1623 results about "JSON" patented technology

The invention relates to the technical field of operation and maintenance management and control, and discloses an intelligent building remote operation and maintenance management and control system based on a large model and a cloud edge collaborative architecture, and the system comprises a configuration subsystem which is used for carrying out the reverse proxy and persistent connection configuration of edge gateway equipment in an intelligent building, and obtaining a TLS encryption bidirectional communication channel; the construction subsystem is used for constructing a cloud-edge-end three-layer cooperative computing architecture; the conversion subsystem is used for performing unified semantic abstraction and conversion on the heterogeneous protocol data to obtain semantic data in a standard JSON (JavaScript Object Notation) format; the rule processing subsystem is used for performing rule processing through a near-end decision engine of the edge gateway to obtain a local intelligent response result; the multi-dimensional analysis subsystem is used for carrying out multi-dimensional analysis through a cloud large model engine to obtain an equipment health score, a fault prediction result and an energy optimization strategy, seamless fusion and interoperation of heterogeneous system data are achieved through the system, and then global optimization and linkage control of the intelligent building are achieved.

The invention discloses an Internet of Things data integration method and system based on dynamic semantic atlas and edge collaboration, and the method comprises the steps: carrying out the equipment access and protocol conversion, capturing the original data of equipment through an edge node, analyzing the average communication interval of 100ms + / -20ms through an LSTM model, generating an equipment fingerprint hash value, analyzing a message structure through a TinyBERT model, and extracting a key field, a temperature value and a timestamp. The method comprises the following steps: establishing a dynamic semantic graph, converting the dynamic semantic graph into a standard JSON format, uploading the standard JSON format to a cloud end through a service grid, constructing a dynamic semantic graph, collecting multi-source metadata by the cloud end, generating an entity relationship by utilizing a Transform model, dynamically optimizing a mapping rule based on reinforcement learning, automatically associating a Pressures field to a database column PSI, and carrying out collaborative optimization by an edge cloud; according to the method, the knowledge graph and the Transform model are combined, the data entity relation is updated in real time, the semantic drift problem of a traditional static graph is solved, the cross-domain data association accuracy is improved by 35%, the unique device fingerprint is generated through the message frequency and load distribution, and dynamic tracking of the mobile device is supported.

The invention discloses a charging station operation maintenance method based on a multi-dimensional data system, and relates to the field of intelligent optimization, and the method comprises the steps: collecting the operation, environment, user and power grid data of a charging pile, preprocessing the data into JSON, carrying out the real-time analysis and classification through Kafka-Flume, storing the data into a distributed database, building a composite index, and carrying out the retrieval through Presto; after cleaning, combining GraphQL and a graph query language to construct a space-time atlas, and extracting a multi-dimensional feature vector; a digital twinborn model is generated, multi-source data are fused through virtual-real mapping, and residual correction and reinforcement learning optimization are combined; fusing multi-modal data by using a hypergraph neural network, and optimizing topology in combination with an entropy regularization multi-target strategy and meta-learning; power is dynamically distributed, LSTM-GAN predicts and positions faults, and PID regulates and controls output; and the block chain contract automatically triggers equipment freezing and power limiting, and on-chain data feedback optimizes scheduling and load balancing. According to the invention, through multi-dimensional data fusion and a dynamic optimization mechanism, the operation and maintenance accuracy and response efficiency of the charging station are significantly improved.

The invention provides an intelligent data production method and system based on a graph neural network and adaptive learning, and the method comprises the steps: collecting a text, a time sequence, an image and sensor data in real time, and converting unstructured data into a structured JSON format through a format analysis tool; extracting text entity features based on a large language model, modeling a cross-modal entity association relationship in combination with a graph neural network, and fusing multi-source heterogeneous data through a dynamic knowledge graph; the detection threshold is dynamically optimized by adopting a reinforcement learning algorithm, and the false alarm rate is reduced in combination with a double-track verification mechanism; and predicting a data trend by using the space-time diagram convolutional network, and outputting an interpretability analysis report through the generative large model. According to the invention, a multi-protocol adaptation engine and a distributed stream processing architecture are adopted, and a protocol analysis layer automatically identifies a plurality of industrial protocols, so that the manual adaptation time is reduced; and real-time synchronization of production line-level data is realized through edge node parallel acquisition and compression transmission.

Some embodiments of a method may include: obtaining Avatar JSON Interchange File (AJIF) data; decoding the AJIF data to generate avatar data; obtaining animation parameters from the avatar data; obtaining user movement data corresponding to a movement of a user; generating updated avatar data based on the user movement data; and sending the updated avatar data to a client device. For some embodiments, a method may include: generating Avatar JSON Interchange File (AJIF) data corresponding to an avatar; sending, to an application server, the AJIF data; obtaining user movement data corresponding to a movement of a user; sending, to the application server, the user movement data corresponding to the movement of the user; receiving, from the application server, scene update data, wherein the scene update data includes updated avatar data corresponding to the movement of the user; and rendering a scene update based on the scene update data.

The invention provides a low-code dynamic configuration method based on a natural language, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out semantic analysis on a natural language description input by a user through an intelligent agent, extracting a page type, a field attribute, a verification rule and interaction logic, and generating structured configuration data; dynamically generating JSON metadata for describing page layout, field components, verification rules and interaction logic according to a preset template on the basis of the structured configuration data; according to page function requirements, interface metadata matched with the JSON metadata is generated, and the interface metadata comprises a request address, a parameter list and corresponding SQL (Structured Query Language) operation logic; and analyzing the JSON metadata by utilizing a low-code configuration rendering engine, dynamically generating an interactive management function page, and modifying the JSON metadata in real time and re-rendering the page through natural language interaction between a user and an intelligent agent.

The invention provides an enterprise process intelligent analysis system based on a large language model. The enterprise process intelligent analysis system comprises a master control scheduling module, a data preprocessing module, a hierarchical analysis module, an insight extraction module, a report generation module and a knowledge retrieval module. The master control scheduling module generates a scheduling plan based on chain thinking reasoning, and dynamically calls each module; the data preprocessing module carries out cleaning and structured conversion on the enterprise event logs and outputs standardized JSON (JavaScript Object Notation) data; the knowledge retrieval module is combined with an RAG technology and a vector database to provide context support for a large language model; the hierarchical analysis module drives a model to execute process discovery and bottleneck identification through a structured cue word template; the insight extraction module converts an analysis result into a commercial insight text containing reasons, influences and suggestions, and has a self-repairing mechanism to guarantee consistency; and the report generation module automatically generates an image-text report. The system can improve the efficiency and accuracy of process analysis.

The invention discloses a medical image report generation method and system based on a large language model, and relates to the field of image report generation, and the method comprises the steps: firstly obtaining original image data and a clinical background text of a patient, and respectively extracting an image embedding vector and a background embedding vector; then, case retrieval based on priori knowledge is carried out by utilizing the embedded vectors, and K highly related historical case reports are screened out from massive historical data; and inputting the image embedding vector and the historical case report into an observation large language model, and outputting the image embedding vector and the historical case report in a structured JSON (JavaScript Object Notation) format. And finally, a large language model is written to integrate the visual evidence JSON, the clinical background text and the historical case report, and a final medical image report is generated. According to the mode, through a staged and multi-modal fusion mode, the problems of incoherent report logic, inaccurate information and the like are effectively solved, and the report quality and the generation efficiency are remarkably improved.

The invention relates to the technical field of text data analysis, in particular to an electronic medical record free text analysis method, system and device, which improve the efficiency of information extraction and reduce the demand of manual intervention. The method comprises the steps of receiving free text data of the electronic medical record, performing cleaning, word segmentation and medical term standardization processing, and converting an unstructured text into structured data; based on deep learning and a medical knowledge base, text features are extracted through a pre-training language model, entity boundaries are captured on an output layer in combination with a conditional random field, medical entities in a text are recognized, and the recognized entities are classified and labeled; extracting a causal relationship, a treatment relationship and an examination relationship among entities through a dependency syntactic analysis and semantic role labeling technology, and constructing an entity association network; performing dynamic correction and supplementation on entity classification and relationships in combination with medical record context information; and outputting an analysis result in a structured JSON format to generate a computable semantic map.

The invention discloses an abnormal behavior detection method and system based on a multi-source log and electronic equipment, and the method comprises the steps: collecting log data, carrying out alignment processing and data preprocessing, and generating normalized log data; carrying out attribute screening on the standardized log data set, and packaging the screened logs into a JSON format; extracting features of the log data, converting the features into data points, clustering the data points, and retaining a log cluster according to a clustering result; mining a hidden association mode between log attribute items in the log cluster by using an improved Apriori algorithm, generating an association rule, and constructing a knowledge graph based on the association rule; and constructing a time sequence of the log data by using a self-adaptive time window, and inputting the time sequence and the tag sequence of the log data into the LSTM neural network by combining context information in the knowledge graph and a tag sequence generated by an association rule to obtain an abnormal behavior detection result. According to the method, the log data format is unified, correlation analysis is enhanced, and the accuracy of an anomaly detection result is obviously improved.

The invention relates to the technical field of financial analysis, and discloses a multi-agent financial analysis system and method based on a large language model, and each agent of a multi-role agent module realizes specialized analysis through a field customization LLM; the hybrid communication protocol module integrates a structured report and a natural language dialogue, and the structured report adopts a JSON or XML format; the dynamic coordinator module is configured to dynamically allocate an agent weight according to the difference between the market stage and the confidence coefficient; the interpretability enhancing module is used for outputting an inference logic chain through a natural language generation technology and visually presenting the inference logic chain; and the data fusion and strategy generation module is used for integrating basic surface, emotion, technology and macroeconomic data and generating a dynamic investment strategy. According to the method, by simulating the organization structure and the cooperation process of a real financial analysis team, the multi-source data are integrated, and the interpretable comprehensive investment strategy is generated, so that the efficiency, the depth and the credibility of financial analysis are remarkably improved.

The invention discloses a data analysis problem generation method based on image input and large model combination, and the method comprises the following steps: S1, obtaining original image data, and carrying out the preprocessing of the original image data; s2, constructing a visual language model based on a Qwen-VL architecture, and performing bidirectional alignment to generate a visual feature vector; s3, constructing a cue word template, and fusing the cue word template through a cross attention mechanism to generate structured text description; s4, constructing a large language model based on a Transform architecture, and performing supervision and fine tuning by adopting a LoRA method to generate a data analysis problem candidate sequence; s5, semantic consistency detection and structural rule matching are carried out, and sequences which do not meet semantic specifications or structural constraints are removed; and S6, constructing an image-text alignment triple, and writing the image-text alignment triple into the data structure in the JSON format for coding and storage. According to the method, the image can be converted into a data analysis problem, and the text generation quality and efficiency are remarkably improved.

The invention discloses a heterogeneous data conversion method and system based on a multi-modal large model, and aims to solve the problems of single-modal limitation, high migration cost and the like in traditional heterogeneous data processing. The method comprises the following steps: firstly, carrying out format adaptation and classification preprocessing on multi-source heterogeneous data such as images, texts, audios and videos; then, through a ViT, BERT and Transform cross attention mechanism, deep analysis and cross-modal semantic association of visual, text and audio features are realized, and visual-text-audio semantic mapping is established; and based on a dynamic prompt project, guiding the multi-modal large model to generate structured data according to a preset Schema zero sample. And then error correction and format normalization are carried out through a Drools rule engine to ensure that the data meet the business standard. And finally, supporting multi-format output of JSON / XML / database table and the like, and meanwhile, associating original data storage to realize data traceability. According to the method, the limitation of single-modal processing is broken through, and cross-modal data deep association is realized.

The invention discloses a circuit netlist generation method supporting deep learning model multi-stage reasoning, and belongs to the technical field of electronic circuit design and automation, and the method comprises the steps: obtaining a circuit diagram image through optical scanning, carrying out the preprocessing of the image, carrying out the recognition of a component through a deep learning model YOLOv5, and determining the boundary frame coordinates and class information. And further performing port fine identification on the component, and determining the position and the direction of the port. And eliminating character interference by using an optical character recognition technology, performing wire recognition, including Hough straight line detection and jumper processing, and determining starting point and ending point coordinates of the wire. And matching the identification results of the components, the ports and the wires, constructing a topological structure of a circuit diagram, finally generating a circuit netlist, and performing error elimination and JSON format output. According to the method, automatic netlist generation of the analog circuit diagram can be realized, the analog circuit diagram file is converted into the netlist file with device function annotations, and the efficiency and quality of circuit design and simulation are improved.

The invention belongs to the technical field of artificial intelligence, and relates to a multi-modal large model-based document identification method, system and device and a medium, and the method comprises the following steps: 1) image preprocessing: preprocessing a document image input by a user; 2) multi-modal large model reasoning: based on the pre-processed document image, the configured JSON template and the cue word template, performing reasoning by a multi-modal large model to obtain a JSON result; 3) OCR identification: identifying the document image input by the user by using an OCR identification technology to obtain an OCR identification result; and 4) verification: performing similarity comparison on the OCR recognition result and the JSON result, and determining a document recognition result based on a similarity comparison result. The method is high in generalization, can adapt to various types of receipts, and can provide an efficient and accurate recognition result.

The invention provides a conflict resolution method and system for realizing multi-person collaborative editing based on WebSocket and JSON-PATCH, and relates to the technical field of I T and software development, the method comprises the following steps: obtaining an editing operation of a user at a client of a low-code platform, and converting the editing operation into operation information in a JSON format; the method comprises the following steps: receiving operation information in a JSON format sent by a plurality of clients through a WebSocket server; storing the received operation information in the JSON format in a database according to a user account; based on the operation type defined by the JSON-PATCH, conflict detection is carried out on the concurrent operation of the multiple users; according to a preset priority rule, carrying out merging processing on the detected conflict operations; a result obtained after combination processing is broadcasted to all the clients through a WebSocket server; and the client receives the broadcast combination result and updates the low-code platform interface. According to the method, the user operation is formatted by using the JSON-PATCH, so that operation conversion is realized to solve conflicts.

The invention provides a risk conduction prediction method and system based on combined deduction of a time sequence diagram and a large model, and relates to the technical field of artificial intelligence and knowledge engineering, and the method comprises the steps: extracting a prospective time sequence fact based on a hierarchical cue word and a structured constraint mechanism; extracting conflicts and generating a sequential relationship; the historical time sequence knowledge graph is updated based on the time sequence relation and a confidence coefficient weighted updating strategy; determining a central entity, and initiating a structured query to the updated historical time sequence knowledge graph by taking the central entity as a starting point to generate a context knowledge sub-graph; converting the context knowledge sub-graph into a natural language description with a logic relationship, injecting a risk hypothesis event, constructing a cue word as a new input of a large model, and outputting to obtain a structured JSON object containing a complete reasoning chain; and converting the structured JSON object based on a risk path extraction and visualization algorithm influencing weight attenuation to obtain risk early warning information and a visual conduction path diagram.

The invention relates to the technical field of configuration editing, in particular to an intelligent page editing method and system based on component dynamic generation, and the method comprises the steps: extracting data binding, event response and rendering rules by analyzing teaching component codes, generating JSON Schema, an event mapping table and a view template, and storing the JSON Schema, the event mapping table and the view template in a database after logic optimization and intelligent completion. And setting priority rules of equipment performance, user intention and domain knowledge, generating a preliminary strategy instruction by utilizing a rule engine in combination with stored data, predicting component combination probability through machine learning, and fusing to generate a final strategy. And generating a DOM tree based on the strategy instruction dynamic loading component, collecting page interaction data and feeding back the page interaction data to the rule engine to form a strategy optimization closed loop. According to the method, dynamic generation and intelligent adaptation of the teaching component are realized, and the flexibility and cross-end adaptation capability of page editing are improved.

The invention discloses a food production traceability management system based on a block chain, which belongs to the field of food management systems, and is characterized in that a unique identifier ID of an inheritor is generated based on an SHA-256 Hash algorithm, a Merkle tree root Hash value of food production data is dynamically associated, a non-tampering digital identity file is constructed, a data island of traditional decentralized management is broken through, and the traceability of food production is improved. According to the technical scheme, full-chain credible association between the non-abandoned technology and the production process is ensured, validity of an electronic signature is automatically verified by means of an intelligent contract, data tampering or identity failure risks are recognized in real time by comparing a public key decryption result with a signature hash value, JSON format alarms are pushed by means of Apache Kafka message middleware, cross-department collaborative response is achieved, and the security and reliability of the electronic signature are improved. The risk prevention and control efficiency is remarkably improved, encryption signature is performed in combination with an ECDSA key pair of a hardware security module, strong binding of an inheritor identifier, a production batch and equipment is ensured, identity fraudulent use or qualification counterfeiting is prevented, and traceability of the whole life cycle of non-abandoned skill inheritance is achieved.

The invention belongs to the technical field of mine ventilation monitoring, and aims to solve the problems that a traditional knowledge system based on a rule base has bottlenecks in the aspects of dealing with sudden working conditions, knowledge updating and semantic reasoning, and is high in construction cost and complex to maintain. The invention provides a mine ventilation knowledge graph construction method based on a large model, and the method comprises the following steps: S100, obtaining and cleaning a multi-source heterogeneous text related to mine ventilation, and obtaining a JSON format knowledge fragment of a unified structure; s200, constructing an ontology model and defining a semantic structure; s300, constructing four types of knowledge extraction tasks, and extracting entities, attributes and relationships; s400, constructing an entity alignment module; and S500, constructing a graph database structure. According to the method, automatic structured expression, semantic consistent fusion and intelligent visual query of knowledge in the mine ventilation field can be realized, and an interpretable, extensible and reasonable intelligent support platform is provided for a mine ventilation system.

The invention discloses a chemical material field knowledge graph construction method and device based on an inference large model prompt instruction. The method comprises the following steps: acquiring basic data information and experimental chart data; establishing a domain rule base and a knowledge graph basic framework document; establishing structured JSON data; constructing a Neo4j database according to the structured JSON data and a field rule base to perform structured data quality verification and dynamic enhancement so as to obtain the updated field rule base and the verified structured data; acquiring preprocessed atlas data according to the verified structured data; acquiring a multi-modal fusion preliminary map according to the preprocessed map data; and establishing a knowledge graph containing a reasoning result according to the multi-modal fusion preliminary graph. According to the method, the logical relationship in the article content can be better extracted, so that the content and the relationship of the established knowledge graph are more comprehensive.

The invention discloses a complex task structured decomposition method, system and device based on a large language model and a medium, and the method comprises the steps: converting natural language query into a standardized atomic task flow through the combination of a dynamic prompt template and a strong type verification mechanism; the core innovation comprises the steps that a structured prompt template is adopted to guide a large language model to generate a subtask description object meeting the JSON Schema specification, it is ensured that an output result meets the expected structure requirement in combination with a format constraint mechanism, and the logic completeness of a task flow is ensured through a dependency relationship consistency verification mechanism; the system realizes an end-to-end processing process from natural language input to structured task output through task processing link series prompt template construction, model reasoning and result analysis links; according to the method, the problems of adaptability limitation, difficulty in unstructured output docking and weak dependency relationship verification of a traditional rule engine are effectively solved, and efficient and reliable task decomposition support is provided for a complex business scene.

The embodiment of the invention provides a dynamic page rendering method and system based on a front-end framework, electronic equipment and a storage medium, and belongs to the technical field of page rendering. According to the scheme, rule expansion processing is conducted on JSON structure specifications, a metadata structure model is obtained, and the metadata structure model is used for defining page rendering rules; obtaining JSON configuration data, and performing data preprocessing on the JSON configuration data based on the metadata structure model to obtain structure data; dynamic structure analysis is conducted on the structure data through an analysis engine, a virtual document object model tree structure is obtained, and the analysis engine adopts a layered architecture; and performing difference rendering according to the virtual document object model tree structure to obtain a target page. According to the method, seamless connection between a low-code platform and professional development can be realized, the development efficiency is improved, and the page rendering dynamic capability is enhanced.

The invention relates to an LLM-based industrial control programming ladder diagram generation method, which is based on a ladder diagram in a text graph form generated by processing a natural language target programming demand through a large language model, generates a large language model by applying a pre-training base, generates a corresponding target XML file, converts the file into a corresponding target JSON file, and finally analyzes the file by applying a GOJS graphic component. Generating a graph componentized target programming ladder diagram; compared with the prior art, the generation rate and accuracy of the graphic componentization programming ladder diagram are greatly improved, the natural language serves as input, the technical threshold and difficulty of ladder diagram programming are reduced, the readability of the generated programming ladder diagram is high, and the corresponding XML file and JSON file can be directly imported into an industrial control programming platform to be compiled and debugged.

The invention belongs to the field of intelligent music generation, and relates to an intelligent music production system and method based on a model context protocol, and the system comprises a large language model, the model context protocol, and a digital audio workstation execution layer. The large language model is used for deconstructing a natural language instruction into structured operation logic and generating a semantic instruction based on the music knowledge base; the semantic instruction at least comprises a device parameter and a chord sequence; the model context protocol is used for compiling the semantic instruction into a JSON command and synchronizing working state data of the digital audio workstation in the digital audio workstation execution layer to a large language model so as to update a model context; the model context comprises track configuration, effect chain parameters and time sequence positioning information of the digital audio workstation; the digital audio workstation execution layer is used for converting the JSON command into music operation to obtain a music operation file; a man-machine cooperative music production mode is reconstructed, and the quality of music production is improved.

The invention discloses a multi-modal large model-based seal detection and identification method and system, and the method comprises the steps: carrying out the size standardization processing of obtained document image data through an image preprocessing module, and generating a standardized image which accords with the input specification of a multi-modal large model; the standardized image and the structured recognition instruction are combined and then input into a multi-modal large model subjected to fine tuning training for end-to-end reasoning, and a recognition result character string conforming to the JSON format specification is output; and analyzing the recognition result character string to extract the bounding box coordinate, the type label, the text content and the text recognition confidence coefficient of each seal object, performing hierarchical decision processing based on the text recognition confidence coefficient, and finally outputting a structured recognition result. According to the invention, the accuracy and the automation level of seal identification are obviously improved, the normalization and the reliability of an output result are ensured, and the problems of low seal identification precision and non-standardized output in a complex scene are effectively solved.

An AI-based system generates personalized educational content tailored to individual students' abilities and needs. The platform analyzes academic standards, curriculum guidelines, and student data including IEP status using natural language processing to build customized profiles. Large language models (LLMs) including GPTs, BERT, and custom models as well as generative adversarial networks (GANs), diffusion models, and other AI algorithms are leveraged to dynamically generate lessons, assignments, recommendations, visual aids, and other educational content adapted for each learner. The system stores output in JSON format for further transformation. Educators can review auto-generated content, provide guiding prompts, and collaborate via social features. Administrative controls enable customization across districts and schools. By leveraging AI to automate personalized content creation, the system reduces teacher workload while improving educational outcomes.

The invention discloses an automatic financial data collection system based on a web crawler technology. The system comprises an intelligent crawler collection module, a financial data processing module and a data collection module. The intelligent crawler acquisition module comprises a dynamic scheduling crawler cluster unit, a multi-format capture engine and a metadata marking unit; the dynamic scheduling crawler cluster configures a routing algorithm based on reinforcement learning, and dynamically adjusts an IP proxy strategy through an anti-crawling feature matrix; the multi-format capture engine is integrated with a PDF / HTML / JSON heterogeneous data analysis channel; the metadata marking unit generates a data source label by adopting multi-layer coding conversion; and the financial data processing module comprises a cross-format analyzer, a data cleaning pipeline and a structured storage unit. According to the scheme, through innovative designs such as multi-modal data analysis, self-adaptive anti-creeping confrontation, dynamic resource scheduling and intelligent association analysis, the core problems of low heterogeneous data integration efficiency, weak dynamic anti-creeping confrontation capability and the like are solved.

The invention provides a multivariate document analysis method and system, relates to the field of computer information processing, and solves the technical problems of low information extraction efficiency and low accuracy caused by incapability of uniformly analyzing and integrating formats of various types of documents. The method comprises the following steps: identifying the type of a to-be-processed document; the document types comprise a table type, a text type and a demonstration type; the text class comprises a Word format and a PDF format; calling a corresponding analysis function according to the type of the to-be-processed document, and analyzing the to-be-processed document to obtain an analysis result, the analysis result comprising the extracted structure information and content data of the to-be-processed document; and converting an analysis result into a standard JSON format and outputting the standard JSON format. The method and the device are used in a document analysis process.

The invention discloses a photovoltaic inverter data monitoring plug-and-play method, and the method comprises the steps: constructing a model list supported by a model conversion program for various inverter brands; the initialization information of the acquisition terminal is configured, so that the acquisition terminal can correctly read the type ID and firmware version information of the inverter; establishing a mapping relation between the model ID and the Modbus protocol, wherein the mapping relation at least comprises a register address, a field type, a byte length, a unit and an alarm and state ID description corresponding to a standard object model field; the acquisition terminal reads the type ID and firmware version information of the inverter, and dynamically constructs a Modbus protocol frame according to a physical model demand field after verification of the support of the model library; and after the fields required by the object model are collected, assembling the object model into a Json structure model with a unified standard, and sending the Json structure model to a cloud for analysis and display. The cloud has the capability of remotely updating firmware, the timeliness and accuracy of the system are ensured, user feedback and technical support requests can be collected, and a model conversion program can be continuously optimized and improved.