352 results about "Adaptation method" patented technology

The invention relates to an AI-based automatic adaptation method for a work approval process, and the method comprises the steps: collecting multi-source approval data, and carrying out the preprocessing of the multi-source approval data, and forming standardized approval data; analyzing the system rule text by using the pre-trained AI large model, and extracting a structured approval rule comprising a trigger condition, an approval role and a process node sequence; matching a basic examination and approval template according to the form field and the user permission information, and dynamically generating an adaptive process comprising multiple stages of examination and approval nodes, aging parameters and an additional examination and approval link; and through resource scheduling optimization and time domain correlation analysis, establishing an optimized mapping relation based on flow execution characteristics such as approval timeliness deviation and node skipping frequency, and outputting a visual flow chart and execution parameters. A business logic writing mode is replaced by automatic analysis of an AI large model on an unstructured rule; the dynamic template matching and continuous iterative optimization mechanism can quickly respond to business changes, and resource scheduling optimization and automatic process generation shorten the implementation period and reduce the operation and maintenance cost.

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 belongs to the technical field of intelligent control, particularly relates to a deep learning-driven intelligent home scene dynamic adaptation method, and aims to solve the problem that an existing intelligent home system is difficult to realize high-precision personalized scene adaptation in a multi-user and multi-device environment due to dependence on a static rule. The method comprises the steps of collecting multi-source heterogeneous user behavior data and performing semantic enhancement preprocessing, constructing a hierarchical time sequence behavior coding model to extract local time sequence dependence and cross-equipment long-range association features, clustering to generate a dynamic scene prototype and mapping the dynamic scene prototype into an executable condition-action rule, after the rules are deployed, a closed-loop optimization mechanism is constructed through explicit and implicit user feedback, and online incremental updating and self-adaptive evolution of the behavior model and the scene rules are achieved. According to the technical scheme, the user complex behavior mode can be deeply understood, the scene adaptation precision is continuously optimized, the individuation level, logic consistency and system robustness of intelligent services are improved, and meanwhile privacy safety and real-time response are guaranteed through edge calculation.

The invention discloses a Java-based large language model dynamic adaptation method and system, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of overcoming the interface difference of multiple model interfaces, simplifying the development process and providing standardized and high-expansibility large model management. Comprising the following steps: providing a model registration and dynamic loading service, a protocol conversion and parameter standardization service and a load balancing and failover service; a streaming transmission protocol service, a function call dynamic injection service and a global error processing mechanism are provided; when a user provides a document analysis and partitioning service, a vectorization index construction service and an RAG enhanced generation service to ask questions, relevant document blocks in the Pinecone are retrieved through the RAG enhanced generation service to serve as contexts to be injected into cue words of the large language model, and answers generated by the large language model are returned.

The invention discloses a dynamic form generation and cross-database adaptation method based on metadata driving, and belongs to the technical field of data architectures. The method comprises the steps that an original metadata attribute graph is established; on the basis of the original metadata attribute graph and user roles and equipment parameters for calling the forms, generating an optimal equipment form interaction data graph corresponding to each user permission; verifying an interaction path between the forms and a cross-database demand of a target database type to carry out cross-database mapping adaptation analysis, and generating an optimal form data adaptation database of the corresponding equipment of each user permission; and performing rule compiling based on multi-source heterogeneous data input by a user in real time and execution logic in the original metadata attribute graph, marking an incremental propagation path in the optimal form interaction data graph of the corresponding equipment of each user permission, and updating the optimal form data adaptation database of the corresponding equipment of each user permission. The method has the advantages that the data interaction efficiency is improved, and the interaction round-trip times and redundant data transmission are reduced.

The invention discloses an intelligent routing and unified adaptation method for a large language model, and the method comprises the steps: defining all access details of the model through a declarative configuration file, and achieving the zero-code access of the large language model without writing any code for a newly-added model; a completely consistent calling interface is provided for an upstream application, the isomerism of all downstream large language models is shielded, and a unified request and response abstraction layer is constructed; through a strategy engine and a JSON path technology, complex streaming response including content thinking is precisely processed, and intelligent analysis and content extraction are carried out; dynamic configuration and intelligent strategy hot update are supported; intelligent routing of the model is realized, and an optimal large language model instance is dynamically selected; and meanwhile, enterprise-level governance capability is provided, governance functions such as fusing, degradation, current limiting and monitoring are integrated, and stability guarantee is provided for model calling. Therefore, the maintainability, the expandability and the user experience consistency of the system are comprehensively improved.

The invention provides a training and pushing integrated scene-based neural network parameter adaptation method, a medium and equipment, and belongs to the technical field of neural network parameter adaptation. Comprising the following steps: selecting initial parameter configuration, carrying out initial evaluation on a neural network model, and recording the performance of the neural network model; according to an initial evaluation result, positioning the parameters in an optimal parameter region by adopting Bayesian optimization; in the optimal parameter region, local optimization of parameters is carried out by using a gradient descent method; and for the neural network model after parameter optimization, the neural network model is compressed to a smaller version through pruning and quantization. According to the method, the parameter adaptation capability of the super-large-scale model can be effectively improved, the calculation overhead in the adaptation process is reduced, and meanwhile it is guaranteed that the model performance is not affected.

The invention relates to the field of artificial intelligence, in particular to a heterogeneous GPU-oriented large model reasoning platform adaptation method and system. The method comprises the following steps: continuously and dynamically collecting and maintaining all available GPU resources in a server cluster and attribute and state information of a deployed reasoning acceleration framework instance through proxy service deployed in each server node; providing a single entry point to receive a reasoning service request, and analyzing the request to determine a large model needing reasoning; according to the analyzed large model needing reasoning and the currently aggregated resource information, selecting a reasoning acceleration framework instance which is optimal in load state and compatible for the request through a decision algorithm, and routing the task to the reasoning acceleration framework instance; heterogeneous response formats returned by different inference acceleration framework instances are converted and packaged into a unified standardized format, and then the unified standardized format is returned to a requester. Scattered and diversified computing resources are effectively utilized, and dependence on a single hardware manufacturer is reduced.

The invention discloses an efficient large language model adaptation method based on server-free edge computing. The method comprises the steps that an LLM reasoning task is divided into four computing stages of lightweight preprocessing, shallow reasoning, deep reasoning and lightweight post-processing; carrying out compression optimization on the model through pruning, quantification and hierarchical optimization technologies; on the basis of an MOSEC server-free scheduling framework, a sensitive index CSI and a real-time load state are calculated in combination with a layer-device mapping table, and a scheduling decision is dynamically generated by taking minimization of total delay as a target; and finally executing cloud edge collaborative reasoning according to a decision result. According to the method, the calculation and communication overhead is effectively reduced, and the efficiency and the resource utilization rate of large language model reasoning in the edge environment are improved.

The invention relates to an industrial protocol adaptation method, system and device based on dynamic feature recognition and a medium. The method comprises the following steps: acquiring original communication data of industrial equipment; performing feature extraction on the original communication data to obtain a depth feature vector; performing feature matching on the depth feature vector in a known protocol feature library to obtain a protocol matching result; if the protocol matching result is the unknown protocol parameter, marking the depth feature vector as unknown to obtain an unknown feature vector; clustering all the unknown feature vectors based on the protocol matching result to obtain candidate protocol groups; and for each group of candidate protocol groups, generating a new protocol analysis rule set. The method can support parallel operation of multiple communication protocols, is suitable for different manufacturer devices, automatically classifies unknown protocols through a machine learning algorithm, and enhances self-learning and self-adaptive capabilities.

The invention relates to the technical field of computers, and provides a cross-platform installation package automatic adaptation method and system, and the method comprises the steps: obtaining a unified description file and an operating system fingerprint of each target platform; based on the operating system fingerprints of the target platforms and the installation package adaptation parameters, matching is carried out, and dependency trees of the target platforms are generated; and generating an adaptive installation package based on the dependency tree of each target platform and the installation package adaptation parameter. According to the cross-platform installation package automatic adaptation method, specific parameters and shared parameters of the platform are managed in a centralized mode through unified description files, environment intelligent perception is achieved and the dependency tree of the platform is generated in combination with dynamically obtained operating system fingerprints, and the adaptive installation package is generated through analysis of the installation package adaptation parameters of the platform and the dependency tree. The problems of low adaptation efficiency, high configuration error rate, high maintenance cost and the like are solved; in addition, a unified construction management mechanism realizes cross-platform centralized management and control, and the accuracy and efficiency of cross-platform installation package adaptation are improved.

The invention provides an AI flow chart driven test step dynamic adaptation method, device and equipment, and relates to the technical field of software testing. The AI flow chart is subjected to semantic analysis to obtain a plurality of executable operation tuples, a dynamic equipment capability library is updated in real time through technical parameters and real-time states of all the matching and testing instruments, then dynamic matching and mapping are carried out, a first mapping relation between the executable operation tuples and the matching and testing instruments is established, and the dynamic equipment capability library is updated in real time through the technical parameters and the real-time states of all the matching and testing instruments. According to the second mapping relation between the signal features and the port of the matching and testing instrument, the AI flow chart is executed based on the executable mapping scheme, automatic matching and mapping of all the testing steps in the AI flow chart are achieved, manual participation is not needed, the problem that the testing efficiency of AI flow chart driving is low is solved, and the equipment testing efficiency of AI flow chart driving is improved.

The invention provides a gear tooth surface precision detection method based on a deep learning network, a medium and equipment, and mainly solves the problem of insufficient model generalization ability caused by scarcity of real labeled data and domain difference in an existing method. The method comprises the following steps: collecting a plane workpiece image marked with a roughness level as a source domain data set, and pre-training an intelligent detection model; based on a gear shaping machining meshing motion relation, establishing a tooth surface morphology geometric simulation model, and generating virtual gear tooth surface images of different roughness grades; extracting a real gear tooth surface area image as a target domain sample; a domain adaptation method based on clustering optimal transmission is adopted, the feature distribution difference between a source domain and a target domain is calculated and minimized, model parameters are optimized, and self-adaptive migration of a feature space from a plane image to a gear tooth surface is achieved. According to the method, virtual sample generation and a domain adaptation technology are combined, so that the data acquisition cost is effectively reduced, and the detection precision and the model generalization ability under the small sample condition are remarkably improved.

The invention provides a low-delay video stream code rate adaptation method and system based on off-line element reinforcement learning. The method comprises the following steps: constructing an off-line expert track data set; constructing a code rate adaptation strategy network, and outputting a downloading code rate selection result and a network throughput correction value by taking a video stream transmission state as input; and adopting a meta-implicit Q learning algorithm to perform double-layer alternate optimization of an inner layer and an outer layer on the policy network, the inner layer updating policy parameters of the policy network based on the offline expert trajectory data set, and the outer layer further adjusting the policy network parameters through meta reinforcement learning to improve the adaptability of the policy network to unseen tasks. According to the method, multiple expert algorithm experiences are integrated through offline reinforcement learning, high generalization and adaptive ability can be realized in multiple target time delays and complex network environments without online trial and error exploration, and finally, high-quality and smooth playing of video streams in a low-time-delay live broadcast scene is supported.

The invention discloses an advertisement creativity dynamic generation and adaptation method and device, equipment and a medium. The method comprises the following steps: constructing a media characteristic knowledge graph for storing context characteristics, content format specifications and user portraits of different media platforms; carrying out multi-modal semantic analysis on the input initial creative material and extracting semantic elements; inputting the knowledge graph and the semantic elements into a generative adaptive model for processing, and outputting adaptive creative content which is matched with a target media platform in context and format and retains core semantics; putting the adaptive creative content on the target media platform and collecting deep behavior data generated by interaction between the user and the adaptive creative content; and performing iterative optimization on the generative adaptive model based on the depth behavior data, and reversely updating the media characteristic knowledge graph according to the optimization. According to the invention, deep and real-time intercommunication between creative design and media characteristics is realized, and the propagation effectiveness and brand consistency of advertisement creativity are improved.

The invention discloses a multi-mode graphical interface self-adaptive adaptation method and system based on environment perception, and relates to the technical field of graphical interface adaptation. The method comprises the steps that environment detection is conducted on a target display platform; rendering engine matching and rendering optimization are carried out based on the system environment information, a rendering engine module is constructed, and rendering optimization is completed based on balance of performance and power consumption; and seamless switching of the graphical interface is carried out by utilizing the rendering engine module according to the input and output modes of the user, and abstract compatibility of interface elements is carried out by adopting a virtualization technology in the switching process. The technical problems that in the prior art, a graphical interface is poor in compatibility under different platforms and input modes, and performance and power consumption are difficult to balance are solved, and the technical effects that multi-mode graphical interface self-adaption is achieved, and the compatibility and performance of the graphical interface under the different platforms and input modes are improved are achieved.

The invention relates to the technical field of computer vision, in particular to an adaptation method and system for continuous testing for differences between construction scene domains. According to the method, the similarity between Gram matrixes between adjacent domains is calculated, an elastic adjustment factor is set, the elastic adjustment factor is utilized, different weights are given to strong data enhancement and weak data enhancement, an elastic data enhancement strategy is provided, and an enhanced target domain image data set is input into a teacher model; updating the pseudo-tag by combining the elasticity regulation factor to obtain an elastic pseudo-tag; and inputting the target domain image data set into the student model to obtain a prediction result, constructing a global elastic symmetric cross entropy loss function based on the elastic adjustment factor, the cross entropy loss and the reverse cross entropy loss, updating student model parameters through the loss function, updating teacher model parameters, and finally obtaining a target model. According to the method, the construction scene monitoring model can adapt to complex domain changes when continuously learning test data, and the prediction result precision of the model in different environments is improved.

The invention discloses a rail transit construction safety identification model dynamic adaptation method and system. The method comprises the following steps: initializing an urban rail transit engineering construction potential safety hazard identification model and new scene training parameters; reading an urban rail transit engineering construction potential safety hazard identification image and a corresponding label in a new scene, and performing data enhancement operation; selecting a historical scene similar to the current new scene in feature, and setting a weight parameter of a new scene model by taking a corresponding encoder weight as an initial value; carrying out image coding and category prediction on the enhanced image data; calculating a total loss value comprehensively considering classification accuracy and model parameter stability through a multi-task optimization objective function; selecting specific parameters in the image encoder to update through adaptive methods such as a dynamic expert hybrid mechanism and dynamic low-rank adaptation; the problems that an existing continuous learning method is poor in cross-scene adaptability and insufficient in sustainable learning ability in urban rail transit engineering potential safety hazard identification can be effectively solved.

The invention provides a dynamic adaptation method and system for a multi-mode power supply compatible charging pile system, and the method comprises the steps: building a multi-protocol physical layer compatible connection between a charging pile and a power supply module through a multi-communication interface layer, and receiving a data frame inputted by the charging pile; analyzing a baud rate, a check bit and an instruction set of the data frame, and identifying a charging pile protocol type and a power module protocol type; querying a matching mapping relationship between the charging pile protocol type and the power supply module protocol type in the dynamic adaptation matrix; if the matching mapping relation exists, data field conversion is executed, and a target protocol frame is generated; and if the matching mapping relation does not exist, starting a machine learning process, creating a temporary mapping rule, and effectively solving the problems of poor compatibility and high manual configuration cost of the charging pile and the power supply module in a traditional protocol mode through the modes of protocol analysis, intelligent matching and dynamic learning.

The invention provides an image semantic segmentation active domain adaptation method and system based on a segmentation cutting model. The method comprises the following steps: generating a full-image mask for an image of a target domain by using a pre-trained segmentation cutting model; the images of the target domain are sampled in proportion, initial labeling is carried out on the full-image mask of each sampled image, an initial target domain labeling data set is obtained, and labeling is that a semantic category is given to a full-image mask area generated by each image; training the semantic segmentation model through semi-supervised learning; wherein in the training iteration of the semi-supervised learning, active learning is triggered according to a preset period, and an updated target domain labeling data set is obtained and used for continuous training of the semi-supervised learning. According to the method, the technical purpose of obtaining initial annotation data with complete semantics and clear structure at relatively low labor cost is achieved, and the technical problems that existing semantic segmentation training depends on large-scale pixel-level manual annotation, the annotation efficiency is low and the cost is high are solved.

According to the intelligent terminal equipment intelligent adaptation method and device, intelligent terminal automatic discovery is carried out based on scene-based adaptive ARP scanning and multi-fingerprint UPnP filtering, access equipment is obtained, automatic and precise discovery of intelligent terminal equipment is achieved, and the intelligent terminal equipment can be automatically and accurately found by standardizing the scanning frequency and the filtering rule. The equipment discovery efficiency and accuracy are improved, the access equipment is subjected to prefabricated API library matching and AI reverse analysis to obtain interface information of the access equipment, automatic analysis of a standard interface and a private interface is realized based on a multi-dimensional analysis technology of an AI large language model, and the equipment discovery efficiency and accuracy are improved by utilizing the AI large language model and combining a built-in multi-language programming template. According to the method, the adaptive code matched with the interface information is automatically compiled, the adaptive code is automatically generated based on the AI technology, the compatibility of multi-protocol and multi-interface type equipment is supported, and finally the core problems of low efficiency, poor compatibility, difficult maintenance and high threshold in the existing intelligent terminal adaptation process are solved.

The invention provides an interactive simulation engine for electronic countermeasure simulation and a general adaptation method thereof, relates to the technical field of electronics, and solves the problem that the general adaptation performance of a simulation engine to each simulation model is insufficient. The simulation engine is used for obtaining a corresponding algorithm result to realize simulation application in a mode of calling a function of each simulation model after each simulation model is initialized, and storing data needing to be interacted by each simulation model in a pointer form. Enabling each simulation model to obtain a pointer corresponding to the required data from the simulation engine according to the own demand, and achieving data interaction; and each simulation model judges whether data corresponding to a pointer is needed or not according to the type of the pointer in the simulation engine. According to the method, a simulation engine code does not need to be modified after the new simulation model is added into simulation, the method can adapt to complex and changeable electronic countermeasure scenes, and higher environmental adaptability and application ductility are shown while the simulation precision is guaranteed.

The invention relates to the technical field of data mapping, and discloses a dynamic data mapping and adaptation method and system for heterogeneous data, and the method comprises the steps: obtaining initial data, and extracting features to obtain an initial data group set; if the matching degree of the type features and the type library is lower than a threshold value, grouping the sets, calculating the association strength of semantic tags and service attributes, and constructing a candidate mapping graph of attachment positions to obtain an attachment position candidate set; integrating the features to the attachment position to obtain an initial data framework; formatting the framework to obtain a standard data framework; generating a logic rule according to the data framework, and fusing the report template to obtain an analysis output report; calculating the matching strength of the semantic tag and the service attribute according to the output report, and if the matching strength is lower than a threshold value, optimizing the standard data framework to obtain an optimized data framework; and updating the candidate mapping graph of the attachment position according to the output report, and combining with the optimized data framework to obtain a time sequence prediction report. The method can solve the problem that data mapping lacks accuracy.

The invention discloses a communication network diagnosis and dynamic arrangement adaptation method and system oriented to a multi-source heterogeneous rule engine. The method comprises the following steps: acquiring logic circuit, environment and energy consumption data as initial data; and after feature extraction and fusion processing, a fusion fault diagnosis model is utilized to analyze the fault probability of each node and the environmental influence weight thereof, the fault is positioned, and the influence of the environment on the fault is evaluated. Comparing the equipment energy consumption data with an energy efficiency standard, identifying energy consumption abnormity, and determining an energy loss source by using a comprehensive energy loss analysis model based on a fault tree and a Bayesian network; comprehensively optimizing equipment parameters, layout, greening and software configuration according to a fault diagnosis result to formulate adjustment measures; the effectiveness of measures is verified through power devices, communication performance and greening environment simulation, the future performance trend is predicted, and a basis is provided for network optimization and greening planning; by implementing the method provided by the invention, the internal logic circuit and external environment factors of the equipment can be considered at the same time.

The invention relates to an accelerator adaptation method for AI algorithm reasoning, and the method comprises the steps: carrying out the structural analysis of a to-be-deployed AI reasoning model, recognizing a calculation path having an influence on a reasoning result based on a target reasoning output result, and cutting or recombining a model structure, so as to generate a group of subtask sets for representing main reasoning behaviors; according to a basic calculation unit structure of a target accelerator, the subtask set is subjected to modular division, the calculation granularity of each module is made to be matched with an execution unit supported by the accelerator, and the operation sequence between the modules is adjusted; aiming at the execution path of the modular task, analyzing a resource mapping relation on a target accelerator, and reorganizing a calculation path execution sequence located in a resource bottleneck region; based on the execution state of the current accelerator, including thread activeness, cache use condition and resource occupation condition, an adaptive instruction sequence is dynamically generated and optimized, and multi-level adaptive optimization is scheduled from a model structure level to an instruction level.

The invention relates to an interface adaptation method and system for reconstruction and integration of a complex information system module. The method comprises the following steps: acquiring a protocol mapping rule and constructing a rule base; monitoring and receiving a calling request which is sent by a source module through a transmission layer protocol and carries source module side interface protocol format data; analyzing the request, stripping a source module side transport layer protocol header to extract application layer data, and packaging the application layer data into an internal general data model; performing structure mapping, data conversion and dynamic logic processing on the model through a rule execution engine according to a request mapping rule of a rule base, reconstructing the model into target request data according to a target module interface format, and sending the target request data to a target module through a target side transport layer protocol; receiving target response data, stripping a transport layer protocol header of the target response data, analyzing the target response data to the internal general data model, and performing reverse conversion according to a response mapping rule to enable the data to conform to a source side interface format; and packaging the back-to-source module side transport layer protocol back to the source module. By adopting the method, the adaptation flexibility and the maintenance efficiency can be improved.

The invention provides an automatic adaptation method, system and device for multi-source heterogeneous alarms, a medium and a program product, and the method comprises the steps: receiving alarm requests from different alarm sources through a unified access entry, and extracting path aliases from the path information of the alarm requests, querying corresponding target alarm source configuration information in a preset alarm source mapping relation library; based on the target alarm source configuration information, a corresponding parser is adaptively matched and loaded from a parser resource pool, and the parser supports hot plug loading and unloading; and analyzing the data content of the alarm request by using an analyzer, and converting an analysis result into a unified and standardized data format, so that a downstream processing module executes alarm strategy processing. According to the method, an alarm source identification and resolver dynamic self-adaption mechanism based on path alias is introduced, automatic identification and dynamic adaption of multi-source alarm access are achieved, and the expansibility, reliability and operation and maintenance automation level of the system are remarkably improved.

The invention provides a rapid database adaptation method and device, a storage medium and electronic equipment. The method comprises the steps that an execution request which is initiated by an application program and contains a source SQL is obtained; enhancing the database execution object to obtain an enhanced object; intercepting the source SQL through the enhanced object; performing structured analysis on the source SQL to generate a corresponding intermediate representation structure; loading a corresponding dialect conversion rule set according to the type of the target database; generating a target intermediate representation structure according to the dialect conversion rule set; according to the target intermediate representation structure, rendering and generating a target SQL (Structured Query Language) compatible with a target database grammar; calling a native drive of the target database to execute the target SQL to obtain a native execution result; performing compatibility packaging processing on the native execution result to obtain an adaptive execution result; and returning an adaptive execution result to the application program. According to the method, large-scale modification of business codes and SQL statements is not needed, and the migration cost and risk are effectively reduced.

The invention provides a desktop browser plug-in compatible adaptation method and system based on a credential terminal Web application, and relates to the technical field of credential terminal Web application, firstly, behavior data of a plug-in during operation of a credential terminal is captured, including calling a system interface, accessing local resources and outputting a data rendering record, and time sequence correlation analysis is performed on the data; the method comprises the following steps: establishing an interaction behavior fingerprint, identifying an abnormal interaction node, calling a mismatching pattern library to determine a mismatching reason and generate an adaptive adjustment instruction for the abnormal node, injecting the instruction into a plug-in running process, starting a behavior monitoring process, recording the adjusted interaction behavior fingerprint, and judging a plug-in function completion condition based on adjusted data. The effective adjusting parameters are extracted to construct the adaptation operation process, the final compatible adaptation scheme is generated, and the compatible adaptation efficiency and accuracy of the plug-in and the credential terminal can be improved.

The invention relates to the technical field of non-intrusive load monitoring, in particular to a domain adaptation method and system for non-intrusive load monitoring. Comprising the steps of obtaining source domain synthetic data and target domain real data; constructing a domain separation network, wherein the domain separation network comprises a shared encoder, a private encoder, a decoder and a regression device; public features and private features are extracted from the source domain data for reconstruction and energy consumption prediction; fusing features of the target domain data through an external attention mechanism to obtain enhanced features and reconstructing the enhanced features; a total loss function is constructed, characteristic orthogonal decoupling of the difference loss is realized through a Floribenius norm, and distribution alignment of the similarity loss is realized through domain adversarial training; a gradient descent method is adopted to optimize parameters, and when the iteration step number reaches a threshold value, a gradient inversion layer is introduced to carry out domain confrontation training. According to the method, the distribution difference between the synthetic domain and the real domain is effectively relieved, negative migration is avoided, and the cross-domain generalization ability is improved.