517 results about "Data domain" patented technology

The invention discloses an energy consumption prediction and scheduling control method based on machine learning. According to the method, operation parameters, energy consumption curves and environment disturbance data of multiple devices are collected through a distributed sensing terminal, the data are input into a pre-trained machine learning model, and a probability prediction result of future energy consumption distribution is generated. On the basis of prediction, an intervention signal is applied in an equipment safety boundary, equipment response characteristics are obtained according to the difference before and after intervention, and an energy consumption risk map is constructed by combining the equipment response characteristics with a probability prediction result. And based on the energy consumption risk map, generating an extreme disturbance scene by using digital twinning, performing consistency check on a probability prediction result and a scheduling scheme in a data domain and a physical domain, and performing multi-stage scheduling in combination with task delays to generate a scheduling result. And finally, issuing the scheduling result to the equipment. The method can improve the accuracy of energy consumption prediction and the reliability of scheduling decision making, and is suitable for intelligent management of data centers, industrial production and high-energy-consumption scenes.

The invention provides a small sample plankton image enhancement recognition method and a small sample plankton image model building method, and belongs to the technical field of underwater image enhancement. The method comprises the following steps: firstly, acquiring and sorting plankton microscopic image data, and constructing a normalized data set; secondly, designing a small sample plankton image enhancement recognition model based on a feature map reconstruction technology, performing feature extraction on the image by adopting a deep convolutional neural network, generating an intermediate layer feature map, and realizing category discrimination in combination with a feature map reconstruction mechanism; the model fuses multiple key designs oriented to cross-domain adaptation and structure perception, the distribution difference between different data domains is relieved, and the characterization capability of the model on the complex morphological structure of plankton is also remarkably improved; and finally, performing end-to-end training and optimization on the model under a meta-learning framework to obtain an optimal model. Experimental results show that the method shows excellent classification performance on a plurality of plankton data sets, and high-precision recognition can be achieved under the condition that the number of samples is limited.

The invention discloses a space-variant self-focusing imaging method for a missile-borne synthetic aperture radar sea surface moving target. The method comprises the following steps: acquiring a radar fundamental frequency echo signal; performing range pulse pressure and range migration correction on the radar fundamental frequency echo signal to obtain a correction signal, and further obtaining a coarse focusing radar image; extracting a single defocusing target image in the coarse focusing radar image, and inverting the single defocusing target image to an echo data domain to obtain data of single target energy; performing correction to obtain data after correction of the residual range walk; selecting a part of distance units from the data after the residual range walk is corrected, performing Doppler frequency modulation rate estimation on the selected part of distance units by adopting fractional Fourier transform, fitting the estimated Doppler frequency modulation rate by adopting a space-variant phase error model, and constructing a space-variant phase error compensation function of the target; and compensating the data after the correction of the residual range walk, and imaging. According to the invention, the refocusing effect of the moving ship target can be improved.

The present application is suitable for the technical field of fault prediction, and in particular relates to a transfer learning-based cross-server fault prediction system and method. The method comprises: carrying out data collection, collecting server data, and preprocessing the server data to obtain preprocessed data; constructing a neural network model, constructing a data set, and, on the basis of the data set, pre-training the neural network model to obtain a pre-trained model; on the basis of a deep domain confusion mechanism, aligning feature distributions of data domains of different servers to obtain a deep domain confusion neural network model; collecting sample data, and tuning the neural network model to obtain a prediction neural network model; and performing data information testing on a server requiring prediction to obtain a test result, and, on the basis of the test result, performing visualization processing. The present application takes preventive measures to improve the stability and reliability of servers, shortening shutdown time and reducing maintenance costs, thus improving the overall performance of systems and user experience.

The invention relates to the technical field of information and data processing, in particular to an enterprise digitalization-oriented data management and application method, which comprises the following steps of: acquiring multi-source heterogeneous data and recording metadata; establishing a cross-department management system to standardize the treatment process; building an asset catalog based on the business section and the data field classification; formulating unified data description of a multi-level data standard system; designing a quality control rule to filter low-quality data; constructing a business index system and embedding the business index system into a business system; identifying a cross-system and cross-type association mode through a metadata association technology and a rule engine; and dynamically adjusting the treatment process by adopting a multi-objective optimization algorithm in combination with service feedback and treatment parameters. According to the method, the defects of existing data governance in comprehensiveness, depth and business adaptability are overcome, standardized governance and potential value mining of multi-source data are achieved, and effective data support is provided for intelligent decision making of enterprises.

A system and method for cross-domain knowledge transfer in federated compression networks. The system enables efficient lossless data compression across diverse data types by intelligently sharing compression strategies between domains. A cross-domain knowledge transfer system identifies relationships between different data domains, adapts compression parameters accordingly, and optimizes learning processes to maximize knowledge reuse. The architecture may include a knowledge repository for storing domain features and compression patterns, domain mapping components that identify similarities, and transfer learning optimization that enables efficient adaptation with minimal examples. This approach significantly accelerates model training for new domains while improving compression performance. Applications include satellite telemetry systems where efficient compression is critical for transmitting large information sets between distant locations. The system may employ probability prediction driven arithmetic coding paired with long short-term memory networks, enhanced by cross-domain knowledge sharing that adapts successful compression strategies from one domain to another while preserving domain-specific optimization.

The invention provides a dialogue and multi-agent collaboration-based data knitting system implementation method, which comprises the following steps that: a main agent performs user intention recognition, splits and distributes a data processing task according to the user intention, and issues a data retrieval task to a data retrieval agent in a domain agent; the data retrieval agent executes a data retrieval task on the managed data domain to obtain a retrieval result, the main agent issues a data calculation task to the data calculation agent in the domain agent, and the data calculation agent executes the data calculation task to obtain a result data set; and the main agent executes a data fusion task on the retrieval result and / or the result data set, and returns the fused data set to the user. According to the invention, through the precise division and efficient cooperation mechanism of the main agent and the domain agent in the multi-agent system, the implementation process of the data knitting system is simplified, the system is easy to expand, and the high efficiency and intelligence of the enterprise-level data knitting process are realized.

Disclosed herein are methods, systems, and computer-readable media for prompting a machine learning model to generate answer data based on a recording. Some embodiments involve preprocessing a prompt corresponding to a query for a first system by receiving the prompt and a timestamp corresponding to a time position of the query in a recording, acquiring a text transcript based on the recording, and selecting, based on the timestamp and the text transcript, a first data domain from the text transcript. Some embodiments involve transmitting at least one of the prompt, the text transcript, and the first data domain to a second system, the second system including a machine learning model. Some embodiments involve generating answer data corresponding to the prompt by querying the machine learning model with the prompt, receiving answer data from the machine learning model, and transmitting the answer data to the first system.

Systems and methods are configured to generate a set of potential responses to a prompt using one or more data models with data from at least a plurality of data domains of an enterprise information environment that includes access controls. A deterministic response is selected from the set of potential responses based on scoring of the validation data and restricting based on access controls in view profile information associated with the prompt. These enterprise generative AI systems and methods support granular enterprise access controls, privacy, and security requirements. enterprise generative AI providing traceable references and links to source information underlying the generative AI insights. These systems and methods enable dramatically increased utility for enterprise users to information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems.

The invention relates to a cross-domain voice classification method and device based on feature decoupling and multi-task learning. The method comprises the following steps: firstly, acquiring a multi-data-domain voice file, and preprocessing the multi-data-domain voice file to obtain a cross-domain voice classification data set; then, constructing a cross-domain voice classification model which comprises a voice feature encoder module, a data domain classification module, a supervised comparative learning module and a multi-task classification module; then, a joint optimization loss function is constructed based on data field classification loss, supervised contrast learning loss and task classification loss, and a gradient descent algorithm is adopted to train and optimize the cross-domain voice classification model based on the cross-domain voice classification data set and the joint optimization loss function; and finally, inputting to-be-classified voice into the trained cross-domain voice classification model to obtain a voice corresponding category. The discrimination ability and generalization ability of the model in a cross-domain scene are significantly improved, so that the model still maintains high classification precision in a complex multi-source voice environment.

The invention discloses a matrix completion sparse meshless chromatography SAR (Synthetic Aperture Radar) super-resolution three-dimensional imaging method, which comprises the following steps of: constructing a multi-channel observation vector in a data field, and then further constraining an observation signal in an MMV (Modulation Modulation Virtualization) into a Hankel Lift matrix with a joint low-rank structure characteristic by utilizing an elevation consistency hypothesis between neighborhood pixels. And constructing a corresponding low-rank matrix completion model in combination with the tomographic SAR signal model. And modeling is carried out on the joint matrix completion problem. The model is solved based on a symmetric projection gradient descent algorithm, Root-Music solution is utilized to obtain elevation normalization frequency, the normalization frequency is inverted to obtain elevation information, and a scene three-dimensional imaging result is obtained by combining distance-orientation two-dimensional information of a target scene. According to the method, the elevation information of the tomographic SAR model is solved through a joint matrix completion method, tomographic SAR three-dimensional point cloud imaging is realized, a clear and complete imaging result is obtained, and a good miscellaneous point suppression effect is achieved.

The invention discloses a firmware-level security protection system and method for a hard disk of industrial automation equipment, particularly relates to the technical field of storage security protection, and is used for solving the problem of abnormal tampering of firmware. According to the method, instruction calling, interface access and interrupt timestamps are collected in a sliding window, a behavior sequence set containing an instruction sequence, calling frequency and sensitive marks is constructed and compared with a reference sequence library, firmware-level judgment on operation abnormity, access border crossing and tampering risks is triggered based on a transfer relation and an offset index, and the operation safety is improved. The method comprises the following steps of: dividing firmware into a control domain, a data domain and a configuration domain, realizing minimum permission and dynamic isolation by relying on state and permission mapping and permission register and interface shielding, forming a flow from detection to isolation to closed loop recovery by combining protected segment integrity verification and local copy hot switching, writing an exception into a protected loop log area for threshold updating, and performing protection on the protected loop log area. And the tamper-proof performance, the operation safety and the maintainability of the firmware are integrally improved.

The invention provides an automatic data management method and system based on a large model, and the method comprises the steps: obtaining a native data set corresponding to a to-be-managed data field, carrying out the cross-domain feature association mining of the native data set based on a pre-trained large model, generating a semantic feature map of data entities and an implicit association link between the data entities, and carrying out the data management through the implicit association link. Calling a pre-trained large model, performing quality dimension evaluation based on the semantic characteristic spectrum and the implicit association link, outputting a quality defect vector of a data entity, performing source tracing analysis based on the quality defect vector, and determining a data quality defect type existing in a to-be-treated data field and a cross-entity propagation path of the data quality defect; a strategy evolution module in the pre-training large model is driven to generate a self-adaptive governance strategy containing defect positioning anchor points, and the self-adaptive governance strategy is deployed to a data governance execution environment to complete automatic data governance, so that the governance strategy can better adapt to different data quality scenes; and the pertinence and effectiveness of data management operation are improved.

The invention discloses a depressive disorder recognition system based on multi-modal domain adaptation, and the system achieves the dynamic fusion and parameter optimization of signals through modeling the interaction relation of electroencephalogram, skin electricity and electrocardio signals into a deep learning framework. According to the method, complementary information of multi-modal signals is fully utilized, so that the model can extract more discriminative features from complex physiological data, and the accuracy and robustness of depressive disorder recognition are effectively improved; the cross-domain common features are successfully mined by drawing the data distribution of the source domain and the target domain twice; according to the cross-domain alignment strategy, the adaptive capacity of the model in different data domains is effectively improved, and the negative influence caused by domain offset is remarkably reduced. Generally speaking, the recognition method provided by the invention shows remarkable advantages in the aspects of improving the depressive disorder detection accuracy and enhancing the model generalization ability.

The invention discloses a cross-domain structural damage identification method based on digital twinning and time-space feature fusion, belongs to the technical field of structural health monitoring, and provides a cross-domain transfer learning damage identification technical scheme fusing a digital twinning model and structural multi-sensor data time-space features. A measurement point connection relation of a digital twinborn model and an actual physical structure is abstracted into a graph structure, and a spatial-temporal feature learning model for simultaneously processing multi-sensor time domain data and spatial feature information is designed based on classic time domain convolution and graph convolution network. A specific model architecture is provided for deep fusion of a structural digital twin model and spatial-temporal features of multi-sensor data of an actual structure to realize structural damage detection in a cross-data-domain scene, so that the problems of insufficient actual damage data, insufficient fusion of structural multi-dimensional feature data and the like are solved.

The invention relates to the field of computer information security and access control, and discloses a block chain power data security sharing method. The method comprises the following steps: acquiring a data field list, a measuring point list and a configuration mapping table, executing naming specification, time anchor registration and de-identification processing, and forming a fixed field sequence and an on-chain index record structure; based on context legality verification and policy mapping, generating an access constraint policy and completing generation and binding of a one-time session token, solidifying a policy snapshot on a contract side and registering a validity period; zero-knowledge proof is constructed, witness materials are assembled, and dual-channel checking, event account falling and failure evidence registration are carried out through a verifiable session smart contract; and an end-to-end key negotiation and control channel is established, so that safe data minimum access and result sealing are realized. According to the method, credible sharing and traceable access of the power data among multiple subjects are realized, and the data security, compliance and chain governance capability are improved.

The invention provides a grain pile surface pest detection model training method, a detection method and equipment, and relates to the technical field of image recognition, and the training method comprises the steps: respectively generating different types of data samples based on grain pile surface image data marked with pest bounding box labels in a plurality of data fields in advance, respectively inputting the two model branches, so that the two model branches sequentially carry out backbone network feature extraction and backdoor adjustment on the data sample to obtain a target feature map and pest detection result data; and determining the causal feature alignment total loss corresponding to the current iteration round according to the target feature maps obtained by the two model branches so as to optimize the two model branches. According to the method, the domain generalization ability of the grain pile surface pest detection model and the adaptability to complex and changeable actual detection scenes can be effectively improved, the application convenience and effectiveness of the grain pile surface pest detection model can be improved, and then the accuracy and reliability of a grain pile surface pest detection result can be improved.

The invention provides an ore grinding grading intelligent decision-making method and system based on machine learning, and the method comprises the steps: S1, collecting the manual control data of a sensor data field, and constructing a training data set and a test data set; s2, analyzing and decoupling a plurality of groups of manual control data; and training a decision tree model according to the training data set, and predicting parameters of the ore grinding classification equipment through the decision tree model to obtain a prediction result. According to the method, the multivariable coupling nonlinear dynamic optimization problem in the ore grinding and grading process can be effectively solved, and the operation efficiency, the product quality and the production efficiency of ore grinding and grading are comprehensively improved.

The invention is suitable for the technical field of radar signal processing, and provides a tomographic SAR super-resolution imaging method based on a structured sparse network, and the method comprises the steps: firstly obtaining multi-channel SAR observation data, constructing multi-channel observation vector MMV data in a data domain, and constructing a multi-pixel signal model corresponding to the MMV data based on a neighborhood pixel elevation consistency hypothesis. The method comprises the following steps: designing a kernel principal component analysis KPCA expansion network model, carrying out dimension reduction and enhancement on MMV data, introducing a norm compressed sensing model on the basis of the MMV data after dimension reduction and enhancement, solving the compressed sensing model by using an ADMM iterative algorithm, expanding the ADMM algorithm into a deep network, reconstructing an elevation spectrum, and obtaining an SAR image super-resolution three-dimensional reconstruction result. According to the method, the super-resolution performance and the solving efficiency of three-dimensional reconstruction can be effectively improved, and high-resolution three-dimensional reconstruction of a large-scale scene can be efficiently realized.

The invention discloses an AI auxiliary diagnosis and treatment system and method for plateau acute critical symptoms, which comprehensively receives and stores patient acute critical symptom data of a plurality of plateau medical institutions in real time by integrating a data field, a pointer field, a cluster sequence module and an AI auxiliary diagnosis and treatment suggestion unit, reduces redundancy, improves data access efficiency, accelerates diagnosis and treatment decision, and improves diagnosis and treatment efficiency. The pointer domain module constructs a data association pointer, deeply mines a logic relation, reveals risk factors, development paths and symptom relations of plateau acute and critical diseases and enhances data availability, and the cluster sequence module performs clustering analysis by applying a deep learning algorithm, accurately identifies plateau disease features, establishes a targeted treatment suggestion library and improves diagnosis and treatment pertinence. The AI auxiliary diagnosis and treatment suggestion unit combines real-time data and a treatment suggestion library, automatically provides emergency treatment, treatment scheme optimization and illness state prediction, relieves the burden of medical staff, improves the decision-making efficiency and accuracy, and achieves personalized precise medical treatment.

The invention discloses a data and model heterogeneous federated learning method of global decision boundary distillation learning. Two key problems of data heterogeneity and heterogeneous model performance difference are respectively solved through two sub-methods. According to the first sub-method, aiming at the problem of data heterogeneity, multiple prototypes are locally clustered, private data field distribution information is fully condensed, irrelevant distillation is introduced in a local supervised learning stage, and local knowledge forgetting is relieved. According to the second sub-method, global decision boundary distillation learning is provided for the problem of performance difference of the heterogeneous model, a global decision boundary learner is maintained and updated in a server, the local model is optimized from the perspective of the global decision boundary, and the influence of conflict information on the local model is reduced. The performance of the method is superior to that of various most advanced federal learning methods, the method can better adapt to challenging scenes, and meanwhile, the method has higher communication efficiency and better privacy reliability.

The invention belongs to the technical field of seismic exploration and artificial intelligence, and relates to a physical perception collaborative fusion-based full waveform inversion neural network construction method, which comprises the steps of required model data construction, full waveform inversion neural network construction, loss function construction, network pre-training, and network fine tuning and application. Physical feature perception and collaborative fusion are carried out through a U-Net network and an adaptive residual learning module, so that the problem that traditional full-waveform inversion depends on synthetic data training and is not matched with an actually measured data field is solved. The designed pre-training strategy not only can get rid of dependence on a large-scale synthetic data set, but also can effectively relieve the domain offset problem through fine adjustment of measured data, thereby improving the generalization ability of the model. And the adaptive residual learning module enables the neural network to adaptively and dynamically optimize feature expression, and improves inversion precision through collaborative fusion of a physical perception mechanism. According to the method, the physical consistency and imaging reliability of speed model prediction can be remarkably improved while the labor cost is reduced.

The invention relates to the technical field of visual recognition, and particularly provides a bottle sorting-oriented three-dimensional visual posture recognition method and related equipment, and the method comprises the steps: obtaining three-dimensional point cloud data and RGB image data of bottles on a conveyor belt, carrying out the preprocessing, feature extraction and geometric analysis of the three-dimensional point cloud data, and obtaining six-degree-of-freedom posture parameters; and the six-degree-of-freedom attitude parameters are projected to RGB image data, the overlap ratio of the projection contour and the actual bottle contour is calculated, and when the overlap ratio is larger than a preset threshold value, it is confirmed that the attitude is effective, and the corresponding attitude parameters are output to a mechanical arm sorting control system. Six-degree-of-freedom posture parameters of the bottle body can be obtained through posture alignment and template point cloud registration, reliability confirmation of a posture recognition result is achieved through overlap ratio verification in an RGB image data domain, and the defect that the posture recognition result is not reliable under the conditions that the posture change of the bottle body is large, the appearance of the bottle body is diversified and the bottle body is inclined or shielded and interfered is overcome. The problems of insufficient recognition precision and incomplete attitude parameters exist.

The invention discloses a knowledge distillation-based passive domain data domain adaptive fault diagnosis method and system, and the method comprises the following steps: collecting fault acceleration data of a generator bearing of a wind turbine generator in different working conditions, dividing the data into source domain data and target domain data, and randomly dividing the data into training samples and test samples; establishing a source domain model, training the model by using source domain data, and storing trained model parameters; establishing a target domain model of knowledge distillation by taking the source domain model as a reference, including a teacher model and a student model, and initializing the target domain model by using the stored source domain model parameters; and adopting the target domain data training sample to adapt to the student model, and inputting the test sample into the trained student model to realize diagnosis of the generator bearing fault. According to the method, the dependence on the source domain tagged data in cross-domain fault diagnosis is reduced, the problem of cross-domain fault diagnosis when only part of source domain fault data can be obtained or the source domain fault data cannot be obtained is solved, and meanwhile, the data privacy is guaranteed.

The invention belongs to the technical field related to data processing, and particularly relates to a data authority evaluation method under a multi-source social governance data fusion scene, which comprises the following steps: extracting metadata of to-be-evaluated data; inputting the metadata into the fine-tuned large model to obtain a matching score of the to-be-evaluated data and all preset social governance business fields, a professional authority score of a to-be-evaluated data source department or institution and weights of representation form standardization degree evaluation, data field association degree evaluation and data source department level evaluation; calculating a data field association degree evaluation score based on the matching scores of the to-be-evaluated data and all preset social governance business fields; determining a data source department level evaluation score based on the professional authority score; calculating a representation form standardization degree evaluation score by comparing the to-be-evaluated data with an industry data specification; and weighting each score to obtain an authority quantitative evaluation score. According to the invention, authority of multi-source social governance fusion data can be guaranteed.

The invention provides a change analysis method and device based on cross-temporal frequency domain information interaction and a readable storage medium thereof. The method comprises the following steps: acquiring a dual-temporal remote sensing image and partitioning the dual-temporal remote sensing image; extracting spatial features through a shared encoder; fourier transform is carried out on the double-tense features in a feature domain, high and low frequency components are divided according to a step length r, low frequency information is exchanged, and after inverse transform, splicing is carried out to obtain a fusion feature F; performing same frequency domain interaction on an original image in a data domain, and modeling through a video encoder to obtain a feature E; and finally fusing F and E, and decoding and outputting a change prediction map. According to the method, the high and low frequency information proportion is dynamically regulated and controlled through the frequency domain step length, dual interaction of the data domain and the feature domain is combined, structural difference perception is enhanced, pseudo change interference is suppressed, and change detection precision and robustness are remarkably improved. Experiments show that the method is superior to the prior art in F1 score, intersection-union ratio and other indexes, and is suitable for the fields of smart cities, disaster monitoring and the like.

The present disclosure provides a method for fault analysis using a knowledge graph, comprising: obtaining raw data related to field faults of devices, the raw data being from a plurality of data domains; creating an integrated knowledge graph for the plurality of data domains; and performing the fault analysis based on the integrated knowledge graph.

The invention discloses an EEG motion image classification modeling system based on self-supervised contrast learning and a modeling method thereof, and the system comprises an obtaining module which is used for obtaining EEG data; the domain self-adaptive alignment module is used for aligning EEG data of the source domain and the target domain; the data enhancement module is used for performing enhancement operation on the unlabeled source domain EEG data; the mask modeling module is used for performing mask of partial time points on the enhanced EEG data and extracting EEG feature representation through an encoder; the multi-view space-time attention module is used for processing EEG features and generating EEG feature vectors; the comparative learning module is used for realizing reconstruction of an original sequence through a reconstruction decoder and carrying out comparative learning and self-supervised pre-training; and the classification module optimizes model parameters through classification loss and applies the model parameters to target domain data to realize cross-domain classification. According to the method, the individual variability of the EEG signals can be effectively processed, and the accuracy of motion image classification and the cross-domain adaptive capacity of the model are improved.

The invention provides a cross-domain data sharing method based on a block chain. The method comprises the following steps: generating a public parameter, sending the public parameter to a data field, and calculating a main public key and a main private key; receiving a user attribute set, and establishing mapping; the data owner selects a symmetric key to encrypt the shared data to obtain a data ciphertext, encrypts the symmetric key to obtain a key ciphertext, and uploads the key ciphertext to the block chain; the block chain verifies the reputation value and the attribute of the data requester, and sends the data request to the data owner after the verification is passed; the data owner generates a re-encryption key for the data requester after receiving the data request, sends the re-encryption key to the block chain, calls the smart contract to generate a re-encryption key ciphertext for the data requester, and sends the re-encryption key ciphertext to the data requester; the self attribute private key decrypts the re-encryption key ciphertext to obtain a symmetric key, and the data ciphertext is decrypted through the symmetric key to obtain target data; according to the invention, the security and privacy of data sharing are improved.

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.