1949 results about "Microservices" patented technology

The invention discloses a hydraulic engineering equipment data intelligent management system based on digital twinning, and belongs to the technical field of hydraulic engineering. Comprising an intelligent perception and data fusion module for realizing real-time acquisition and standardized processing of cross-modal data; the knowledge graph construction and causal reasoning module is used for constructing an intelligent knowledge system capable of autonomously learning and semantic reasoning; the digital twin modeling and simulation module is used for realizing dynamic simulation and scene deduction of a full life cycle and providing limit working condition simulation and risk assessment support; the intelligent prediction and health management module is responsible for performing real-time monitoring, fault early warning and residual service life prediction on the equipment state, and generating personalized intelligent maintenance strategies for different working conditions; the visualization and decision support module is used for visually presenting the equipment operation data and the analysis result and providing intelligent decision recommendation; and the cloud edge collaboration and system integration module realizes cross-platform interoperation and continuous integration through distributed computing and micro-service architecture.

The invention discloses a real-time interactive digital human system supporting high concurrency and an implementation method thereof, and relates to the technical field of digital human interaction.The system comprises a model instance pool module used for loading the weight of a deep learning model to a shared memory area through the memory mapping technology; the multi-thread scheduling module is used for scheduling user requests by adopting a lock-free queue and a dynamic priority algorithm; the asynchronous pipeline processing module is composed of decoupled micro-services, and the modules are connected in series through asynchronous message queues; the client SDK is used for dynamically switching a rendering mode according to terminal hardware performance and network conditions; the elastic capacity expansion and contraction module is used for monitoring resource loads in real time and automatically adjusting the number of service instances; and the audio and video synchronization calibration module is used for ensuring that the synchronization error of the audio and video frames is lower than a preset threshold value through a timestamp alignment algorithm. According to the scheme, core challenges in a high-concurrency digital human interaction scene can be systematically solved, and innovative support is provided for large-scale real-time application.

Apparatus and methods are disclosed for implementing a copilot as a network of microservices including specialized large language models (LLMs) or other trained machine learning (ML) tools. This architecture supports flexible, customizable, or dynamically determinable dataflow. Compared to much larger competing LLMs, comparable or superior performance is achieved for certain tasks, while significantly reducing computation time and hardware requirements, even to a single compute node with a single GPU. An expansion microservice converts client input tokens into associated tokens to diversify targeted tasks presented to a core microservice. An ML tool in the expansion microservice is pretrained in multiple stages for varying tasks using varying general and target-specific corpora. Synthesized training data derived from a knowledge graph can also be used. The expansion ML tool is subsequently fine-tuned in one or multiple stages. Variations and additional techniques are disclosed.

The invention relates to the field of computers, in particular to a micro-service fault positioning method and device based on causal inference and a knowledge graph. The method comprises the following steps: firstly, collecting an original calling link, a multi-dimensional time sequence index and log data of a micro-service system, analyzing the correlation of micro-service indexes by using a partial correlation algorithm, then inferring the causal relationship of the indexes by using a structural causal model, and mapping the causal relationship of the indexes to specific service calling by using a principal component analysis method. And constructing a service dependency relationship. And then defining a knowledge graph ontology, constructing a fault dependency graph according to an index causal relationship and a service dependency relationship, and fusing the fault dependency graph with a calling link graph to form a complete fault dependency knowledge graph with multi-level influence. And finally, storing the graph in a graph database, taking exception as a service node starting point, and outputting a fault root cause candidate set through exception detection, random walk and node score calculation. According to the invention, the efficiency and accuracy of fault positioning are improved, and the stable operation of the micro-service system is ensured.

The invention discloses an intelligent livestock breeding traceability method and system, and the method comprises the steps: building an overall framework of the intelligent livestock breeding traceability system based on a micro-service architecture, and employing Spring Cloud Al ibaaba as a micro-service technology stack; livestock physiological data and environmental parameters are collected through Internet of Things equipment, and a livestock electronic file is established; an AI intelligent analysis engine is constructed, and livestock health state evaluation, oestrus prediction and abnormal behavior recognition are achieved; the whole life cycle data of livestock from breeding to selling is recorded based on the block chain technology, and a full-chain traceability system is constructed. The method further comprises the functions of integrating a financial service interface, designing a multi-terminal collaborative application platform, constructing a matchmaking transaction platform and the like. According to the invention, the problems of data islanding, incomplete traceability system, low intelligent level, insufficient system expandability and the like in the prior art are solved, and the accuracy, traceability and system stability of livestock breeding are improved.

The invention provides a multi-agent-based low-code component development method and system and electronic equipment, and the method comprises the steps: obtaining a user demand, and converting the user demand into an initial business model through a first agent; obtaining a prefabricated component library of the low-code platform, and automatically generating a demand component by using a second agent according to the initial business model and the prefabricated component library; automatically adapting and integrating a cross-platform data source by using a preset cross-domain data integration mechanism, and performing intelligent scheduling on the automatically generated demand component through a third agent; and establishing an automatic service arrangement mechanism based on the micro-service architecture, automatically identifying each service dependency relationship, and dynamically adjusting the execution sequence and resource allocation of each demand component according to the service dependency relationship. Through modular component design, the low-code platform has high flexibility and expandability, efficient collaboration of the components in the complex industry is guaranteed, and resource allocation and use efficiency is optimized.

The invention discloses a computer big data information processing system, which comprises a data acquisition layer, a data processing layer and a data processing layer, wherein the data acquisition layer is used for accessing structured, unstructured and streaming data by using a multi-source adapter and Apache NiFi, executing format standardization, and extracting basic metadata and semantic tags through a rule engine and an NLP model; the metadata intelligent management layer integrates four modules, namely a federal learning framework for realizing cross-domain dynamic classification labels, an intelligent contract for real-time uplink storage evidence blood relationship change, a Neo4j combined graph neural network for constructing a knowledge graph for mining implicit association, and a reinforcement learning engine for optimizing a storage strategy based on frequency and risk indexes; the distributed storage calculation layer is used for processing batch and real-time metadata by adopting a Cassander + MinIO mixed framework and Spark / Flink, and dynamic partition balance performance is realized; and the application service layer is used for outputting functions of blood relationship query, classified browsing, compliance report and the like through a Vue.js portal and a Spring Cloud micro-service API (Application Program Interface) to form a full-link closed loop.

The invention discloses a digital product online leasing system based on a cloud native micro-service architecture and a construction method thereof, and the method specifically comprises the steps: firstly, carrying out containerized deployment and micro-service hierarchical design, including a plurality of business modules of user service, product management, leasing order, payment settlement, credit evaluation and recommendation, and enabling each module to operate in an independent service form; efficient cooperation is realized through service registration and discovery; a cloud native platform is utilized to realize elastic expansion and automatic operation and maintenance of the system, lease life cycle management is realized through an event-driven mechanism, links of ordering, payment, delivery, lease renewal, return and maintenance are covered, and a credit determination and risk control mechanism is introduced to support multiple lease modes such as detention-free lease and the like. The digital product leasing platform supports multi-terminal access and unified entrance management, has good system expansibility and leasing service flexibility, improves the concurrent processing capability, deployment efficiency and user experience of the digital product leasing platform, and has wide commercial application prospects.

The invention relates to an automatic protocol adaptation method for a real-time access system of multi-source equipment. The data acquisition layer receives original protocol data sent by equipment and transmits the original protocol data to the protocol adaptation layer. And the protocol metadata analysis module extracts, analyzes, compares and matches the data, and automatically identifies the protocol type of the equipment. The rule engine converts original protocol data into a system unified internal data format according to a conversion rule and then transmits the original protocol data to the data processing layer, and after cleaning, integration, analysis and other operations are carried out, the original protocol data are transmitted to the application interface layer to be called by an upper-layer application system. And when new protocol equipment is accessed, the hot plug function allows the system to dynamically load the new protocol adapter module in a state of not stopping running. According to the method, automatic adaptation of a new protocol is achieved through rapid deployment of the containerized micro-service architecture in combination with newly added protocol rules and metadata of the protocol management module, the whole process does not need to compile and publish the system again, and efficient and stable operation of the multi-source equipment real-time access system is guaranteed.

The invention discloses an internet of things multi-protocol fusion limo intelligent control system, which relates to the field of limo intelligent control and comprises a hardware irrelevant layer, a multi-protocol adaptation layer, a data abstraction layer, a programmable logic layer, a security and redundancy mechanism and an edge AI module. According to the invention, a physical bus is packaged into an independent module, an MCU / SoC and a protocol transceiver are built in, hot plugging is realized through a standardized PC I e / EdgeConn backboard, a core processing board only exposes a unified backboard interface, module drive localization execution is realized, master control firmware does not need to sense protocol details, only the module needs to be replaced when a protocol is newly added, and the master control firmware or upper software does not need to be modified. The defect that the whole vehicle firmware needs to be refreshed when a bus is replaced in a traditional system is overcome, each protocol corresponds to an independent drive micro-service deployed in a containerization mode, the drive micro-service analyzes a message into a unified data model and uploads the unified data model to a data abstraction layer, a downlink control instruction is packaged and issued according to a target protocol, and it is guaranteed that the protocols are diversified and interfaces are consistent.

The invention provides a service scene collaboration-oriented cross-service system micro-service processing method and system, and the method comprises the steps: firstly obtaining an initial interaction data set with a service scene label of a cross-power service system in a preset time period, and then carrying out the scene feature analysis of the initial interaction data set; obtaining electric power business scene features and interactive behavior features corresponding to each interactive data unit, then calling a pre-constructed business collaborative analysis model to perform collaborative correlation analysis on the features, and generating a collaborative processing strategy containing micro-service calling priorities and data interactive format specifications; and determining a business process adjustment scheme of each business system in the target power business scene according to the cooperative processing strategy, and finally generating a cooperative execution instruction containing a micro-service calling instruction sequence based on the business process adjustment scheme, and sending the cooperative execution instruction to the target business system to trigger a cross-system cooperative processing operation. Therefore, barriers among power business systems can be effectively broken, the cooperative processing efficiency and flexibility are improved, and the reliability of power supply is guaranteed.

Systems and methods for decentralized orchestration of event records within decentralized computing environments. A processing system including decentralized execution nodes dynamically allocates microservice instances based upon real-time node performance metrics. Transaction requests including transaction data digitally associated with decentralized identifiers (DIDs) are processed by allocated microservice instances. The processing includes verifying transactions, generating cryptographically-linked immutable memorialization records associated with respective DIDs, and distributing these records to a tamper-evident distributed ledger configured to enforce immutability through consensus nodes. The processing system dynamically reallocates microservice instances among decentralized execution nodes based upon monitored utilization metrics and predefined scaling thresholds. Each memorialization record is cryptographically secured and linked permanently to the DID for data immutability, enhanced security, and dynamic scalability for event data processing in distributed infrastructures.

The invention discloses an optical cable intelligent label full life cycle management method and system, and the method comprises the steps: extracting structured data through an XML analysis engine based on a configuration file, carrying out the semantic analysis of an unstructured text, carrying out the regression quantification of text features through combining symbol quantiles, and constructing a knowledge graph; according to the knowledge graph, dynamically adjusting the character size, the two-dimensional code position and the error-tolerant rate by adopting a generative adversarial network technology according to the optical cable type and the pasting scene, and generating optical cable intelligent labels adaptive to different scenes; collecting a label image, and converting the label image into structured label identification data including an optical cable type, a connection relation and a transmission signal type by adopting a computer vision technology; the identification data and the knowledge graph are compared, consistency is checked, potential abnormity is analyzed, influences are evaluated, and graded early warning is generated; a micro-service architecture system is constructed, data analysis, AI generation, an intelligent recognition engine and knowledge graph service are integrated, and full-life-cycle management is achieved. The intelligent level of full-life-cycle management of the optical cable label is improved.

The invention relates to the field of wireless communication and intelligent networks, in particular to a software defined radio-based ad hoc network dynamic slicing architecture and a management and control method, comprising a basic communication layer, an intelligent control layer and a service execution layer. The basic communication layer adopts a multi-distribution multi-center clustering architecture; the intelligent control layer is responsible for intelligent management and resource scheduling of the self-organizing network, and the intelligent control layer comprises a plurality of command and control nodes; and the service execution layer adopts a micro-service-based architecture, and encapsulates different network functions and service logics into independent micro-service modules. According to the method, the technical problems of high plane coupling, resource scheduling rigidness, insufficient survivability and high hardware dependency in the prior art are solved.

The invention relates to a large-model-driven adaptive closed-loop optimization method for machining process parameters of an aviation complex thin-wall component. The method comprises the following steps: acquiring multi-source heterogeneous machining data and quality detection data of the aviation complex component; constructing and training an aviation complex component processing quality prediction model and a multi-target process parameter optimization model; packaging the trained model into a micro service and deploying the micro service to a DeepSeek large model container platform; a DeepSeek large model is used as a controller, and closed-loop calling of the micro-service is achieved; an online self-adaptive optimization process is formed; an analysis report is automatically generated based on an optimization result; and optimizing the processing quality prediction model by using the new data increment and designing an automatic rollback strategy. According to the method, deep learning and a large model technology are fused, accurate prediction of the machining quality and efficient regulation and control of the technological parameters are achieved, and meanwhile the manufacturing efficiency and quality of the aviation complex component are effectively improved.

The invention discloses an optimized registration method and system based on a micro-service architecture. The invention relates to the technical field of Nacos systems. The health monitoring center is started and starts to send a health examination request to a downstream service at regular time; the gateway access layer service receives a client request; obtaining a network address of a target service instance through a Nacos service discovery mechanism according to a service name in the request; then forwarding the request to a target service instance for processing; when the configuration information is changed, the Nacos configuration management center pushes an update notification to all subscribed service instances; the service instance pulls the latest configuration information and updates the local cache; according to the method, a secondary hand waving mechanism is implemented in Nacos cluster node, gateway service and downstream service communication, so that the offline condition of the downstream service can be accurately judged, and the problem of data inconsistency caused by node faults or network partition is avoided.

The invention discloses a resource dynamic allocation method and system under a micro-service architecture, relates to the technical field of resource allocation, and aims to overcome the defect that an existing dynamic resource allocation scheme cannot meet operation requirements of micro-services, and the adopted scheme comprises the following steps: acquiring resource use data in a service operation process in real time; cleaning and filtering the collected data of the data collection module to generate a standardized time sequence data set; for the time sequence data set, analyzing a resource use trend and predicting a future resource demand by adopting a time sequence model, and then generating an elastic capacity expansion and contraction suggestion in combination with a preset service priority rule to provide data support for subsequent resource scheduling; and on the basis of the elastic capacity expansion and contraction suggestion, carrying out actual allocation operation on the resources in combination with a pre-formulated resource allocation strategy. According to the method, the resource use condition of the micro-service is monitored in real time, and a scientific and reasonable dynamic adjustment strategy is combined, so that flexible allocation of resources is realized.

In order to extract more contextualized metrics associated with a particular service in a microservices-based application, a method of aggregating metrics comprises analyzing tags of spans for each trace and, based on a first predetermined set of rules, extracting one or more tags pre-selected by a user of the monitoring service. A pre-selected tag may appear in one or more spans associated with a particular service in a trace and may be extracted as a numeric metric. The method comprises computing a set of metrics for the particular service using the numeric metric extracted from the one or more spans of the trace. The method also comprises determining a set of aggregated metrics over the selected time duration for the particular service by aggregating the set of metrics over multiple traces.

The embodiment of the invention provides a method for realizing automatic service discovery by transforming a service registration center based on xxl-job, and the method comprises the steps: registering a micro-service gateway, a multi-copy xxl-job-admin scheduling center and a micro-service timed task executor to the service registration center, and carrying out the centralized storage and management of configuration items in the service registration center. And meanwhile, the microservice gateway is used for providing access and load balancing based on routing for the xxl-job-admin dispatching center and the microservice timed task executor. And the xxl-job-admin scheduling center obtains the online micro-service timed task executor from the service registration center, and calls the executor to execute the timed task instead of depending on active registration of the executor. According to the method, the problems of high management cost and difficult dynamic expansion caused by independent deployment of the xxl-job are solved, the xxl-job deployment method which is easy for unified management, high in availability and flexible in configuration is provided, and automatic discovery and dynamic scheduling of timed tasks in a micro-service environment are realized.

The invention provides a visualization system capable of synchronously displaying multiple types of data according to time. Comprising a distributed service cluster composed of a micro-service, a user management micro-service, a data transmission micro-service, a data storage management micro-service, a data extraction micro-service, a data visualization display micro-service, a data visualization template management micro-service and a resource monitoring micro-service; the micro-services are packaged in a containerized manner and interact by adopting a lightweight communication protocol, and automatic deployment and dynamic expansion are realized through a container arrangement tool; the system is provided with a basic parameter set with customizable micro-service configuration and a graphical configuration interface which are used for cooperatively generating a system operation environment configuration strategy. The user management micro-service establishes a multi-dimensional authority system based on a role access control model, and records an operation log to realize audit tracking; the data transmission micro-service integrates a plurality of heterogeneous data access modes, carries out transmission process verification and carries out secure channel transmission through the service gateway.

The invention provides a ship comprehensive energy efficiency improvement platform and a data management and application method thereof, and belongs to the technical field of ship comprehensive energy efficiency optimization and big data. The ship comprehensive energy efficiency improvement platform comprises a ship end, a cloud end and a shore end, wherein the ship end realizes equipment data acquisition, edge processing and scheduling through a data acquisition module, an edge calculation module and a data scheduling module; the cloud end completes data aggregation, isolation and distributed storage through the data aggregation module, the data isolation module and the distributed storage module; and the shore end realizes data standard management, algorithm version control and micro-service business application through the data center layer, the algorithm center layer and the business center layer. The ship energy efficiency comprehensive improvement platform provides comprehensive management and optimization for various energy-saving devices and various ship energy efficiency improvement software, and optimization and improvement of ship comprehensive energy efficiency are achieved.

The invention relates to the field of power equipment data processing, in particular to a multi-target dynamic optimization unit load intelligent distribution method, which adapts a micro-service cluster through a containerization protocol, adopts an OPC UA protocol and an MQTT protocol to respectively collect thermal power and new energy data at different frequencies, dynamically analyzes an IEC 61850 power grid instruction, converts and stores the IEC 61850 power grid instruction into a Kafka message queue, and sends the IEC 61850 power grid instruction to a network server. The problem of communication protocol heterogeneity is solved. Based on an IEEE 1588 protocol, sub-millisecond clock synchronization of edge nodes is realized, a cubic spline interpolation algorithm is adopted to align multi-frequency data, an interpolation window is dynamically adjusted in combination with a genetic algorithm, and a spatial-temporal characteristic matrix is generated and stored in a Redis database. And performing multi-objective optimization solution through an NSGA-II algorithm, and dynamically adjusting the weight of an objective function to adapt to the frequency modulation requirement of the power grid. A closed-loop feedback mechanism corrects model parameters through Kalman filtering, actual data are written back to a feature matrix, the robustness of the system is improved in combination with a hierarchical fault-tolerant strategy, the problem of space-time mismatch of data is effectively solved, and the economical efficiency and safety of load distribution are improved.

The invention discloses a self-adaptive micro-service resource management method and device, and the method is applied to a resource-limited micro-service system, and comprises the steps: receiving a task, carrying out the modeling of the time sequence data and spatial dependency of a micro-service based on a space-time diagram neural network, and predicting the resource demands of a future time step, including a CPU, a memory and a network bandwidth; classifying the micro-services into resource redundancy, resource insufficiency and resource self-sufficiency according to the predicted resource demand, the current resource allocation and the resource guarantee threshold; according to the resource state and the business importance, the dynamic priority of the micro-services is calculated, the micro-services with redundant resources are sorted in an ascending order, and the micro-services with insufficient resources are sorted in a descending order; and recovering redundant resources from the low-priority micro-services, and allocating the redundant resources to the high-priority micro-services until available resources are exhausted or resource requirements are met, so that the resource utilization rate is optimized, and a service level protocol is guaranteed. According to the method, when resources are limited, the adaptability of the micro-service in different load environments can be improved.

Apparatus and methods are disclosed for implementing a copilot as a network of microservices including specialized large language models (LLMs) or other trained machine learning (ML) tools. This architecture supports flexible, customizable, or dynamically determinable dataflow. Compared to much larger competing LLMs, comparable or superior performance is achieved, while significantly reducing computation time and hardware requirements, even to a single compute node with a single GPU. Examples incorporate a retrieval microservice, as least one data producer, and a core microservice. Based on client input, the retrieval microservice can perform multiple iterations of retrieval augmented generation (RAG). At each iteration, output (based on any preceding iterations' results or the client input) is transmitted to a data producer, and results received therefrom. Eventually, based on these results, an output is transmitted toward the core microservice for generation of a response to the client input. Variations and additional techniques are disclosed.

According to the student personalized learning path generation method based on the neural network, a data acquisition module is deployed on a teaching platform, a learning terminal and an interactive interface, and multi-dimensional data such as student learning duration, interactive frequency and wrong question records are acquired in real time. A knowledge graph is constructed based on a course outline and a prior textbook structure, and a graph neural network GNN is utilized to dynamically adjust association weights of nodes and edges of the graph neural network GNN to reflect weak knowledge areas of students. A Bi-LSTM encoder is constructed for the collected data to generate a behavior feature vector, and the behavior feature vector and a knowledge graph node embedded vector are aligned and fused through an attention mechanism to obtain student personalized multi-modal representation. And taking the representation and the knowledge graph node weight as an environment state, selecting a next knowledge point to be learned as an action space, designing a reward function by integrating multiple indexes, updating a target by using a deep Q network, and training a strategy network to generate a personalized learning path. The trained strategy is deployed as micro-service, a path is displayed according to knowledge module sorting, and related parameters are iteratively updated according to student feedback to improve the recommendation effect.

The invention discloses a business and financial integrated management system, which constructs a high-elasticity and easy-to-expand business and financial integrated management system through deep fusion of cloud native and micro-service architecture. The system realizes real-time acquisition and cleaning of business and financial data through a standardized data interface and an ETL technology, and data islands are thoroughly broken; a business process modeling tool cooperates with a workflow engine, financial management and control nodes are embedded into the whole business process, and the real-time performance of budget execution and cost collection is ensured; the intelligent analysis module combines OLAP and machine learning, provides multi-dimensional data insight and risk prediction, and significantly improves decision scientificity. In addition, due to the distributed log and role grading permission design, the safety and auditing performance of the system are enhanced. The system not only reduces the manual operation error rate and IT operation and maintenance cost, but also assists an enterprise in realizing refined operation and agile decision making through an automatic process and an intelligent early warning mechanism.

The invention discloses a fault determination method and device for a micro-service system, a product and electronic equipment. Relates to the technical field of artificial intelligence. The method comprises the following steps: determining an abnormal event flow according to abnormal monitoring information; an abnormal event causal graph is determined based on an abnormal event flow, and then a fault point, a fault chain and a fault classification are determined by utilizing an intelligent agent driven by a pre-training model and combining the event causal graph and a fault mode standard. According to the method, anomaly detection, fault classification and root cause positioning of the micro-service system are realized, and closed-loop diagnosis from anomaly discovery to root cause positioning is realized; secondly, performing depth feature engineering and semantic abstraction on the refined data from the perspective of events and causal relationships, fusing system behaviors and dependency relationships dispersed in different modal data into a unified event causal graph through a graph modeling technology, and providing comprehensive and high-dimensional input for fault reasoning of an intelligent agent; and the accuracy of micro-service fault determination is improved.

The invention discloses a microservice system time sequence anomaly detection method and device, equipment, a medium and a product, and the method comprises the steps: carrying out the preprocessing of original multivariate time sequence data, converting the data into a sliding window sequence, carrying out the channel independent division of the sliding window sequence into a univariate time sequence of a plurality of channels, carrying out the periodic coding, and carrying out the calculation of the univariate time sequence; the method comprises the following steps: generating a periodic coding sequence, carrying out feature analysis on the periodic coding sequence, obtaining local features and global features, carrying out feature fusion on the local features and the global features, decoding, generating reconstructed time sequence data, and carrying out mean square error calculation on the reconstructed time sequence data and original multivariate time sequence data. According to the method, the abnormal score of each time step is obtained, anomaly detection is carried out based on the abnormal scores, semantic differences of different variables in multivariate time sequence data are accurately distinguished, relation characteristics of the different variables in the micro-service system are fully extracted, and abnormal fluctuation in the time sequence of the micro-service system is accurately detected.

A method for configuring a system is provided. The method comprises a first entity obtaining function information identifying a Cloud-Native Network Function, CNF, to be provided by the system and using the obtained function information, the first entity identifying the CNF to be provided by the system. The method further comprises the first entity transmitting to a second entity a request for deployment information, wherein the deployment information is about a deployment of one or more microservices for providing the identified CNF. The method further comprises the first entity receiving from the second entity the requested deployment information and using the deployment information, deploying in the system said one or more microservices for providing the CNF.

The invention provides an application environment and version management method and system suitable for a low-code platform, and relates to the technical field of platform management.The application environment and version management method comprises the steps that a directed dependency graph is constructed by obtaining change records, and a dependency loop is detected and intelligently decomposed through a depth-first traversal algorithm; and packaging the node clusters into micro-services based on a function relevancy model to form an acyclic directed dependency graph, constructing a version backtracking index and a difference analysis matrix to determine a change influence range, executing incremental deployment according to a topological sequence, and recording deployment information. According to the method, the problem of version management and environment dependence in a low-code platform is solved, and the application deployment efficiency and the system stability are improved.