1666 results about "Scalability" patented technology

The invention provides an optical storage charging and discharging station aggregation control and optimization method based on a virtual power plant, and aims to solve the problems of multi-target collaborative optimization, dynamic resource response and uncertainty robustness. By introducing a Markov decision process and an adaptive clustering algorithm, the system can dynamically aggregate photovoltaic, energy storage and charging pile resources according to equipment characteristics, and power dispatching is optimized. A multi-objective optimization model is adopted, economical, technical and environmental objectives are combined, a dynamic weight factor is introduced, and optimal scheduling is generated in combination with a fuzzy decision theory. And real-time compensation is carried out by adopting a rolling time domain control framework and deep reinforcement learning, so that the scheduling precision and the response speed are improved. The edge computing and cloud collaboration mechanism reduces the communication load through a lightweight federated learning model, and improves the scheduling response efficiency. According to the invention, the scheduling efficiency of the optical storage charging station can be obviously improved, the operation cost is reduced, the system stability is improved, and the system has good adaptability and expandability.

Systems and methods are disclosed for optimization, management, and merging of processes. This system integrates a multifaceted technological framework, including generative artificial intelligence, quantum computing simulations, and blockchain technology. It features a user interface for inputting diverse workflow requirements, a generative AI module for processing these inputs, and a quantum computing module for simulating and optimizing workflows. The system utilizes blockchain for secure workflow deployment and a suite of specialized engines for prompt management, data extraction, analysis, optimization, deployment orchestration, and continuous monitoring. These components ensure the system's adaptability to user-specific needs, scalability across various industries, and capability for integration with existing enterprise systems. This invention revolutionizes BPM by streamlining processes, enhancing efficiency, and maintaining high security and customization standards.

The invention relates to the technical field of data processing, in particular to a modular task management system based on dynamic process configuration, which comprises a dynamic process configuration module, a task state circulation engine, a visual interaction interface module, a historical track tracing module and a modular expansion interface. The dynamic process configuration module dynamically binds enterprise configuration data with regional process original data through a process template ID to generate an enterprise exclusive process topological structure comprising a regional identifier, a node association relationship and a process classification label; and the task state circulation engine updates the task state data based on an event-driven and atomized transaction lock mechanism, and synchronizes the task state data to the change management general table. According to the method, flexible configuration of process logic, visual display of task states and cross-enterprise rapid adaptation are realized, and system expansibility and operation efficiency are improved.

The invention relates to the technical field of data acquisition, and discloses a multi-protocol extensible data acquisition system architecture and method with a unified interface standard, and the architecture comprises a data source management module which is used for storing data source configuration information and distributing protocol identifiers; the protocol adaptation module comprises an analysis unit and a matching unit and is used for realizing data format conversion and protocol compatibility verification; the data conversion module is used for generating target data through format conversion and semantic mapping; the data verification module and the anomaly detection module are used for comparing and monitoring data integrity based on an integrity algorithm and a threshold value; the data quality monitoring module and the early warning module are used for comparing and evaluating data quality through a consistency model and a threshold value; and the result output module transmits the early warning information to a target system. The method comprises the steps of data source configuration, protocol adaptation, data conversion, integrity and quality monitoring, early warning and the like. The system expansibility and the data reliability are improved, and the method is suitable for multi-scene data acquisition.

The invention relates to the technical field of semantic analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a dialogue processing method, device, equipment and medium based on historical dialogue recognition, which comprises the following steps: receiving an interaction request, judging whether a stored historical dialogue identifier is associated, directly generating first response data when the stored historical dialogue identifier is not associated, and sending the first response data to a server; and extracting historical session data from the database when the data are associated, stopping extraction after a capacity threshold value is reached, integrating the interaction request and the extracted data into context parameters, submitting the context parameters to the intelligent processing module to obtain second response data, and outputting the first response data or the second response data. According to the method, on the basis of judging the session relevance, the historical session data is subjected to limited extraction, and the context parameters are constructed, so that the capability of keeping session continuity under the condition that computing resources are limited is realized, and the processing efficiency of multiple rounds of sessions and the system expandability are improved.

The invention provides a multi-agent-based low-code component development method and system and electronic equipment, and the method comprises the steps: obtaining a user demand, and converting the user demand into an initial business model through a first agent; obtaining a prefabricated component library of the low-code platform, and automatically generating a demand component by using a second agent according to the initial business model and the prefabricated component library; automatically adapting and integrating a cross-platform data source by using a preset cross-domain data integration mechanism, and performing intelligent scheduling on the automatically generated demand component through a third agent; and establishing an automatic service arrangement mechanism based on the micro-service architecture, automatically identifying each service dependency relationship, and dynamically adjusting the execution sequence and resource allocation of each demand component according to the service dependency relationship. Through modular component design, the low-code platform has high flexibility and expandability, efficient collaboration of the components in the complex industry is guaranteed, and resource allocation and use efficiency is optimized.

The invention relates to the field of unmanned aerial vehicle identification, in particular to a low-altitude target identification method and system based on reinforcement learning multi-modal fusion, and the method comprises the following steps: 1, constructing a multi-source data fusion architecture; step 2, carrying out feature alignment based on Transform; 3, reinforcing a learning training mechanism; and step 4, a lightweight deployment scheme. Compared with the prior art, the method has the following advantages that the recognition performance breaks through that the model recognition accuracy rate is 92.4% (industry average 68%), and the false alarm rate is reduced to 0.7% (5.2% in the traditional technology); dynamic environment adaptation: through dynamic weight adjustment, the recognition stability in an electromagnetic interference scene is improved by three times; the hardware cost is reduced to 30 thousand yuan / node (the traditional system is greater than or equal to 20 thousand yuan) by a lightweight model; technical extensibility: a three-dimensional feature matrix architecture can be compatible with novel sensors such as quantum radar and the like; the reinforcement learning framework can be migrated to civil scenes such as low-altitude logistics supervision and the like.

The invention discloses a digital product online leasing system based on a cloud native micro-service architecture and a construction method thereof, and the method specifically comprises the steps: firstly, carrying out containerized deployment and micro-service hierarchical design, including a plurality of business modules of user service, product management, leasing order, payment settlement, credit evaluation and recommendation, and enabling each module to operate in an independent service form; efficient cooperation is realized through service registration and discovery; a cloud native platform is utilized to realize elastic expansion and automatic operation and maintenance of the system, lease life cycle management is realized through an event-driven mechanism, links of ordering, payment, delivery, lease renewal, return and maintenance are covered, and a credit determination and risk control mechanism is introduced to support multiple lease modes such as detention-free lease and the like. The digital product leasing platform supports multi-terminal access and unified entrance management, has good system expansibility and leasing service flexibility, improves the concurrent processing capability, deployment efficiency and user experience of the digital product leasing platform, and has wide commercial application prospects.

The invention relates to the technical field of blockchain privacy protection, and discloses a homomorphic encryption and blockchain privacy protection method and system, and aims to encrypt a transaction amount through multi-key fully homomorphic encryption, store transaction metadata in combination with a Hash commitment mechanism, ensure that data on a chain is always in an encrypted state, and prevent unauthorized access. And secondly, in order to solve the problem that the smart contract cannot directly process the ciphertext, homomorphic calculation is combined with zero-knowledge proof, so that the smart contract can verify the transaction legality under the condition of not decrypting data, and the calculation burden is reduced by combining multi-party calculation. And finally, in order to optimize the storage and query efficiency, in combination with under-chain encryption storage, the encryption hash value is only stored on the chain, and meanwhile, proxy homomorphic signature is adopted to enhance data access authority management, so that the expandability and calculation efficiency of the block chain are improved.

The invention provides a task disassembly and multi-agent arrangement execution system and method based on a large language model, and belongs to the technical field of computers, and the system comprises an instruction analysis module, a DAG construction module, a scheduling execution module and a cache optimization module. Analysis is carried out according to the dependency relationship between the tasks, a task execution DAG is automatically constructed, and concurrent calling is carried out on the sub-modules without dependency; by caching an authentication result, a context reasoning result and the like, a universal module is executed in advance, the result is reused, and repeated calculation is reduced; the maintainability and the expandability of the system are improved through graph structure visualization and node element information injection; and performing context analysis and scheduling optimization in combination with the reasoning ability of the language model to realize an intelligent decision execution path.

The invention discloses a data analysis method and device based on multi-agent cooperation and a storage medium. The method comprises the following steps: receiving a natural language request by a workflow arrangement agent, and generating an execution plan comprising a plurality of subtasks and corresponding task processing units by consulting a data exploration agent and a data engine understanding agent; in the execution stage, the professional agent matched with the subtask converts the subtask into an executable instruction, and the data engine dispatch agent calls and coordinates the corresponding task processing unit to execute the instruction. According to the method, through a layered collaborative architecture, the separation of planning, translation and execution is realized, and the automation level, modularization and expandability of data analysis are remarkably improved.

Systems and methods for decentralized orchestration of event records within decentralized computing environments. A processing system including decentralized execution nodes dynamically allocates microservice instances based upon real-time node performance metrics. Transaction requests including transaction data digitally associated with decentralized identifiers (DIDs) are processed by allocated microservice instances. The processing includes verifying transactions, generating cryptographically-linked immutable memorialization records associated with respective DIDs, and distributing these records to a tamper-evident distributed ledger configured to enforce immutability through consensus nodes. The processing system dynamically reallocates microservice instances among decentralized execution nodes based upon monitored utilization metrics and predefined scaling thresholds. Each memorialization record is cryptographically secured and linked permanently to the DID for data immutability, enhanced security, and dynamic scalability for event data processing in distributed infrastructures.

The invention relates to a large model safety shield system and a safety protection method. The large model safety shield system comprises three layers of input layer safety protection, business scene processing layer safety alignment and output layer safety auditing, and the safety of the whole processing flow is ensured through a multi-layer protection framework. According to the large model safety shield system, the cue word shield module, the multi-dimensional alignment expert model module and the output supervision module are utilized, and real-time safety detection and multi-dimensional alignment processing of user input and real-time monitoring and auditing of output content are achieved. The method has the advantages of high-efficiency security detection, high-accuracy interception, optimized user experience and good expandability and adaptivity, and the security and reliability of application of a large model in the fields of finance and the like are effectively improved.

The invention discloses a TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and a medium. The method comprises the following steps: deploying a lightweight flow detection module in a switch or a router, and after a controller receives a data request, automatically identifying a newly arrived unknown service flow by using the lightweight flow detection module, and judging whether the newly arrived unknown service flow is a periodic TT flow or an unpredictable burst flow; the controller performs classification management on the identified service flow types, collects topological information and flow requirements of the whole network and issues the topological information and the flow requirements to the terminal nodes through a network management interface; the controller constructs an intelligent queue scheduling task based on the collected network topology information and traffic demand and converts the task into a Markov decision process MDP, network resources, queue states and priorities are used as a state space, a scheduling strategy is used as an action space, a reward function is designed in combination with throughput and delay indexes, and an intelligent queue scheduling task is obtained. Driving a dynamic environment through real-time data and training a DRL model; an enhanced queue scheduling mechanism Pro-CQF is adopted, different priority labels are configured according to classified flow types, and then mixed flow scheduling is carried out; the controller periodically collects time delay, packet loss rate and end-to-end transmission delay indexes and feeds back the indexes to the DRL model, and a scheduling strategy is updated online. According to the method, the traffic sensing and scheduling efficiency is greatly improved in a network environment in which multiple service flows coexist and end-side equipment functions are different, and the reliability and the expandability of the TSN in industrial Internet of Things, edge computing and other high-real-time application scenes are remarkably enhanced.

The invention discloses an MCP-based multi-agent collaboration method and device and a medium, and relates to the technical field of artificial intelligence, the method comprises the following steps: obtaining a model registry form through a standardized model registration interface, analyzing model metadata in the registry form, and extracting a core field of a current registration model; creating a model node in the model map, and initializing the model node based on the core field; receiving a task request, triggering a scheduling engine, determining a plurality of candidate model nodes in the model atlas according to task content, respectively calculating task execution scores of the candidate model nodes, and generating a model execution chain based on the task execution scores; and based on the model execution chain, scheduling a candidate model corresponding to the candidate model node, and executing the corresponding task content. Dynamic access of multiple types of models is supported through a standardized model registration interface, system expansibility and integration efficiency are improved, and matching precision of tasks and models and multi-model cooperation continuity are enhanced.

The invention provides a data processing method and system based on cloud computing, belongs to the technical field of data processing, and realizes high concurrent processing and automatic load balancing by dynamically scheduling computing power through elastic resource allocation and a distributed computing framework and combining a storage and computing separation framework. The system supports cross-node disaster recovery backup and multi-layer encryption, and data security is guaranteed; and an on-demand payment mode is adopted, so that the hardware input cost is reduced, the resource utilization rate is improved, and the large-scale data processing efficiency and the system expandability are remarkably improved.

The invention relates to the technical field of industrial internet data processing, in particular to a cross-platform multi-source data integration analysis method and system based on the industrial internet, and the system comprises a data collection layer, a data preprocessing layer, a data integration layer, a data storage layer, an intelligent analysis framework, an application layer and a data security layer. Through the data adapter and middleware technology, efficient collection and integration of multi-source heterogeneous data are achieved, multiple data sources and communication protocols are supported, the compatibility and expansibility of the system are improved, and the system can rapidly adapt to different industrial scenes and data source changes; according to the method, advanced big data and artificial intelligence technologies are applied, a specific analysis model for industrial production is combined, deep analysis can be performed on the data, potential values in the data are mined, an accurate analysis result is provided for equipment fault prediction and production process optimization, and the intelligent level of industrial production is improved.

The invention discloses a storage cluster, a system, a data processing method, equipment, a medium and a product, and relates to the technical field of data storage. A plurality of devices are arranged in a controller unit, a switch unit and an extended memory unit; a plurality of switches of the switch unit are correspondingly connected with a plurality of extended memories of the extended memory unit, and copy data of a plurality of controllers are respectively stored in the extended memories. The technical problem that the high availability of a storage system is poor due to the fact that the expansibility of the number of controllers and the number of data copies is poor is solved, and the purposes that memory cache data of the controllers are decoupled from DRAM memories in the controllers to external extended memories, deployment operation is simplified, N-1 controllers are allowed to fail at the same time, and the reliability of the controllers is improved are achieved. The data access processing can be completed under the condition that the M-1 switches simultaneously fail and / or the extended memories corresponding to the P-1 data copies fail, so that the technical effect of high availability of the storage system is improved.

The invention discloses a high-precision three-dimensional point cloud visualization method, electronic equipment and a storage medium, and belongs to the technical field of geographic space information systems.The high-precision three-dimensional point cloud visualization method comprises the steps that S1, a point cloud file is input, and meta-information of the point cloud file is obtained; s2, performing scalable data geographic gridding processing on the point cloud file meta-information to obtain a basic grid object; s3, adopting a data stratified sampling method to obtain a multi-level grid object; s4, constructing a multi-level index file based on the multi-level grid, and outputting the multi-level index file; s5, performing high-availability, high-concurrency and compression processing on the output multi-level index file; and S6, performing real-time hierarchical scheduling rendering on the point cloud based on the processed multi-level index file. A point cloud is quickly split into small blocks by using a gridding technology, then parallel processing is performed, a hierarchical rendering level is generated by performing sub-sampling on a whole data set from bottom to top by using a plurality of sampling strategies, and meanwhile, a quick layering strategy is introduced, so that the throughput is remarkably improved.

The invention discloses a heterogeneous computing system, a cache consistency maintenance method and device, equipment and a medium, and relates to the technical field of heterogeneous computing. The method comprises the steps that when a data operation request is detected, if a receiving party and a transmitting party are a cluster cache layer and a global cache layer, the protocol format of the data operation request is converted according to the mapping relation between a group consistency protocol and a global consistency protocol. And when the data is forwarded to the local cache layer, executing corresponding operation based on the memory consistency model of the calculation unit to which the data belongs, and maintaining the consistency of the local cache. And when the data is forwarded to a cluster cache layer, corresponding operation is executed based on the cluster consistency protocol, and the consistency of the cluster shared cache is maintained. And when the data is forwarded to a global cache layer, maintaining the consistency of the shared caches of the clusters based on a global consistency protocol. The method and the device can solve the problem of poor compatibility and expansibility of related technologies, can be compatible with various computing units and different memory consistency models thereof, and are easy to expand.

The invention provides an agricultural machinery multi-source data fusion and collaborative operation method based on edge calculation. The method comprises the following steps: step 1, dividing a multi-source sensor group into three units to collect data; 2, constructing a data fusion and collaborative operation model through four parts of space-time calibration, dynamic weight data fusion, a real-time decision engine and multi-agricultural machinery collaborative operation; 3, realizing practical application of the data fusion and collaborative operation model in the step 2 through edge-cloud collaboration; and 4, performing test detection on the data fusion and collaborative operation model. According to the invention, real-time collection and fusion of agricultural machine multi-source data are realized through an edge computing architecture, and the data transmission delay is significantly reduced; the heterogeneous computing architecture design greatly reduces the power consumption of edge nodes, and meets the long-time operation requirement; and the edge-cloud collaboration mechanism supports incremental updating and exception verification of model parameters, so that the reliability and the expandability of the model are ensured.

The invention relates to the technical field of data processing, and discloses a method and a system for dynamically generating declaration receipts based on custom configuration, the method comprises the following steps: constructing a component library, the component library comprises multiple types of data item components, and each data item component is packaged into an independent module; based on the graphical configuration process, adding the components in the component library to a working area, and setting field attributes; setting a dependency relationship between fields based on conditional logic, defining a legality rule of the fields based on a data verification rule, and automatically storing a historical version in a configuration process based on version control; and based on preset authority and distribution setting, determining a filling person range and a filling time limit of the declaration receipt, and notifying a filling person. According to the invention, the form creation efficiency is improved, the maintenance cost is reduced, and the expandability and applicability of the system are enhanced.

The invention discloses a data sharing processing method and system applied to human resources, and relates to the technical field of data processing.The method comprises the steps that after multi-source data are collected and preprocessed, the data are classified, shared and stored, and a public storage area and an authorized access area are included; performing role and access authority management on the stored data, and executing data sharing compliance auditing; the access permission is dynamically adjusted based on the access environment, permission change records are stored through a Fabric alliance chain, anomaly detection is carried out according to the real-time access data, and a security policy is executed; and performing two-way matching of the job seeker and the enterprise based on the stored data. According to the method, the sharing efficiency and security of the human resource data are effectively improved, the expandability of data storage and the intelligence of authority management are enhanced, the method is suitable for application scenes such as enterprise human resource management, recruitment platform and government data sharing, and the value and application potential of the human resource data are greatly improved.

The various embodiments herein provide a system and method for networked digital twins with autonomous collaborative decision-making. The system comprises a Digital Twin Engine for real-time data acquisition, model synthesis, and simulation, an AI Module for advanced data analysis, an autonomous collaborative decision-making module for optimized decision-making, a communication layer for secure data exchange, and supporting modules for coordination, storage, security, and user interaction. The method for generating and deploying digital twins comprises data collection, transmission, preprocessing, model synthesis, simulation, validation, and deployment. The method for networking and collaboration comprises AI-based data processing, complex event processing, autonomous decision-making, task distribution, decision communication, real-time monitoring, and continuous improvement. This system enhances operational efficiency, scalability, and security, reducing the need for human intervention and providing a comprehensive management solution for complex systems.

The invention discloses a network data integration analysis system and method based on a model context protocol MCP, and relates to the technical field of computer networks. The system comprises an MCP-LSP adaptation layer, a network analysis language server, an IDE plug-in, a remote cooperation management module, an MCP analysis engine, an LSP client and an IDE UI component. According to the invention, deep fusion of the network protocol analysis capability and the integrated development environment is realized; by constructing an MCP-LSP adaptation layer, a network analysis language server can convert a semantic analysis result of an MCP analysis engine into an LSP standard message format, and standardized services of functions such as protocol analysis, session state and anomaly detection are realized; the working efficiency of a developer is improved by the IDE plug-in; the remote cooperation management module realizes synchronization and sharing of session states, improves integration, expandability and cooperation efficiency of network analysis, and is suitable for diversified requirements of modern software development teams.

The invention discloses an attribution analysis method based on a BI platform, a medium and equipment, and the method comprises the steps: collecting multi-dimensional business index data and an associated index data set, detecting abnormal fluctuation through employing a self-adaptive sliding window algorithm, and calculating an absolute growth contribution degree in combination with an improved contribution rate decomposition model; and fusing to generate a composite influence matrix. According to the technical scheme, chart type limitation can be broken through, and attribution adaptation of most mainstream charts is achieved; eliminating the risk that a denominator is zero through an improved contribution rate model, and precisely recognizing a proportion mutation dimension in combination with a JS divergence algorithm; through an automatic attribution map model and a rule engine, the manual operation time is shortened, and the hidden danger of data leakage is eliminated; the constructed unified data query structural body significantly improves the expansibility of the system, and meets the real-time analysis requirements of complex service scenes.

The invention relates to the technical field of information security, in particular to a security baseline setting method and system equipment based on clustering decision and a medium, and the method comprises the steps: collecting original data, carrying out the construction of a feature vector through feature conversion, generating a sample, and marking and extracting the sample; constructing a classification model by using manifold learning, inputting the extracted feature vectors into the constructed classification model, optimizing the model through training, and outputting classification labels; processing the decision boundary by using constraint optimization and feature scaling to obtain a standardized feature vector converted by a decision boundary model, grouping based on the standardized feature vector, and extracting a baseline; the security policy is generated through data analysis, and the data is dynamically updated and continuously monitored, so that the unified configuration and centralized management of the security policy are realized, and the policy implementation efficiency and the system expansibility are remarkably improved.

The invention discloses an end-side safe path planning method based on a small language model, which comprises the following steps: generating a task prompt by fusing the small language model SLM with an A * algorithm and a structured map token, storing the task prompt in a vector database, retrieving historical map information similar to the current task from the database when the path planning task is executed, and performing path planning according to the historical map information. The method comprises the following steps: constructing a COT logic chain prompt, guiding a small language model to gradually generate path key nodes and weights thereof in combination with the constructed COT logic chain prompt, then executing greedy path search by an A * algorithm by taking the key nodes as local targets, and finally generating a safe and efficient path. The method provided by the invention has end-side deployment capability, can be operated in scenes without network, resource limitation and the like, greatly reduces node traversal and improves planning efficiency compared with the existing A * algorithm, is particularly suitable for path planning tasks in a large-scale or high-density obstacle environment, gives consideration to intelligent reasoning and planning safety, and has wide application prospects. The method has good interpretability and expansibility, and has a wide application prospect.

The invention discloses a storage system, method and apparatus, an electronic device, a medium and a product, relates to the technical field of data management, and uses a memory device outside a controller to store write-in data without being limited by the controller, thereby improving the expansibility of the storage system.

The invention provides a data driving and multi-element cloud edge cooperative computing method for intelligent workshop construction, which comprises the functions of multi-protocol heterogeneous equipment sensing, edge node adaptive load balancing, lightweight real-time edge computing processing, cloud centralized management control, industrial intelligent application service deployment scheduling, cloud edge task unloading and the like. The system comprises a data acquisition and transmission middleware, an edge stream processing engine and a cloud service center. The method has the characteristics of real-time performance, low delay, flexibility, expandability, loose coupling, low resource occupancy, high availability and privacy security.