65 results about "Prospective data" patented technology

The embodiment of the invention provides a memory controller, an error address positioning method and a computing system. The memory controller comprises an algorithm control circuit, a data generation circuit, a data comparison circuit and a monitoring circuit, and the algorithm control circuit is configured to generate control information for memory testing based on preset steps of a test algorithm; the data generation circuit is configured to generate test data used for being written into the memory to be tested and expected data obtained after the test data is read; the data comparison circuit is configured to detect whether target data read from the to-be-detected memory is consistent with expected data or not; and the monitoring circuit is configured to respond to the inconsistency of the read target data and the expected data, send a mark for stopping the memory test to the algorithm control circuit so as to stop the memory test and record a test address corresponding to an error and corresponding command data, so that stop and quick positioning are realized when the error occurs, and the repair efficiency is improved.

The invention belongs to the technical field of machine room environment control, and particularly relates to an air conditioner and fresh air control method and system based on an enthalpy value. The method comprises the steps that a prediction model obtains prospective data according to obtained equipment layer and environment layer data and historical data; generating reference judgment data according to the look-ahead data and the first real-time data; constructing a mode instruction according to the reference judgment data and the second real-time data; and generating an execution instruction according to the mode instruction and the third real-time data, controlling an air conditioner and fresh air by the execution instruction, and feeding back the data in real time to adjust parameters of the prediction model. According to the system, linkage between equipment and real-time interaction of data are achieved through connection, and a complete closed loop covering'perception-prediction-decision-execution-correction 'is formed. Through the complete process, intelligent collaborative operation of the machine room air conditioner and the fresh air system is finally achieved.

A method includes receiving a query to determine anomalies in a set of multivariate time series data values including an endogenous variable and an exogenous variable. The method includes determining an impact of the exogenous variable on the endogenous variable. The method includes determining a set of univariate time series data values and training one or more models using the univariate time series data values. The method includes determining an expected data value for a respective time series data value and determining a difference between the expected data value and the respective time series data value. The method includes determining that the difference between the expected data value for a particular time series data value and the particular time series data value satisfies a threshold. In response, the method includes determining that the particular time series data value is anomalous and reporting the anomalous value to a user.

The present invention relates to the technical field of control systems. A PID-based ROV motion control system and method provided by the present invention solve the deficiencies of existing PID control algorithms in the control of complex ROV systems. The system includes data acquisition, processing, detection, model matching, and optimization modules. By acquiring historical data and constructing an algorithm model knowledge base, it can match environmental data in real time to select the optimal control model, send control commands, and collect ROV operation data. If the real-time data is inconsistent with the expected data, a genetic algorithm is used to optimize the control model to generate optimized control commands. The present invention improves the control accuracy and environmental adaptability of the ROV, and enhances the system robustness and operation efficiency through dynamic optimization of the control strategy.

A data flow is intercepted, the intercepted data including a set of data fields carrying a corresponding set of content. An intent is identified behind a transaction including the intercepted data. A conformity indication is set relative to a first data field in the set of data fields to indicate nonconformity of a first content of the first data field, the setting including comparing the first content with expected data corresponding to a mapping of the intent in a knowledge graph. Based on the conformity indication, the first content is modified to form modified content. In the transaction in the data flow, the first content is replaced with the modified content, the replacing forming a modified transaction. The modified transaction is transmitted to a receiver data processing system over a data integration channel.

A method performed by a user equipment (UE), the method comprising: receiving, from an access network node, a measurement configuration for requesting information regarding expected data communication with the access network node; and transmitting, to the access network node, a measurement report including the information, the information being used to output at least one parameter using a model for energy saving.

The invention discloses a labor capability allocation method and device based on a dynamic weight, a medium and a product, and the method comprises the steps: obtaining the complexity and personnel skill data of each task in response to a project labor capability allocation request; based on the dynamic coupling model, calculating the real-time matching degree of the personnel and the tasks in combination with the dynamic weight factors corresponding to the tasks; then, according to a matching result and multi-dimensional constraints such as project cost and time, a multi-objective optimization algorithm is adopted to generate an optimal labor force and energy distribution scheme and expected progress data; after the scheme is executed, the actual progress of the project is monitored in real time and compared with expected data; if the deviation exceeds a set threshold value, a feedback mechanism is triggered, the personnel skill data and the dynamic weight factor are updated, the matching and optimizing process is restarted, and closed-loop self-adaptive management of the whole period of the project is achieved until the project is completed, and the technical scheme of the embodiment of the invention remarkably improves the accuracy of resource allocation and the self-adaptive capacity of project management.

Techniques for determining one or more expected categories of data for receipt by a skill or application and comparing the expected categories of data to user disclosed data, such as from a spoken natural language user input. The techniques include determining the spoken natural language user input is directed at an interaction with a particular skill and identifying a set of data categories registered for receipt by the skill. The techniques may further include determining if the skill solicited the disclosed data of the user input or if the user mistakenly provided the disclosed data. In some embodiments, the interaction with the skill may end if the skill is not authorized to receive the disclosed data. In some embodiments, the unsolicited disclosed data may be used to identify misunderstood or confusing requests for user input.

Methods include training a machine learning module to predict one or more target product properties for a prospective chemical formulation, including (a) constructing or updating a training data set from one or more variable parameters; (b) performing feature selection on the training data set; (c) building one or more machine learning models using one or more model architectures; (d) validating the one or more machine learning models; (e) selecting at least one of the one or more machine learning models and generating prediction intervals; (g) interpreting the one or more machine learning models; and (h) determining if the one or more target product properties calculated are acceptable and deploying one or more trained machine learning models, or optimizing the one or more machine learning models by repeating steps (b) to (g). Methods also include application of trained machine learning modules to predict formulation properties from prospective data.

Demand and capacity management system (1), configured as a web-based data service (200) in an automotive network for communication between supplier and customer, comprising: - at least one data management system (2) configured as a cloud-based or central server, - at least one customer device (3) comprising an associated demand and capacity management application (3.3), - at least one supplier device (4) comprising an associated demand and capacity management application (4.3), - at least one first data room connector (3.1) which can be detachably connected or linked to the customer device (3) as a communication interface, - at least one second data room connector (4.1) that can be detachably connected to or is connected to the supplier device (4) as a communication interface, wherein the first data room connector (3.1) is configured to communicate with the data management system (2) via an associated connection and with the supplier device (4) directly via communication channels (6.1, 6.2), and the second data room connector (4.1) is configured to communicate with the data management system (2) via an associated connection and with the customer device (3) directly via the communication channels (6.1, 6.2), wherein the web-based data service (200) is configured to: a) Entering a coupling of at least one of the data room connectors (3.1, 4.1) and receiving a data room connector identifier or network identifier (108) and writing the data room connector identifier or network identifier (108) to the data management system (2), b) Creating a data space connector asset (112) for expected data on this coupled data space connector (3.1, 4.1), c) Receiving an initial request (101) of the expected data directly via the communication channels (6.1, 6.2) between the data space connectors (3.1, 4.1), d) Comparing the expected data with relevant internal data, e) based on the matching results, creating a second requirement, f) optionally retrieve from the data management system (2) a network identifier (108) of the receiving data room connector (3.1, 4.1), g) Transmitting the second request (103) and the associated network identifier (108) of the receiving data space connector (3.1, 4.1) to the coupled data space connector (4.1, 3.1) directly via the communication channels (6.1, 6.2) between the data space connectors (3.1, 4.1), h) Establishing one of the communication channels (6.1, 6.2) between the coupled data space connector (3.1, 4.1) and the receiving data space connector (3.1, 4.1) and i) Sending the second request (103) to the receiving data room connector (3.1, 4.1).

Systems and methods are disclosed for software security analysis. In certain embodiments, a method may comprise identifying a set of potential security vulnerabilities, comparing the potential vulnerabilities to a catalog identifying elements on a client system, and generating risk mitigation advice based on potential security vulnerabilities matching an element in the catalog. The method may include evaluating a data field of a service request to a third party to determine an expected data type for the field, determining whether the actual data included in the service request matches the expected data type, and quarantining the service request when the actual data does not match the expected data type. The method may comprise operating as a proxy for the service request, including receiving the service request from the client system, evaluating the service request for irregularities, and forwarding the service request to the third party once the irregularities have been addressed.

The invention relates to a regression test method and system for position information release and electronic equipment, and the method comprises the steps: obtaining a plurality of groups of test case data suitable for a target position based on a preset test case data set, the test case data comprising legal user identity data, test data and expected data; performing simulation release of target position information based on the test data of each test case; release response data of multiple dimensions are obtained, and verification is carried out based on the expected data of each test case in each dimension and the release response data; and recording data generated during simulation release of the target position information, release response data of multiple dimensions and verification data, and generating a regression test report for release of the target position information based on the recorded data. According to the invention, the test efficiency is improved, the manual labor amount is reduced, the test accuracy is high, the coverage is wide, and the expandability is high.

Systems and methods are disclosed related to triggering of data collection for User Equipment (UE)-side Artificial Intelligence (AI) / Machine Learning (ML) models. In one embodiment, a method performed by a UE for UE-side data collection for training, retraining, or fine tuning a UE-side AI / ML model comprises transmitting, to a network nod, information related to one or more UE-side conditions related to an expected quality of data samples that can be collected by the UE. The method further comprises receiving, from the network node, an indication to start data collection at the UE using a predefined, preconfigured, or indicated radio resource configuration or an indication to send a request to the network node for a radio resource configuration for data collection for training, retraining, or fine tuning the UE-side AI / ML model.

In an approach for managing data continuity of a data storage system, a processor analyzes queries received by the data storage system from a plurality of data sources. A processor identifies a query frequency and response time for queries from each data source of the plurality of data sources. A processor derives an expected data pipeline continuity requirement for each data source based on respective query frequency and respective response time for the respective queries from each data source. A processor learns patterns of the analyzed queries to enable predictions for when future queries from each data source will occur and identification of an unavailability pattern for each data source. A processor determines recommendations for maintaining data continuity for the data storage system based on the expected data pipeline continuity requirement for each data source, the query predictions, and the unavailability pattern for each data source.

The invention provides a test method of an expansion card, electronic equipment and a test system. The test method comprises the following steps: configuring a configuration file according to instruction contents of a controller which can be received by the expansion card and expected data contents corresponding to the instruction contents; generating a test instruction according to the configuration file; sending a test instruction to the expansion card; receiving return data returned by firmware of the expansion card in response to the test instruction; and testing whether the expansion card successfully communicates based on the returned data. And according to the instruction content configuration of the controller which can be received by the expansion card and the expected data content configuration file corresponding to the instruction content, generating a test instruction according to the configuration file, and sending the test instruction to the expansion card. And then, receiving returned data returned by the firmware of the expansion card in response to the test instruction, and finally, testing whether the communication of the expansion card is successful based on the returned data.

A system for mitigating biases in a training dataset for a machine learning model is disclosed. The system determines that the training dataset is biased based on determining that the training dataset is missing at least one expected datapoint, a first datapoint is associated with a first label that is incompatible with the machine learning model, or a second datapoint is associated with an incorrect label compared to a counterpart expected datapoint. In response, the system generated a transformed training dataset by adding the at least one expected datapoint that is missing from the training dataset to the transformed training dataset, changing a first data structure of the first label to a second data structure with which the machine learning model is compatible, or updating a second label of the second datapoint to correspond to a third label associated with the counterpart expected datapoint. The system outputs the transformed dataset.

The invention provides a dust explosion-proof monitoring control method and system, and relates to the technical field of industrial equipment monitoring, and the method comprises the steps: collecting environment information through a dust collection device disposed in a production region, building a safety threshold evaluation standard based on an evaluation report, and building an optimized feedback circulation mechanism in the dust collection device, and early warning of potential explosion risks is completed. According to the method, the potential explosion risk can be early warned, the safety state of the production environment can be automatically adjusted to optimize the management process and improve the production efficiency, and compared with the prior art, the prior art focuses on traditional protection measures or post-event processing and is lack of prospective data analysis capability and comprehensive management optimization function, so that the prior art has the potential safety hazard. The safety coefficient of equipment operation is remarkably improved, and the risk of dust explosion is effectively reduced.

The invention relates to a method for monitoring misuse of a user identification profile, wherein an exchange of data via the communication network between a user and a respective recipient can be carried out in encrypted form, and wherein a data traffic analyst is further provided, which is configured to analyse the data traffic between the user and the respective recipient, wherein the traffic analyst comprises an alarm device for issuing a fraud report, wherein the method comprises at least the following steps in the order mentioned: a. monitoring an exchange of data between the user identified by an individual user identification profile and at least one recipient; b. comparing the monitored actual exchange of data with a maintained expected exchange of data; and c. using the alarm device to issue a fraud alert if the monitored actual exchange of data deviates from the expected exchange. The method proposed here for monitoring misuse of a user identification profile makes it possible to identify fraudulent use of a user identification profile within an IoT application. Furthermore, the corresponding user identification profile can be remotely deactivated or deleted (if necessary).

The invention relates to a time sequence data transmission method and device, equipment and a storage medium. The method comprises the following steps: in the process of receiving time sequence data sent by a sending end, extracting a data logic sequence number from data when receiving one piece of data; determining a data type corresponding to the data according to the data logic sequence number of the data; under the condition that the data type corresponding to the data is expected data, submitting the data to a preset processing unit; under the condition that the data type corresponding to the data is advanced data, storing the data into a buffer slot position corresponding to the data logic sequence number, and updating buffer slot position state data; data completion control is performed based on the buffer slot status data in order to attempt to complete missing data prior to the data. According to the method, dynamic classification, expected data submission and advanced data caching are performed on the data through the data logic sequence numbers, so that the cache resource occupancy rate can be effectively reduced, overall sorting is not needed in the whole process, and meanwhile, the data integrity can be guaranteed.

In some embodiments, the present disclosure is directed to an exemplary system, including: an access controlling platform, executing software instructions that instruct to generate an expectation data record as an access control mechanism to access an access-restricted digital resource, the expectation data record including: access code and identity linked to a computing device; where the access code is associated with the access-restricted digital resource; receive a mobile originating communication having data that includes a particular access code and a particular identity; perform a confirmation of the expectation data record with the mobile originating communication; instruct a cellular communications network to perform, based on at least one part of the data, at least one action with the cellular network hosted access controlling schema to generate a session record associated with the particular identity; and instruct to unlock the access-restricted digital resource for the accessing via the computing device.

To provide a technology for effectively confirming an effect by supervised machine learning, and for delivering a machine learning model in the middle of learning or after learning to another device.SOLUTION: The model setting unit (6) performs setting related to the first chunk of the input data and the second chunk of the teacher data based on the read learning data, and setting related to the third chunk of the test input data and the fourth chunk of the expected data based on the read test data. The model computation unit (5) performs a learning computation using the machine learning model based on the first chunk and the second chunk, performs an inference computation using the machine learning model based on the result of the learning and the third chunk, and performs a comparison computation between the result of the inference and the fourth chunk. The machine learning model after performing at least a part of the learning operation is non-temporarily stored. Each chunk is a mass of data for one time when data is sequentially supplied to the machine learning model.SELECTED DRAWING: Figure 2

Methods and apparatus, including computer program products, are provided for processing analyte data. In some exemplary embodiments, there is provided a method. The method may include selecting at least one module from among a plurality of modules, the selection performed based on metadata including one or more of the following rules: whether the at least one module can be used with a type of device, whether the at least one module can be used with a glycemic state of a host, and whether the at least one module can be used with an expected volume of data generated by the type of device; and generating a report including the selected at least one module configured to present information representative of the glucose concentration level measured in the host. Related systems, methods, and articles of manufacture are also disclosed.

A method for verifying data and control couplings of software under test comprises obtaining software architecture and design information for the software; extracting expected control coupling information based on an order in which software component calls are in the software architecture; extracting expected data coupling information from one software component to another software component in a sequence diagram; updating the expected control and data coupling information in an expected control and data coupling sequencing table; deriving actual control coupling information from execution trace data obtained from a requirements based test execution; deriving actual data coupling information from data trace information; and updating the actual control and data coupling information in an actual control and data coupling sequencing table. The method then compares the expected and actual control coupling information, and compares the expected and actual data coupling information, to verify whether software components are interacting with each other as expected.

The application provides a data detection method and device, equipment and a storage medium, wherein the method comprises: querying data of a distributed application server node, and performing format processing on the queried data to obtain a data model object conforming to a predefined data model; shunting the data model object to a corresponding channel for aggregation to obtain an aggregated data sequence pair, and collecting each data model object associated with the aggregated data sequence pair in a data stream; setting a dynamic data window range based on a starting time of each data model object entering the data stream and an end time of each data model object in the data stream; and processing the aggregated data sequence pair in the data stream based on the set dynamic data window range to generate a detection result for the queried data, wherein the detection result comprises whether the queried data is consistent with expected data.

Techniques for generating replication data from a data source to be stored in a target location are described herein. A computing system can receive, from a client device, client requirements associated with a dataflow from the data source to the target location. The client requirements can include an expected data freshness value and an expected data query latency value. Additionally, the computing system can process the expected data freshness value with one or more machine-learned models to generate an extraction framework for extracting data from the data source. Moreover, the computing system can process the expected data query latency value with the one or more machine-learned models to generate a loading framework for loading data to the target location. Furthermore, the computing system can copy the replication data from the data source to the target location based on the extraction framework and the loading framework.

The embodiment of the application discloses a kind of data processing link verification method, device and equipment based on multiple monitoring nodes.The method comprises the following steps: obtaining a set of expected data packets;At different links of the data processing link, at least two monitoring nodes are used to collect data packets respectively, to obtain at least a first set of collected data packets and a second set of collected data packets;Based on the characteristic identification information carried in the data packets, a data packet matching relationship is established between the set of collected data packets and the set of expected data packets, and the data packet matching relationship is independent of the order of the data packets in the collection sequence;Based on the data packet matching relationship, data packet content verification is performed, and based on the respective matching verification results of each monitoring node, the abnormal occurrence position is determined.The application can realize multi-source data collection, solve the problem of inconsistent data sequence caused by multi-thread parallel packet sending, and improve the accuracy, flexibility and problem positioning ability of data verification.

A system that includes the following: a processor; a storage facility; and a machine-readable memory that stores instructions, whereby the instructions can be executed by the processor to: to receive inputs (400) specifying an expected amount of data and a minimum retention period for a deduplication data store (150); to perform a comparison of the at least one retention period with a stored data structure (170), wherein the stored data structure (170) comprises different combinations of respective storage multipliers corresponding to the respective maximum retention periods; to determine at least one storage multiplier of the respective storage multipliers based on the comparison of at least one retention period with the stored data structure (170); to determine a storage quantity to be provided for the deduplication data store (150) based on the expected amount of data and the determined minimum storage multiplier; and to provide the deduplication data store (150) based at least on the specified amount of storage.

The invention discloses an automatic diagnosis system for central axis spinal arthropathy. The system comprises a data acquisition module, a data processing module, a model construction module and a model interpretation module, and the model construction module performs deep learning on the MRI image, and constructs a joint prediction model in combination with clinical variables to obtain a central axis spinal arthropathy prediction result. According to the invention, through a method of combining deep learning and machine learning, the diagnosis accuracy and efficiency of the central axis spinal arthropathy are improved; multi-center retrospective and prospective data are adopted, comprehensiveness and representativeness of the data are ensured, the MRI image is analyzed through a deep learning model, image features related to diseases can be recognized more accurately, and meanwhile prediction performance is further optimized in combination with a machine learning model of clinical variables. The interpretability analysis of the model enables a doctor to understand and trust the prediction result of the model, and promotes the application and popularization of artificial intelligence in medical image diagnosis.

Systems, methods, and non-transitory machine-readable media for provisioning a deduplicated data store are provided. Example embodiments relate to provisioning a deduplicated data store. Examples include receiving input indicative of an expected data volume for a deduplicated data store and at least one retention period; performing a comparison of the at least one retention period to a stored data structure; determining at least one storage multiplier based on the comparison of the at least one retention period to the stored data structure; and determining a storage volume to provision for the deduplicated data store based on the expected data volume and the at least one storage multiplier.

Computing platforms, methods, and storage media for managing input data compatibility with a software application are disclosed. Exemplary implementations may: receive an input file having input data content arranged according to a first data format; compare the input data content with expected data content in relation to which the software application has been configured; compare the first data format with an expected data format in relation to which the software application has been configured; in response to determination that the input data content substantially overlaps with the expected data content and the first data format differs from the expected data format: generate a rearranged input file by rearranging the input data content according to the expected data format; and provide the rearranged input file to the software application for processing. Exemplary implementations avoid processing incompatibility and errors while avoiding the need to reconfigure the application.