3727results about "Reservations" patented technology

An Automated AI-Driven Lifestyle Orchestration System proactively manages a user's daily life by intelligently populating calendars with relevant, time-sensitive marketing objects. This is achieved through sophisticated AI-driven analysis of user directives, preferences, and real-time location data obtained via computing devices like smartphones. The system leverages advanced media identification modules employing fingerprinting and watermarking to accurately match user-captured media (via a Percipient Sample Pack (PSP)) to specific content and associated products. It facilitates dynamic commerce through hierarchical linked lists for purchasing authentic or similar items, supported by an affiliate program. This inventive solution significantly improves personal organization and digital commerce by transforming passive interaction into a proactive, intelligent, and monetizable user experience.

The invention discloses an intelligent government affair service platform and a data processing method applied by the intelligent government affair service platform, and relates to the technical field of government affair service management, and the method comprises a public configuration module which is used for carrying out hierarchical configuration on system service capability according to a configuration instruction, and the hierarchical configuration comprises capability application management configuration, public template configuration and service model configuration; the service model configuration comprises a service overall planning model and a service implementation model; the reservation rule engine module comprises multi-level reservation rules of a hall reservation rule, a special area reservation rule and an item reservation rule, rule parameters can be dynamically adjusted, and the multi-level reservation rules comprise a reservation time period, a government affair service number allocation strategy and a conflict detection mechanism; the business handling module is used for realizing automatic material verification and document generation in a certificate-free business scene; and carrying out data interaction with an external system through the capability application management module, and carrying out system service capability authorization. The service efficiency of government affair services is improved, and the government affair handling experience of the masses is improved.

The invention provides a reservation and resource scheduling system based on cognitive intelligence and a self-evolution rule engine, and the system comprises a rule cognition and self-evolution engine, an enhanced visual configuration, deduction and auditing engine, and a dynamic loading, real-time decision and predictive optimization module. A rule cognition and self-evolution engine of the multi-target intelligent conflict resolution and collaborative scheduling module is used for constructing a knowledge graph and generating a reservation rule in a current state by adopting a deep learning algorithm; the enhanced visual configuration, deduction and auditing engine is used for providing a rule editing function for a user; the rule demand edited by the user is transmitted to the rule cognition and self-evolution engine for detection; the dynamic loading, real-time decision-making and predictive optimization module is used for enabling the changed rule to take effect immediately; and the multi-target intelligent conflict resolution and cooperative scheduling module is used for processing the conflict problem of the reservation requests or the conflict problem between the reservation requests and available resources.

A computer-implemented method comprises receiving a first chat message at a server computer from a chat interface of a reservation application executing on a mobile computing device; determining, by the server computer, from the first chat message, whether the first chat message specifies an intent for a recommendation of a travel item by programmatically classifying the first chat message to output the intent; in response to determining that the first chat message specifies the intent, generating a first plurality of travel items and a second chat message comprising one or more descriptors of the intent, outputting the first plurality of travel items and the second chat message in the chat interface, and including, in each travel item of the first plurality of travel items, a graphical user interface (GUI) widget that is programmed when selected to initiate a reservation dialog based on the travel item; in response to determining that the first chat message does not specify the intent, outputting in the chat interface a third chat message comprising a prompt for contextual information, receiving, in the chat interface, a fourth chat message specifying the contextual information, executing an inference stage of one or more trained machine-learning models over the contextual information to output one or more named entities, deriving the intent from the first chat message and the one or more named entities, generating, using the server computer, the intent, and the one or more named entities, a second plurality of travel items and a fifth chat message comprising one or more descriptors of the intent, outputting in the chat interface the second plurality of travel items and the fifth chat message, and including, in each of the second travel items, a second GUI widget that is programmed when selected to initiate the reservation dialog based on one or more of the second travel items.

As disclosed herein, a computer-implemented method for travel planning is provided. The computer-implemented method may include receiving, via a conversational user interface (UI), a user input associated with a product offered by a booking application. The computer-implemented method may include retrieving, from a database associated with the booking application, at least one attribute of the product. The computer-implemented method may include determining, using a first machine learning (ML) model of a plurality of ML models, a user intent associated with the user input. The computer-implemented method may include generating, based on the at least one attribute and the user intent, a first response to the user input. The computer-implemented method may include providing, via the conversational UI, the first response to the user input. A system and a non-transitory computer-readable storage medium are also disclosed.

The invention discloses a real-time reservation and scheduling system for shared parking, and relates to the technical field of intelligent traffic and smart city management, and the system comprises a demand analysis module which is used for extracting parking lot scene types, time requirements and priority information from user parking request data, generating standardized demand description through dynamic weight distribution, and sending the standardized demand description to a user; the process decomposition module is used for decomposing a parking scheduling process into demand receiving, resource matching and path planning business units according to parking lot scene types and time requirements in the user demand analysis result, and determining a business unit set; according to the real-time reservation and scheduling system for shared parking, through dynamic weight distribution and real-time response optimization, efficient resource distribution and path planning are ensured, and the parking scheduling efficiency and the user experience are remarkably improved.

A vehicle including an energy storage device and processing circuitry. The processing circuitry detects a charging session being initiated to charge the energy storage device. The processing circuitry further obtains a current state of charge (SoC) associated with the energy storage device and first vehicle usage information that indicates a usage of the vehicle over a historical time-interval. The processing circuitry further predicts vehicle usage over a first time-interval based on the obtained first vehicle usage information and rate the charging session at current time-instance based on the predicted vehicle usage and the current SoC. The processing circuitry further generates an alternate charging plan based on the charging session at the current time-instance being rated suboptimal and outputs the generated alternate charging plan at the current time-instance.

The invention relates to an intelligent stadium resource scheduling optimization method and device, equipment and a medium. The method comprises the following steps: acquiring multi-source data of a stadium, and processing the multi-source data to obtain a multi-modal time sequence set; performing stadium resource current situation analysis according to the multi-modal time sequence set to obtain a current resource occupation result; resource demand prediction is carried out according to the current resource occupation result based on the real features, and a conflict record table and a regional demand thermodynamic diagram are obtained; and performing scheduling optimization according to the conflict record table and the regional demand thermodynamic diagram based on a preset scheduling target strategy to obtain a resource scheduling table and a corresponding execution mechanism. By adopting the method, the stadium resource state can be accurately perceived by fusing multi-source heterogeneous data, and dynamic coordination and optimal allocation of internal resources of the stadium are realized in combination with a prediction algorithm and a multi-target scheduling strategy, so that the resource utilization rate and the use experience of the stadium are improved.

The invention provides a method for checking dynamic allocation of reservation resources, which belongs to the technical field of intelligent allocation and comprises the following steps: defining a reservation resource set and a reservation request set; a three-layer conflict detection and cooperative compensation mechanism is established, conflict detection is carried out on each reservation request to obtain a conflict set of each reservation request, and the conflict set comprises various conflicts of each reservation resource matched with the corresponding reservation request; determining the time utilization rate of each matching resource based on all the reservation requests and the capacity utilization rate of each request time moment, and establishing an idle possible set of each request time moment according to a preset triggering condition; and according to the conflict set of the reservation requests and a corresponding reservation request configuration priority obtained according to the conflict set of the reservation requests, performing mapping matching on the idle possible set to realize dynamic allocation. The accuracy of appointment resource allocation is ensured, and the user experience and the resource operation efficiency are improved.

The invention discloses an outpatient queuing and calling scheduling system based on an AI dynamic decision engine. The outpatient queuing and calling scheduling system comprises a multi-source data acquisition and preprocessing module, a DeepSeek intelligent prediction and scheduling engine, a dynamic priority ranking and calling module and a cross-department resource collaboration module. According to the method, the whole outpatient service process is reconstructed through AI, a closed loop of data driving, intelligent prediction, dynamic scheduling and cross-section collaboration is achieved, a traditional static queue is converted into a dynamic system capable of sensing disease severity, special crowd requirements and resource loads, the medical resource allocation efficiency is improved, the average waiting time of patients is shortened, and the medical resource allocation efficiency is improved. The rescue response speed of critical patients is improved by multiple times, meanwhile, the diagnosis period of difficult cases is shortened through automatic triggering of multidisciplinary consultation (MDT), pregnant women / disabled persons / elderly patients are automatically recognized in cooperation with the multi-modal recognition technology, dynamic priority values are given, and the priority service missed recognition rate is obviously improved.

The invention relates to the technical field of car rental management, in particular to a car rental intelligent management method and system, and the method comprises the following steps: obtaining the time difference between reservation and use, judging whether to give an early warning or not, counting the number of early warning times, adjusting the credit rating if the number exceeds the limit, judging the renewal time and the return time, and recognizing abnormal renewal. Behavior nodes are constructed, abnormal statistical behavior chain early warning numbers are aggregated, resource request priorities are judged in combination with multiple factors, and a vehicle planning table is established. In the invention, through difference calculation, a short-time frequent reservation behavior is identified, abnormal renewal is determined, deep modeling of continuous abnormity on a user behavior rule is aggregated, credit rating records and behavior chain characteristics are combined, a user resource request priority is dynamically adjusted, and interference of a high-risk behavior on resource scheduling is controlled. The early warning response capability to abnormal behaviors and the pertinence of resource management are improved, behavior risk constraint and intelligent cooperation of vehicle configuration are realized, and a vehicle rental management system with timeliness, hierarchy and adaptive characteristics is constructed.

An example operation includes one or more of reserving an available charging point at a charging station for a vehicle at an arrival time at the charging station when one other charging point at the charging station will be available for one other vehicle in a time less than a threshold time after the arrival time.

The invention discloses a pedestrian behavior simulation operation control method based on an unreal engine. Efficient and vivid pedestrian behavior simulation is realized through five core modules: a three-dimensional scene construction module converts a BIM model based on an unreal engine Datasmith plug-in and constructs an interactive dynamic scene; the pedestrian model construction module defines multi-dimensional pedestrian attributes and supports data-driven initial distribution configuration; the hierarchical behavior control module realizes refined regulation and control of pedestrian behaviors through macroscopic path planning, microcosmic behavior decision and emotion driving; the dynamic resource optimization module adopts level-of-detail control and multi-thread task allocation to improve the operation efficiency of the system; and the real-time interaction and data analysis module provides scene regulation and control, data visualization and simulation data recording and playback functions. According to the method, the authenticity, the dynamic response capability and the controllability of pedestrian simulation are remarkably improved, and reliable technical support can be provided for traffic planning, emergency drilling and building design evaluation.

The invention discloses a hotel service sharing method for tourism resource linkage, and belongs to the technical field of hotel management. Hotel data are collected and standardized through a multi-source interface, an identifier associated with a scenic spot is generated, and a system aggregates regional hotels according to a target scenic spot of a user and performs preliminary screening; by analyzing user demands and combining historical behaviors to generate weights, comprehensive scoring and sorting are performed on candidate hotels by using a multi-dimensional weighted scoring model, and finally, the system automatically executes reservation operation of an optimal hotel, feeds back a result to a user, updates room state data in real time, and improves the user experience. The technical problem of dynamically matching hotel recommendation and real-time reservation on the basis of linkage of multi-source hotel information and scenic spot resources is solved, data structure standardization is ensured, real-time updating is supported, hotel recommendation and candidate hotel screening of scenic spot linkage are realized, dynamic scoring and optimal sorting are realized, and the efficiency of hotel recommendation is improved. The data is consistent with the actual house resource, and the user reservation experience is improved.

Some implementations to a multi-pane graphical user interface (GUI) where, during a dialog session between a user and a generative model system, the generative model system generates first pane responses that are rendered in a first pane of the GUI and generates a second pane response that is rendered in a second pane of the GUI and that is dynamically updated over the dialog session. Further, first pane user inputs, that are directed to the first pane, can cause an additional first pane response to be generated and rendered at the first pane and / or can cause an update to the second pane response. Likewise, second pane user inputs, that are directed to the second pane, can cause a corresponding update to the second pane response and can cause an additional first pane response to be generated and rendered at the first pane.

The invention relates to the technical field of charging infrastructure management, and particularly discloses a charging pile appointment scheduling and path planning cooperation system and a charging pile appointment scheduling and path planning cooperation method. According to the method, continuous wavelet transform and fast Fourier transform are adopted to extract a charging rate fluctuation characteristic value and a power grid power supply stability characteristic value for quantitatively evaluating the service stability of a charging pile and the power supply reliability of a regional power grid, and the system fuses the characteristic values into a comprehensive characteristic vector as the input of a gradient boosting tree model so as to obtain a gradient boosting tree model; and training the prediction model to output a charging planning score so as to judge whether the user can smoothly complete the charging task, and if a prediction result does not meet a set threshold value, dynamically adjusting a reservation scheduling strategy and re-planning an optimal charging path.

The invention provides a charging pile reservation intelligent regulation and control method and system, and belongs to the technical field of new energy vehicle charging operation management, and the method comprises the steps: determining the vehicle charging emergency degree based on the current vehicle information and user demand information when a charging reservation request is detected; based on the number of the current idle charging piles and a preset charging pile scarcity degree threshold value, the scarcity degree of charging resources corresponding to the target charging station is determined; determining a power grid load degree based on the current power grid load information; generating charging strategy information based on the vehicle charging emergency degree, the scarcity degree of the charging resources and the power grid load degree, or determining the charging strategy information corresponding to the target vehicle through the reserved charging priority corresponding to the target vehicle based on the vehicle charging emergency degree and the current idle charging pile number; and guiding the user to select charging appointment time according to the charging strategy information. Reasonable reservation charging can be carried out, the utilization rate of the charging piles in the charging station is improved, and resource waste is reduced.

The invention provides a micro-nano processing platform user machine-hour management method and device, a storage medium and equipment. The method comprises the following steps: acquiring an equipment reservation request sent by a user; calling a user initial machine hour from the equipment machine hour management system according to the user identity information; if the initial machine time of the user does not meet the equipment machine time sum of the equipment reservation request, sending a prompt message that the machine time is insufficient to the user; real-time equipment operation parameters and user real-time operation records of a user in the equipment operation process are obtained, and a user machine time adjustment strategy is obtained according to the real-time equipment operation parameters, the user real-time operation records and the equipment reservation request; and adjusting the initial machine time of the user according to the machine time adjustment strategy to obtain a final user machine time, and sending the final user machine time to the user. Through the user machine-hour management method, transparent machine-hour management of the micro-nano machining platform is realized, and meanwhile, the opinions of standard operation of the user and the use efficiency of the micro-nano machining platform equipment are improved by utilizing the equipment machine-hour adjustment strategy.

The invention relates to the technical field of scientific research instrument management, in particular to an intelligent supervision method and system for a scientific research instrument, and the method comprises the following steps: extracting instrument reservation task information and constructing a timetable, collecting and analyzing control instruction abnormal behaviors to generate risk information, collecting power trend data to recognize an operation state, and carrying out management. And collecting an identity instruction and power information to construct a use record, and analyzing power fluctuation and load degree to generate a maintenance cycle evaluation value. According to the method, dynamic sorting and conflict screening of the reservation requests are realized by combining the task dependency relationship and the priority information, the risk level is evaluated by using the user instruction behavior characteristics, the real-time identification of the operation risk is realized, the accuracy of the operation state identification is enhanced by using the power fluctuation trend analysis, and the user experience is improved. A structured binding relationship of use data is established in combination with the identity and the device number, the information tracing integrity and the data tracing efficiency are improved, and the prejudgment capability of the maintenance period is optimized by using the combined evaluation of the operation load and the power trend.

The invention relates to the technical field of self-service, and discloses a campus self-service clothes washing management system based on a cloud platform, and the system comprises the steps: obtaining clothes washing tasks of all appointment users in future appointment time, and selectable washing machines; obtaining fatigue degree data about the selectable washing machine; target washing machines with the same number as the reservation users are matched from all the selectable washing machines according to the fatigue degree index, and a plurality of washing matching strategies are formed according to all the target washing machines and the reservation users; acquiring an optimal washing matching strategy from the plurality of washing matching strategies by using a pre-configured genetic algorithm; distributing each target washing machine to the corresponding reservation user according to the optimal washing matching strategy; the use frequency of the low-frequency washing machine is optimized, the maintenance period difference between the washing machines is reduced, and the return on investment of the low-frequency washing machine is improved.

A system is described for training a virtual agent of a listing network platform using a machine learning model. The system establishes a communication session with a virtual agent of a listing network platform and generates, by a first machine learning model, conversation data representing a customer support issue associated with the listing network platform. The system transmits, by the first machine learning model, at least a portion of the conversation data to the virtual agent via the communication session. The system receives one or more responses to the at least the portion of the conversation data from the virtual agent in the communication session and stores a simulated conversation comprising the at least the portion of the conversation data generated by the first machine learning model and the one or more responses received from the virtual agent.

A maintenance reservation system according to a first aspect includes: an equipment information acquisition unit configured to acquire equipment information including information about equipment; a maintenance contractor information acquisition unit configured to acquire maintenance contractor information including information about maintenance contractors who provide maintenance services on the basis of information transmitted from maintenance contractor terminals; a reservation information acquisition unit configured to acquire reservation information including desired conditions for the maintenance services on the basis of information transmitted from a user terminal; a processing unit configured to select maintenance contractor candidates on the basis of the equipment information and the maintenance contractor information, and narrow down the candidates on the basis of the desired conditions and the maintenance contractor information corresponding to the maintenance contractors included in the candidates; and a notification unit configured to notify the user terminal of the candidates narrowed down by the processing unit.

The present invention relates to the technical field of transportation demand management and parking pricing, and specifically to a parking reservation method in the urban central business district based on dynamic pricing, comprising the following steps, S1, traffic data acquisition, S2, key value calibration, S3, perceived utility calculation, S4, parking probability measurement, and S5, pricing strategy realization. The method of reserving parking spaces in the CBD of this city is aimed at addressing the serious imbalance between the supply and demand of parking resources. To alleviate the problem of parking difficulties in urban CBD areas and appropriately increase the revenue of parking lots by dynamically adjusting parking costs. At the same time, this method greatly reduces the cruising time of vehicles when searching for parking spaces, effectively reduces fuel waste and exhaust emissions during cruising, and largely avoids traffic congestion caused by the convergence of cruising vehicles and through traffic.

The invention relates to the field of new energy automobile power supply, and provides a charging pile reservation charging method and device and a storage medium, and the method comprises the steps: receiving a charging reservation request sent by a user terminal; obtaining time-sharing load data of a current power grid based on the power grid load prediction model, and collecting real-time state data of the target charging pile group through the Internet of Things interface; generating a charging queue sequence through a multi-objective optimization algorithm; sending a charging authorization instruction to a target charging pile corresponding to the target charging pile identifier according to the charging queue sequence, and synchronously updating a reservation state identifier of a charging pile interaction interface; collecting multi-dimensional operation data of the charging pile, and uploading the multi-dimensional operation data to a cloud platform in real time through an encrypted communication link; carrying out abnormal mode identification on the multi-dimensional operation data, and triggering a power redistribution strategy when an abnormal feature is detected; and when the charging is completed or abnormally terminated, generating a billing voucher based on the block chain smart contract, and writing the key parameters of the charging process into a distributed account book.

The invention discloses a reservation type vehicle and cargo matching method and device applying an AI model and a storage medium, and the method comprises the steps: receiving a cargo owner transportation plan, and generating a vehicle finding demand; obtaining reserved driver information, and determining a transport capacity pool to which a reserved driver belongs and a corresponding transport capacity classification attribute; the AI model with the built-in vehicle and cargo matching rule is combined with the transport capacity pool and the corresponding transport capacity classification attribute to intelligently match a target driver, and a transport task is generated to notify the target driver to receive an order and carry; and the intelligent matching process improves the priority of the allocated transportation tasks of the reservation drivers with the goods-fixing certificates. And receiving an activation signal of the order certificate, distributing the AI digital person to follow up the performance condition of the target driver in real time, automatically identifying the risk, and sending out a guide prompt. According to the invention, on the basis of an AI large model technology, reservation type vehicle and goods matching based on a goods fixing certificate is realized, the space-time matching efficiency of a vehicle source and a goods source is optimized, the vehicle and goods matching efficiency is improved, a driver is assisted in conveniently and rapidly completing a performance transaction in a full-service process through an AI digital person, and the service quality of the driver is improved.

The invention provides a multi-mode online car-hailing demand uncertainty prediction method. The method comprises the following steps: constructing a hybrid expert network structure formed by an exclusive expert module and a shared expert module, obtaining OD demand data from a historical starting point to a terminal point of the urban multi-mode online car-hailing platform, and constructing an OD three-dimensional tensor comprising a plurality of travel modes; taking the OD three-dimensional tensors of the plurality of travel modes as training data, and training the hybrid expert network structure by using the training data to obtain a trained hybrid expert network structure; and inputting the OD three-dimensional tensors of the plurality of travel models in a period of historical time into the trained hybrid expert network structure, and predicting the OD three-dimensional tensors of the plurality of travel models in a future time slice. According to the method, through learning of the space-time dependency relationship and the operation dependency relationship, the accuracy of carpooling demand prediction is improved, and important support is provided for a platform to deploy vehicle resources more reasonably, meet user travel demands and improve service quality.

The invention relates to the technical field of information, and particularly discloses an online mall platform intelligent management method and system, and the method comprises the following steps: S1, constructing a data collection system, and obtaining platform operation data, user behavior data and external market data in real time; s2, acquiring historical sales data, analyzing the historical sales data by adopting a Prophet time sequence model, predicting a future sales trend and inventory requirements, and generating a dynamic replenishment suggestion; s3, optimizing inventory management through a dynamic programming algorithm, calculating an optimal inventory threshold value in combination with external market data, and performing automatic replenishment triggering; s4, establishing a user portrait according to the user behavior data, and generating a personalized coupon strategy based on a collaborative filtering algorithm; s5, dynamically matching the work order and the customer service according to a multi-attribute decision algorithm; and S6, storing quality assurance information, and ensuring non-tampering and traceability of construction time, material batches and technician ID data. Therefore, the operation efficiency and the user experience of the shopping mall platform are improved.

The invention provides an STGAT-informer-based charging scheduling method and an STGAT-informer-based charging scheduling device in an inter-city car-pooling task of an electric car, and relates to the technical field of inter-city electric network car-hailing charging scheduling. Constructing a double-layer optimization model, wherein the upper layer aims at minimizing the vehicle charging cost, and the lower layer aims at maximizing the new profit sum at multiple scheduling moments; then, each charging station is mapped into an independent agent, real-time and implicit associated features of the vehicle and the charging station are requested by utilizing dynamic bipartite graph convolution and multi-head graph attention fusion, a ProbSparse attention mechanism of an Informer network is introduced to construct a centralized reviewer, and multi-agent collaborative decision making is realized; in each charging scheduling interval, charging stations are recommended in advance for vehicles which are about to complete inter-city journeys in the future, and car-pooling order matching is synchronously executed, so that the empty driving and queuing waiting time of the vehicles is remarkably shortened, and the inter-city electric network car-hailing transport capacity utilization rate and the platform income are improved.

The invention relates to the field of traffic safety, and discloses an online car-hailing risk order monitoring method and device, which are used for solving the problem that data acquisition is difficult when a large amount of labeled data is needed to train a model, and the method comprises the steps: obtaining order data in an online car-hailing platform within nearly 180 days through an API interface, the order data comprises order information, recording information and track information, according to the order information, order data feature engineering is carried out, a first type of order features is constructed, the recording information is processed, a second type of order features is constructed, the track information is processed, and a third type of order features is constructed; according to the first type of order features, the second type of order features and the third type of order features, network model training is performed, the accuracy of the model is calculated, and the constructed model is used for data prediction and abnormal order recognition, so that tasks are effectively simplified, the calculation complexity is reduced, and the processing speed and the accuracy of abnormal order judgment are improved.