562 results about "Online and offline" patented technology

The invention relates to the technical field of computer deep learning, in particular to a deep learning task hybrid deployment method and system. The method comprises the steps that S1, a user submits a task through a Kubernetes native interface; s2, a task scheduling optimizer analyzes tasks according to resource requirements and service levels and allocates the tasks to proper nodes; s3, the SLO manager tracks real-time data of various performance indexes of the system and compares the real-time data with a preset service grade target; s4, the Koordlet component executes a specific resource allocation operation on the target node; and S5, the traffic safety monitor monitors the data streams among the nodes in real time. According to the method, a mixed deployment strategy for different types of resources is provided by analyzing the periodical rule of deep learning task resource use, dynamic resource sharing of online and offline tasks is achieved, and meanwhile it is ensured that the system can still run stably and efficiently under the high-load condition.

The present invention relates to the technical field of statistical analysis of retail commodity sales data, and discloses a statistical analysis system for retail commodity sales data based on big data, including a data acquisition module, a data cleaning and preprocessing module, a data storage and management module, a data analysis and mining module, a report generation and visualization module, a permission management module, a real-time monitoring and early warning module, an external data integration module and a decision support module; the data acquisition module is used to collect online and offline sales data in real time or in batches from multiple heterogeneous data sources. The system introduces an adaptive data cleaning mechanism, uses a machine learning algorithm to dynamically identify and repair missing values ​​and abnormal values ​​in the data, and adaptively adjusts the cleaning rules according to the real-time data mode. The intelligent data cleaning system can process structured and unstructured data, and can dynamically respond to data fluctuations from different sources, significantly improve the accuracy and consistency of data analysis, and reduce manual intervention.

The invention relates to the technical field of energy storage technology, integrated circuit design and battery management systems, and discloses a passport-sensing integrated battery management chip and a battery management system architecture, battery information is collected based on the passport-sensing integrated battery management chip, and unique battery passport information is generated. And then real-time interaction with the BMS main control module is carried out through the daisy chain communication module, so that the health state of the battery is evaluated and managed. The battery cell state data acquisition and traceability functions are realized through the battery cell slave board, the BMS master control module has a fine management function on the battery state, battery safety early warning and daisy chain communication channels are provided to realize real-time interaction between the battery cell slave board information and the BMS master control module information, online and offline information disclosure modes are fused, a collaborative management architecture is established, and the battery cell state data acquisition and traceability functions are realized. Intelligent management and data tracking of the whole life cycle of the battery are realized, and the safety, reliability and traceability of the battery are improved.

A method for analyzing density-type coal quality parameters on the basis of big data, and an analyzer and a system. The method comprises: on the basis of (II) and a large number of collected coal quality parameters A, M, V and FC, and collected Q and S data, establishing a coal quality parameter database and an identification and analysis software package; and on the basis of a coal quality parameter change signal U measured by a coal quality parameter measurement apparatus (1), the software package performing analysis, so as to obtain coal quality parameters A, M, V, FC, Q and S of coal. With regard to the coal quality parameter analyzer that uses the method, the analyzer is at least composed of a coal quality parameter measurement apparatus (1) and a control and calculation unit (2), wherein the coal quality parameter measurement apparatus (1) measures a coal quality parameter change signal U, the control and calculation unit (2) receives the signal U, and all coal quality parameters are rapidly analyzed by means of an identification and analysis software package. Compared with the establishment of a mathematical model for measurement, the method in the present invention is not affected by changes in coal types and coal quality, and has high measurement accuracy. The present invention is suitable for the online and offline measurement of coal quality parameters.

The invention relates to the technical field of mixed learning, in particular to an online and offline combined digital interactive teaching method and system, and the method comprises the following steps: collecting online and offline learning data of students, carrying out the statistics of the number and frequency of various interaction types, including the number of discussion times, the number of question answering times and the group cooperation frequency, and generating an initialized interactive data set. According to the method, redundancy and deficiency of various interactions are calculated through the information entropy technology, information vacancy in learning is helped to be recognized, teaching content is optimized, student requirements are matched, the optimized design defines a learning target, an expected interaction frequency is set, the interaction adjustment effect is verified through a simulation test, it is ensured that teaching activities are more targeted, and the teaching efficiency is improved. The interaction frequency is dynamically adjusted in real-time teaching, the sense of participation of students is improved, the learning effect can be monitored in real time, the learning motivation and achievement of the students are improved, and implementation of personalized learning is promoted.

The invention discloses a base liquor full-life-cycle traceability management method based on a sovereign block chain. The base liquor full-process management is realized by constructing systems such as double-chain cooperation, full-process digital monitoring and credible evidence storage. In the raw material link, a block chain is used for establishing a quality file, and in the production link, the industrial Internet of Things is used as a basis, and a layered chaining and double-code anti-counterfeiting technology is adopted; a dynamic environment monitoring chain is built in a warehouse logistics link, and a multi-source sensing and condition triggering evidence storage strategy is applied; constructing a two-way verification system in a sales link to combine with risk assessment; and a carbon footprint tracking mechanism is introduced in the recovery link. The method can reduce the risk of raw material adulteration, improve the quality inspection efficiency, guarantee the production quality and brand safety, improve the safety and efficiency of warehouse logistics, achieve the guarantee of full-link certified products, improve the full-life-cycle management, improve the reliability of a block chain network, employ the verification mechanism of an oracle machine to verify and guarantee the real chaining of the data of each link, and improve the efficiency of the whole-link certified products. And consistency of online and offline data is ensured.

The invention discloses a system smooth online and offline method and device based on a registration center, and relates to the technical field of computers, and the method comprises the following steps: deploying a mirror image file to be subjected to online service to a working node, distributing a virtual container, and constructing a corresponding container instance; performing health detection on the container instance to be on-line service, and if it is judged that the container instance to be on-line service is healthy and normal and corresponding IP information is registered in the registration center, judging that the container instance to be on-line service is successfully registered; based on the IP information of the container instance of the to-be-offline service, issuing an offline request for the to-be-offline service, and executing an inter-service smooth switching process; and executing an offline operation on the container instance of the to-be-offline service. According to the method, specific online and offline operation processes and rules are configured, lossless online and offline service is realized, good adaptability and operability are achieved, and the actual requirements of smooth online and offline are met.

The invention discloses a power system frequency risk quantification and coordination control method and system, and belongs to the technical field of power systems and automation thereof. The method comprises the following steps: collecting power grid online operation data and system working conditions; frequency response curves of the system after different faults are obtained through fault simulation; dividing the frequency into different frequency bands according to a binary criterion formed by a plurality of frequency threshold values and corresponding duration time, and distributing an initial weight coefficient for each frequency band; obtaining a dynamic weight coefficient of each frequency band by combining the frequency change rate in the frequency band and a preset weight gain coefficient; based on the dynamic weight coefficient of each frequency band, performing time integration on the frequency deviation value in each frequency band to obtain a transient frequency risk assessment value; and judging the system stability and the risk level, and giving a corresponding frequency control strategy according to the risk level so as to coordinate and control resources. According to the method, the short-time power disturbance and the permanent power disturbance can be effectively distinguished, and a basis is provided for on-line and off-line safe and stable control.

The invention discloses a test method and system for starting and stopping trusted proxy services of DCS (Distributed Control System) upper computers in batches, belonging to the technical field of industrial control, comprising the following steps: running a pre-configured executable program by using a multi-thread mechanism, reading a protocol file in the pre-configured executable program, and remotely connecting to each DCS upper computer in batches based on the protocol file; printing a process of connecting to each DCS upper computer and executing a command at a terminal in real time, and if a failure occurs, immediately returning failure reasons such as connection timeout and incorrect reply of a connected target host; after the operation of the pre-configured executable program is finished, returning the number of threads and a prompt that the operation of the threads is finished; and based on the real-time printing result and the returned prompt, checking the online state of each DCS upper computer, and verifying whether the batch online and offline functions take effect or not. According to the method, the trusted proxy service of the DCS upper computer is started and stopped in batches by one key, so that a large batch of equipment can be online and offline at the same time at one time.

The invention discloses a computer vision-based railway tool online and offline checking method, which comprises the following steps of: acquiring online and offline and warehouse tool images of a high-speed railway work section, cutting a target area through an area focusing algorithm to eliminate interference, and combining enhancement technologies such as perspective transformation and multi-transformation splicing with a generation method based on SAM and a diffusion model. Constructing a diversity training data set; constructing a text vision multi-modal rotating target detection model, and guiding the model to pay attention to key categories by enhancing feature extraction and fusing text information and visual features of a job schedule; detecting parameters are adjusted by adopting a self-adaptive threshold optimization algorithm, a few-sample detection technology is combined, and after category sensing scale filtering, outlier frame suppression and cross-category non-maximum suppression processing are performed, a result is compared with an operation plan, and a result is output; and based on a sample cutting and screening mechanism driven by false detection and missing detection, high-value samples are mined and supplemented to a training set. The method has the advantage that the safety and efficiency of railway tool management are improved.

The invention relates to the technical field of mental health assessment and early warning, in particular to a mental health assessment and early warning analysis system based on health big data, which comprises a data acquisition module, a monitoring module, an analysis and judgment module, an informatization management module and an early warning module. Monitoring data are monitored through the monitoring module and analyzed through the analysis and judgment module, so that whether students have psychological health problems or not can be judged, early warning is carried out in time through the early warning module when the students have the psychological health problems, and in the whole evaluation early warning analysis process, resources of psychological experts, consultants, psychological studios and medical institutions can be called in time; and diagnosis and treatment are carried out in an online and offline combined direction, so that the technical problems that an existing diagnosis and treatment mode seriously depends on experience of doctors, professional psychologists are long in training period and limited in number, and face-to-face psychological interviews and face-to-face diagnosis are difficult to carry out on each subject are solved.

The invention discloses a personalized teaching method and system based on multi-dimensional learning style analysis, and relates to the technical field of teaching. The personalized teaching system based on multi-dimensional learning style analysis comprises a learning style analysis module, and the learning style analysis module is connected with a teaching content management module. According to the system, a personalized learning plan can be made according to the learning style and progress of students through a personalized learning path and an intelligent recommendation engine, learning is flexible and efficient, in addition, rich teaching resources such as videos, interactive courses and textbooks in the system enhance the diversity and interestingness of learning, and the learning efficiency is improved. The real-time feedback and multi-dimensional evaluation mechanism ensures continuous support in the learning process and helps students to adjust strategies in time to optimize the learning effect, the system also combines online and offline mixed teaching, provides wider learning choices, enhances interaction and communication opportunities between teachers and students, and improves the learning efficiency. Therefore, the teaching effect and the satisfaction degree of students are effectively improved.

The invention belongs to the technical field of commercial entertainment recommendation, and discloses a matching method for recommending online and offline script playing methods based on an AI algorithm, and the method comprises the steps: collecting user immersion experience data, carrying out the multi-dimensional deconstruction of the immersion experience data, and obtaining a user preference pedigree; performing script emotion tension and interaction density analysis on the script content to form a cross-scene experience map; designing a dual-channel sensing network, and performing cross fusion on the user preference pedigree and the cross-scene experience atlas based on the dual-channel sensing network to generate a personalized matching matrix; establishing an immersion degree evaluation parameter set, and performing scene adaptability calculation on the personalized matching matrix to obtain a scene conversion coordination degree index; generating a script recommendation sequence according to the scene conversion coordination degree index, and realizing real-time recommendation adjustment and experience feedback collection through context awareness; the difference between online and offline script experiences is effectively quantified, and a scientific cross-scene matching basis is provided.

The invention relates to the field of education data mining and teaching process evaluation, in particular to an online and offline mixed teaching performance dynamic evaluation method and system based on a PDCA (Packet Data Convergence Architecture) circulation, and the method comprises the steps: generating initial Rubric configuration and formulating a multi-source data collection scheme through the analysis of course types and learning condition information around the PDCA circulation; then, online and offline multi-modal information is collected and preprocessed, and data with labels and basic statistics are obtained; and in combination with causal graph construction and quantum multi-group fuzzy weight optimization, carrying out comprehensive evaluation on the data, outputting adaptive Rubric configuration and student scores, and generating a readable report and an early warning through a neural fuzzy abstract. And according to the score and early warning, implementing difference enhancement and holographic AR feedback, collecting intervention process data again, and dynamically adjusting Rubric to complete iteration. According to the invention, the teaching performance can be continuously optimized, and accurate early warning and personalized intervention are realized.

The invention discloses a flow analysis method and device for active PCDN users based on Flink, and the method comprises the steps: screening a user list which accords with PCDN features and PCDN platform domain names from aaa call ticket data provided by an operator, and obtaining a PCDN violation user list; the Flink calculation engine loads the PCDN violation user list, matching is carried out on the basis of user access ip and ports and accessed Netflow traffic data, then traffic light summarization is carried out, and Netflow traffic data associated with PCDN violation users is obtained; the method comprises the following steps: selecting Netflow traffic data associated with PCDN violation users in the online and offline time range of the PCDN violation users to perform activity analysis, calculating the activity ratio of Flow in the online and offline time range of the PCDN violation users, obtaining the activity of the PCDN violation users, and finally outputting a PCDN violation user list with inverted activity. According to the method and the device, the suspected PCDN users are found and sorted according to the activeness, so that subsequent operation is facilitated, the burden of flow settlement is reduced, and the revenue of broadband services is increased.

The invention discloses a platform payment risk control method and system based on new retail e-commerce, and relates to the technical field of network transaction security control, and the method comprises the steps: collecting multi-dimensional data containing user credit data and transaction scene data in real time, building a risk control evaluation model through a machine learning algorithm, and carrying out the risk control evaluation model; the method comprises the following steps: performing weighted scoring on collected multi-dimensional data to obtain a user and transaction combination risk level, dividing the user and transaction combination risk level into a low risk level, a middle risk level and a high risk level, dynamically configuring risk control parameters according to different risk levels, performing real-time monitoring on a transaction whole process, and recording a transaction success rate and a fund safety rate after parameter adjustment. According to the method and the system, through online and offline fusion and data driving of new retail, dynamic adjustment can be performed in combination with user credit and transaction scenes, and the user experience is improved while transaction risk control is guaranteed.

System, devices, and methods facilitate determining positioning using distributed antenna system with service availability monitoring. Positioning methods include network based methods and handset assisted methods in addition to a monitoring system to report any service outage and possible location information loss. The system provides location information also at handset only level through application or apps and operating systems with online and off-line access to location database data. A combined monitoring system that monitors antenna output power for mobile coverage and service availability helps also in monitoring the availability of the localization system and dynamic update of lookup information. Monitoring system provides also asset tracking and service analytics features for the active or passive distributed antenna system.

A system, method, and computer program for updating calibration lookup tables within an autonomous vehicle or transmitting roadway marking changes between online and offline mapping files is disclosed. A LIDAR sensor may be used for generating an online (rasterized) mapping file with online intensity values which are compared against a correlated offline (rasterized) mapping file having offline intensity values. The online intensity value may be used to acquire a lookup table having a normal distribution that is compared against the offline intensity value. The lookup table may be updated when the offline intensity value is within the normal distribution. Or the vehicle may transmit a roadway marking change when the offline intensity value is outside the normal distribution.

The invention relates to the technical field of advertisement putting, and discloses an online and offline advertisement attribution method and system based on multi-dimensional anchor point matching, and the method comprises the following steps: obtaining online advertisement interaction and offline purchase events, and extracting feature vectors; generating a dynamic intention potential field for the online event according to the situational features; in response to offline purchase, calculating a potential energy value of each online event by taking the offline purchase as a multi-dimensional anchor point; and finally, normalizing the potential energy value to obtain an attribution contribution degree, and aggregating to generate an efficiency analysis result. The system comprises a data and feature module, a potential field modeling module, an attribution calculation module and a result aggregation module. According to the method, the intention potential field dynamically determined by the situation is constructed, and offline purchase is used as the multi-dimensional anchor points of the space-time and semantics for matching calculation, so that the real influence of the advertisement is refined and dynamically quantified.

The invention discloses a fast moving consumer goods market state evaluation method and system, and belongs to the technical field of big data, fast moving consumer goods market state evaluation is realized based on a multi-dimensional weighted fitting model, and the method comprises the following steps: data acquisition: acquiring online and offline multi-channel data, deriving acquisition indexes through consumption and inventory records, and integrally constructing a complete index system; standardizing the indexes, constructing a standardization method based on the forward and reverse relationship between the collected indexes and the market state, and realizing the same-order relationship between the standardized indexes and the market state on the premise of eliminating the dimensional difference; index score conversion: based on the index standardization, realizing mapping from an index original value to a [0, 1] interval; combining weights of the indexes, measuring and calculating the indexes, and weighting the indexes; calculating a comprehensive score; and calculating a market state threshold value. According to the method, combination processing of indexes is realized, an end-to-end process from index acquisition to state evaluation is perfected and established, the market state is accurately evaluated, and the prediction capability is improved.

The invention relates to a medical data screening system and method based on AI auxiliary interrogation, the screening system at least comprises a client APP, an AI client and a cloud which are mutually connected, compared with the prior art, the system fully combines the advantages of AI, cloud storage, a user intelligent terminal and an offline diagnosis and treatment end, and realizes data acquisition, classification, fusion and interaction; the upstream of the AI client side can allow a patient to input basic data and image data, the downstream of the AI client side can call a cloud database, pathological data can be accurately screened through feature comparison, and pathological similarity suggestions are provided; in the high-incidence period of infectious diseases, the screening system or the screening method can be used for shunting hospitals, limited medical resources are fully allocated, and cross infection is avoided; by means of big data screening and online and offline diagnosis and treatment combination, the pathology screening accuracy is improved, accurate diagnosis and treatment suggestions can be given, and the medical diagnosis service experience is improved.

The invention relates to the technical field of medical care, in particular to a lung cancer chemotherapy patient resilience intervention system and application thereof. According to the intervention system based on the PERMA mode, intervention is implemented in stages through comprehensive measures such as acute symptom management, psychological construction strengthening, chemotherapy adverse reaction cognition, home nursing skill training, physical recovery training, social support linkage and treatment compliance improvement. The method comprises the following steps: quantifying pain by using digital scoring equipment, and assisting pressing to relieve symptoms by using acupoint diagrams; the psychological toughness is enhanced through the positive voice listening and the anti-cancer brave diary recording; an online and offline combined mode is adopted, group lectures, scene simulation video teaching and online community interaction are carried out, and social support of patients is enhanced; personalized exercise prescriptions and home safety lists are formulated, and treatment compliance is improved. The system can significantly improve the recovery of a patient, improve the life quality during chemotherapy, and provide a scientific basis for innovation of a tumor nursing mode.

The invention relates to an intelligent library system, method and device and a storage medium thereof. The intelligent library system comprises a digital library card handling unit, a book resource management unit, a reader information management unit, a borrowing service unit, a sharing service unit and an interaction service unit. Identity verification is carried out through connection with a government affair platform, resource management and service are optimized through intelligent classification and big data analysis, and interconnection and intercommunication of multiple library heterogeneous systems in a cross-regional range can be achieved. And meanwhile, the system supports the functions of online and offline borrowing, through borrowing and returning, book sharing and the like, provides convenience for readers to enjoy the public library service of'one-certificate-through 'in a cross-regional range, effectively promotes sharing of public culture resources and services among libraries, and improves the management level of public culture services.

The invention relates to the technical field of intelligent control, and discloses a dexterous hand motion planning and control method based on a visual language motion model, and the method comprises the steps: S1, collecting data, and carrying out the time sequence synchronization and consistency verification of multi-source data; s2, performing dynamic adaptive exponential moving average filtering and calculation processing on the joint sequence, and reconstructing a processing result into adaptive structured features; s3, fusing joint states, inputting the fused joint states into an action head network, splicing online and offline samples into a training batch according to a preset proportion, and determining results to jointly optimize model parameters; s4, performing model reasoning to obtain target action information, performing processing in sequence to generate a smooth low-jitter control sequence, and sending the smooth low-jitter control sequence to the dexterous hand for execution; and S5, the deployment thread issues a control instruction by aligning the annular buffer and the timestamp, triggers a rollback track and allows manual intervention when the control instruction exceeds a threshold value, and writes manual intervention data into an offline buffer. According to the method, efficient, stable and smooth motion control of the dexterous hand can be realized under the condition of extremely few teaching data sets.

This application relates in general to a portable ECG monitoring and, in particular, to a process for ECG signal analysis using an artificial intelligence backend software with machine-learning-based with a reinforcement learning algorithm. This invention presents a waterproof portable device for gathering and monitoring of ECG data of an individual, configured to work on either online or offline mode. The process comprises at least the steps of (a) preparing an ECG exam through a first device containing a software; (b) connecting the first device to a portable ECG device; (c) setting up the portable ECG device on a patient's chest; (d) collecting the ECG data; (e) uploading the data to an artificial intelligence backend software; (f) processing and classifying the data using a machine learning algorithm; (g) analyzing the classification provided in step “f” and performing a second classification of data; and (h) providing a signed medical report.

The invention relates to a fire door remote debugging system and debugging method based on WiFi networking, and belongs to the technical field of fire door system debugging, the debugging system comprises a fire door system, a server and a client, the fire door system is connected with a network through a USB-WiFi module and comprises a fire door monitor, a fire door electric door closer and a fire door electromagnetic releaser; the server comprises a communication service module and a remote debugging service module; the client comprises a remote debugging platform and a mobile phone APP. The debugging method comprises an on-line debugging mode and an off-line debugging mode: in the on-line mode, the monitor communicates with the server through a TCP / IP protocol to realize remote parameter modification, state query and fault diagnosis; and in the off-line mode, the WiFi module is converted into a hotspot, and the mobile phone APP is directly connected and completes configuration issuing and debugging.

The invention provides a trusted online and offline federated learning method and system based on Shamir secret sharing, and relates to the technical field of distributed computing and cryptography. The method comprises the following steps: a secret key generation center distributes a secret key for a newly registered user; the model requester determines a threshold value according to a task demand, selects a secret value, performs secret segmentation according to the total number of pre-distributed data providers, and transmits a secret share to the server; the data provider trains a local model by using local data, encrypts the local model by using a public key of a model requester, and sends a ciphertext parameter to the server; the server performs secret recovery on the secret share; if the number of the obtained secret shares exceeds the threshold value requirement, the secret is recovered successfully, and model parameters are aggregated and sent to a model requester; otherwise, the server continues to wait until the number of the obtained secret shares reaches the threshold value requirement. The method has remarkable advantages in the aspects of data privacy, model security and calculation complexity.

The invention relates to the technical field of education information, in particular to a student group classification prediction method, device, system, equipment and medium, and the method comprises the steps: collecting the multi-dimensional data of each student through an online and offline combined mode, and obtaining a multi-dimensional data set; clustering the multi-dimensional data set by adopting a clustering algorithm to obtain a classification result of the student group; and constructing a neural network classification prediction model based on the multi-dimensional data set and the classification result, training the neural network classification prediction model, and inputting the multi-dimensional data of the to-be-tested student into the trained neural network classification prediction model for analysis to obtain the student group category of the student. Therefore, the diversity and comprehensiveness of data sources are ensured through online and offline multi-channel data collection, the accuracy of data analysis is improved by combining a clustering algorithm and a neural network classification prediction model, detailed classification of student groups is realized, the potential value of students is rapidly evaluated, and the students' experience is improved. And the purpose of helping enterprises and educational institutions to carry out strategic planning is achieved.

The invention belongs to the technical field of power robots. According to the single-wire X-ray detection robot on-line method, the single-wire X-ray detection robot off-line method and the robot, an unmanned aerial vehicle drives the robot to move, so that a to-be-detected wire enters an opening space of the robot through an obliquely downward opening; when the to-be-detected wire is located in the gravity center range of the open space of the robot, the unmanned aerial vehicle drives the robot to vertically move, so that the driving wheels of the robot fall on the to-be-detected wire; and after the driving wheels make contact with the to-be-tested wire, the unmanned aerial vehicle is separated from the robot, the robot is put on line, and after the robot is put on line, the driving wheels and the auxiliary wheels of the robot are used for conducting stable walking control over the robot with multi-wheel cooperation. According to the invention, the robot carried by the unmanned aerial vehicle can be quickly and stably loaded and unloaded, the interference on the upper-phase wire and the middle-phase wire of the double-circuit line is avoided, and the stable walking control of the multi-wheel cooperative robot on the single wire is realized.

The invention discloses a quality infrastructure service efficiency evaluation method based on a large model, which is based on full-chain elements such as construction, management, service and propaganda and promotion of a quality infrastructure one-stop service platform, integrates application of a key technology of the one-stop service digital platform, deeply explores influence factors of online and offline service efficiency of the platform, and improves the quality infrastructure service efficiency evaluation efficiency. And a gradient grading platform evaluation system model is constructed. The objective of the invention is to provide a powerful tool for platform self-service efficiency evaluation and provincial platform gradient grading.