78 results about "Prospective data" patented technology

The invention discloses an exhibition and conference information processing method and system, and relates to the technical field of exhibition and conference information processing, and the method comprises the following steps: at the initial stage of system operation, according to the expected data updating demand of an exhibition and conference, setting a predefined synchronization period to periodically execute data updating operation; in each data updating process, information issuing operation in the exhibition is monitored in real time, and all related data inputs are captured to construct an information data set. The method combines the prediction capability of a machine learning model and a self-adaptive dynamic adjustment algorithm, not only can identify the activity degree of information release in real time, but also can dynamically shorten the synchronization period under the trend of high-frequency release, so as to ensure that a high-frequency update request is processed in time, avoid missing of key information and logic conflicts, and meanwhile, improve the efficiency of updating the high-frequency update request. And a predefined synchronization period is kept under a conventional release type trend, so that system resources are saved, the operation cost is reduced, and the optimal balance between resource utilization and response efficiency is realized.

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.

The invention relates to the technical field of control systems, provides an ROV motion control system and method based on PID, and solves the defects of an existing PID control algorithm in ROV complex system control, the system comprises a data acquisition module, a data processing module, a data detection module, a model matching module and an optimization module, historical data are acquired and an algorithm model knowledge base is constructed, and the ROV motion control algorithm is optimized. The method comprises the steps that environment data are matched in real time, an optimal control model is selected, a control command is sent, ROV operation data are collected, if real-time data are not consistent with expected data, the control model is optimized through a genetic algorithm, and an optimized control command is generated. And the robustness and the operation efficiency of the system are improved.

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.

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.

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.

The embodiment of the present application provides a method, system and readable storage medium for intelligent prediction and evaluation of projects based on big data. The method includes: obtaining project attribute information and feature information of the project to be evaluated, extracting evaluation information data based on the project attribute information and feature information of the project to be evaluated to generate a feature portrait of the evaluation project, extracting an evaluation data package based on the feature portrait of the evaluation project, performing an indicator fit analysis on the project evaluation data set in the evaluation data package according to a first preset model to obtain project indicator fit data, performing project evaluation on the feature portrait of the evaluation project according to a second preset model to obtain project expected data, and correcting the project expected data based on the project evaluation data set and the project indicator fit data of the project to be evaluated to obtain project target data; thereby generating an evaluation project feature portrait based on the evaluation information data to perform project evaluation to obtain project expected data and correcting the project target data in combination with the project indicator fit data and the project evaluation data set to obtain project target data, thereby obtaining a high-precision technology for intelligent prediction and evaluation of projects based on big data.

Solutions are disclosed that enable efficient federated graph queries across multiple isolated data stores. Examples leverage the connectedness of the expected data that spans the data stores by defining the entities and relationships and inferring the intent of the queries. These are used to optimize data searches in the individual data stores. Examples map each of two or more variables of the input query to elements of a public schema and use the mapping to determining a storage tag (identifying a data store) for each of the variables of the input query. Store-specific queries are scheduled and performed based on at least the storage tags.

The invention discloses a transaction docking test method and device, equipment, a storage medium and a program product, and relates to the technical field of system testing, and the method comprises the steps: receiving transaction basic data inputted by a test requester, inputting the transaction basic data to a business system, and carrying out the first transaction liquidation, and obtaining actual data; in response to a transaction test request initiated by a test requester, performing second transaction liquidation according to the transaction basic data and an automatic interface obtained by a transaction rule of a packaging business system to obtain expected data; and comparing the actual data with the expected data to obtain a transaction docking test result. According to the invention, the transaction docking test efficiency is improved.

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

A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: providing the data center asset to a prospective owner; establishing a secure communication channel between an onboarding system and the data center asset; establishing a communication channel between a rendezvous server and the data center asset; exchanging information between the onboarding system and the data center asset via the secure communication channel, the information including a data center asset ownership voucher; maintaining the data center asset ownership voucher at the rendezvous server; and, using the data center asset ownership voucher to associate the data center asset with the prospective owner.

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.