332 results about "Data feed" patented technology

The invention discloses an unmanned forklift dynamic task scheduling method and system based on deep reinforcement learning of a cold chain warehouse, and belongs to the field of unmanned forklift scheduling. The method comprises the steps that a sensor is arranged in a target cold chain warehouse, environment data of the target cold chain warehouse is collected, and the environment data of the target cold chain warehouse is acquired; constructing a digital twinborn model of the target cold chain warehouse at the cloud based on a digital twinborn technology; a dynamic environment state of the target cold chain warehouse is constructed according to data fed back by the sensor, an initial unmanned forklift track is generated based on a path generation algorithm, the initial unmanned forklift track is optimized by using a multi-feature coupled space-time joint path planning algorithm deployed at a cloud end, and an optimal path and scheduling plan are generated; and the cloud end issues the instruction to the edge gateway, and the edge gateway decomposes the instruction and issues the decomposed instruction to each unmanned forklift. According to the invention, the optimization effect of the scheduling scheme is improved.

A system and method for predictive analysis, scenario simulation, and decision optimization is provided. The system includes a prediction management system executed on a distributed computing infrastructure, and a prediction engine configured to receive input data, including event parameters, user-defined constraints, real-time data feeds, and historical trends. The prediction engine generates predictive models using algorithms trained on historical event outcomes, assigns probability scores and confidence intervals to potential outcomes, and dynamically updates the models based on new input data. Actionable insights are generated and ranked according to predefined success criteria. A non-transitory computer-readable medium is used to store the predictive models, outcome probabilities, and actionable insights for subsequent analysis and reporting. This system facilitates enhanced decision-making by offering real-time insights and continuously refined predictions, thereby optimizing responses to complex events and scenarios.

A system for converting a source data feed schema into a canonical data product including a memory for storing computer-executable instructions and a processor for executing the instructions stored on the memory. Execution of the instructions programs the processor to perform operations that include receiving a source data feed having a source schema, identifying a plurality of data fields of the source schema, assigning a data category from a plurality of predefined data categories to each data field of the plurality of data fields, modifying the source schema based on predefined parameters, wherein the source schema is modified to match a target schema, comparing the modified source schema to the target schema, and in response to a determination that the modified source schema matches the target schema, converting the source data feed to a canonical data product having the target schema based on the assigned data categories.

The invention, which relates to the technical field of energy management, discloses a multi-target collaborative optimization prediction system and method for regional-level water-wind-light power generation power, and the system comprises a data collection module which is used for obtaining power generation power data, meteorological environment data and power grid load demand data in real time; the optimization processor is used for receiving the data fed back by the data acquisition module, and constructing a multi-target optimization model based on spatial-temporal feature extraction to generate a plurality of candidate optimization paths; the screening module is used for screening the candidate paths through a dynamic weight distribution algorithm and screening out an optimal collaborative optimization path; and the dynamic feedback module is used for carrying out online correction on the optimal collaborative optimization path in the real-time optimization process according to the power grid frequency deviation, the energy storage charge state and the meteorological abrupt change signal. The optimization processor constructs the multi-target optimization model based on the spatial-temporal characteristics, generates a plurality of candidate optimization paths and screens out the optimal path, and compared with a traditional single energy prediction and simple scheduling mode, the precision of power generation power prediction is improved.

The invention belongs to the field of artificial intelligence, can be applied to the field of finance, and relates to a product recommendation method, which comprises the steps of classifying historical insurance policies according to a preset dimension, and generating a corresponding classification label for each classification category; matching the customer tags of the potential customers with the classification tags to obtain target classification tags; screening out a target customer group according to the target classification label; inputting the obtained target customer information of the target customer group and the target claim settlement case into a verbal skill recommendation model, outputting a target recommended verbal skill, pushing the target recommended verbal skill to potential customers, and receiving voice data fed back by the potential customers; recognizing the voice data to obtain recognized text data; and matching a target product based on customer demand information extracted from the identified text data, and recommending the target product to potential customers. The invention further provides a product recommendation device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the client tag can be stored in a block chain. According to the method, clients can be accurately positioned, and the product recommendation effect is improved.

The invention relates to the technical field of mechanical arm control, and discloses a multi-degree-of-freedom mechanical arm-based visual identification grabbing method, which comprises the following steps of: acquiring point cloud data of a target object by utilizing a depth camera, and screening the point cloud data to determine a target feature point; the target feature points are converted into mechanical arm base coordinate system pose data; solving an optimal joint angle; acquiring tail end pose data fed back by the depth camera in real time, and calculating a joint angle correction amount based on a preset formula and the tail end pose data; a control signal is output through the PID controller, and the seven-degree-of-freedom carbon fiber mechanical arm is controlled to grab a target object; and whether the seven-degree-of-freedom carbon fiber mechanical arm successfully grabs the target object or not is verified through point cloud collision detection. By implementing the method, the problems that mechanical arm visual grabbing has obvious defects in the aspects of flexibility of degree of freedom, visual perception robustness, control cooperation precision, grabbing verification reliability and the like, and high-precision and high-stability grabbing requirements in a complex environment are difficult to adapt are solved.

A fault-recoverable firmware detection system and method, a storage medium, and a server. The method comprises: acquiring target fault information stored in a fault register, and sending the target fault information to a BMC (201); upon receiving the target fault information sent by a BIOS, the BMC analyzing the target fault information, and determining a fault type (202); when the fault type of the target fault information is a recoverable fault, the BMC controlling a virtual external device to send a target heartbeat request to a server operating system according to a preset sending period, and receiving response data fed back by the server operating system on the basis of the target heartbeat request (203); and within a preset duration during which the BMC sends the target heartbeat request to the server operating system, if an interruption occurs in the response data fed back by the operating system, determining that the server operating system is crashed, and on the basis of the target fault information, determining a faulty component in a server (204).

The invention relates to the technical field of dynamic price adjustment of electronic price cards, and discloses a dynamic price adjustment strategy optimization method for electronic price cards based on machine learning, and the method comprises the steps: constructing a multi-source data sensing network, and collecting historical transaction data, competitive product price information and user interaction behavior data; generating a multi-dimensional composite feature set covering time, commodity attributes and market environment through feature engineering processing; training a dynamic price adjustment prediction model by using the feature set, and outputting an initial price adjustment strategy parameter table; calculating a strategy deviation value and triggering a correction mechanism based on sales data fed back by the electronic price tag in real time; and synchronizing the optimized price adjustment strategy to the electronic price card terminal, and updating the training sample library to continuously improve the model. According to the invention, data-driven intelligent price adjustment is realized, the accuracy, the real-time performance and the adaptability of a price strategy are remarkably improved, and the problems that a traditional price adjustment method is lagged in response and depends on artificial experience are effectively solved.

The embodiment of the invention relates to the technical field of testing, and discloses a system-on-chip debugging system and method. The test system comprises a debugging host, at least one communicator, at least one message facility and at least one analysis module, each analysis module is sequentially connected with at least one message facility and one communicator in series and then is connected with the debugging host to form a debugging channel between the debugging host and each analysis module; and the debugging host sends debugging information to each analysis module through each debugging channel so as to control each analysis module to carry out debugging tracking on the kernel module, and receives debugging tracking data fed back by each analysis module through each debugging channel. According to the scheme, the analysis modules suitable for different types of kernel modules can be conveniently integrated, the working state of each kernel module on the system-on-chip is comprehensively monitored, the real-time performance of a debugging tracking function is ensured, and the expandability and convenient migration of the debugging system are ensured along with upgrading iteration of the system-on-chip.

The invention relates to the technical field of data analysis, and provides a CAN bus waveform message analysis method, device and equipment and a medium, and the method comprises the steps: issuing an operation instruction carrying a preset operation parameter to an oscilloscope; in response to waveform data fed back by the oscilloscope, storing the waveform data as a message file in a preset format; analyzing and converting the message file according to the data head structural body identified by the oscilloscope to obtain a message array; performing time domain segmentation, normalization processing and de-noising processing on the data in the message array by using a Numpy library to obtain a message signal; and performing error frame screening on the message signal by adopting a strategy of supplementing one after five, and performing layer-by-layer analysis on the screened message signal based on a CAN protocol structure to obtain message analysis content. According to the invention, through the software design of the whole process, a universal scheme is adopted from data acquisition, storage and analysis to signal processing and message generation, so that the analysis universality of the CAN bus waveform message is effectively improved.

The invention relates to a smart home equipment control method and system based on QUIC point-to-point communication, and belongs to the technical field of smart home control, and the control method comprises the steps: receiving a natural language instruction of a user terminal, and carrying out the analysis to obtain a target equipment identifier and an operation intention description text; retrieving a pre-constructed equipment registry according to the target equipment identifier, and obtaining a corresponding cue word template and a QUIC connection address; calling an artificial intelligence model deployed locally, and outputting a structured equipment control instruction in combination with the operation intention description text and the cue word template; performing signature encryption on the structured equipment control instruction, establishing a QUIC end-to-end encryption transmission channel between the user terminal and the target equipment, and transmitting the QUIC end-to-end encryption transmission channel to the target equipment; receiving operation response data fed back by the target equipment after the target equipment verifies and executes the structured equipment control instruction; the device registry is dynamically optimized based on the operational response data. According to the invention, the risk of cloud service privacy leakage can be reduced, and the data security of the smart home system is improved.

Some embodiments of the present application include obtaining a trained machine learning model that was trained using training data. Production data may be obtained from a data feed selected based on the trained machine learning model. In some embodiments, feature sets and observed results may be extracted from data output from the data feed, and one or more features or results may be masked to generate the production data. Predicted results data may be generated with the trained machine learning model based on the production data and an accuracy score for the trained machine learning model may be determined based on the predicted results data. If the accuracy score satisfies a threshold accuracy condition, the trained machine learning model may be caused to be rebuilt or the training data may be caused to be updated.

In accordance with some embodiments, systems, apparatus, interfaces, methods, and articles of manufacture are provided for providing information incorporating additional data feeds, creating common arrangements, and improving performance in a virtual meeting. In some embodiments, a video conferencing system hosts a customized video conferencing and includes video conference settings data, participant display device data, supplemental data feed data, and processing instructions. The video conferencing system may identify (based on the video conference settings data) a number of data feeds for display on a display screen of each participant in a video conference and (based on the participant display device data) a screen specification for each display screen of each participant. The system may then compute an arrangement for the number of data feeds that fits within an available display area.

Aspects of the disclosure relate to using machine learning models to automatically prioritize and orchestrate spatial data. A computing system may generate, based on inputting scene data and spatial positioning data into a machine learning model, a prioritized context that indicates physical objects that are significant within a physical environment. The scene data may comprise images of the physical environment and the spatial positioning data may comprise an indication of states of a user in the physical environment. Based on the prioritized context meeting user criteria, generate indications associated with the prioritized context. Based on the scene data, one or more spatial anchors at which to position the one or more indications of the prioritized context within a virtual environment based on the physical environment may be generated. The virtual environment comprising the indications of the prioritized context positioned at the one or more spatial anchors may be generated.

A computer-implemented method for detecting and managing burnout risk in a contact center associated with an enterprise. The method includes (a) receiving, by at least one computing device, real-time data associated with each of a plurality of agents servicing incoming communications at the contact center associated with the enterprise, wherein the real-time data is a data feed from one or more of a communication distributor server, a workforce management server, or a back office case system server, (b) determining, by the at least one computing device, a relative burnout risk for each of the plurality of agents by processing the real-time data associated with each of the plurality of agents using a trained supervised machine learning model having a plurality of input features, wherein the plurality of input features includes at least one operational metric related to one or more of the communication distributor server, the workforce management server, or the back office case system server, (c) determining, by the at least one computing device, based on a specification of a logical directive, an action to be executed by a server in relation to at least one agent in the plurality of agents, wherein the specification has at least one condition relating to the determined relative burnout risk, and wherein the specification defines the action to be executed in relation to the at least one agent in the plurality of agents upon the condition being satisfied, and (d) sending, by the at least one computing device, an instruction to the server to cause the server to execute the action in relation to the at least one agent instance in the plurality of agents. A computing system and article of manufacture are also provided.

The invention discloses a 10kV overhead line adaptive evaluation method and system for non-power-cut operation, and the method comprises the following steps: obtaining the field data of a target line, and constructing the field data into a multi-dimensional evaluation feature vector according to a preset evaluation dimension system for non-power-cut operation; inputting the multi-dimensional evaluation feature vector into a non-power-cut operation adaptability evaluation model to obtain a comprehensive evaluation score and an optimization suggestion of the target line; outputting the comprehensive evaluation score and the optimization suggestion; wherein the non-power-cut operation adaptability evaluation model is constructed by the following steps: training to obtain an initial machine learning model; simulating a preset non-power-cut operation task flow in the digital twinborn body; and performing incremental training and parameter optimization on the initial machine learning model by using operation effect data fed back after actual operation of the simulation data vector. The problems of single evaluation dimension, model stiffness and the like in the prior art can be solved.

The invention discloses an electric power inspection operation robot anti-misoperation system and method based on video analysis, and relates to the technical field of electric power equipment, and the system comprises an inspection robot which is used for collecting first real-time state data of to-be-operated equipment, and executing an operation instruction issued by a user; the video acquisition module is used for acquiring second real-time state data of the equipment to be operated; the data acquisition and monitoring control module is used for acquiring third real-time state data of the equipment to be operated; and the main control module is used for judging whether equipment state data fed back by the first real-time state data, the second real-time state data and the third real-time state data are consistent or not, and controlling the inspection robot to execute a corresponding operation instruction when the equipment state data are consistent. The situation of misoperation of the inspection robot is avoided, and therefore stable operation of a power system is guaranteed.

The invention relates to the technical field of intelligent traffic and smart city waste management, and discloses a path planning method and system for a garbage transport vehicle based on multi-source data, and the method comprises the steps: generating a reference path based on historical collection and transportation data, the method comprises the following steps: performing data cleaning on pre-acquired garbage quantity data, vehicle state data, real-time traffic data, hydroelectric data and fuel consumption data, performing multi-source time sequence feature fusion anomaly detection on a cleaned data set, performing trajectory inversion on the cleaned data set, performing target constraint through a mixed integer programming model, and performing multi-source time sequence feature fusion anomaly detection on the cleaned data set. Then gradient descent parameter optimization is carried out on the initial optimization model, elastic resource scheduling is carried out on an optimized path scheme based on a large event plan library, a path instruction set is generated, dynamic re-optimization evaluation is carried out on the path instruction set based on garbage amount change and traffic condition data fed back in real time, and an updated path is generated; according to the invention, the path planning efficiency of the garbage transport vehicle based on the multi-source data can be improved.

The embodiment of the invention discloses a data desensitization method and device, electronic equipment and a storage medium, and the method comprises the steps: transmitting a first desensitization model to at least one data producer corresponding to a data acquisition request when the data acquisition request sent by a data consumer is received; receiving first desensitization data fed back by at least one data producer, and performing secondary desensitization processing on the first desensitization data to obtain target desensitization data; wherein the first desensitization data is data processed through a first desensitization model; the target desensitization data is fed back to a data consumer, the highly-imitated data subjected to secondary desensitization processing can simulate a real business scene and finely describe business characteristics, the privacy of production data can be protected, the contradiction between data availability and privacy protection is balanced in a cross-mechanism and cross-industry data sharing scene, and the privacy protection efficiency is improved. And the security and availability of cross-mechanism and cross-industry data interaction are improved.

A system and method for enhancing data feed using a machine learning (ML) model are disclosed. In some embodiments, the method includes receiving multimodal data associated with a plurality of data items and providing, from the received multimodal data, a set of multimodal data samples to the ML model, each multimodal data sample associated with two or more modalities. The method also includes training the ML model using the set of multimodal data samples by optimizing a similarity value computed for each multimodal data sample based on whether the multimodal data sample is associated with a same data item or from different data items. The method further includes receiving new data associated with a new data item, the new data including one or more data components to be enriched, and automatically populating the one or more data components using the trained ML model.

A system for automated real-time publication processing may comprise a data collection module configured to automatically gather data from multiple external sources. A data analysis module may be configured to process the gathered data using artificial intelligence models. A data visualization module may be configured to generate interactive visual representations of the processed data. An update control module may be configured to automatically update published content and maintain version history with timestamps. The data collection module may utilize application programming interfaces and web scraping tools to gather data from government databases and real-time data feeds. The data analysis module may employ machine learning libraries to process and analyze the collected data. The data visualization module may use visualization tools to create interactive charts and graphs. The update control module may use Git-based version management and automated scheduling scripts to implement updates and record modification dates.

An integrated system for generating innovative solutions is disclosed. The system comprises a literature review AI agent, a knowledge graph, and a TRIZ Module. The AI agent continuously analyzes diverse information sources, feeding data into the knowledge graph. The knowledge graph organizes and connects information, identifying patterns and relationships. The TRIZ Module interacts with both components to identify contradictions and generate innovative solutions. This system accelerates innovation by processing vast amounts of information, recognizing cross-disciplinary patterns, and applying systematic problem-solving techniques.

Techniques for automatically determining metadata for fields of a data string, byte slice, or byte array using a semantic data model framework (SDMF) and a large language model (LLM) are discussed herein. The LLM can provide field descriptions to the SDMF which outputs additional or finer field descriptions. The techniques can include determining descriptions for fields of a non-standardized data string from a third-party or other entity thereby enabling analysis of third-party data strings for a potential security threat. The techniques can reduce an amount of time to identify missing metadata caused by lack of standardization of field names and evolving data feeds (e.g., third-parties).

This invention discloses a method and system for dynamic task scheduling of unmanned forklifts based on deep reinforcement learning in cold chain warehouses. This method belongs to the field of unmanned forklift scheduling and includes: placing sensors in a target cold chain warehouse to collect environmental data of the target cold chain warehouse, and constructing a digital twin model of the target cold chain warehouse in the cloud based on digital twin technology; constructing the dynamic environmental state of the target cold chain warehouse based on the data fed back by the sensors, and generating an initial unmanned forklift trajectory based on a path generation algorithm; optimizing the initial unmanned forklift trajectory using a multi-feature coupled spatiotemporal joint path planning algorithm deployed in the cloud to generate an optimal path and scheduling plan; and finally, the cloud sends instructions to an edge gateway, which decomposes the instructions and sends them to each unmanned forklift. This invention improves the optimization effect of the scheduling scheme.

A system and method are disclosed for aggregating product trends. Embodiments include receiving an initial set of images from one or more data feeds by a trend aggregation system comprising a server, identifying one or more attribute values from the initial set of images, and quantifying a social affinity score of one or more items of an inventory of a supply chain entity based on recentness, relevance, and similarities of the identified one or more attribute values to an attribute value of a potential product for a product assortment. Embodiments may further include receiving an image of at least one additional item, identifying a product attribute from the image, and assigning an attribute value to the at least one additional item based, at least in part, on the identified attribute value from the image of the least one additional item and the attribute values of the at least two items.

A multi-stage deep learning method is provided, comprising training a deep learning network using a set of training pairs interchangeably including input data from a first noisy dataset with a first noise level and target data from a second noisy dataset with a second noise level, and input data from the second noisy dataset and target data from the first noisy dataset; generating an ultra -low noise data equivalent based on a low noise data fed into the trained deep learning network; and retraining the deep learning network on the set of training pairs using the target data of the set of training pairs in a first retraining step, and using the ultra-low noise data equivalent as target data in a second retraining step.

The invention discloses a target address information obtaining method and device, equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: processing multiple kinds of historical load evaluation index data fed back by each slave node obtained in advance, and obtaining a historical load evaluation tensor; inputting the historical load evaluation index tensor corresponding to the ith slave node into a pre-constructed load prediction model to obtain a predicted load evaluation index tensor; determining a single-value load index according to the historical load evaluation index tensor and the predicted load evaluation index tensor; selecting a preset number of target slave nodes from the plurality of slave nodes according to the single-value load index, and obtaining target address information; and feeding back the target address information to the client. By predicting the future load condition and pre-judging the node load change in advance, the flow is prevented from being guided to the node which is about to face high load or the node which is about to be fully occupied by temporary activities such as backup tasks, so that the queuing flood peak is reduced, and the overall performance of the system is improved.

A computing system for applying post-authorization modeling tools to a payment transaction for enhancing a decision intelligence (DI) score is disclosed. The computing system is configured to: (i) build a pre-authorization and a post-authorization model to analyze backward velocities and forward velocities; (ii) receive an authorization message for a current transaction; (iv) input into the pre-authorization model current transaction data and the backward velocities for the current transaction to output a DI score; (v) based upon the DI score, authorize the current transaction; (vi) continuously monitor a data feed for a predefined period of time after authorizing the current transaction; (viii) update a data register to include the forward velocities for any post-authorization transactions received during the predefined period of time; (ix) update the DI score for the current transaction by inputting the forward velocities into the post-authorization model to transmit the updated DI score to the merchant.

A threat management facility for an enterprise network integrates native threat management capabilities with threat data from a cloud service provider used by the enterprise. By properly authenticating to the cloud service and mapping data feeds from the cloud service to a native threat management environment, the threat management facility can extend threat detection and management capabilities beyond endpoint-centric techniques.

The invention provides a large-model multi-agent interaction coordination system, method and server, and relates to the technical field of agent coordination control, the large-model multi-agent interaction coordination system provided by the invention comprises a user access layer, an agent service layer, a protocol layer, a capability layer and a data layer; request information can be obtained through a user access layer; the intelligent agent service layer comprises an intelligent agent dispatching center and a plurality of intelligent agents communicating with the intelligent agent dispatching center, and the plurality of intelligent agents exchange data through the protocol layer; the agent service layer carries out instantiation decomposition on the request information to obtain a subtask agent to execute a subtask and generate feedback task data; the task data fed back by each agent is summarized to obtain the feedback information corresponding to the request information, and the feedback information is displayed in the user access layer, so that the whole system can realize efficient collaboration and cooperation of multiple agents, the ability of the whole system to deal with complex tasks is improved, and the problem solving efficiency is greatly improved.