65 results about "Document model" patented technology

The invention provides a general document data conversion method and device, and the method comprises the steps: carrying out the business scene analysis and rule field extraction of an input text of a user through a large model business flow processor and an intelligent agent, generating a recommendation rule template, and carrying out the processing of the recommendation rule template; updating a configuration field of the recommendation rule template according to an adjustment requirement text input by a user to generate a target document conversion rule template; and according to the target document conversion rule template, performing general data mapping conversion on the source document data to generate a document data model of the target document, and realizing dynamic code-free automatic configuration by configuring the document conversion rule template, so that the reusability is improved, and the user experience is improved. Automatic conversion of isomorphic data and heterogeneous data to a general document model in a complex scene can be met, the delivery period is shortened, and the delivery cost is reduced; multiple cancel-after-verification relationships can be recorded according to the cancel-after-verification relationship table, an arbitrary cancel mechanism is supported, the bill redundancy is reduced, and the flexibility of cancel-after-verification relationship processing in bill conversion is improved.

The invention discloses a laser-based surface treatment method and equipment, and the method comprises the steps: obtaining drawing document data of a to-be-treated metal material, carrying out the graphic layering processing of the drawing document data, and generating a multi-layer drawing document model; generating a first laser processing parameter set by combining the type, thickness and surface characteristics of the metal material; the metal material is placed on a jig provided with an automatic adjusting device, and the position of the jig is sequentially adjusted; the metal material is marked through a first laser according to the multi-layer drawing and document model, and machining feedback information of the metal material is collected through a sensor in the marking process; according to the difference between the processing response data and the drawing file data, generating a single-layer drawing file for correcting the processing deviation through an image processing and neural network identification method; the laser marking process and data feedback analysis are deeply fused, so that the precision and consistency of pattern forming are effectively improved; and meanwhile, the machining deviation can be remarkably reduced, so that the overall surface treatment quality and the product yield are improved.

System, method, and apparatus for transforming computer files are provided. The method, implemented by one or more processors, comprises: inputting a source file and a source file specification, the source file including one or more source data items, the source file specification defining formats of the one or more source data items; generating, using a generative AI model based on the source data and the source file specification, a source file model including one or more source nodes, each of one or more source nodes associated with a respective data item of the one or more source data items; generating, using the generative AI model, a transform map mapping the one or more source nodes to one more target nodes of a target file model; invoking, a semantic model-based transformation service to generate an output file; and invoking a comparison module to compare the output file to an output sample.

Systems and methods are provided for creating a machine learning (ML) model staging repository, where ML models are stored as an open container image (OCI). The OCI comprises layers or portions of the complete ML model, so that the model can be stored separately and as a smaller files. The ML models may be pre-packaged for automated downloads and integration at the customer site. In some examples, the OCI can identify / store the model in a directory structure that defines the model and its profile. The OCI can comprise a combination of layers and profiles that allows the AI platform to optimize the storage and simplify the process of downloading or updating the given user namespace instead of downloading a single large file for the model.

A method may include detecting one or more selection boxes and one or more text lines in a primary document. The method may include determining respective vectors associated with the selection box and adjacent text lines to the selection box in a plurality of directions. The method may include determining a set of respective vectors associated with a unique selection box. The method may include determining a variance between respective vectors in the set of respective vectors and identifying a particular direction corresponding to a minimal variance between the respective vectors in the set of respective vectors as compared to a variance of other sets of respective vectors. The method may include generating a key-value pair based on the set of respective vectors characterized by the minimal variance. The method may include generating a document model, including the key-value pair, and extracting data according to the document model.

Techniques for cycle management including: determining, based on historical unstructured CRM data, a document model to determine document attributes of a set of documents; determining, based on historical structured CRM data, a stage prediction model to determine attributes of a current sales cycle based on a current sales cycle stage and current document attributes; determining, based on current sales cycle structured data, a current stage of the current sales cycle; determining, based on application of current sales cycle unstructured data to the document model, a current set of attributes for the current sales cycle; and determining, based on application of the current stage of the current sales cycle and the current set of attributes for the current sales cycle to the stage prediction model, a predicted outcome for the current sales cycle.

The invention provides a document processing method and device, electronic equipment, a computer readable storage medium and a computer program product. The method is applied to an online document program. The method comprises the steps that in response to an opening operation for a first document, a first editing tool corresponding to the document type of the first document is inquired from at least one editing tool, and the first editing tool is called to display the content of the first document; in response to an editing operation for any page in the first document, calling a first editing tool to obtain an editing parameter corresponding to the any page, and updating a first document model set corresponding to the first document based on the editing parameter to obtain a second document model set; calling a first editing tool to display an editing result of any page based on the second document model set; and calling the first editing tool to generate a second document based on the second document model set in response to meeting the saving condition of the document. According to the method and the device, the contents of the documents of different document types can be edited quickly.

The invention provides an underwriting billing algorithm based on a general insurance product model, and the algorithm comprises the steps: S1), designing a unified product baseline and a file model for the characteristics of insurance types of non-vehicle insurance lines; s2) designing a unified document model for the insurance slip and the approval slip; s3) developing a business factor library capable of dynamically expanding on the basis of the unified document model to adapt to business characteristic index expression and calculation rules with changeable scenes; s4) under the support of the unified document model and the business factor library, defining an insurance amount and insurance premium structure, and designing a unified insurance premium calculation engine to execute a charging task, the insurance premium calculation engine generating an insurance premium by applying a business factor calculation rule, and charging by superposing a business adjustment coefficient; and S5) executing rate base line rule constraint on a charging result, and prompting a charging risk. According to the invention, convenient and flexible charging, supervision, backtracking and auditing support can be provided for complex and changeable non-vehicle insurance product insurance, and the delivery efficiency is improved.

The invention provides an online labeling, training and reasoning platform and method for identifying an AI model of a 3D point cloud, and belongs to the technical field of artificial intelligence. The system comprises a file management module, an online labeling module, an online training module and an online reasoning module. The file management module is used for managing a point cloud file, an algorithm file, an AI model file and a classification label file; the online labeling module is used for performing category labeling on the point cloud file and storing a labeling result back to the file management module; the online training module is used for training the AI model and storing the trained AI model in the file management module; and the online reasoning module performs reasoning classification on the point cloud file by using an AI model to obtain a point cloud classification result. According to the invention, the implementation difficulty of 3D point cloud marking, training and reasoning can be reduced, and the 3D point cloud deep learning efficiency is improved.

The embodiment of the invention relates to a multi-system AI repository controller. Systems and methods are provided for creating a hierarchical repository of machine learning (ML) models in which ML models are stored as open container images (OCIs). The OCI includes a layer or portion of a complete ML model so that the model can be stored separately and as a smaller file. The ML model may be pre-packed for automated downloading and integration at the customer site. In some examples, the OCI may identify / store the model in a directory structure defining the model and its configuration files. The OCI may include a combination of layers and configuration files that allow the AI platform to optimize storage and simplify the process of downloading or updating a given user namespace rather than downloading a single large file for the model.

A computer-implemented method includes generating a user interface; receiving a set of object models; processing the received object models; and rendering the document model via a viewport. A non-transitory computer readable medium contains program instructions that when executed, cause a computer to generate a user interface; receive a set of object models; process the received object models; and render the document model via a viewport. A computing system includes a processor and a memory having stored thereon instructions that when executed by the process, cause the computing system to generate a user interface; receive a set of object models; process the received object models; and render the document model via a viewport.

Techniques for detecting file similarity based on the characteristics and semantics of files are disclosed. A machine learning (ML) model may be trained to recognize and group files based on a hierarchy of file characteristics. The trained ML model may be used to process a set of files to generate a feature vector database comprising a set of feature vectors that are grouped based on the hierarchy of characteristics. In response to receiving a query file to be compared to the set of files, the ML model may be used to process the query file to generate a query feature vector. The query feature vector may be used to search the feature vector database to identify feature vectors that are similar to the query feature vector. A file corresponding to each feature vector that is similar to the query feature vector may be retrieved and presented to a user.

The embodiment of the invention discloses a retrieval enhancement generation method and device, equipment and a storage medium. According to the technical scheme, firstly, type classification is carried out on received initial query questions, target question types are determined, the target question types comprise at least one of document questions and non-document questions, then target models corresponding to the target question types are used, target answers corresponding to the initial query questions are output, and the target answers corresponding to the initial query questions are obtained. The target model is one of preset models, the preset models comprise a document model and a non-document model, the document model is used for obtaining answers to document questions, and the non-document model is used for obtaining answers to non-document questions. According to the retrieval enhancement generation method, the initial query question is subjected to type classification, and then the target answer corresponding to the initial query question is output by using the corresponding target model, so that the accuracy of the answer generated by retrieval enhancement can be improved, and the user experience is improved.

This application provides an AR application method and system based on CATIA software modeling. The method utilizes the secondary development environment of Navisworks software to convert the CATProduct file (CATIA's unique output format) into an NWC file. Then, Navisworks' secondary development is used to extract the geometric, material, and attribute data from the NWC file model, outputting the analytical results, which are then loaded into the AR application. This application uses a low-cost and lossless analytical method to convert CATIA data into a data type recognizable by AR applications, helping AEC-related enterprises reduce costs and increase efficiency, expand AR application scenarios, and improve user satisfaction with model AR applications.

The one or more embodiments of the specification provide a method for collaborative editing of a mixed document, applied to a client in a document collaborative editing system for collaborative editing of the mixed document, the mixed document including document content objects corresponding to multiple types of document editors; the method includes: creating a corresponding collaborative document instance for each document content object in the mixed document, the collaborative document instance participating in collaborative editing through a collaborative document model; determining a corresponding original atomic operation of a collaborative editing instruction for the collaborative document instance in the mixed document; expanding the original atomic operation into an expanded atomic operation for the mixed document; and sending the expanded atomic operation to a server of the document collaborative editing system for sharing to other clients for collaborative editing of the mixed document.

The invention belongs to the technical field of artificial intelligence, and relates to a data management method and device, computer equipment and a storage medium, and the method comprises the steps: receiving a model uploading request triggered by a user; wherein the model uploading request carries a model file, file information of the model file and authentication information of a user; verifying the model file based on the file information; if the model file passes the verification, authenticating the user by using a multi-factor authentication strategy based on the authentication information; if the user passes the authentication, the user is authenticated based on the authentication information; if the user passes the authentication, generating target version information of the model file; obtaining metadata information of the model file; and based on the target version information and the metadata information, performing storage processing on the model file. In addition, the invention also relates to a block chain technology, and the model file can be stored in a block chain. The method can be applied to model management scenes in the medical science and technology field and the financial field, and the processing efficiency of model data management is improved.

The application discloses a kind of offshore task planning and deduction simulation analysis method, and the knowledge data of war knowledge base is set war ontology knowledge base, and the model based on event ontology is designed in combination with ontology modeling language, the class of sea battlefield event ontology and the hierarchical structure setting of class are determined, and the sea battlefield rule file model is established to describe sea battlefield combat event, and the sea battlefield combat rule of the sea battlefield rule file model is obtained based on war semantic net rule;Sea battlefield combat rule is inferred according to BN reasoning engine module to obtain the execution event of sea battlefield combat rule;And the execution event is visualized by man-machine interactive system, and the expression of battlefield combat rule based on offshore task planning and deduction simulation is realized.

1. Name of the design product: Graphical user interface for file model view analysis of electronic equipment. 2. Purpose of this design product: for use in an electronic device. 3. The key design point of this design product lies in the graphical user interface. 4. The picture or photo that best illustrates the key points of the design: main view. 5. Purpose of the graphical user interface: to provide users with view analysis services related to file models. 6. Description of the change status of the graphical user interface: The main view is the graphical user interface displayed in the file model view analysis interface; in the main view, click the bar chart in the upper right corner to enter the interface change status diagram 1. In the interface change state diagram 1, click the "XXX" text content in the "Number of Clauses" table to enter the interface change state diagram 2. 7. Other situations requiring explanation: “XXX” in the graphical user interface is variable text, numbers or letters.

Systems and methods for periodically modifying data privacy elements are provided. The systems and methods may identify a set of data privacy elements. A data privacy element can characterizes a feature of a computing device and can be detectable by a network host. A first artificial profile can be generated by modifying a first data privacy element based on an artificial profile model that defines a relationship associated with one or more constraints between the set of data privacy elements. Subsequent to generating the first artificial profile, a second artificial profile can be generated by periodically modifying a second data privacy element in accordance with the relationship defined by the artificial profile model. The computer device can be masked from being identified by the network host by sending the second artificial profile including the second data privacy element to a requested network location.

The invention provides a document object model updating processing method, electronic equipment and a storage medium. The method comprises the steps of obtaining a node sequence of a new document model tree; according to the text content of each new node and an update time difference, determining the priority of each new node, the update time difference being used for indicating the time difference between a new document model tree and the current time; traversing the new nodes in sequence according to the priorities of the new nodes, and for the traversed current new node, determining a target node matched with the current new node from an old document model tree; determining a multi-dimensional distance between the current new node and the target node, and determining a target updating strategy according to the multi-dimensional distance; and updating the target node according to the target updating strategy and the current new node. And the real-time performance and the accuracy of updating processing of the document object model are improved.

The application discloses a document data updating method and device, a medium, equipment and a program product, and relates to the technical field of computers. The method comprises the following steps: in response to a document updating event of a target online document, obtaining document updating data corresponding to the document updating event and updating event information, wherein the updating event information is used for indicating an updating character position of the document updating data in the target online document; determining a target document node corresponding to the updating character position in a visual document model of the target online document, wherein the visual document model comprises a plurality of document nodes which are divided based on the order of document content, and the target document node is a document node with changed document display content; determining a target rendering node corresponding to the target document node in a rendering object tree of the target online document; and updating rendering data of the target rendering node based on the document updating data and the updating event information. The application can reduce the consumption of rendering resources and time consumption, and improve the dynamic updating frame rate of the document.

The application provides a flow version software integration implementation method based on an MVC architecture, relates to the technical field of data processing, and aims to solve the problems of low conversion efficiency, information loss and software redundancy caused by the separation of flow documents and version documents in traditional office software. The method builds a unified office software kernel, so that the view layer of the flow document processing module is directly extended from the model layer of the version document processing module, and the two share basic technical units such as display, printing, encryption and decryption. Based on the fusion architecture, the flow document generates a version document model in memory synchronously when parsing and typesetting, so that the format conversion can be completed through single traversal, and structured information such as outline and cross-reference can be holographically preserved. The application significantly improves the document conversion efficiency and quality, eliminates software redundancy through deep component sharing, and provides an efficient, unified and reliable document processing basic capability for an office system.

Systems and methods for controlling the exposure of data privacy elements are provided. The systems and methods may generate an artificial profile model. The artificial profile model may include a constraint for generating new artificial profiles. A signal may be received indicating that a computing device is requesting access to a network location. One or more data privacy elements associated with the computing device can be detected. An artificial profile can be determined for the computing device. The artificial profile may be usable to identify the computing device. The one or more data privacy elements may be automatically modified according to the constraint included in the artificial profile model. The method may include generating a new artificial profile for the computing device. The new artificial profile may include the modified one or more data privacy elements. The new artificial profile may mask the computing device from being identified.

The invention provides an artificial intelligence whole-process automatic learning system based on an assembly line. Comprising an assembly line arrangement unit used for performing metadata association, state synchronization and task scheduling on each function unit in the same assembly line; the data management unit is used for acquiring the training data from the heterogeneous data source and performing format standardization, preprocessing and versioning storage on the training data; the training management unit is used for executing multiple rounds of model training and parameter tuning on the computing resource cluster to generate a temporary model file; the model evaluation and management unit is used for extracting a corresponding temporary model according to the identifier of the temporary model file, and performing evaluation index calculation and index comparison on the temporary model; and the release management unit is used for constructing a service package and pushing the service package to a preset DevOps mirror image warehouse to trigger a deployment process. According to the method, data version traceability, training process standardization, continuous retention of evaluation indexes, transparent model release and easy rollback can be realized.

The application relates to the technical field of cross technology of artificial intelligence and information security, and discloses a multi-level security protection system and method of a service-oriented artificial intelligence model. The system comprises a model producer terminal used for enabling a model producer to initiate an identity authentication request to a model security platform and upload an original model file after authentication; a model user terminal used for enabling a model user to initiate an identity authentication request to the model security platform and initiate a model calling request after authentication; and a model security platform used for receiving the original model file uploaded by the model producer terminal, encrypting the original model file, and performing security encapsulation on the model type, model serial number, check value and security token to generate a model security container. A model registration module synchronously generates a globally unique model identity, and the model identity and the model security container are bound and stored after encryption. The application can guarantee the security and credibility of the model from development to retirement.

1.The name of the design product: smart magic box (delicate). 2.The use of the design product: the design product is used for operation through voice recognition technology, and the intention is input into the AI search system according to the voice, all the qualified text, picture, video and design file (model, drawing) are searched out, the automation demand matching is realized, and the individualized display of the user is met. 3.The design points of the design product: in shape. 4.The picture or photo that can best show the design points: perspective view.

Systems and methods for previewing documents associated with a headless object are provided. A client device may generate a primary user interface having a first viewport with a first object focus. In response to a first event, the first object focus may be set to a document model corresponding to the object. The document model may be loaded into a data memory of the client device, and the first object focus may be set to display a rendering of the document model. In response to a second event, a secondary interface of the client device may display the rendering of the document model that was loaded into the data memory.

The invention discloses a mechanical assembly generation method based on semantic driving and multi-modal retrieval, and belongs to the field of computer aided design. The method comprises the following steps: constructing a multi-modal data set containing a mechanical part geometric file, B-Rep model data, a professional and amateur dual-channel description text and ISO standard information; in combination with RAG and CLIP retrieval strategies, a natural language instruction of a user is accurately mapped into candidate three-dimensional parts; an assembly relation and a pose matrix between the parts are calculated through a JoinAble assembly inference engine; automatic visualization generation of the assembly is realized by means of a three-dimensional software API, and key engineering indexes are controlled. The method reduces the professional threshold of mechanical design, simplifies the traditional tedious process, ensures that the assembly meets the engineering standard and physical rationality, and solves the problems that the traditional CAD depends on professional knowledge and the natural language intention is difficult to convert in the prior art.

The invention relates to the technical field of model deployment, and provides a deep learning model deployment method and device, equipment and a storage medium, and the method comprises the steps: obtaining the structure data of a first deep learning model and a corresponding first deployment file, the first deployment file comprises a weight transformation instruction corresponding to first weight data in the first deep learning model; a model file of a second deep learning model is obtained, the model file comprises second weight data in the second deep learning model, and the structure of the second deep learning model is the same as that of the first deep learning model; performing weight transformation on the second weight data according to the weight transformation instruction to obtain target weight data; and deploying a target deep learning model corresponding to the structural data of the first deep learning model and the target weight data. According to the method and the device, the model file to be deployed does not need to be compiled, so that the time for compiling the model file before the target deep learning model is deployed is saved, and the deployment efficiency of the deep learning model is improved.

The invention discloses a data backtracking method based on FATE privacy calculation, which comprises the following steps: S1, FATE service installation and unilateral test completion; s2, FATE interconnection is carried out, and bilateral testing is completed; s3, accessing a user FATE through an independently developed FATE federation service; s4, accessing the FATE federated service to an FATE federated algorithm; s5, the FATE federated service is accessed to the FATE task; s6, the model providing side customizes an encryption algorithm, and provides a mapping API or an offline file; s7, the model providing side extracts the receipts and encrypts the receipts through a user-defined encryption algorithm; s8, the model providing side initiates an FATE uploading task for the encrypted data through the federal service platform, uploads the data to an FATE server and notifies the data acquisition side; s9, the data acquisition side selects an FATE algorithm, and initiates an FATE federated modeling task through the federated service platform; s10, the data acquisition side initiates an FATE downloading task through the federated service platform after the federated modeling task is completed; and S11, after the data downloading task is completed, the data acquisition side processes the data through a customized decryption algorithm and an offline file and then stores the data. According to the method, the problems of data flow, privacy protection and cross-platform compatibility are effectively solved; meanwhile, convenient operation is provided, and the risk of data leakage is reduced.