149 results about "Model Number" patented technology

Sharing video footage having audio / video (A / V) recording and communication device (A / V device) model identifiers in accordance with embodiments of the present disclosure are provided. In one embodiment, a method for communicating in a network is provided, the method comprising receiving, from a first audio / video (A / V) recording and communication device (A / V device), at one or more servers in network communication with the first A / V device, first image data captured by a camera of the first A / V device; receiving, from a first client device associated with the first A / V device, at the server(s), a command to share the first image data with a network of users; determining a model identifier associated with the first A / V device; and transmitting the first image data and the model identifier associated with the first A / V device to a second client device of at least one user of the network of users.

The invention discloses a computer system based on a cross-model Internet of Things equipment access efficiency calculation model, which integrates an equipment performance sub-model, a network state sub-model and a response prediction sub-model. The identification module is used for identifying a device inherent identifier of the Internet of Things device to be accessed so as to obtain a response time statistical characteristic value and a historical communication parameter set; the prediction module is used for acquiring network state parameters in real time, calling the response prediction sub-model to calculate expected response time, and optimizing the historical communication parameter set to generate an initial communication parameter set; the comparison module is used for monitoring the actual response delay and the data packet loss rate of the current access equipment and obtaining a delay deviation value; and the adjusting module is used for dynamically adjusting the bandwidth allocation strategy in the initial communication parameter set when a preset condition is met, and synchronously updating the data analysis priority to generate a target communication parameter set. According to the invention, differentiated access control of different types of Internet of Things equipment can be realized.

The invention discloses a classification code and description code combination-based electronic material coding method and system, and the method comprises the steps: formulating an 11-bit pure digital coding rule, taking the first three bits as classification codes, and distinguishing the paster / plug-in packaging, basic types and specific types of materials; the last eight bits are description codes and are divided into two types according to material characteristics: for materials with nominal values, such as resistors and capacitors, nominal value codes converted based on a scientific counting method are combined with conventional attribute codes of precision, packaging and the like; for materials without nominal values, such as chips, PCB finished products and the like, the combination of model codes and serial number codes is adopted; a classification code and a description code are automatically generated by analyzing the BOM and are compared with a database to process conflicts. According to the method, the coding rule has intuition and uniqueness, the problems that traditional serial number coding is poor in readability and prone to code repetition are effectively solved, and the efficiency and accuracy of electronic material management are remarkably improved.

The invention relates to a power transformation equipment defect text standardization method and system in combination with deep semantic matching and a knowledge graph, and the method comprises the steps: inputting an actual power transformation equipment defect text into a text entity recognition model, and extracting an entity in the defect text as a to-be-standardized entity, querying a device model and a defect level corresponding to the defect text, and obtaining a defect device type according to the device model; all the standard descriptions corresponding to the entity types in the defect standard are used as candidate standard entities, the candidate standard entities and the to-be-standardized entities form entity pairs in sequence, the formed entity pairs are input into the FTS-BERT, and the standard descriptions of the entities are obtained through matching; constructing a power transformation equipment defect knowledge graph; in combination with FTS-BERT and knowledge graph search, standardizing a power transformation equipment defect text into a unified form; and adding the new knowledge into the original knowledge graph through incremental updating. The method and the system can automatically and effectively complete a power transformation equipment text standardization task, and solve the problem that defect text data is not standard due to manual recording.

The invention provides a maintenance spare part prediction system based on a big language model plug-in knowledge base, and the system comprises the steps: carrying out the information data extraction and preprocessing of a historical work order, and obtaining a preprocessing text; performing semantic vector conversion and association binding processing on the preprocessed text to obtain a vector database; extracting attribute information from the new work order, and performing semantic vector conversion on the attribute information to obtain a query vector; retrieving a work order related to the query vector from a vector database; semantic reasoning is carried out on a new work order and a related work order, a maintenance spare part prediction result is generated, natural language fault description and equipment model information in a maintenance record are fully utilized, natural language information is fully understood in the face of complexity and diversity, the spare part prediction accuracy and practicability are improved, reasonable configuration of maintenance resources is achieved, and the maintenance efficiency is improved. Spare part inventory cost and inventory waste are reduced, and maintenance efficiency is improved.

A method for a system provider in a communication with a user over a computer network, allowing a determination of an optimum dimensioned breast flange for employment by the user with a breast pump. The identification of a model identifier of a flange which is best dimensioned for the physical characteristics of the breast of the user is determined from measurements taken and provided by the user remotely.

The invention discloses a device upgrading method, system and device and a computer readable storage medium, and relates to the technical field of embedded device upgrade.The device upgrading method comprises the steps that after an upgrading mirror image file uploaded by a user is received, the upgrading mirror image file is analyzed to automatically extract a to-be-upgraded firmware identifier and a to-be-upgraded component identifier; according to the method, the matched reference upgrading process of the to-be-upgraded component identifier in the pre-stored corresponding relation and the link information of the reference component are utilized, after firmware identifier consistency verification is carried out, the link information of the reference component is embedded into the reference upgrading process and then is automatically executed, operation and maintenance personnel do not need to manually screen scripts and configure communication parameters, and the efficiency is improved. And the risk of script calling errors and link parameter input errors is avoided. The technical problem of upgrade errors caused by model misjudgment and script calling errors in the manual upgrade process is solved, and the technical effects of non-inductive upgrade of the control unit of the reference component and improvement of the upgrade efficiency are achieved.

To provide an information processing device capable of searching for a component constituting a mobile body or a candidate of the component.SOLUTION: An information processing device 1 acquires information on at least one of a mobile identification number in a predetermined mobile body and a combination of a model designation number and a classification number, identifies a component constituting the predetermined mobile body associated with the information on at least one of them stored in a storage section, and outputs information on the identified component. The information processing device 1 identifies, by acquiring the information on the mobile identification number or the information on the combination of the model designation number and the classification number, the component on the basis of any of the acquired pieces of information. The information processing device also identifies, by acquiring the information on the mobile identification number and the information on the combination of the model designation number and the classification number, the component on the basis of the information assigned priority between both pieces of information.SELECTED DRAWING: Figure 1

The invention relates to a PCell code data enhancement method and system and a storage medium, and belongs to the technical field of artificial intelligence training data construction.The PCell code data enhancement method comprises the steps that an LLM large model unit is constructed, and an interface key value pair vector database is constructed; retrieving a vector database and a process rule base based on the PCell code structure template and the code configuration information to generate a first PCell code sample; performing logic error correction on the first PCell code sample to obtain a second PCell code sample, and performing compilation error correction to obtain a third PCell code sample; and performing instantiation error correction on the third PCell code to obtain a fourth PCell code sample, and storing the fourth PCell code sample in a sample library. According to the method, PCell code samples of different types and models of devices can be automatically generated, a large number of high-quality PCell code samples can be generated through multiple error correction, and rapid amplification of PCell code sample data is achieved.

The invention relates to the technical field of intelligent terminals and network testing, in particular to an automatic testing system and method based on an AI large model.According to the system, parameters are configured and manually rechecked according to the testing type and the network environment, knowledge base data are retrieved through the RAG technology, an optimal testing case combination is dynamically generated in combination with the testing stage, the equipment model and the like, and the testing efficiency is improved. Testing of reliability, stability, performance and the like is automatically executed, key indexes are collected and re-checked by professionals after AI intelligent merging analysis, finally, a standardized report is output and fed back to a data optimization system and a knowledge base, a computing power-model-agent collaborative system is constructed, and automation of a whole test link is achieved; according to the method, the test period is shortened by more than 50%, the test precision and coverage are remarkably improved, the method is adaptive to multiple types of terminals and multiple scenes, test resources are optimized, it is ensured that results are objective and repeatable, and the method has sustainable evolution ability.

The invention relates to the technical field of electronic design automation, and provides a PCB schematic diagram component information extraction method based on text recognition and a graph algorithm. The method aims at solving the problem that component models in a complex PCB schematic diagram are extracted inaccurately, and the method mainly comprises the steps that preprocessing of binaryzation, denoising and text area enhancement is conducted on the PCB schematic diagram, and an optimized image is generated; performing character direction detection and rotation processing on the obtained optimized image, inputting the fine-tuned OCR model to perform multi-scale character recognition, and outputting a recognition result containing character content, coordinates and confidence; constructing a graph model containing MCU position nodes and character block nodes, and establishing associated edges through a weight calculation module; performing global optimal matching on the graph model based on a Hungary algorithm, and screening associated pairs of the MCU and the character blocks; and performing editing distance calculation on a matching result and a preset MCU model template library, correcting character confusion errors through a multi-stage verification mechanism, and outputting final component information.

The invention discloses a circuit knowledge graph library construction and application method, electronic equipment and a storage medium. The circuit knowledge graph library construction method comprises the following steps: carrying out semantic function module extraction on an original electronic circuit drawing uploaded to electronic equipment; performing standardization processing on the extracted function module to obtain a standardized function module template; and constructing a circuit knowledge graph library by taking the standardized functional module template as a basic storage unit. The invention provides a brand-new data processing mode with a reusable functional module as a core, circuit knowledge is precipitated into reusable functional module knowledge units by performing semantic and modular disassembly and standardized reconstruction on electronic circuit drawings, and on the basis, the reusable functional module knowledge units are obtained. A modularized cross-equipment-model circuit knowledge graph database is constructed, and finally, cross-equipment-model circuit comparative analysis capability is provided for maintenance personnel, and powerful support is provided for intelligent fault reasoning when the maintenance personnel face brand-new unknown equipment.

The invention discloses an equipment energy consumption statistical method and device, a storage medium and a computer program product, and relates to the field of smart home, and the equipment energy consumption statistical method comprises the steps: determining a first equipment model of first target equipment; a first algorithm model is matched according to the first equipment model, and the first algorithm model is used for carrying out equipment energy consumption statistics on the first target equipment; acquiring first operation parameter data of the first target equipment in a first time period, and processing the first operation parameter data through the first algorithm model so as to count equipment energy consumption of the first target equipment in the first time period, the first operation parameter data is at least used for indicating power data of the first target equipment; by means of the scheme, the problems that in the related technology, due to the fact that a traditional air conditioner energy consumption calculation method does not distinguish data differences between different models, the generalization ability of an energy consumption calculation model is poor, and prediction precision is insufficient are solved.

The invention discloses a BIM model number inputting and auditing method based on GH and Revit. A two-dimensional drawing is cleaned and number data is extracted through Rhino, the centroid of a BIM model component is extracted by utilizing Grasshopper and is projected to generate a point cloud, the number of the two-dimensional drawing and the BIM model component are matched based on a nearest neighbor algorithm, and batch inputting and dynamic synchronous updating of the number are realized. And reading an instance parameter value of the BIM model component through Grasshopper, and automatically comparing the instance parameter value with the two-dimensional drawing number data set to generate an Excel auditing report containing a consistency mark. The invention relates to the technical field of building modeling, and can solve the technical problems in the prior art.

To save time and effort of a user in migrating migration data of an information processing apparatus of a migration source to especially a plurality of information processing apparatuses of migration destinations.SOLUTION: An information processing system includes a first information processing apparatus, a server apparatus, and a second information processing apparatus. An export processing unit of the first information processing apparatus sends, to the server apparatus, an export instruction, a first device number identifying the first information processing apparatus itself, and migration data to be migrated which is recorded in its own apparatus. An export management unit of the server apparatus receives, from the first information apparatus, the export instruction and the first device number and migration data and stores the first device number and migration data in a storage medium in association with each other. An import instruction unit of the second information processing apparatus sends an import instruction including the first device number to the server apparatus. An import management unit of the server apparatus receives the import instruction from the second information processing apparatus and sends, to the second information processing apparatus, the migration data associated with the first device number included in the import instruction.SELECTED DRAWING: Figure 6

The invention belongs to the technical field of part machining, and relates to a machining program sequence rapid pushing method and system. The standard process parameters are obtained according to the types of the features, the material attributes and the machine tool model, the manual parameter setting time is shortened, the processing sequence sorting efficiency is improved, and the initial program is generated according to the standard process parameters. Attribute assignment is carried out on the features of the parts, and automatic decision making of follow-up sorting is supported. And according to the process number attributes, the inter-group process logic and the intra-group feature category priorities, processing sequence sorting is performed on the features after attribute assignment, and a feature sorting result is obtained. And through inter-group process logic and intra-group priorities, it is ensured that a sorting result conforms to process constraints, and the sorting accuracy is improved. The initial program is reordered according to a feature ordering result, a machining program of the part is generated, a dynamic mapping relation from feature ordering to a program sequence is established, when the feature ordering is adjusted, the program is automatically and synchronously updated, and the multi-version flexible production requirement is met.

A method, system, electronic device, and medium for storing code library data relate to the field of data processing technology. The method includes: obtaining a data file for a target product, parsing the data file to obtain key parameters; determining multiple device models that the target product can adapt to, adjusting the key parameters according to each device model, and obtaining first adaptation parameters corresponding to each device model; adjusting each first adaptation parameter according to device brand information to obtain target adaptation parameters, where the device brand information includes at least one device model; and storing the target adaptation parameters in the database of the target product. Parameter adaptation based on the device model improves the accuracy of the adaptation parameters.

This application relates to the field of BIM modeling design, and particularly to a drawing generation method, a computer device, and a storage medium for support and hanger. The method includes: obtaining the support model and construction specialty of each support and hanger in the global modeling environment; determining the first number of each support and hanger in the global modeling environment according to the support model and construction specialty of the support and hanger, and generating the support structure drawings corresponding to each support and hanger with the first number; in response to the user's project number request, determining a number of target support and hangers in each construction project; determining the second number of each target support and hanger according to the support model and / or construction specialty of the target support and hanger; performing duplicate removal and screening on the first numbers corresponding to the number of target support and hangers, determining a number of target first numbers, and exporting the support structure drawings corresponding to each target first number; generating and exporting the corresponding project overview drawings based on the second number. While achieving lightweight drawing generation, it improves the effectiveness of the drawings in guiding construction.

The article to which this design relates can be used, for example, when searching for the model number or part number of an automobile part, such as a battery, that is compatible with a vehicle during car repair or part replacement.

The invention provides a fault diagnosis method and a computer program product. The method comprises the steps that the equipment type, the equipment model and fault information of target equipment are obtained; then, the fault reason of the target equipment is determined based on the equipment type, the equipment model and the fault information, so that fault diagnosis is automatically carried out without depending on artificial experience, and misjudgment and missed judgment are avoided; then, on the basis of the fault reason, finding out disposal data from a target knowledge graph corresponding to the equipment model; if the disposal data comprises disposal measures and a spare part identifier of at least one recommended spare part, determining detailed information of each recommended spare part based on the equipment type and the equipment model of the target equipment and the spare part identifier of each recommended spare part; and finally, outputting a fault diagnosis result based on the fault reason, the disposal measure and the detailed information of each recommended spare part. According to the invention, spare part recommendation is realized through the knowledge graph, so that fault diagnosis and spare part recommendation are combined to help to improve fault processing efficiency and improve user experience.

The invention discloses an FCT equipment IO function test information compiling method. A first module, an IO number obtaining module, a jointed board IO distribution module and an IO information import and export module are included. The upper computer software of the same FCT device can adapt to lower computer hardware modules of different models, the flexibility of the device is enhanced, other ports without faults can be rapidly used for replacing faulty ports, and IO information setting can be rapidly carried out; and the working efficiency of the IO function test of the FCT equipment is greatly improved.

The article to which this design relates can be used, for example, when searching for the model number or part number of an automobile part, such as a battery, that is compatible with a vehicle during car repair or part replacement.

The disclosure provides a gamma adjustment method, a computer readable storage medium and a computer program product. The gamma adjustment method comprises the following steps: obtaining a model of a to-be-tested module, determining a target model corresponding to the model and an error probability density distribution function according to the model, the target model being used to represent a mapping relationship between a gamma register value and a target influence factor, and the error probability density distribution function being used to represent an error distribution between a predicted gamma register value output by the target model and an actual gamma register value; obtaining a target influence factor value when the to-be-tested module displays a target gray scale picture, and inputting the target influence factor value into the target model to obtain a first gamma register value; detecting whether a display picture of the to-be-tested module meets a requirement when the to-be-tested module performs gamma adjustment by using the first gamma register value; and when the display picture does not meet the requirement, adjusting the first gamma register value by using the error probability density distribution function until the display picture meets the requirement.

The invention relates to a model optimization method and device, terminal equipment and a computer program product, and the method comprises the steps: obtaining model information of a to-be-optimized model and chip information of a target chip, the model information comprises structural features, a target type and a performance index, and the chip information comprises a target model and a chip parameter; determining a first constraint based on the chip parameter, and determining a second constraint based on the performance index; candidate layer instances matched with the target model and the target type are selected from a pre-constructed model structure library, the model structure library comprises a plurality of layer instances, the layer instances are labeled with labels, and the labels identify chip models and task types matched with the layer instances; combining and screening the plurality of candidate layer instances based on the structural features, the first constraint and the second constraint to obtain a candidate layer combination; and generating a first optimization model based on the candidate layer combination. Through the method and the device, the problems of relatively low optimization efficiency and relatively poor optimization effect of the model structure in related technologies are solved.

The invention relates to the technical field of airborne system hardware abstraction, and discloses a hardware abstraction method and system for an airborne system, computer equipment and a medium, and the method comprises the steps: abstracting each physical equipment in the airborne system from different types of physical equipment into abstract equipment with the same interface, shielding the physical difference between different types of equipment, and improving the hardware abstraction efficiency. And forming the same abstract device with the same function. And meanwhile, analyzing and counting the interfaces of different types of physical equipment of each type of physical equipment to form a group of uniform interfaces of the abstract equipment. For the upper software, the method shields the physical difference of different hardware of different manufacturers, obviously improves the development efficiency and cross-platform portability of the upper software, and facilitates the development and transplantation of the upper software; for manufacturers, the method unifies the interface requirements of different models of aircrafts, and facilitates formation of lineage design and research and development of products with unified functions and different performances.

The present invention relates to the technical field of artificial intelligence, and provides a resource management method, an electronic device and a storage medium, the method comprising: obtaining first resource information reported by a device plug-in of a node, the first resource information comprising a first name of a computing device of the node, and computing devices of different device information corresponding to different first names; a first name in the first resource information is converted into a second name, second resource information is obtained, and the computing devices of different device information correspond to the same second name; and reporting the second resource information to a cluster end through a node agent component of the node. According to the method, the electronic equipment and the storage medium provided by the invention, the intermediate agent component is additionally arranged on the node, and the first name in the first resource information reported by the equipment plug-in is converted into the second name through the intermediate agent component, so that unification of resource names of computing equipment of different manufacturers and different models is realized; and the resource management difficulty under the heterogeneous cluster is greatly reduced.

The application discloses an operation and maintenance knowledge intelligent question and answer interaction method, device and equipment based on an LLM large model. The method receives a question from an operation and maintenance personnel through a natural language interaction interface, analyzes semantics by using a pre-trained LLM to generate a candidate answer, verifies the accuracy of the answer in combination with a dynamic operation and maintenance knowledge graph (containing a device model and fault code mapping, a historical case library and a real-time updating knowledge base), and finally returns a verification result. The device contains an input module, a processing module, a verification module and an output module, wherein the processing module integrates field knowledge fine-tuning and real-time data set functions, and the verification module supports multi-source data fusion and confidence evaluation. The platform adopts a multi-tenant architecture, supports visual diagnosis, automatic device operation and knowledge usage report generation, and ensures that data cannot be tampered with through blockchain technology.

The invention provides an automatic burning method and system suitable for a laser radar production line. The implementation method comprises the steps of order analysis, power supply configuration, burning configuration, firmware package downloading and function verification. The production line automatic burning system is designed and realized based on the method, is verified and used in a production line, and comprises an upper computer control system, a burner, a burning jig and a programmable power supply, the method has the following advantages that 1, the method is combined with an MES system, the radar model, the hardware version and the firmware package issued by the MES system are automatically mapped, and mixed burning of a production line is achieved; 2, the replicability is high, convenience is provided for copying of a production line, the integration level is high, a downward compatible mechanism facing full automation and semi-automation is realized, and a foundation is laid for automatic upgrading of a subsequent production line; 3, a read-back-function verification-closed loop mechanism is adopted, key information read-back is performed on each radar, and meanwhile, through dual verification of chip self-weight starting and power supply external restarting, the reliability of the system is improved; the method has the advantages of being simple in structure, convenient to operate and capable of executing relevant functions to conduct good detection on radar key chips, preventing non-benign radars from flowing into follow-up stations, indirectly reducing production cost, recording burning information of the key chips, and achieving traceability of the radars in the whole life cycle.

The application provides a MaaS platform large model inference parameter automatic optimization acceleration method, comprising the following steps: detecting hardware configuration, constructing a standardized container environment, and preparing pressure test data; using an intelligent search algorithm to traverse a multi-dimensional parameter space, automatically discovering optimal inference parameters of a specific hardware and model combination through pressure testing and a performance scoring formula; persistently storing optimal parameter combinations corresponding to various hardware models and model versions to a structured database to form a reusable parameter knowledge base; automatically identifying a current hardware environment during model deployment, intelligently matching and loading corresponding optimal parameter configurations from the parameter base, and realizing zero-configuration optimization deployment. Through a parameter combination feasibility verification mechanism, the application can timely discover and exclude parameter combinations that may cause system errors or memory overflow, ensuring that the finally selected parameters can both improve performance and guarantee stable operation of the system.

The invention discloses a method for binding order numbers of three-in-one products in LCD (liquid crystal display) module production, which comprises the following steps of: 1, when a preset model production order of an LCD module is received, writing an order number code containing model information corresponding to the model product into a background system; 2, the LCD module product to be processed is fed into a three-in-one automatic machine table, a product two-dimensional code on the LCD module product is scanned through a code scanning gun arranged on the three-in-one automatic machine table, and the scanned product two-dimensional code is transmitted to an upper computer of the three-in-one automatic machine table; and step 3, the upper computer sends the received product two-dimensional code to a background system. Compared with a traditional mode, order number recognition is added in three-in-one software, the situation that production line workers misplace products is intercepted, and the method has the following effects that the use probability of secondary burning is reduced; the product scrapping probability is reduced; customer complaints are reduced, and the quality of company products is improved; the cost and the reworking cost are reduced.