552 results about "Middle tier" patented technology

The invention belongs to the field of intelligent edge systems and privacy enhancement computing, and particularly relates to a privacy protection-oriented robot large model cloud edge end collaborative reasoning and federated learning system, which comprises a cloud server layer used for deploying a large-scale pre-training model and executing complex reasoning and global federated learning coordination; the edge calculation layer is used for deploying an intermediate layer model and executing local data aggregation, privacy protection processing and intermediate feature calculation; the terminal equipment layer is used for deploying a lightweight model and executing data acquisition, primary processing and lightweight reasoning; the federated learning framework is used for optimizing the model; the privacy protection module is used for integrating data localization, differential privacy, homomorphic encryption, secure multi-party computing and a block chain verification mechanism; the adaptive allocation module is used for dynamically adjusting computing resources. According to the method, the problems of privacy leakage risk, computing resource limitation, network delay, insufficient data isolation and the like of the traditional AI service in a robot scene are solved, and efficient privacy protection and data security isolation are realized.

The invention relates to the technical field of intelligent fusion terminal communication, and discloses an intelligent fusion terminal multi-protocol communication method and system based on edge computing. According to the method, a protocol adaptive engine is deployed at an edge node, an original data stream of a communication link is collected and analyzed in real time, and a current protocol type is dynamically identified in a fuzzy matching mode. And based on an identification result, the system dynamically loads a corresponding protocol analysis module, generates an adaptive instruction set, and realizes standardized data frame encapsulation through a protocol conversion intermediate layer. And meanwhile, the system monitors the link state, triggers incremental updating of the protocol feature library, and realizes seamless protocol switching. According to the invention, the communication compatibility and reliability are improved, and the requirements of high-reliability scenes such as the industrial Internet of Things are met.

The invention discloses a knowledge cross validation question-answering method and system for reducing hallusion of a large language model, and belongs to the technical field of artificial intelligence, and the method is implemented by the following steps: generating results through multiple times of sampling: when a user puts forward a question, controlling the large model to perform multiple times of sampling, and generating a specified number of results; calculating hidden state related indexes: extracting the hidden state of the last token of the middle layer of the large model corresponding to the result, and calculating covariance matrixes and answer discrete feature values of the hidden states; mLP model prediction: inputting the discrete feature value of the answer and the length of the answer into a multilayer perceptron MLP, and outputting a hallucination-free probability; querying and summarizing a knowledge graph; and calculating a final illusion-free score and outputting a result. According to the method, the answer quality and credibility of a large language model can be remarkably improved, and the method is particularly suitable for application scenes with extremely high requirements on the accuracy of single-mode text generation contents.

Disclosed herein are system, method, and device embodiments for providing a mobile interface powered by artificial intelligence. A user remains on a single user-interface page, conducting interactions with a customer relationship management tool using natural language. The technique leverages a large language model as an intermediary middle-layer, allowing a user to engage core functions. The technique builds an appropriate prompt including the natural language and uses the large language model to build an execution plan that references tools and tasks performable in the customer relationship management tool. By chaining prompts, the technique incorporates prior interactions into subsequent prompts. Mobile-specific information such as location, images, and scanned barcodes may be included in a prompts. Running the large language model on the client device allows the user to perform CRM functions while operating in an offline mode, a mode that secures user data and enhances privacy.

The invention provides a communication segmentation learning system and method for adaptive channel compression, and a medium, and relates to the technical field of segmentation learning and communication compression. The system comprises a plurality of clients, a server side and a channel compression device arranged between the clients and the server side, the channel compression device comprises a channel sensitivity modeling module, a rate distortion adaptive compression module and a cross-client fair coordination module. In a channel sensitivity modeling module, channel importance is dynamically evaluated through intermediate layer activation value fusion; differentiated quantization and compression strategies are designed on the basis of sensitivity scores in a rate-distortion self-adaptive compression module, so that key information is reserved while communication overhead is reduced; and meanwhile, the cross-client fair coordination module realizes balanced distribution of communication resources among multiple clients through a fairness regularization and dual optimization mechanism, so that the influence of excessive compression on global convergence is avoided. According to the method, the communication efficiency and the training stability of segmentation learning in a complex heterogeneous environment are remarkably improved.

An interaction assistance system for a user computing device can operate as an intermediate layer in a human-machine interface to receive user action data that describes user actions with a user computing device, interpret the actions in context, and intelligently instruct or command the host system to perform tasks associated with the user action data. An example interaction assistance system can enable faster and more efficient human-machine interfaces by simplifying a number or complexity of inputs to perform a given task.

The invention relates to the technical field of artificial intelligence model optimization, and discloses a multi-granularity dynamic pruning method and system for a generative AI model. The system comprises a model state acquisition module which acquires output of a middle layer in real time through a probe, and constructs a feature information set of neuron activation distribution, weight matrix norm and connection topology; the sparseness evaluation module outputs a sparseness risk value based on the feature information set, the initial sparseness parameter and the real-time computing resource state; a pruning planning collaborative analysis screening module generates a multi-granularity pruning scheme when the risk value exceeds a threshold value, calculates a collaborative interference value of precision recovery operation and screens an optimal scheme; and the pruning strategy execution feedback module updates model parameters, generates logs and feeds the logs back to the management terminal. The system realizes adaptive structure optimization and resource scheduling of the generative model in the reasoning process.

The invention relates to an industrial part defect classification method capable of realizing inter-domain category self-adaption, which comprises the following steps of: acquiring a source domain data set with label information and a target domain data set without label information, and preprocessing the source domain data set and the target domain data set; inputting a to-be-detected target domain sample into the trained neural network detection model for defect detection; and removing a domain adaptation structure which is not needed in the domain adaptation detection network, and carrying out defect detection on the target domain scene. According to the industrial part defect classification method and device for realizing inter-domain category self-adaption, the processor and the computer readable storage medium, the domain invariant feature information aiming at the category is decoupled from the middle layer of the feature extraction network and is fused into the original feature so as to enhance the classification capability; an ELA attention mechanism is added to solve the key problems of small defect size and difficult feature extraction of part detection, and finally, class labels are taken as conditions during classification, inter-domain alignment is carried out for classes, and the cross-domain classification capability is further improved.

The invention discloses a method for improving private domain operation and maintenance knowledge retrieval quality based on RAG, knowledge retrieval quality is improved through multi-stage cooperative processing, in a document preprocessing stage, a basic DOM tree is constructed, visual blocks are identified by using OCR and spatial clustering, a dynamic semantic slicing mechanism is adopted, and the knowledge retrieval quality is improved. A large language model and a sliding window technology are combined to generate fine-grained knowledge blocks, a cross-document context tracking network is innovatively constructed, a knowledge block reference relation graph is established based on semantic vector calculation, the traditional retrieval islanding effect is broken through, header alignment and merging optimization are implemented for table data, retrieval integrity is ensured, and the retrieval efficiency is improved. A lightweight knowledge injection strategy is adopted in a model level, intermediate layer parameters of a pre-training model are migrated, TripletLoss and contrast loss function training are combined, the field adaptability is enhanced while the generalization ability is kept, finally, a structured vector index is output, and timeline normalization processing of multi-version logs is supported.

The invention relates to the technical field of embedded maps, and provides a vector map display method under a VxWorks system, which comprises the following steps: S1, analyzing a vector map source file through an MITAB library, and extracting geometric space topological data, geographic coordinate system parameters, style attributes and projection parameters; s2, constructing a hierarchical storage structure based on SQLite3, and storing vector map information including a spatial data table, a style configuration table and a metadata table into a geographic information database; s3, creating a map display control MapWidget inherited from a QGraphics View class based on a Qt framework, loading the geographic information database through a dynamic configuration interface, and initializing a coordinate system conversion module, a multi-level view window module and a covering management system; and S4, dynamically rendering and outputting an interactive vector map interface through a layer priority scheduling strategy in a VxWorks real-time operating system. Through the design of the cross-platform data middle layer, the extensible general symbology system and the resource efficiency optimization, the data compatibility problem of the embedded system and the commercial GIS software is solved.

The invention discloses a large visual language model vulnerability detection method and device based on security sensitive layer activation guidance and a storage medium, and belongs to the technical field of artificial intelligence security, the method comprises the following steps: S1, constructing a security sensitive layer activation induction sample data set; s2, performing quantitative analysis on activation differences of the model intermediate layer under attack and normal input; s3, on the basis of the recognized security sensitive layer combination and an output layer of the large visual language model, generating an in-process confrontation image with an attack attribute through optimization of a loss function; and S4, recording an optimal security sensitive layer combination and a harmful response path guided by the optimal security sensitive layer combination, and constructing a structured representation of the potential security vulnerabilities of the model. According to the method, the security fragile links of the model are accurately positioned, the attack guiding force is improved in combination with multiple losses, the generated confrontation disturbance is controllable and imperceptible, the good cross-instruction migration capability is achieved, and the method adapts to various security assessment and red team test tasks.

The invention discloses a cloud edge collaborative reasoning method based on an auto-encoder and an intelligent agent, and aims to solve the problems of calculation bottleneck and high delay faced by resource-constrained equipment when the resource-constrained equipment executes deep learning reasoning. Splitting the complex deep neural network model into a plurality of parts for edge cloud cooperative execution according to calculation complexity and equipment performance; then, the middle layer data is compressed through a lightweight self-encoder, and the data transmission amount is reduced; and finally, a proper reasoning strategy is selected in real time according to the equipment load, the network bandwidth and the real-time task requirement by using a multi-task reasoning strategy generated by the intelligent agent, the method performs intermediate data compression by splitting the deep neural network model and the lightweight auto-encoder, and realizes multi-equipment collaborative reasoning by training the strategy generated by the intelligent agent. The reasoning efficiency is remarkably improved, the reasoning time is shortened, and the system resource utilization rate is optimized on the premise that the reasoning precision is guaranteed.

The embodiment of the invention provides a design method and device for layout of passing points, computer equipment, a medium and a program, and the method comprises the steps: determining a plurality of to-be-matched signal paths, and enabling the to-be-matched signal paths to be matched with part of path points; determining a current middle layer of a current to-be-matched passing point of the to-be-matched signal path; determining a weight data structure corresponding to the plurality of signal paths to be matched in the current intermediate layer, wherein the weight represents the connection quality; based on the weight data structure, determining a passing point matching scheme corresponding to connection quality maximization; adding matched path points of a to-be-matched signal path to candidate passing points, matched in a current intermediate layer, of the to-be-matched signal path in the passing point matching scheme; and after each to-be-matched signal path is matched with the candidate passing point in each intermediate layer, determining the candidate passing point, matched in each intermediate layer, of each to-be-matched signal path as a target passing point of the layout. According to the embodiment of the invention, the design efficiency and design quality of the passing point layout can be improved.

The invention relates to a man-machine cooperation fabricated construction management and control system and method based on a BIM and a Unity model.The BIM is integrated into a man-machine cooperation construction workflow through an interactive closed-loop digital twin framework, the closed-loop digital twin framework comprises a model building module, a man-machine interaction module, a middle layer module and a data collection module, the model building module is used for building a BIM model for the prefabricated part; the man-machine interaction module realizes manual remote control through a graphical user interface, issues an instruction to the robot, intervenes and supervises the construction process, and transmits the instruction to the middle layer module through a programming language; the middle layer module is provided with a data processing module, a machine autonomous control module and a decision information integration module; and the data is transmitted to the BIM through the data processing module to update the BIM model so as to reflect the change between the design construction state and the actual construction state. The method has the advantages that the participation degree of the building robot in site construction work is improved, and a solution is provided for intelligent construction of the fabricated building.

The invention discloses a BeeGFS-based metadata query efficiency improvement method and system. The BeeGFS-based metadata query efficiency improvement method comprises the following steps: constructing a three-dimensional index network consisting of a PMEMKV hot data layer, a RocksDB common data layer and a ZFS cold data layer; dividing all metadata into three independent sets of hot data, common data and cold data according to the heat values, and respectively storing the three independent sets to corresponding layers; after a query request is received, a top layer, a middle layer and a bottom layer are queried in sequence, and corresponding metadata is returned if the query request is hit; monitoring query indexes in real time, wherein the query indexes are used for updating the heat value of the metadata and adjusting the storage configuration of each level; performing dynamic promotion or degradation on the metadata between the three-dimensional metadata index networks according to the latest popularity value of the metadata and a preset popularity threshold value, and performing data synchronization and query path switching in the promotion or degradation process; the method can reduce query delay, improve throughput and balance storage resources.

The invention relates to the technical field of computer networks, in particular to an intelligent agent system and method based on the MCP technology, and the system comprises an MCP intermediate layer protocol engine, an intelligent scheduling module, a channel management system, a security enhancement mechanism and an edge node self-learning mechanism. The method has the beneficial effects that support for various communication protocols (such as HTTP, MQTT, gRPC and the like) is realized through the MCP technology. Through the protocol adapter module, the system can automatically identify and load different protocol adapters, and parallel work of multiple protocols can be supported without manual intervention. The multi-protocol support greatly improves the flexibility of the system, so that the system can be seamlessly connected with equipment of different manufacturers and standards, and the requirements of complex and diverse application scenes are met.

The invention discloses a heterogeneous instruction dynamic adaptation remote label printing method and system, a terminal and a medium. The method comprises the steps that a server side receives a printing request remotely sent by a user through terminal equipment; a target printer driver is dynamically read and loaded through a built-in equipment driver abstraction layer, and a registry of a target printer is inquired to obtain an instruction set supported by the registry; calling an adapter mode of a device driving abstraction layer to analyze the printing request, and generating label data matched with the target printer special instruction according to the label content; and the label data and the printing parameters are sent to the target printer, and label output is completed. According to the method, the pre-installed printer driver is read and matched, the unified middle layer adaptive interface is extracted from the upper layer, and instructions used by the printer do not need to be distinguished during printing, so that a user does not need to perceive the difference of the printer, the operation is simple and convenient, and the printing efficiency is improved.

The invention provides an execution system and method for a hybrid measurement and power calculation task and electronic equipment, and the system comprises a measurement and power calculation service platform which is used for analyzing the hybrid measurement and power calculation task to generate a sub-task set, and generating a task work queue corresponding to the sub-task set according to a dependency relationship between sub-tasks; and the scheduling adapter feeds back the task execution progress in the task work queue in real time, and the electricity measuring calculation service platform selects to issue the sub-tasks in the task queue to the calculation task scheduling execution system according to the task execution progress. The invention provides an execution system and method for a power measurement hybrid calculation task and electronic equipment, and the system employs a distributed task scheduling execution system, enables a power measurement calculation service platform to interact with the task scheduling execution system through employing a scheduling adapter as a middle layer, and distributes a calculation task to the corresponding task scheduling execution system. The resource allocation is more reasonable, the coupling degree of different task scheduling execution systems is reduced, and a new quantum computing cluster can be conveniently expanded.

A learned model comprises an input layer; one or two or more intermediate layers connected to the input layer; and an output layer connected to the intermediate layer, wherein the learned model causes a computer to operate to perform machine learning based on initial data and ground truth information, wherein the initial data represents a result detected while a molded product is being manufactured, or a result of measurements of the molded product after the molded product has been manufactured, for each initial value set or detected with respect to a predetermined element used to manufacture the molded product by an injection molding machine, and the ground truth information represents ground truth information regarding a setting of the predetermined element derived from evaluation information indicating an evaluation of the molded product.that is produced when the first value is set or the first value is detected, for each first value, and output information from the output layer regarding a setting of the predetermined element when second data is input from the input layer, wherein the second data indicates a result that is detected while the molded product is being produced, or a result that is measured after the molded product has been produced.

The invention provides an FPGA (Field Programmable Gate Array) multi-modal control method based on dynamic partial reconfiguration, which comprises the following steps that: a main body adopts a layered hardware architecture to realize multi-modal control, firstly, a system module of a whole system is divided, so that FPGA logic resources are divided into a static region and a reconfigurable region; wherein the static area is responsible for global control and a communication interface part, the reconfigurable area realizes a multi-mode function by loading different configurations, and the middle layer is used for deploying the dynamic part to reconfigure a controller and is responsible for time sequence coordination and bit stream management. Compared with the prior art, the method has the advantages that the mapping relation between the modal feature library and the hardware module is established, the priority preemptive reconfiguration strategy and the modal switching cost model are adopted, the real-time matching of the sensor data flow and the hardware architecture is achieved, the dynamic data path reconstruction technology supports multi-modal parallel processing, and the real-time matching of the sensor data flow and the hardware architecture is achieved. Meanwhile, time sequence constraint, resource occupancy rate and power consumption budget are considered.

The invention discloses a cascade speculation inference method and system based on hierarchical decline KV cache compression, and the method comprises the steps: firstly inputting a context prompt text into a target model for coding, generating a KV cache, and calculating an attention score between tokens; secondly, descending sorting is carried out based on the attention scores of the final input token, KV cache blocks corresponding to the first k attention scores are selected as cascading middle layer cache, a lightweight large language model is loaded to serve as a draft model, and a hierarchical decline KV cache compression strategy is adopted to maintain draft model cache. Then, based on all the caches, a double-layer cascade speculation reasoning framework is constructed, a target reasoning path is obtained, and the caches are updated; and finally, repeating the operation until target response data corresponding to the context prompt text is output according to the target reasoning path. According to the method, the KV cache proportion is reduced, meanwhile, the draft token acceptance rate of the target model of the full KV cache is improved, and reduction of precision is reduced.

The invention discloses a CAD application system design scheme data management method and system, and relates to the computer cartographic technology, and the method comprises the steps: in a target design scheme creation stage, separately creating design model data and design view data of a target design scheme, and copying to obtain intermediate design model data and intermediate design view data; a design scheme data editing stage: loading part of design model data associated with editing data, executing editing, and storing edited update data to the intermediate design model data and the intermediate design view data after editing; and synchronizing the intermediate design model data and the intermediate design view data to the design model data and the design view data of the target design scheme in a design data storage and submission stage. According to the method, through the data middle layer, high consistency and integrity of design scheme data can be well guaranteed through two-stage submission.

The embodiment of the invention provides a dynamic mapping method and system for a cloud host with a direct connection device, and the method comprises the steps: in the initialization process of the cloud host, initializing a first virtual device through a virtual layer to obtain the memory configuration of the cloud host, and enabling a memory space address required by a driver directly connected to the cloud host in the memory configuration to be default; initializing a corresponding memory space in the direct connection equipment in a process of initializing an operating system of the direct connection equipment in the virtual layer; physical memory missing page interruption is caused by default of the configured memory space address required by the driver, the memory space address required by the driver is allocated to the cloud host, and the allocated memory space address required by the driver is associated with a virtual machine hardware address of the cloud host in a middle-layer data table; and when the cloud host is operated, applying for a corresponding memory space in the direct connection device according to the memory space address required by driving in the memory address mapping relationship, so that the direct connection device is directly connected to the cloud host. According to the invention, the time of direct connection of the direct connection device to the cloud host can be accelerated.

The invention discloses a hierarchical cloud storage adaptive migration method based on data popularity, a medium and equipment. The method comprises the following steps that a migration management platform composed of a statistics and alarm module, a heat model calculation module, a strategy control center module, a metadata management module and a migration action module is added to a middle layer of service side storage and archiving side storage; when the data written by the client reaches an alarm threshold, the modules cooperate with each other to adaptively migrate the data from the service side storage to the archiving side storage according to the data popularity; and when the client needs to relocate the data, the modules cooperate with each other to store the relocated data from the filing side to the service side. The invention provides a self-adaptive migration method according to data popularity in a hierarchical cloud storage environment, and the overall performance and the resource utilization rate of a storage system are improved.

The invention provides a multi-protocol data interaction system, a data interaction method and a data interaction device. The system comprises a process simulation service and a field device, the process simulation service comprises a computing system and a middle layer system, the computing system is in communication connection with the middle layer system, and the computing system is used for computing control parameters of the field device and sending the control parameters to the middle layer system; the intermediate layer system comprises a plurality of protocol interfaces, is in communication connection with the field devices through the protocol interfaces, sends the control parameters to the corresponding field devices through the protocol interfaces, receives action information of the field devices through the protocol interfaces, and uploads the action information to the computing system; and the field equipment is used for receiving the control parameters, executing response actions according to the control parameters and feeding back action information to the intermediate layer system. The problem that in the prior art, data interaction with different field devices can be achieved only by developing multiple sets of communication systems, and consequently development cost and complexity are high is solved.

The invention discloses a multi-core platform-oriented collaborative scheduling method and system and terminal equipment thereof, and belongs to the technical field of collaborative scheduling of operating systems. The method comprises the steps of obtaining a task initial process based on a multi-core heterogeneous chip platform; a task dependency graph is obtained, loop dependency detection and parameter consistency detection are executed based on the task dependency graph, and an intermediate layer task description file is obtained; acquiring real-time operation data and a master control heartbeat survival table based on the intermediate layer task description file; obtaining a target master control node based on the real-time operation data and a preset election method when it is judged that the preset failure condition is met; and carrying out a breakpoint snapshot action based on the target master control node, and carrying out degradation processing based on the original master control node. According to the collaborative scheduling method and system for the multi-core platform and the terminal equipment thereof, the technical problem that an effective master control hot switching and task migration mechanism is lacked in the prior art can be solved, and the intelligent level and reliability of heterogeneous collaboration of multiple operating systems are remarkably improved.

The invention belongs to the technical field of data processing, and particularly discloses a multi-source heterogeneous data acquisition access method and system based on intelligent mapping and a dynamic interface, and the method comprises the steps: registering an input data source class, generating a data source identifier corresponding to the data type, and storing the data source identifier; respectively executing meta-model analysis operation to generate a standardized meta-model associated with the data source identifier and a container instance template; generating a mapping rule set through a twin neural network architecture with multi-modal feature fusion and a preset mapping rule; a middle layer API interface is constructed through interface logic, and real-time conversion and access between input and output data are achieved; and realizing containerized parallel access based on the container instance template and the containerized interface service instance. According to the method, data source change is dynamically adapted, the real-time performance and accuracy of the mapping rule are ensured, and meanwhile, multi-source heterogeneous data can be accessed efficiently in real time in combination with containerized parallel processing and a data source change monitoring mechanism.

The invention discloses an intelligent design research and development system based on domestic AI and an implementation method thereof, the system adopts a modular loose coupling architecture, and comprises a front end layer, a middle layer, a core layer, a base layer and a data layer; the front-end layer is a Web front-end interface, and the middle layer is an API gateway layer; the core layer comprises a knowledge graph module, a simulation engine module, a decision center module and a digital twin manager module; the basic layer comprises a system management module, the data layer comprises a multi-layer data storage and communication component, and the method comprises the steps of initializing a system manager, constructing a knowledge graph, executing multi-physics field simulation and evaluating simulation confidence; generating a decision strategy based on reinforcement learning and knowledge graph reasoning; according to the method, digital twin bodies are constructed, virtual-real linkage and real-time data interaction are achieved, domestic AI is comprehensively adopted and comprises a MindSpore framework and an ancient big model, the technology is autonomous and controllable, and the method is suitable for the whole process of power equipment design, inspection and detection and supervision and construction.

The invention relates to the technical field of voice processing, in particular to a real-time intention recognition method and system based on streaming incremental reasoning, and the method comprises the following steps: receiving the voice input of a user through a voice collection module, slicing the voice into a plurality of audio frames, and carrying out the recognition of the intention of the user through an incremental large language model module by adopting an Early-Exit reasoning mechanism; side outlets are arranged at multiple levels of the model, incremental reasoning is performed on token streams based on a QLoRA4-bit quantization technology, prediction results of multiple tokens are smoothed by using a stream ASR decoding module and an accumulative fusion module, and a stable final label is generated; the method has the beneficial effects that by combining streaming ASR decoding with incremental large language model reasoning, intention recognition and risk assessment can be immediately performed on each speech token after the speech token is generated. Through an Early-Exit reasoning mechanism, under the condition of high confidence, the system can output a fraud intention in advance in a middle layer in the reasoning process and stop subsequent calculation, and unnecessary calculation overhead is reduced.

The embodiment of the invention discloses a distillation method based on a sparse large language model, electronic equipment and a storage medium, and the method comprises the steps: obtaining a teacher model and a student model, and carrying out the at least one round of training of the teacher model and the student model; calculating output of the teacher model and the student model for forward propagation of each batch of data of at least one round of training, calculating distillation loss through a dynamic coefficient strategy, and calculating middle layer alignment loss through a knowledge alignment module; obtaining task specific loss, and calculating total loss by combining the task specific loss, the dynamic coefficient strategy distillation loss and the intermediate layer alignment loss; the student model parameters are updated through back propagation, the student model performance is evaluated on a verification set, and dynamic coefficient parameters are adjusted according to the verification performance; and storing the student model with the best performance, and optimizing and adjusting hyper-parameters by using Bayesian distillation.