413 results about "Data feed" patented technology

The invention discloses a cold chain warehouse unmanned aerial vehicle obstacle avoidance and path planning checking method and system based on AI intelligence, and belongs to the field of intelligent obstacle avoidance, and the method comprises the steps: arranging different types of sensors used for sensing the environment change of a cold chain warehouse in the cold chain warehouse, the edge gateway integrates different types of sensors, preprocesses the collected data, and sends the processed data to a cloud database; constructing a digital twin model of the target cold chain warehouse and an intelligent inventory path planning algorithm in the cloud database; the cloud sends a path scheme output by the intelligent inventory path planning algorithm to the unmanned aerial vehicle terminal and the digital twin model at the same time; the unmanned aerial vehicle terminal completes a checking task according to the path scheme, and the digital twin model is updated in real time according to the path scheme and data fed back by the unmanned aerial vehicle terminal. According to the invention, it can be ensured that the unmanned aerial vehicle efficiently and safely performs inventory tasks in a complex cold chain warehouse environment.

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 hybrid multi-agent orchestration system and methods is proposed where one or more multi-domain primary computational conversation agents are configured to interoperate with a plurality of secondary domain specialized computational conversation agents in query handling. The hybrid terminology refers to a hybrid of primary computational conversation agents and a plurality of secondary domain specialized computational conversation agents that are coupled together in a specific computer architecture designed to enhance the computing capability of both types of agents working together in an effort to improve the accuracy and relevancy of the generated results, albeit at a significantly increased complexity and computational processing cost. The secondary agents can include trained machine learning models having a diversity of data feeds and machine learning architectures.

The invention discloses a tower crane path intelligent planning and dynamic control method and system in bridge construction and a medium, and belongs to the field of bridge construction.The method comprises the steps that a bridge construction digital twinborn model is constructed and used for describing the state and position of a tower crane; the construction progress data is incorporated into the constructed digital twinborn model, the operation task and the hoisting target of the tower crane are synchronously updated according to a construction progress plan, and the state of the tower crane is adjusted in the digital twinborn model according to the construction progress; an optimization control system is constructed in combination with a tower crane path planning algorithm, and the change of the surrounding environment of the tower crane is obtained in real time through a sensor real-time data set digital twin model, so that the digital twin model can reflect the relative position between the tower crane and an obstacle; according to the relative position between the tower crane and the obstacle and the data fed back by the sensor in real time, if the position of the tower crane deviates from a preset path or potential collision risks occur, the movement direction of the tower crane is automatically adjusted or the tower crane decelerates. The collision risk can be effectively avoided.

A system and a device for active threat monitoring in a user worn helmet is provided. The system comprises an Internet of Things (IoT) cloud server having an IoT database. The IoT database is configured to generate virtual data feed. The system comprises a head mounted wearable device. The head mounted wearable device comprises a plurality of physical sensors configured to sense and generate at least one of visual feed and radar feed and a multimodal communication interface. The system comprises a user database designated to store user profile data. The system comprises an environment monitoring unit configured to map an environment based on at least of one of the visual feed, the radar feed, and the virtual data. The system comprises an awareness unit configured to generate an environmental context based on position of a user. The system comprises a controller. The controller is configured to receive the user profile data from the user database, the mapped environment from the environment monitoring unit, the environment context from the awareness unit, and the virtual data feed from the IoT database; analyse the user profile data, the visual feed and the radar feed, the environment context, and the virtual data feed to predict occurrence of threat based on the analysed data; and warn the user about a threat based on the predicted occurrence of the threat via the multimodal communication interface.

Systems and process for identifying anomalies and changes in supply chain networks, in real-time, are disclosed. The system leverages machine learning models trained on change point detection algorithms and historical supply chain data to detect supply chain network anomalies in real-time. The system can be operatively connected to, and receive data feeds from, supply chain entities and clients. The system can provide the received data feeds to the machine learning models to detect specific points in time at which a change occurs in a supply chain network. In response to identifying anomalies and changes in supply chain networks, the system can generate alerts or modification recommendations for one or more entities or clients in a supply chain network to mitigate against network inefficiencies resulting from the identified anomalies and changes in the supply chain network, or the system can automatically override inventory management configurations in the supply chain network.

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 discloses an electric power marketing digital inspection method and system, and relates to the technical field of electric power marketing. According to the main technical scheme, the method comprises the steps of obtaining real-time power data of a designated area, wherein the real-time power data comprises real-time power consumption data fed back by an intelligent electric meter and real-time power transmission data fed back by a sensor installed at a key node of a power grid; obtaining an inspection result corresponding to the real-time power data by using a preset inspection model, wherein the inspection model is used for identifying a potential abnormal behavior corresponding to the real-time power data based on the feature representation of the real-time power consumption data and the real-time power transmission data; and if the potential abnormal behavior exists in the inspection result, triggering a corresponding power adjustment measure according to a behavior type corresponding to the potential abnormal behavior. The main purpose is to improve the accuracy of power marketing inspection and reduce power loss. The method and the device are used for performing digital inspection on power marketing.

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 is suitable for the technical field of GPU testing, and provides a software and hardware combined GPU automatic testing method and system, which comprises a hardware platform and a software platform, and is characterized in that the software platform is used for setting a voltage source output voltage value, power-on time and power-off time of the hardware platform, obtaining current, voltage and temperature data fed back by the hardware platform, and sending the current, voltage and temperature data to the hardware platform; associating the data with the automatic test GPU performance, and establishing a GPU test fault case database; and the hardware platform is used for providing power for the GPU, controlling power-on and power-off of the GPU and a power supply voltage value of the GPU according to the setting of the software platform, reading real-time current, voltage and temperature data near a GPU pin and feeding back the real-time current, voltage and temperature data to the software platform. According to the method, automatic testing of the GPU is achieved in a software and hardware combination mode, data graphical analysis can be achieved, abnormal problems occurring in the testing process can be positioned by comparing analysis results, and the GPU detection efficiency is improved.

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.

The invention relates to the field of power grids, in particular to a processing method and device for a digital twinborn model of a cable facility, and the processing method comprises the steps: obtaining the satellite original observation data of the cable facility, and obtaining the initial coordinates of the cable facility through differential GPS calculation; transmitting the initial coordinates to a satellite base station, and combining coordinate information recorded by the satellite base station with the received information of the initial coordinates to obtain coordinate correction data; according to the coordinate correction data fed back by the satellite base station, correcting the initial coordinates by adopting a least square method to obtain coordinate values higher than a precision threshold value; according to the coordinate values higher than the precision threshold value, a preset database is inquired, and a digital twin model of the range threshold value of the position where the cable facility is located is obtained.

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 invention relates to the technical field of laser processing, in particular to a polyhedron cutting path generation method, device and equipment and a storage medium, and the method comprises the following steps: acquiring reference line information on a blank to be processed, and acquiring point cloud data fed back by a line scanning module; based on the reference line information, realizing alignment between the point cloud data and the to-be-processed blank to obtain alignment data; obtaining a finished product effect drawing and finished product related parameters, and confirming tangent point cloud data of each facet based on the finished product related parameters and the alignment data; according to the cloud data of the points to be tangent of each facet and the finished product effect drawing, determining a cutting surface corresponding to the facet, and generating a cutting path corresponding to the cutting surface; the method disclosed by the invention is used for realizing laser processing of the special-shaped polyhedron, the point cloud data corresponding to the to-be-processed blank is obtained through the line scanning module, the point cloud data can provide multi-angle and high-density surface information related to the special-shaped polyhedron, and the accuracy and reliability of a subsequently generated cutting path 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.

Described are techniques for sharing a media item to a video conference session. The techniques include receiving an instruction to share the media item to one or more other user devices participating in the video conference session. The techniques further include capturing an independent data feed for the media item that is separate from a video conference data feed. The techniques further include sending the independent data feed to the one or more other user devices using a messaging protocol that identifies the independent data feed for the media item as being separate from the video conference data feed. The identifying of the independent data feed as being separate from the video conference data feed enables the one or more other user devices to provide the independent data feed in an application window that is independent of a video conference application window of the video conference session.

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.