205 results about "Online and offline" patented technology

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 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 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 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.

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 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.

The invention relates to the technical field of marketing, and particularly discloses a service optimization method and system based on combination of online marketing and offline drainage. By acquiring online marketing and offline service multi-dimensional data in real time, constructing a quantitative evaluation system and dynamically optimizing service collaboration, the method comprises the following steps: firstly, acquiring original data from a user behavior log, an activity configuration library and an offline business system, covering core information such as a user identifier, an activity identifier, a service network state and the like; sequentially calculating a marketing load coefficient, a drainage congestion coefficient, an online marketing behavior coefficient and an offline service bearing coefficient, finally carrying out weighted fusion on the online and offline coefficients to generate a comprehensive service coefficient, verifying an over-limit state by adopting a synchronous non-blocking mechanism and a dynamic threshold value, predicting a resource demand increment in combination with a time sequence, generating a coordination strategy set, and carrying out resource scheduling. Accurate adaptation of online drainage and offline service is realized, the experience is improved, and the system risk is reduced.

The invention relates to the technical field of e-commerce, and discloses an Ai-based multi-store O2O smart mall system, which comprises a user portrait module, a store portrait module and a recommendation module. According to the application, the user portrait module integrates the online data to generate the first user portrait, and updates the first user portrait to the second user portrait in combination with the offline data, thereby comprehensively capturing user demand changes; the store portrait module is fused with online and offline data to construct a store portrait, and the operation quality and the supply capability are accurately reflected; the recommendation module dynamically screens and recommends stores based on portraits, updates the stores in real time, and generates a scenarized scheme in combination with multi-target optimization; precise matching between the user and the store is achieved, recommendation accuracy and timeliness are improved, user offline experience is optimized, the purchase conversion rate is improved, meanwhile, the store is assisted to efficiently allocate resources, and the operation efficiency and commercial value of a multi-store O2O mode are enhanced.

The invention relates to a bank online and offline channel fusion method, device and system and a program product. The method comprises the following steps: receiving a reservation request sent by an online channel, and carrying out risk verification on the reservation request; under the condition that the verification is passed, according to the reservation request, calling a corresponding queuing machine assembly to perform remote reservation number taking; determining that the target service needs to be pre-filled based on the reservation request, and sending a voucher generation request to a unified voucher library; in response to the received electronic certificate file fed back by the unified certificate library, calling the unified communication component to send the electronic certificate file to the client, and sending a reservation success message to the online channel; receiving a service handling completion notice from the bank service component, and associating the electronic receipt information in the service handling completion notice with the reservation request to generate service handling full-cycle data; and in response to the reservation record query request, obtaining and displaying corresponding service handling full-cycle data. By adopting the method, the problems of online and offline flow breakage and data blockage can be solved.

This application belongs to the field of digital push technology and relates to a method and system for collaborative online and offline marketing content delivery, aiming to solve the problems of rigid delivery, low accuracy, and disconnect between online and offline in existing technologies. The method includes: acquiring user behavior characteristics and scenario characteristics collected by offline display terminals when displaying content, and determining interests; when preset conditions are met, sending an instruction to cause the offline display terminal to dynamically generate a unique interaction identifier and output an interaction entry point; receiving online access requests and extracting the unique interaction identifier from them, associating display events and online interaction events based on the unique interaction identifier to obtain event-level attribution results; and adjusting the generation control parameters and / or delivery strategy parameters of subsequent target marketing content based on the attribution results. This application can achieve event-level association and attribution between offline display and online interaction, forming a closed loop of content generation, display, interaction, and optimization, improving the efficiency of marketing content generation, delivery accuracy, and conversion effect.

The invention aims to provide a K8s and Slurm hybrid deployment management and control method, and particularly relates to a Kubernetes and Slurm hybrid deployment management and control method based on dynamic resource awareness. According to the method, an intelligent node agent (Node Agent) is introduced, trend prediction is carried out according to the real-time water level and historical load characteristics, and therefore refined resource arbitration is carried out on K8s (online) and Slurm (offline) on the Linux Cgroup level. According to the method, the problems of resource conflict and invisibility of heterogeneous scheduling systems (K8s and Slurm) during hybrid deployment at the same node are solved, the problem of performance interference between online services and offline operations is solved, the resource utilization rate is maximized, and meanwhile, the quality of service (QoS) of the online services is ensured. The system has higher stability and safety and anti-interference performance at a micro-architecture level.

The invention discloses a monitoring video delay sweep generation method based on scene change self-adaptive frame extraction, and belongs to the technical field of video monitoring and streaming media processing. The method comprises the following steps: acquiring a sweep strategy containing a time period, a reference interval, a difference threshold value, a forced retention period, a window length and a quality threshold value; receiving a real-time code stream or reading a storage side key frame intermediate file; calculating an inter-frame difference degree by using a brightness channel of a multi-scale structure similarity algorithm, determining a frame extraction interval in combination with a static duration, and marking candidate frames; performing quality detection according to definition, brightness, contrast, stripes and noise, and rejecting unqualified products; when a forced period is reached, a bottom frame is reserved; and performing segmented coding according to a time window, and performing assembly output at the end of a time period. According to the method, online and offline processing links are unified, key information is reserved, and redundancy and time delay are reduced.

This invention discloses an offline multi-scale training method for online remaining life prediction of electric vehicle lithium batteries, relating to the field of electric vehicle battery health monitoring technology. This invention combines online and offline data streams, collecting parameters such as battery voltage, temperature, and charge / discharge status in real time. The online component handles real-time monitoring and data acquisition, while the offline component performs deep learning and model training based on historical degradation data. Online data and the offline model work collaboratively through a closed-loop system, updating the battery health status in real time and adjusting the prediction results in each cycle. Through this closed-loop prediction system combining online real-time data and offline historical data, this invention can provide a more accurate health status assessment under limited historical degradation data conditions. This system is particularly suitable for battery management in practical applications, improving the accuracy and stability of battery health monitoring and remaining life prediction.

The invention discloses a RESTFUL API rapid generation and release device and method, and the device comprises an API service management module which is used for providing an API interface development and management interactive interface for a data server user, achieving the configuration, testing and release of a newly-added API, and achieving the management function of the existing API, project and environment; the API service opening module is used for providing an API reading and subscribing interaction interface for a user at the service system end; and a network management and control module, a data basic service module and the like. The device provides an interface mode or an SQL configuration mode to generate an API; dynamic online and offline management can be carried out on the API; multiple sets of environments can be managed, the APIs can be dynamically published to the specified environment, and the registration publishing and management efficiency of the APIs is effectively improved.

The invention discloses a mediation strategy dynamic adjustment system based on multi-modal emotion perception. The core of the system comprises six modules: obtaining text, voice and video data through a multi-modal data acquisition module; high-accuracy emotion perception is realized through an emotion recognition and fusion analysis module; key emotional events are captured through an emotional event detection and early warning module; strategies such as verbal skills and rhythms are intelligently recommended / adjusted by means of a mediation strategy dynamic adjustment module in combination with a knowledge base; a visual interface and a feedback channel are provided through the user interaction and feedback module; and compliance is guaranteed by a data security and privacy protection module. The system can realize real-time accurate sensing of multi-modal emotions, dynamically adapt to a mediation strategy, reduce experience dependence of mediators, and support online and offline multi-mediation modes. The trial application shows that the multi-modal emotion recognition accuracy rate reaches 92%, the strategy suggestion adoption rate reaches 87%, the mediation success rate is increased by 15%, mediation intelligence and standardization can be enhanced, and the method has wide popularization prospects and social benefits.

The embodiment of the application provides a to-do message distribution method and system, and relates to the technical field of data distribution. The to-do message distribution method comprises the following steps: obtaining to-do member group data and parallel distribution number data; querying the to-do member group data according to a preset account type to obtain a plurality of selected to-do members; initializing the weight of the plurality of selected to-do members as a preset weight value; processing the plurality of selected to-do members according to a preset weight calculation condition and the online and offline states of the plurality of selected to-do members to obtain member weight data; performing random screening based on the member weight data to obtain a plurality of target members, wherein the number of the parallel distribution number data corresponds to the number of the plurality of target members; and distributing a to-do message to the plurality of target members. The to-do message distribution method can achieve the technical effect of improving to-do message processing efficiency.

The invention relates to a social subject memory simulation system and method based on a large language model, and belongs to the technical field of computers. According to the method, online and offline multi-source information fusion is realized, and behavior and situation characteristics of social subjects can be comprehensively described; a large language model semantic comprehension capability is introduced, so that the intelligent level of memory retrieval and information fusion is improved; dynamic dump and index retrieval of long-term memory are supported, so that subjects can keep semantic coherence and behavior consistency in multiple rounds of interaction; the authenticity of information spreading and interaction between social subjects is enhanced, and technical support is provided for an intelligent social system, virtual human interaction and social simulation.

The utility model relates to automatic shell manufacturing line technical field, and specifically is a kind of precision casting automatic shell manufacturing line feeding and discharging vehicle, including rack, fixed with mounting mechanism and two fixed rods on the rack, the mounting mechanism is engaged with lifting mechanism, rotatingly installed with material storage mechanism on the fixed rod, the material storage mechanism includes pivot and connecting rod, pivot is rotatably connected in the fixed rod, the pivot is fixedly installed with connecting rod, connecting rod is fixedly connected with carousel on it, carousel is engaged with the installation of several clamping blocks in ring shape equidistant on it, clamping block is fixedly connected with hanger bar and lifting ring on it;Pivot drives connecting rod and carousel rotation, and through tool lifting ring, make it will hanger bar and clamping block be limited in carousel, further in the shell mold of being hung in the end of hanger bar is conveniently fed and discharged while being convenient for installing hanger bar on carousel.

The application belongs to the technical field of online transaction, and discloses a blockchain integral transaction method, system, device and storage medium. The blockchain integral transaction method comprises the following steps: generating a payment code and a repurchase gold account for a merchant; receiving and executing a payment transaction sent by a user and transferring repurchase gold to the repurchase gold account; receiving an integral exchange request of the user; triggering a transaction when the accumulated amount of repurchase gold in the repurchase gold account is greater than or equal to a trigger value, repurchasing all integrals with the repurchase gold until the repurchase gold is exhausted or the qualified integrals are completely repurchased. The application breaks the mode of only active buy orders in the traditional integral transaction system, creates passive buy orders of consumption orders, links online and offline consumption scenes, changes the traditional active buy and sell transaction into an order repurchase passive buy and sell transaction mechanism, and can promote consumption, increase additional income of consumers, improve customer return rate, increase customer stickiness and stimulate user consumption.

A method of determining whether an end user device coupled to a voice over Internet Protocol (VOIP) network is online or offline assembling source data from a variety of network monitoring devices and services including at least one selected from the group consisting of: of asset inventories, VoIP Call Control Servers, routers, firewalls, and intrusion protection systems (IPSs) concerning an online and offline status of a plurality of end user device in the network. The method also comprises training a machine learning algorithm using the source data to determine whether the end user devices coupled to the VoIP network are online or offline, and after training, executing the machine learning algorithm to determine whether a specific end user device currently coupled to the VoIP network is online or offline.

The invention discloses a drill bit machining parameter intelligent optimization method based on machine learning, and particularly relates to the technical field of industrial intelligent manufacturing and machine learning, and the method comprises the steps: carrying out the real-time sensing and evaluation of a machining state through multi-modal sensing data fusion, achieving the dynamic precise prediction of a machining effect and a risk based on the online and offline dual-model cooperation, and achieving the real-time monitoring of a machining state. According to the real-time working condition and the production demand, the weight of the multi-objective function is dynamically adjusted and optimization is carried out to generate processing parameters, finally, the parameters are optimized, the search space of subsequent optimization is adaptively managed according to risk prediction, and an intelligent closed loop of perception, prediction, optimization and execution is formed. According to the method, comprehensive and accurate sensing of the machining state, continuous evolution of a prediction model, dynamic self-adaption of an optimization strategy and intelligence of closed-loop control are realized, so that the machining efficiency is improved, the service life of a cutter is prolonged, and the workpiece quality and the process safety are improved.

This invention provides a method and system for optimizing ticket allocation under a hybrid fare system for urban and suburban railways, belonging to the field of transportation operation management technology. This invention breaks through the limitations of a single fare system, achieving online and offline dual-system collaboration through a dynamic ticket conversion mechanism, effectively solving the problem of commuter passenger flow being squeezed and significantly improving passenger flow satisfaction; it solves the difficult problem of high-dimensional optimization of ticket allocation by designing an improved particle swarm optimization algorithm (integrating dynamic parameter adjustment and diverse restart mechanisms), greatly improving computational efficiency and supporting minute-level optimization of a network of hundreds of stations; it accurately matches passenger flow characteristics, constructing a dual-system demand coupling model based on OD passenger flow distribution, significantly improving seat utilization and transportation revenue, and directly supporting the "four-network integration" fare system interoperability project.

This application provides a method, system, and related equipment for offline reproduction of an autonomous driving system. The method includes: acquiring stored data, utilizing the stored data, and, according to preset business logic, calling a preset business framework module to perform offline initialization and calculation operations to obtain offline results identical to those of the online operation of the autonomous driving system, thereby completing the offline reproduction. The solution of this application uses the same preset business logic and the same preset business framework module for both online and offline operation of the autonomous driving system. Furthermore, both the preset business logic and the preset business framework module are pre-configured according to business requirements, enabling accurate control of data calculation and ensuring accurate offline reproduction of the autonomous driving system's operation process.

The invention particularly relates to a cluster error code correction parameter learning method and system, and relates to the technical field of distributed cluster communication and error code correction. A dynamic neighborhood topology construction module; a local transfer rule engine module; an asynchronous random updating engine module; and an entropy monitoring and stability detection module. According to the method, cellular neighbors are defined according to physical and logic two dimensions, and cluster dynamic changes such as node online and offline, link faults and replica migration are accurately adapted; and physical parameter microsecond acquisition is realized through direct reading of a hardware register, communication and calculation overhead is greatly reduced through discrete coding and lightweight custom messages, and the core requirement of lightweight interaction of the cluster is met.

The application relates to barrage control technology, and provides a barrage control method and device, a server and a computer storage medium, the method comprises the following steps: acquiring original barrage sent by a user in an online live room; performing online risk filtering on the original barrage, and screening online barrage meeting an online barrage control strategy; performing offline risk filtering on the online barrage, and screening offline barrage meeting an offline barrage control strategy; and delivering the offline barrage to an offline live room. The application realizes double risk filtering of the barrage, and ensures effective control of the barrage content in an online and offline linkage scene.

This invention discloses a universal multi-level referral bonus and anti-fraud customer referral operation method and system, involving customer operation, referral marketing, automatic profit-sharing settlement, and offline cash payment risk control technology. This invention establishes a store administrator system and integrates a biometric module through system initialization and role configuration; generates unique invitation codes for users; supports merchants to dynamically configure two-level bonuses and self-purchase cashback ratios; effectively determines online and cash payment orders through multi-dimensional verification; automatically settles bonuses and generates a traceable ledger after order completion; and establishes a four-tiered anti-fraud risk control system encompassing devices, accounts, orders, and cash payments to prevent fraudulent behavior. This invention solves the problems of low manual efficiency, inaccurate settlement, difficulty in controlling fraud, poor universality, and the inability of cash payment orders to participate in bonus sharing in traditional referral operations, achieving integrated online and offline automated referral operations, applicable to various online and offline integrated operation scenarios.

The embodiment of the present disclosure provides a kind of automatic loading and unloading machine control method, device, control equipment and readable storage medium, the method comprises in the process that the automatic loading and unloading machine and the handling robot execute current task, if detecting fault point, then the sub-equipment of the automatic loading and unloading machine corresponding to the fault point is controlled offline;After the fault point is eliminated, the offline sub-equipment is controlled online, and the handling robot and the offline sub-equipment are initialized to make the handling robot and the offline sub-equipment re-execute the current task.The method provided in the present disclosure can realize the orderly online and offline of automatic loading and unloading machine.

The invention discloses an online and offline course linkage recommendation method and system for a mixed teaching mode, relates to the technical field of course recommendation, and discloses the online and offline course linkage recommendation method and system for the mixed teaching mode. The method comprises the steps of generating a current track fingerprint representing teaching process characteristics for offline classroom knowledge points, calculating a reuse order and a track variation degree by matching historical fingerprints, and judging a high-order variation reuse scene; if yes, calculating a target cognition progressive level in the scene; based on the hierarchy and the trajectory variability, constructing a connection resource sequence composed of a plurality of progressive resources; calculating a dynamic cognitive absorption rate of the learner to determine an optimal pushing moment, and progressively pushing a resource sequence to the learner terminal; according to the method and the device, accurate identification of the differentiated offline teaching scene is realized, the personalized online preposed resource sequence matched with the differentiated offline teaching scene is dynamically generated, and the learning connection efficiency in a mixed teaching mode is improved.

The application provides a signal sending method and device, and belongs to the technical field of semiconductor manufacturing. The signal sending method is applied to an equipment automation control EAP device, and comprises the following steps: obtaining a communication state of a processing equipment, wherein the communication state comprises online and offline, and the processing equipment comprises a main equipment and an auxiliary equipment corresponding to the main equipment; receiving a material monitoring signal of the processing equipment, wherein the material monitoring signal comprises a conveying signal of the auxiliary equipment and an unloading signal of the main equipment; judging whether a preset condition is met according to the communication state and the received material monitoring signal; and in the case that the preset condition is met, sending a material conveying signal for the main equipment to a manufacturing execution system MES client. The technical scheme of the application can send the conveying signal of the processing equipment.