28 results about "Model sharing" patented technology

Methods of enabling Artificial Intelligence (Al) models to be trained in a privacy preserving manner are disclosed. A method comprises the formation of virtual cluster per privacy level and selection of a cluster head that performs the training and coordination per privacy level. A method comprises training an Al primary model on a private user data set. The Al primary model may be used to label a public data set. A secondary model may be trained using the labeled public data set. The secondary model may be shared with a network or other UEs.

In one implementation, a method of decoding point cloud data for a point cloud is presented. A signal indicative of a number of sub-blocks, N1 is decoded, and a neural network is configured to have N1 sub-blocks. In particular, each sub-block of the N1 sub-blocks includes an upsampling function and at least a neural network layer, and each of the N1 sub-blocks is configured with the same neural network parameters. The point cloud data is decoded based on the neural network. At the encoder side, the signal indicative of N1 is encoded, and the neural network is configured to have N1 sub-blocks. In particular, each sub-block of the N1 sub-blocks includes a downsampling function and at least a neural network layer, and each of the N1 sub-blocks is configured with the same neural network parameters. The point cloud data is encoded based on the neural network.

The invention provides a federal learning model copyright protection method and system based on feature fusion, and the method comprises the steps: 1, receiving a watermark triggering sample, and inputting the watermark triggering sample into a pre-established client model for training; 2, updating and aggregating the local shared model parameters and the local classification layer parameters to obtain updated global model shared parameters and personalized classification layer parameters; performing watermark feature extraction on the updated global model sharing parameters based on a feature layer watermark embedding mechanism to obtain a global watermark; 3, performing multi-bit quantization on the personalized classification layer parameters to obtain a set of binary signatures; and performing copyright verification based on the black box watermark and the global watermark, and determining a malicious client based on a set of binary signatures. According to the method, illegal copying and distribution of the model can be effectively restrained, and a key technical support is provided for promoting healthy and sustainable development of a data and model cooperation normal form.

The invention discloses a device for controlling a model question and answer result through role definition, and the device sets different roles through a role model setting module, and carries out the output limitation of the roles. The role automatic verification module verifies the content output by the role model by using a verification rule; a model result output comparison module performs similarity verification on files according to output results of different roles, compares similar text contents in different roles one by one, and confirms role model versions; performing question-answering by using a role model question-answering module; meanwhile, the model optimization module can configure output elements and form a specific role model according to the current role of the user, and defines a content generation method taking figures, things, events, targets and scenery as the center by performing condition combination setting on the model, so that the accuracy is greatly improved; meanwhile, sharing among different role versions is achieved through the model sharing module, and continuous optimization learning is conducted on the role template through the model continuous learning module.

The application discloses a kind of model sharing method, device, equipment, medium and product based on multi-chain structure, which comprises the following steps: model training is carried out on the federal learning network of the multi-chain structure created to the node participating in model sharing, to generate federal learning model;Wherein, the federal learning network includes a plurality of data blockchains supporting cross-chain communication, and the data blockchains correspond to the nodes one by one;According to the data distribution characteristics of each node in the federal learning network, the federal learning model is segmented to obtain a plurality of model blocks, and the plurality of model blocks are encrypted to obtain a plurality of encrypted model blocks;Wherein, each data distribution characteristic corresponds to at least one model block;Each encrypted model block is uploaded to the data blockchain of the corresponding node for storage and sharing;The application combines the advantages of blockchain technology and federal learning, realizes the federal learning model sharing of multi-chain structure, can effectively improve the efficiency of model sharing and reduce communication overhead.

The present invention relates to a method for intelligent and secure sharing of vehicle-road collaborative data, and belongs to the field of mobile communication technology. First, local redundant data and noise data are screened to avoid overfitting. Secondly, a malicious model detection scheme is designed to provide a defense method against label flipping attacks; then, a pre-learning scheme based on dynamic mutual distillation technology is established to improve the generalization ability of the model and reduce the communication load; finally, a personalized aggregation strategy based on CAV is proposed. After the last CAV mutual distillation is completed, the final mutual distillation model is broadcast to all remaining CAVs, and the CAVs evaluate the accuracy of the mutual distillation model and the local training model based on their respective local data sets. It is used to solve the problems of low model accuracy caused by heterogeneous data in the Internet of Vehicles scenario, high model sharing communication overhead, and label flipping attacks on federated learning. This solution improves the generalization ability of the model and maximizes sharing efficiency under the premise of ensuring safe and low-load CAV collaborative training.

The invention provides a WebGL and URDF-based mechanism animation model self-building sharing method, system and device, relates to the technical field of data sharing platforms, and comprises a preprocessing stage, a loading stage, a running stage and an interaction stage. According to the technical scheme, the dependence of traditional mechanism simulation software on specific equipment and complex environments is eliminated, and smooth and high-precision mechanism motion simulation can be completed in a universal web browser by calling the GPU computing power of a local computer of a user. Through a Web technical architecture, a user can access the system through a browser anytime and anywhere and conveniently carry out real-time parameter adjustment and multi-view dynamic observation, so that the movement mechanism and the working principle of a mechanism are intuitively and deeply understood. Besides, an open model sharing ecology is also constructed, different users are supported to upload and share an autonomously constructed mechanism model, and the movement process of a complex mechanism is vividly demonstrated through custom animations, so that the communication and propagation of technical knowledge are facilitated.

The invention relates to an enterprise intelligent modeling platform based on a big language model, and the platform comprises a model capability platform which is used for achieving the integration with a machine learning platform and a big data platform, achieving the configuration arrangement function of a big language model / big language model agent, and providing the support of data, basic algorithm capability and big language model capability for conversational modeling; and the application capability platform is used for realizing related functions of manual modeling, dialogue-type inherent model calling, dialogue-type intelligent modeling, dialogue retrieval of existing knowledge, model sharing and agent arrangement on the basis of the model capability platform. The method can be widely applied to the technical field of enterprise-level artificial intelligence application.

A secure clean room environment for sharing artificial intelligence (Al) models between multiple parties uses access control mechanisms and role-based access control for governing access to the Al models within the clean room environment. Isolated sandbox environments within the clean room environment allow authorized parties to test and use the Al models, monitor and log all activities performed within the clean room environment, and enforce usage controls on the Al models. The clean room leverages a retrieval-augmented generation (RAG) framework, multi-agent architecture, and confidential computing to enable safe, purpose-specific machine learning (ML) model sharing. A centralized knowledge graph and confidential virtual machines (VMs) create isolated environments where partners can securely access and query ML models.

This invention provides a copyright protection method and system for federated learning models based on feature fusion. The method includes: Step 1, receiving watermark trigger samples and inputting them into a pre-established client model for training; Step 2, updating and aggregating the local shared model parameters and local classification layer parameters to obtain updated global model shared parameters and personalized classification layer parameters; extracting watermark features from the updated global model shared parameters based on a feature layer watermark embedding mechanism to obtain a global watermark; Step 3, performing multi-bit quantization on the personalized classification layer parameters to obtain a set of binary signatures; verifying copyright based on the black-box watermark and the global watermark, and identifying malicious clients based on the set of binary signatures. This invention can effectively curb the illegal copying and distribution of models, providing key technical support for promoting the healthy and sustainable development of data and model collaboration paradigms.

In one implementation, a method of decoding point cloud data for a point cloud is presented. A signal indicative of a number of sub-blocks, N1 is decoded, and a neural network is configured to have N1 sub-blocks. In particular, each sub-block of the N1 sub-blocks includes an upsampling function and at least a neural network layer, and each of the N1 sub-blocks is configured with the same neural network parameters. The point cloud data is decoded based on the neural network. At the encoder side, the signal indicative of N1 is encoded, and the neural network is configured to have N1 sub-blocks. In particular, each sub-block of the N1 sub-blocks includes a downsampling function and at least a neural network layer, and each of the N1 sub-blocks is configured with the same neural network parameters. The point cloud data is encoded based on the neural network.

The invention discloses a full-cycle asset security guarantee method and device for a nuclear power industrial model, and the method comprises the following steps: carrying out the preprocessing of a data set, optimization of model parameters, and setting of a corresponding defense strategy for data poisoning in a model compiling stage, i.e., a model generation stage; in the model transmission stage, a secure transmission protocol is set, a virtual private network is adopted, encryption transmission and uploading are carried out on the model, a secret key is managed and stored, and integrity verification is carried out on transmitted model data; in the model storage stage, encryption and access authority control are performed on the stored model data, and a data backup and recovery strategy is set for the stored model data; performing access authority control in a model operation calling stage, and performing operation calling on the model by adopting isolation and sandbox technologies; access authority control is carried out in the model sharing stage, and a corresponding application program interface is set for model sharing. According to the method, the asset safety of the nuclear power industrial model in the whole life cycle can be guaranteed.

The present disclosure relates to a model sharing method, an apparatus, a device, and a readable storage medium. Provided in the present disclosure is the model sharing method, used in a group management device of a first user equipment (UE) group, the first UE group comprising the group management device and a plurality of member UEs. The method comprises: receiving a model acquisition request sent by a first member UE, the first member UE being any one of the plurality of member UEs; and sending a model acquisition response to the first member UE, the model acquisition response being used for the first member UE to acquire a required target model. Provided is the model sharing method based on a UE group.

The application particularly relates to a machine translation model crowdsourcing incremental learning method, which comprises the following steps: providing a translation model array composed of multiple machine translation models, introduction and details of each translation model; model owners can select whether to open crowdsourcing training, if the crowdsourcing training is opened, the system automatically preprocesses and cleans data uploaded by participants for participating in joint training; data quality scoring is performed, and a contribution value of the batch of data to model training is calculated according to the effective data amount and the quality score; after the participants confirm the contribution value, the model incremental training process is entered through a front-end interactive module; after the incremental training is completed, the model is updated to the machine translation array, the model related information is updated, and the opening is continued, and other participants can participate in the training. According to the method, data resources and the model after incremental training can be greatly shared through joint training and model sharing of multiple persons.

The application discloses a training method and device of a longitudinal federal neural network based on MPC. In order to solve the problem that the longitudinal federal split learning is vulnerable to model completion attack, the first n layers of the neural network adopt model fragmentation technology, and there is no complete bottom model, so the model completion attack cannot be initiated. In order to solve the efficiency problem caused by model fragmentation, after the n layers, the model fragmentation and the fragmentation of the calculation result are added by using the existing model sharing patent idea, that is, the training and reasoning mode of a single server is used to speed up the running speed. Since n is usually 1 or 2, the training and reasoning efficiency of the whole system is close to the effect of plaintext, and the security and privacy characteristics are maintained. For the scene of multiple label providers, the model and the model result can be split and combined, and the model and the result are split again to protect the privacy of the label. And the overall structure is still applicable to the application scene of split learning.

The application provides a model sharing management method and device, equipment and a storage medium. The method comprises the following steps: obtaining a model demand of a to-be-subscribed model sent by a model user, wherein the model demand of the to-be-subscribed model comprises a type demand of the to-be-subscribed model and a deployment environment type demand; determining a target model and use data of the target model according to the model demand of the to-be-subscribed model and a published model, wherein the published model is a to-be-published model stored in a model sharing platform and matching audit results meet publishing requirements, and the matching audit results represent the results of matching and auditing of the model data of the to-be-published model and a publishing request sent by a model provider by the model sharing platform; and sending the use data of the target model to the model user. The method improves the efficiency of the user using the model and increases the universal applicability of the model sharing platform.

The invention discloses a model sharing service method, device, system and equipment, a medium and a program product, and relates to the technical field of digital networking, and the method applied to a model sharing platform comprises the steps: carrying out the preprocessing of task request information sent by a model demand node, and obtaining at least one piece of target task information; each piece of target task information in the at least one piece of target task information is matched with multiple private domain models in a pre-registered model library, a first private domain model corresponding to each piece of target task information is determined, and the at least one piece of target task information is sent to a model service node where the corresponding first private domain model is located; receiving a task processing result corresponding to the target task information, wherein the task processing result is obtained based on the first private domain model; and fusing task processing results, and sending response information to the model demand node. According to the embodiment of the invention, the problem of insufficient adaptive capability of model security of a large model in the related art can be solved.

Provided in the present application are a model sharing authorization method, a model inference method, an electronic device, and a storage medium. The model sharing authorization method comprises: acquiring a model sharing acquisition request sent by a network function consumer, wherein the model sharing acquisition request at least carries consumer information of the network function consumer; sending the consumer information and a consumer authentication request to a security main body; and in response to authentication success information, which is fed back by the security main body on the basis of the consumer authentication request and the consumer information, sharing a model with the network function consumer.

The application relates to a model sharing method, device and equipment and a readable storage medium. The application provides a model sharing method used in a group management device of a first user equipment (UE) group, the first UE group comprising the group management device and a plurality of member UEs, and the method comprises the following steps: receiving a model acquisition request sent by a first member UE, the first member UE being any one of the plurality of member UEs; and sending a model acquisition response to the first member UE, the model acquisition response being used for the first member UE to acquire a target model required by the first member UE. The application provides a method for sharing a model based on a UE group.

The application discloses a kind of electromechanical transient simulation model sharing method and system, comprising the following steps: initialization modeling and time domain simulation modeling are carried out to element, and the ID number of current element is obtained, is uploaded to server custom component library by network, according to the ID number is stored, realize the pretreatment function of the element;After the pretreatment, simulation calculation includes obtaining power system data and carrying out power flow program calculation;Analysis power system data, and determine inherent model and custom model;Custom model is extracted by searching the custom component library, and it is judged whether search is successful or not.The application realizes the model custom function for electromechanical transient simulation of power system and cross-user shared use, can allow end user to electromechanical transient simulation main program to be unable to provide the element of model to carry out custom modeling, allow end user to design or receive new element when verifying the dynamic characteristics of equipment access large power grid.

The disclosure describes a method of implementing model information sharing using pre-configured AI / ML (Artificial Intelligence / Machine Learning) in a wireless mobile communication system including a base station (e.g., gNB) and a mobile station (e.g., UE). When AI / ML models are applied to a radio access network, model information signaling for multiple UEs and / or applications can suffer significant load when ML applicability conditions vary across different UEs / applications. Accordingly, the disclosure is capable of adaptive implementation of model sharing between the network and the user terminal, while improving model performance while reducing signaling overhead.

Video processing with a multi-quality loop filter using a multi-task neural network is performed by at least one processor and includes generating model IDs, based on quantization parameters in an input, selecting a first set of masks, each mask in the first set of masks corresponding to one of the generated model IDs, performing convolution of first weights of a first set of neural network layers and the selected first set of masks to obtain first masked weights, and selecting a second set of neural network layers and second weights, based on the quantization parameters, generating a quantization parameter value, based on the quantization parameters, and computing an inference output, based on the first masked weights and the second weights, using the generated quantization parameter value.

The invention discloses a federal learning model sharing and incentive method based on a block chain smart contract, and relates to the technical field of big data processing and distributed artificial intelligence, and the method comprises the following steps: constructing a model sharing community rule; on the basis of the model sharing community rule, an optimized game theory algorithm is implanted into a chain, and decentralization and automatic quantization of contribution degree are achieved; and by introducing an evolutionary game theory, evolutionary game deep modeling of a supply-demand relationship is realized, and then design and construction of the block chain smart contract are realized. According to the method, the native smart contract is constructed at the bottom layer of the block chain, distributed quantitative evaluation of the contribution degree of the federated learning participant is realized, and a set of decentralized point reward and reputation mechanism is constructed based on the Shapley value algorithm and the evolutionary game model; and a safe and efficient digital governance environment with crowd intelligence characteristics is provided for distributed model training.

The invention discloses a model sharing parameter creation method and device, equipment and a storage medium, and relates to the technical field of data processing. The model sharing parameter creation method comprises the steps of obtaining a mapping relation table of a building information model and business data; extracting data information of mapping parameter items in the mapping relation table; creating shared parameters conforming to modeling software configuration based on the data information; and associating the shared parameter with the data information to form an associated and bound target shared parameter. Through automatic analysis of a mapping relation table, automatic creation of parameter items and automatic binding of categories, automatic creation of building information model shared parameters is realized, the time consumption of the whole process from creation to association of the mapping parameter items is greatly shortened, and the efficiency is remarkably improved. And meanwhile, manual intervention of parameter item creation and category assignment links is reduced, and the risk of parameter missing or category definition error caused by manual omission is reduced.

The invention is suitable for the technical field of data annotation, and particularly relates to a weak supervision scene-oriented incremental data annotation framework and annotation quality dynamic evaluation method, the method comprises the steps of constructing a model sharing platform, determining registrants, and clustering the registrants into a plurality of groups, each group corresponding to an industrial device, and obtaining a fault event of each group. According to the method, a model sharing platform is constructed, registrants are determined, the registrants are clustered into a plurality of groups, each group corresponds to one industrial device, fault events of each group are obtained, abnormal data are selected, manual labeling is carried out, and a training set is generated; a sharing analysis model is created, training is conducted through the training set, after training is completed, model parameters are extracted, each registrant corresponds to a set of model parameters, a use scene of the industrial equipment is configured, similar scenes are determined, the model parameters corresponding to the similar scenes are integrated, and a parameter set is generated.

The invention discloses a model sharing method and system for a heterogeneous robot, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a trained target model, and packaging the trained target model into a corresponding meta-skill; obtaining a target task and a corresponding heterogeneous robot; reading the skill description file of the meta-skill based on the target task to obtain the lowest capability configuration; the capacity of the heterogeneous robot is judged on the basis of the minimum capacity configuration, and a meta-action sequence is obtained on the basis of the skill description file; converting into a native control instruction which can be executed by the heterogeneous robot based on the meta-action sequence; and inputting the native control instruction to the heterogeneous robot to execute the target task. The shackle binding between an algorithm model and robot hardware is broken through, and lossless migration and sharing of the intelligent ability are achieved.

The application discloses a model ownership verification method and device, a storage medium and a program product, wherein the method comprises the following steps: sending a public matrix corresponding to an ownership certificate and a fault-tolerant learning result to a coordinator, the coordinator constructing a watermark according to the public matrix and the fault-tolerant learning result, and sharing the watermark to participants; embedding the watermark sent by the coordinator into a preset federated model to obtain a local federated model, and sending the local federated model to the coordinator, the coordinator performing aggregated training on the local federated models of the multiple participants to obtain a target federated model, and sharing the target federated model to each participant; if it is determined that there is a suspicious model according to the target federated model, performing zero-knowledge proof verification according to the ownership certificate, and if the verification is successful, determining that the participant has the ownership of the suspicious model. The application realizes the privacy and security of the ownership certificate when verifying the ownership of the model.

The embodiment of the invention relates to a model sharing method, equipment and device, a model request end sends a model training request to a first network node, and the model training request carries model demand information, so that the first network node queries a model in a shared model set according to the model demand information, and feeds back a model query result; and the model request end obtains a target model matched with the model demand information according to the model query result. According to the embodiment of the invention, the model can be searched from the shared model set according to the model demand information after the model training request is received, and the model does not need to be trained according to the model training request every time, so that the expenses of model training and deployment can be reduced.