132 results about "Learning analytics" patented technology

The invention provides an industrial control network security service security guarantee system based on behavior analysis, which belongs to the technical field of industrial control network security, and comprises a multi-source data fusion acquisition module, a dynamic behavior modeling engine, a federal learning analysis cluster, an attack chain prediction module, a self-adaptive protection strategy executor and a model evolution feedback ring, wherein the multi-source data fusion acquisition module synchronously acquires industrial control network flow (including OPC UA / Modbus / DNP3 protocol analysis), equipment operation logs, user operation behavior fingerprints and physical interface state data, and the physical interface state data comprises electrical characteristic fluctuation monitoring of USB / network interfaces. According to the scheme, through multi-technology fusion and closed-loop design, the problems of static performance, single-dimension analysis defects and response lag of a traditional industrial control security scheme are effectively solved, a comprehensive protection system with dynamic modeling, intelligent decision making, privacy protection and continuous optimization is constructed, and the security and service reliability of an industrial control network are remarkably improved.

The invention belongs to the technical field of power systems, and particularly relates to an audit data early warning method and system, and the method comprises the steps: collecting the node voltage and current of a power supply network and the charging and discharging efficiency data of an energy storage system in real time through a distributed sensor network, and generating an original data flow; carrying out localized cleaning and standardized format conversion through an edge computing node; generating a dynamic risk assessment result and a scheduling scheme based on fuzzy logic reasoning and digital twinborn simulation; and analyzing and predicting deviation through meta-learning, and realizing closed-loop optimization by adopting a model distillation technology. The system comprises a distributed sensor network, an edge computing node, a multi-source data fusion module and the like. According to the method, the anomaly detection real-time performance, the risk assessment accuracy and the energy storage scheduling economy of the power system are improved.

The invention discloses an automatic identification method and system for a wafer internal defect image of a 3D stacked chip, and belongs to the technical field of semiconductor manufacturing and detection. According to the method, optical, X-ray and ultrasonic image data are synchronously acquired based on a multi-modal imaging technology, imaging parameters are dynamically adjusted to adapt to different wafer levels and material characteristics, multi-modal features are extracted in combination with layered filtering and denoising, multi-resolution registration and a self-supervised deep learning method, and micron-sized defects are positioned by using an attention mechanism. And further constructing a defect-process parameter correlation model through reinforcement learning, generating a closed-loop process optimization instruction, and transmitting the closed-loop process optimization instruction to an execution system. The system comprises a multi-modal imaging module, a noise suppression module, a deep learning analysis module and a process optimization module, and supports edge computing deployment. According to the invention, the internal defect detection efficiency and precision of the multi-layer stacked chip are significantly improved, real-time closed-loop control of detection-analysis is realized, the wafer manufacturing quality risk is reduced, and the method is suitable for intelligent defect detection of an advanced packaging production line.

The invention relates to the technical field of medical data integration, in particular to a block chain and AI-based medical whole industry data asset integration system and method. Comprising a data acquisition layer, a block chain right confirmation layer, a federal learning analysis layer, a data asset transaction platform and a supply chain traceability optimization module. The data acquisition layer comprises an edge computing node, a data cleaning engine and a desensitization algorithm are built in the edge computing node, and the edge computing node is used for carrying out localized cleaning and desensitization processing on hospital HIS system data, medicine RFID data, medicine research and development experiment data and medical image data; the block chain right confirmation layer constructs a medical data asset account book based on an alliance chain architecture, the asset account book comprises a data hash record, a contributor identity identifier and a use authorization log, and an integrated intelligent contract module is used for dynamic right management; according to the invention, medical data islands can be broken, and safe integration and efficient utilization of cross-domain data are realized.

The invention discloses a microcomputer synchronous automatic closing analysis system, which relates to the technical field of power system automation and comprises a data acquisition module, a data processing and storage module, a machine learning analysis module, a closing control module and a maintenance decision module. Electrical quantity data and equipment state data are monitored in real time through the data acquisition module, and the machine learning analysis module is utilized to perform modeling analysis on key parts and the overall operation state of the system, so that health assessment and fault prediction of the key parts are realized; when a fault possibly influencing closing safety or a health score of a key component is predicted to be lower than a set threshold value, the closing control module can adjust a closing control strategy to ensure the safety and reliability of closing operation, and the intelligent closing control mechanism remarkably improves the operation safety and reliability of the microcomputer synchronous automatic closing system.

The invention relates to the field of teaching monitoring and recognition, and particularly discloses a practical training monitoring system based on machine vision and deep learning, and the system comprises a data collection module which comprises an image collection unit, a sound collection unit, and a multi-type sensor unit, and is used for synchronously obtaining the image data, sound data, and environment parameter data of a practical training scene; the data processing module is used for preprocessing the multi-modal data acquired by the data acquisition module, realizing multi-modal data fusion through feature alignment and a space-time association algorithm, and generating structured practical training data; the deep learning analysis module adopts an improved convolutional neural network model and is used for performing real-time analysis on the fused structured practical training data and identifying personnel actions, equipment states and environment changes in a practical training scene; by adopting the technical scheme of the invention, the defects of dimension limitation of a single sensor and environment robustness of traditional image processing can be broken through, and accurate pre-judgment of practical training safety risks and objective evaluation of learning effects are realized.

The invention relates to the technical field of computers, and discloses a quality traceability method and system for an electronic device, and the method comprises the steps: collecting and preprocessing the data of each production link in real time; fusing and constructing a digital twinborn model; analyzing quality risks and abnormities by using deep learning; a non-tampering traceability chain is constructed through a block chain technology; and real-time monitoring and early warning are provided. The system comprises a data acquisition module, an edge module, a central platform module, a deep learning analysis module, a block chain traceability module and a visual early warning module. According to the scheme, closed-loop acquisition and intelligent analysis of data in the whole production link are realized, the authenticity of traceability data is ensured, and the quality management and traceability capabilities are improved.

The invention relates to the technical field of numerically-controlled machine tool diagnosis, and discloses a numerically-controlled machine tool fault diagnosis system based on machine learning. The system comprises a multi-source sensing data acquisition module for acquiring multi-dimensional sensing data such as vibration spectrum, spindle current waveform, temperature distribution, servo motor encoder feedback and the like; the operation feature coding module receives the multi-dimensional sensing data, extracts time domain statistical features and frequency domain energy distribution features, and generates a multi-source feature coding result; the incremental learning analysis module dynamically updates the feature weight through an incremental learning algorithm, and constructs an incremental training data set; the genetic optimization module optimizes the network structure and hyper-parameter configuration of the fault diagnosis model according to the incremental training data set, and generates optimized network structure parameters; and the integrated diagnosis decision module receives the current operation state data and the optimized network structure parameters, fuses diagnosis results of a plurality of base classifiers through an integrated learning algorithm, and outputs fault type classification signals.

The invention discloses a logistics freight vehicle transportation supervision system and method based on the Internet of Things, and relates to the technical field of vehicle transportation supervision, the supervision system is constructed based on multi-source data fusion and hierarchical anomaly detection, and the problems of false data and signal interference which are difficult to identify in traditional supervision can be effectively solved; through multi-level comparison and block chain evidence storage, supervision blind areas caused by manual tampering or equipment faults are greatly reduced; meanwhile, the self-learning analysis module dynamically recognizes repeated or regular pseudo data, and by iteratively updating a threshold value and improving an early warning level, false alarms can be remarkably reduced, and accurate positioning of potential anomalies can be enhanced; the real-time performance and traceability of freight vehicle transportation supervision are improved, and the method has significant improvement value for the traditional mode which depends on a single data source and lacks a rapid verification mechanism.

The invention discloses a teaching evaluation method and system based on artificial intelligence, and relates to the technical field of teaching evaluation, and the method comprises the steps: constructing a student learning analysis time series data set based on an LMS learning management system integration platform; constructing a dynamic student learning behavior evaluation model by using an unsupervised learning algorithm, and generating a student personalized learning portrait; based on the personalized learning portrait of the student, combining the historical score data of the student and the learning progress of the student, utilizing a deep neural network optimization model to realize dynamic real-time prediction and self-adaptive adjustment of the learning state of the student, and generating a continuously updated student learning state evaluation map; and based on the continuously updated student learning state evaluation map, analyzing the relationship between the student learning progress and the teacher teaching effect, dynamically adjusting the student learning strategy and the teaching plan, and generating an artificial intelligence teaching evaluation scheme. The method has the beneficial effects that personalized and scientific teaching evaluation and optimization are realized, and the teaching effect and the learning achievement of students are improved.

A system of a telecommunications network that uses IP Multimedia Subsystem (IMS) network elements to screen inbound calls. A Media Resource Function (MRF) intercepts an inbound call and uses various techniques to screen the call. The MRF redirects the call to an interactive voice response (IVR) system, which prompts the caller to state the caller's name, records the caller's response, and relays it to the called subscriber, who can then decide to accept or reject the call. The system can present a random number challenge to the caller. The system can use machine learning analytics and a large language model (LLM) to analyze the response's contents, accepting the call only if it relates to a topic allowed by the subscriber, perform audio analysis to determine whether the caller sounds robotic, or detect whether the response contains other known robocall markers.

There is a need for solutions that classification solutions in hierarchical prediction domains. This need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and unstructured fusion machine learning. In one example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions. Afterward, the structure-based predictions and non-structure-based predictions are processed in accordance with an unstructured fusion model to generate one or more unstructured-fused predictions.

Systems and methods for real-time monitoring and control of well operations at a well site use machine learning (ML) based analytics at the well site. The systems and methods perform ML-based analytics on data from the well site via an edge device directly at the well site to detect operations that fall outside expected norms and automatically respond to such abnormal operations. The edge device can issue alerts regarding the abnormal operations and take predefined steps to reduce potential damage resulting from such abnormal operations. The edge device can also anticipate failures and a time to failure by performing ML-based analytics on operations data from the well site using normal operations data. This can help decrease downtime and minimize lost productivity and cost as well as reduce health and safety risks for field personnel.

The present invention provides a method and system for autonomous trajectory planning and image acquisition of unmanned aerial vehicles, which relates to the field of unmanned aerial vehicle inspection technology. First, through intelligent three-dimensional grid division and dynamic route planning, the coverage and efficiency of feature area monitoring are significantly improved; secondly, the integration of real-time meteorological data and historical feature data enhances the pertinence and adaptability of acquisition, which helps to obtain high-quality images in complex and changeable environments; then, the combination of real-time processing and ground deep learning analysis greatly improves the accuracy and efficiency of feature extraction; based on the adaptive supplementary sampling mechanism of key focus areas, rapid response and precise positioning of suspected feature areas are achieved, providing reliable data support for subsequent detailed analysis and decision-making. The present invention is not only suitable for various recognition tasks of preset features, but also has good scalability and flexibility, and can be adjusted and optimized according to different application requirements.

The invention provides an interpretable automatic machine learning analysis method for designing extrusion casting process parameters, and belongs to the field of extrusion casting process parameter design. The method comprises the following steps: constructing squeeze casting process data, and then establishing a machine learning prediction model of squeeze casting process parameters based on an automatic machine learning framework; and on the basis, an interpretable artificial intelligence algorithm is used for carrying out interpretable analysis on the extrusion casting process parameter prediction decision. According to the design method, an advanced automatic machine learning framework and an interpretable artificial intelligence algorithm are combined, so that the extrusion casting process parameter design process is more transparent and reliable. Meanwhile, the method is combined with an existing squeeze casting process data set, implicit relations between material components and process parameters are mined through high-order knowledge dimensions of a machine learning algorithm, and a new thought is provided for modeling optimization design of materials and process parameters of the squeeze casting process.

The invention is suitable for the technical field of laser spot regulation and control, and provides a laser spot regulation and control system with a self-adaptive feedback mechanism and a regulation and control method thereof, and the system comprises a workpiece detection module, a model generation module, a learning analysis module, a working condition collection module, an optimization adjustment module and a hardware execution module. The workpiece detection module is used for collecting workpiece space data and workpiece material components; the model generation module is used for generating a digital twinborn body according to the workpiece space data and the workpiece material components; the learning analysis module is used for analyzing the digital twins and generating or optimizing process parameters, working paths and to-be-processed data in cooperation with the self-learning processing library; the working condition collecting module is used for collecting actual machining data and external environment data in the workpiece machining process. According to the device, the problem of insufficient laser spot regulation and control precision is solved, and the effects of reducing deviation and improving the machining precision are achieved.

The invention discloses an intelligent early warning method and system for frozen shrimp transportation based on deep learning, and belongs to the technical field of cold-chain logistics and intelligent monitoring. According to the method, a multi-dimensional data analysis model is constructed by using a deep learning technology, and intelligent monitoring and anomaly prediction of a transportation environment are realized. The system is composed of a data acquisition module, a deep learning analysis module and an early warning decision module, can automatically identify potential risks (such as temperature abnormity and equipment faults) in the transportation process, and timely notifies related personnel through a multi-level early warning mechanism (such as short messages, mails and system popup windows). Through self-learning and optimization of the deep learning model, the accuracy and the real-time performance of early warning are improved, the loss risk of frozen shrimps in the transportation process is effectively reduced, the product quality is guaranteed, meanwhile, the intelligent level of cold-chain logistics is improved, and remarkable economic benefits and application values are achieved.

The invention relates to the technical field of electrical equipment maintenance, and discloses an upper limb exoskeleton control method, system and equipment for assisting replacement of an insulator. The method comprises the following steps: acquiring image data of an insulator and a field environment, and acquiring motion vector information according to the image data based on a preset optical flow method; analyzing the motion vector information based on a deep learning algorithm to obtain motion state information of the insulator; based on a dynamic fusion algorithm, according to the motion state information of the insulator and the position information of the upper limb exoskeleton, obtaining adjustment motion parameters of the upper limb exoskeleton; and issuing a control instruction to the upper limb exoskeleton according to the adjusted motion parameters. Dynamic changes of the insulator and the surrounding environment are monitored through the optical flow method, the insulator in motion is accurately recognized and analyzed in combination with deep learning analysis, the motion trail of the upper limb exoskeleton can be adjusted in time, it is ensured that the upper limb exoskeleton stably and accurately tracks and operates the insulator, the physical burden of operators is relieved, and the working efficiency is improved. And the operation safety and efficiency are improved.

The invention discloses an optical domain reflection and machine learning fusion device for optical fiber network fault positioning, and relates to the technical field of optical fiber network fault positioning. According to the invention, the fine reflection characteristics of the optical fiber network are captured by using the synergistic effect of the optical signal transmitting unit and the receiving unit; the signal processing module and the data acquisition unit ensure the data quality through efficient filtering and feature extraction; the machine learning analysis module automatically identifies and classifies fault modes based on a convolutional neural network model, and overcomes the limitation of a traditional method; the fault positioning unit combines algorithm output to calculate accurate position coordinates; seamless coordination between modules is realized through a control interface, and the response efficiency of the system is improved; the display output module provides a visual result, which is convenient for operators to monitor in real time; the power supply control unit optimizes energy management, ensures stable operation of the device and saves energy in idle time, thereby remarkably improving the accuracy, efficiency and reliability of fault positioning, and reducing the maintenance cost.

The invention discloses an operation and maintenance log self-learning analysis system and method based on a large language model, and relates to the field of information technology operation and maintenance management. Comprising the steps of 1, creating an operation and maintenance log self-learning analysis system based on a large language model, 2, obtaining original operation and maintenance log data from servers, devices or application programs in real time through a log collection module, 3, format standardization, noise filtering and necessary sensitive information desensitization are conducted on the collected original operation and maintenance log data through a preprocessing module, and a standardized log event sequence is formed; 4, semantic understanding and intelligent analysis are conducted on the log event sequence through a large language model analysis module by means of a pre-trained large language model, and abnormal modes, error reasons or important event abstracts are recognized; 5, a readable analysis report or warning notification is generated through a result output module according to output of the analysis module, and a result is provided for operation and maintenance personnel through a local interface or an operation and maintenance warning system; and step 6, receiving the feedback of the operation and maintenance personnel on the analysis result through a feedback learning module.

The invention relates to the technical field of power transmission line monitoring, in particular to a power transmission line visual multi-dimensional monitoring system based on an AI model, which comprises an image acquisition module, an edge calculation module, a deep learning analysis module, an intelligent diagnosis module and a time sequence correlation analysis module. The image acquisition module acquires multispectral data through a visible light camera, an infrared thermal imager and an unmanned aerial vehicle; the edge calculation module adopts an FPGA (Field Programmable Gate Array) chip to realize data preprocessing; the deep learning analysis module comprises a feature fusion sub-module, an anomaly detection sub-module and a three-dimensional reconstruction sub-module which are respectively used for extracting multi-scale features, identifying equipment defects and constructing digital twin bodies; the intelligent diagnosis module integrates the knowledge graph to provide decision support; and the time sequence correlation analysis module is combined with an LSTM-GRU model to realize state prediction. According to the invention, through multi-source data fusion and intelligent analysis, the problems of many blind areas, low identification precision and the like in traditional monitoring are solved, and all-weather and intelligent monitoring, operation and maintenance of the power transmission line are realized.

The invention relates to the technical field of substation monitoring, and discloses an indoor substation high-voltage live display state identification method and system. The system comprises a data acquisition module for acquiring multi-modal state data of a display and optimizing an acquisition process; the state feature extraction module is used for performing incremental feature learning analysis on the real-time data; the identification model optimization module is used for optimizing model parameters and structures through a multi-target particle swarm; the state reasoning module is used for performing deep reasoning by using taboo search in combination with newly added data and an optimization model and converting the data into identification signals; the control output module is used for adjusting control constraints by using an approximate gradient method and optimizing output by combining a potential field method so as to generate a control instruction; and the state verification module verifies the state of the equipment through adaptive filtering and feeds parameters back to the related module so as to adjust the identification strategy. The system can comprehensively and accurately identify the state of the display, improves the adaptability and operation and maintenance safety, and adapts to the dynamic operation environment of a transformer substation.

The invention discloses a multi-modal learning data conjoint analysis method and system, a medium and a product, and relates to the field of multi-modal learning analysis. Comprising the following steps: acquiring synchronously acquired multi-modal data and extracting a feature vector; and calculating a confidence score of each modal feature vector and a semantic conflict coefficient between the modal feature vectors. When the semantic conflict coefficient is greater than a preset conflict threshold value, determining the modal feature vector with the maximum confidence score as a final state feature vector; and when the semantic conflict coefficient is smaller than or equal to the threshold value, calculating a fusion weight according to the confidence score, the semantic conflict coefficient and the task priority parameter, performing weighted fusion on each modal feature vector to obtain a final state feature vector, and generating an evaluation result according to the final state feature vector. According to the invention, through a decision-making mechanism that the optimal information source is selected in high conflict and context adaptive fusion is carried out in low conflict, the problem of inaccurate evaluation caused by forced fusion of contradictory signals is solved, and the accuracy and robustness of learning state evaluation in a complex scene are improved.

A method for cybersecurity threat actor severity scoring, the method comprising: receiving public data that includes publicly available information obtained via monitoring of a data connection between one or more networks; parsing first data related to a cybersecurity event from the public data; associating the first data with a first threat actor; obtaining second data that includes information regarding one or more previous cybersecurity events associated with the first threat actor; determining a first threat actor score based on the first data and the second data; receiving a second threat actor score for a second threat actor; causing a graphical user interface to display a graphical depiction of a ranking of the first threat actor and the second threat actor based on the first threat actor score and the second threat actor score.

There is a need for solutions that classification solutions in hierarchical prediction domains. This need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and unstructured fusion machine learning. In one example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions. Afterward, the structure-based predictions and non-structure-based predictions are processed in accordance with an unstructured fusion model to generate one or more unstructured-fused predictions.

The invention relates to a tunnel safety state evaluation and prediction method based on lining damage evolution in the technical field of tunnel engineering. The method comprises the following steps: acquiring tunnel lining crack data, inputting the tunnel lining crack data into a TF-Transform deep learning analysis model, outputting the tunnel lining crack data to obtain damage characteristics, summarizing a damage evolution mechanism and a development rule of a cracked tunnel, and establishing a cracked tunnel safety evaluation index system; a PL-VIKOR comprehensive evaluation model is constructed based on a crack tunnel safety evaluation index system and an improved subjective and objective combination weighting method based on normal distribution goodness of fit; establishing a multi-dimensional crack lining monitoring system, and monitoring real-time tunnel section data; and predicting a tunnel section development result by adopting an LSTM model based on real-time tunnel section data, and evaluating a future safety state of the tunnel in combination with a PL-VIKOR comprehensive evaluation model. And real-time and objective evaluation of the safety state of the cracked tunnel structure is realized.

The invention discloses a learner cognitive diagnosis method oriented to an intelligent education scene, and belongs to the technical field of cognitive diagnosis and education data analysis. According to the method, concept-level multi-dimensional modeling is carried out on the ability and exercise difficulty of a learner by constructing multi-dimensional representation of concept perception so as to describe mastering characteristics of the learner on different cognitive levels; and meanwhile, distinguishing a pre-correction dependency relationship and a semantic approximation relationship, establishing a relationship-perceived concept dependency model, and deducing a potential exercise-concept association structure according to the relationship-perceived concept dependency model. Further, the annotated exercise-concept incidence matrix and the inference incidence matrix are fused in a unified diagnosis layer, and comprehensive evaluation of the knowledge mastering state of the learner is achieved. According to the method, an end-to-end mode is adopted for optimization, the fineness, knowledge coverage and stability of cognitive diagnosis can be effectively improved, better prediction performance and generalization ability are shown on multiple real education data sets, and the method is suitable for intelligent education application scenes such as learning analysis and personalized teaching.

The invention provides a parent terminal applet system for recommending and reporting student learning conditions based on parent portraits. The system comprises a data acquisition module for acquiring a student multi-source learning data set from students after a parent terminal applet logs in; meanwhile, parent portrait information is recommended and determined for parents of the students; the fusion processing module performs data processing on the multi-source learning data set of the student by adopting a data fusion method; the behavior analysis module performs learning behavior analysis according to the student multi-source learning processing data to obtain student learning analysis data; the report generation module is used for generating a learning condition report text based on student learning analysis data in combination with parent portrait information through a natural language processing model to obtain learning condition report text information; and the condition feedback module feeds back the learning condition of the student in the parent-side applet according to the learning condition report text information. According to the invention, learning conditions of students can be reflected more deeply, and parents can be ensured to accurately understand learning condition feedback information of the students.

A robotic insurance platform is disclosed that transforms an autonomous service robot into a tamper-resistant “digital witness” capable of supplying legally probative evidence without human intervention. The robot is equipped with surround video cameras, a spatial microphone array, an on-board processor, a cryptographically isolated secure enclave, a wireless communication module, and a rolling-buffer memory that retains encrypted audio-video data for a configurable period such as forty days. A companion mobile application enables an insured user to register, perform know-your-customer identity verification, and pair the robot with an insurance policy stored on a cloud server that hosts an claim-decision engine and policy database. Following explicit verbal consent from the user, the robot records continuously while performing ordinary tasks. When an incident is detected—either by on-board heuristics or by a user-initiated claim request—the processor extracts a time window surrounding the event, computes a cryptographic hash of the clip inside the secure enclave, and commits that hash as an immutable anchor to a permissioned or public blockchain ledger. Only after blockchain confirmation is the encrypted clip transmitted to the insurance server, where the claim-decision engine verifies integrity, applies machine-learning analytics to determine causation, and issues a coverage determination. Approved claims trigger repair dispatch, replacement shipment, or direct monetary reimbursement, while unclaimed data exceeding the retention interval are securely erased. The platform delivers objective, bias-free evidence, virtually eliminates false claims, and reduces end-to-end settlement time from weeks to minutes, thereby lowering operational costs for insurers and increasing transparency for policyholders.

The invention discloses an efficient data availability and invisibility implementation system, which comprises a trusted data client, a network space security center, a router, a big data platform and a block chain system, and is characterized in that the router is connected with the trusted data client, the network space security center and the big data platform, and the big data platform is connected with the block chain system. According to the invention, the data security client is provided to realize the data availability and invisibility function, and the data learning analysis plug-in platform is provided through the data security client, so that rapid fusion of the data security client and a data analysis system of a client can be realized, and the data can be taken to a user side for analysis and learning; therefore, various defects caused by the fact that the data needs to be learned and analyzed at the data provider are avoided.