasks belong; inputting historical task information of at least one task into a task priority prediction model to obtain a task priority score of each task; acquiring real-time resource information of the Kubernetes cluster and real-time task execution information of each task; and scheduling each task based on the real-time resource information, the real-time task execution information of each task and the task priority score of each task. And a service fine-grained and dynamic resource scheduling strategy is realized. The resource allocation of each task can be flexibly adjusted according to the real-time resource of the current Kubernetes cluster and the task execution information of each task, and the resource can be dynamically preempted according to the task priority score.

The invention discloses a resource processing method and electronic equipment, and relates to the technical field of resource management, and the method comprises the following steps: in the starting process of the electronic equipment, analyzing a group of specified registers indicated by configuration space information of connected equipment in a plurality of connected equipment, the method comprises the steps of obtaining resource information of a plurality of connected devices, obtaining resource configuration information, and allocating specified resources to the connected devices in the plurality of connected devices based on the resource configuration information and the resource information of the connected devices in the plurality of connected devices. According to the resource allocation method and device, the technical problem of resource allocation mismatching caused by firmware information misidentification in the prior art is solved, and the technical effect of improving the accuracy of resource allocation is achieved.

The invention is applicable to the technical field of cloud computing resource management and scheduling, and provides a resource scheduling method, device, equipment and medium based on instance specification awareness, and the method comprises the following steps: collecting multi-dimensional resource information and task load information of a plurality of computing nodes in a cloud computing platform in real time, and according to the multi-dimensional resource information and the task load information, scheduling the computing nodes according to the multi-dimensional resource information and the task load information; a target scheduling action meeting the instance specification requirement at the current moment is obtained through a pre-trained multi-dimensional resource specification perception scheduling model, the multi-dimensional resource specification perception scheduling model is obtained through population-based reinforcement learning training mechanism training, and task resource allocation is executed according to the target scheduling action. And the task execution result is monitored, and the multi-dimensional resource specification perception scheduling model is adaptively updated based on the feedback information obtained by monitoring, so that the cloud environment resource utilization efficiency, the task matching rate and the dynamic environment response speed are improved.

The invention discloses a hierarchical authority control method and platform for a network management platform oriented to cloud side-end collaboration. The method comprises the following steps: constructing a topological structure by collecting equipment resources, user identities and authority level information of all levels of platforms; based on a task scheduling and resource management algorithm and an edge-cloud collaboration model, factors such as task types and resource loads are comprehensively considered, and initial allocation and dynamic adjustment of tasks between cloud edge ends are achieved; verifying a user request by using the permission level information, recycling resources after a task is completed, and feeding back a result; and regularly collecting and analyzing data, and optimizing an algorithm, a model and an authority control strategy. The platform comprises a resource information acquisition unit, a task initial allocation unit, a task cooperative processing unit, an authority verification unit, a resource recovery feedback unit, a strategy optimization unit and the like, and the units are mutually connected and cooperated. According to the method, efficient task scheduling, accurate authority control and reasonable resource utilization of the cloud side-end collaborative network are realized, and stable operation of a network management platform is guaranteed.

The invention relates to the field of artificial intelligence, in particular to a heterogeneous GPU-oriented large model reasoning platform adaptation method and system. The method comprises the following steps: continuously and dynamically collecting and maintaining all available GPU resources in a server cluster and attribute and state information of a deployed reasoning acceleration framework instance through proxy service deployed in each server node; providing a single entry point to receive a reasoning service request, and analyzing the request to determine a large model needing reasoning; according to the analyzed large model needing reasoning and the currently aggregated resource information, selecting a reasoning acceleration framework instance which is optimal in load state and compatible for the request through a decision algorithm, and routing the task to the reasoning acceleration framework instance; heterogeneous response formats returned by different inference acceleration framework instances are converted and packaged into a unified standardized format, and then the unified standardized format is returned to a requester. Scattered and diversified computing resources are effectively utilized, and dependence on a single hardware manufacturer is reduced.

The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. The present invention discloses a method performed by a UE in a wireless communication system, the method includes storing first resource information related to a contention-based random access (CBRA) and second resource information related to a contention-free random access (CFRA); receiving, from a base station, a first message for requesting random access related information of the terminal; and transmitting, to the base station, a second message including the stored first resource information and the stored second resource information in response to the first message.

The invention provides an electric power computing network multi-dimensional resource scheduling method, device and platform and a storage medium, and belongs to the technical field of electric power computing power networks, and the method comprises the steps: obtaining the request information of a current service terminal and the computing power resource information of a cloud side computing node; determining an end-to-end time delay constraint, a cloud edge computing power resource constraint and a cloud edge end network bandwidth constraint of the intelligent server; according to the cloud edge computing power unit price and the network bandwidth unit price, determining an objective function of computing network multi-dimensional resource scheduling optimization by taking minimization of the network bandwidth and the cloud edge computing power overhead as an optimization objective; solving the plurality of scheduling constraint models to obtain an optimal solution; and outputting a computing network multi-dimensional resource scheduling strategy according to the optimal solution. According to the method, under the condition that different computing network resource service quality requirements of multiple services are considered, by dynamically deploying multi-dimensional resources such as network bandwidth, cloud computing power and edge computing power, the low response delay requirements of key services such as environmental risks and equipment faults in power grid operation are effectively met, and meanwhile it is ensured that the computing network resource consumption economical efficiency is optimal.

The invention relates to a GTX Quad resource multiplexing method and system based on a Xilinx FPGA. The method comprises the steps that a public configuration module of a GTX transceiver in a protocol IP core is stripped into an independent shared resource module; constructing a standardized interface bus for connecting each protocol IP core with the independent shared resource module; dynamically allocating resource information of the quaternary phase locked loop and the channel phase locked loop according to a protocol rate demand; and deploying a priority arbitration state machine in each channel in the Quad, monitoring protocol bandwidth requirements of each channel, and performing configuration snapshot switching based on the protocol bandwidth requirements so as to realize time division multiplexing of GTX Quad resources. According to the method and the device, the common configuration module is decoupled into the independent shared resources, the standardized interface bus is constructed, the clock resources are dynamically allocated, and the snapshot is configured in a time division multiplexing manner, so that the problems of resource monopolization limitation and insufficient protocol compatibility in the prior art are solved, the GTX Quad resource multiplexing rate is improved, and multi-protocol dynamic switching can be supported.

The invention relates to a distributed model reasoning method, device and equipment and a storage medium, the method is applied to center equipment of a distributed network system, and the distributed network system comprises one piece of center equipment and a plurality of pieces of edge equipment; comprising the following steps: dividing a to-be-distributed model according to structure information of the to-be-distributed model to obtain a plurality of sub-models, and distributing each sub-model to at least one edge device; wherein the structure information represents a hierarchical structure between the model layers, and the plurality of sub-models form a to-be-distributed model; if the model reasoning request is responded, obtaining resource information of each edge device; wherein the resource information represents the operation state of the edge device; determining a target device from the at least one edge device allocated by the sub-model according to the resource information of each edge device; and controlling the target equipment to execute the corresponding sub-model according to the execution sequence among the sub-models. The operation efficiency of the model is improved, and full utilization of resources is realized.

The invention discloses a distributed AI training task processing method and system, and relates to the technical field of cloud computing. The distributed AI training task processing method comprises the steps that labels are set for hardware resources of nodes of each cluster of each cloud platform, and resource information of each hardware resource is acquired; and intercepting a CRD configuration file containing a hardware resource quantity request through a pre-deployed webhook service in the cluster, and creating an AI training task carrying the hardware resource quantity request after modifying resource information in the CRD configuration file into a parameter format adaptive to the hardware environment of the cluster according to the tag. According to the method and the device, hardware resources in multi-cloud platform heterogeneous resources can be scheduled and used in a unified manner without manual intervention, dynamic parameter adaptation and automatic injection of hardware resource configuration can be completed through the webhook service deployed in the cluster, and seamless resource scheduling and adaptation across cloud platforms and across hardware architectures are realized.

The invention relates to a thermal migration method and device for power distribution network services, electronic equipment, a storage medium and a computer program product. The method comprises the steps of obtaining resource information of each edge node in a plurality of edge nodes contained in a power distribution network and load information of services on each edge node; inputting the resource information of each edge node and the load information of the service on each edge node into a residual network contained in the trained service migration prediction model to obtain feature representation; the feature representation is input into a migration strategy decision module contained in a service migration prediction model, a service migration indication is obtained, and the service migration indication is used for indicating the to-be-migrated service and a migration path and a migration destination edge node corresponding to the to-be-migrated service; and issuing the service migration indication to the edge node containing the to-be-migrated service in the plurality of edge nodes, so that the edge node containing the to-be-migrated service carries out live migration on the to-be-migrated service based on the service migration indication.

The invention discloses a load clustering method based on load adjustable potential, and particularly relates to the technical field of power regulation and control, and the method comprises the steps: collecting the behavior and resource information of a load user, constructing feature vectors, carrying out the bi-clustering analysis, and extracting a user intersection with the behavior and resource dual matching; high-regulation potential users with high response reliability are screened out after identification; and combining the load pressure, resource saturation and historical scheduling feedback of the power grid, dynamically adjusting clustering parameters by using the trained machine learning model, optimizing the classification boundary and precision, and realizing accurate identification of a load adjustment user and stable support of a power grid scheduling strategy. According to the method, synchronous recognition of user response behaviors and resource controllability is achieved through two-dimensional clustering and intersection screening, the neural network is introduced to achieve dynamic self-adaptive adjustment of clustering parameters, the user screening reliability is improved in combination with a response confidence verification mechanism, and therefore the accuracy, practicability and stability of load adjustment resources are remarkably enhanced.

The invention relates to the technical field of data encryption transmission, in particular to an electric power communication resource data encryption method, system and equipment based on multi-algorithm fusion, and the method comprises the steps: dividing data into a core control class, an equipment state class and a resource information class according to the purpose of electric power communication resources, a security label containing data sensitivity, transmission timeliness and storage cycle information is distributed to each type of data; an initial quantum key is generated by adopting a quantum key distribution protocol, an asymmetric key pair is generated in combination with an SM2 algorithm, and the quantum key is signed by using an SM2 private key and then transmitted; the dynamic secret key update is triggered based on the timestamp or the data volume threshold value; encrypting core control type data, dynamically generating confusion layer parameters, and meanwhile, generating a message digest in combination with an SM3 hash algorithm; and the receiver decrypts the confusion layer parameter, decrypts to obtain the plaintext, and verifies the SM3 hash value to confirm the data integrity. And the limitations of a single algorithm on efficiency, strength and aggressiveness resistance are made up.

Systems and methods are described for executing tenant artificial intelligence pipelines by dynamically selecting service providers based on resource consumption. A server can poll a group of service providers and receive resource information that indicates compute, network, storage, and token requirements to perform an action. When the tenant AI pipeline executes, a pipeline engine can select a service provider to execute the action based on stored resource information. The service providers available in the group can also dynamically change based on terms of service.