52 results about "Prospective data" patented technology

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 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.

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.

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.

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 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.

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.

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.

A capability migration prediction method and system in industrial skill practical training relates to the field of occupational skill assessment, and the method comprises the following steps: firstly, obtaining a constraint condition type of virtual environment relative to real equipment simplification, and recording an operation performance difference value as constraint condition dependence intensity by setting a constraint-containing and unconstrained control group and experiment group virtual environment; then, analyzing constraint conditions related to each link of a real machine task, calculating single capability loss based on dependency intensity and a virtual-real difference multiple, and performing weighted summation by considering coupling coefficients among the constraint conditions to obtain a total capability loss predicted value; and finally, the predicted value is converted into real machine expected data, and a virtual-real ability migration evaluation result of the target student is generated by comparing the real machine expected data with actual data. By implementing the method, the refinement degree of virtual and real capability migration evaluation can be improved.

This specification discloses a resource allocation method, apparatus, and equipment. The scheme includes: identifying a resource allocation requester who holds a first resource and requires a second resource to supplement the first resource; identifying a resource holder who can provide the second resource and requires a third resource, wherein the third resource can be derived from the first and second resources; acquiring due diligence data related to the resource allocation requester and the resource holder; extracting a set of characteristic variables from the due diligence data, the set of characteristic variables including: a first subset of characteristic variables reflecting the resource holder's ability to provide the second resource; and a second subset of characteristic variables reflecting the resource allocation requester's ability to attract resources; generating risk measurement data for the first resource based on the first subset of characteristic variables; and generating derived expected data for the third resource based on the risk measurement data and the second subset of characteristic variables, so as to determine whether to allocate the second resource from the resource holder to the resource allocation requester based on the derived expected data.

Method of protecting integrity of data including steps of running web service on primary, secure computer system including original, complete data set created by user. Generating intended state of data of the web service on primary, secure computer system, so intended state of data has frontend data, files and information relevant for visual design and interaction of the web service. Transferring intended data set through secure connection to secondary computer system(s) in potentially insecure network on which the web service is executed. Checking integrity of intended data set by detecting manipulation of the underlying information of intended data by an attacker, after manipulation, manipulated data is placed in quarantine and may be analyzed in further step but is no longer executed and thus cannot cause any damage. Restoring intended state of data at time before manipulation by transferring intended data set stored on primary computer system to secondary computer system(s), so manipulation of data has been detected.

The invention relates to the technical field of memory testing, and discloses a memory testing tool, a memory testing analysis method thereof and a storage medium, and the memory testing analysis method comprises the following steps: before executing each testing step, according to the address generation logic of the memory testing tool, determining a target memory address to be accessed in the current testing step, determining an operated memory area of the current test step based on the target memory address; before the current test step is executed, actual data of the operated memory area are read to serve as original data before operation, and corresponding expected data are generated based on the original data and the test mode of the current test step; after the current testing step is completed, actual data of the operated memory area are read and serve as actually measured data after operation; and combining the original data, the measured data and the expected data into a data recording unit of the current test step, and sequentially storing the data recording unit according to a time sequence of test execution to form a data frame sequence organized according to a test process.

A model setting unit executes setting related to a first chunk of input data and a second chunk of training data on a basis of loaded training-purpose data as well as setting related to a third chunk of test input data and a fourth chunk of expected data on a basis of loaded test-purpose data. A model computing unit executes computations of training with use of a machine learning model on a basis of the first chunk and the second chunk, further executes computations of prediction with use of the machine learning model on a basis of results of the training and the third chunk and arithmetically compares results of the prediction and the fourth chunk with each other. The machine learning model after execution of at least part of the computations of training is stored non temporarily.

This invention discloses a self-governing method for VOCs quality control, belonging to the field of VOCs governance technology. The method includes: setting up a VOCs information model for users; improving the VOCs monitoring system based on the VOCs information model; conducting real-time monitoring through the VOCs monitoring system to obtain VOCs monitoring data; matching applicable measures from a preset measure library based on the VOCs information model; performing governance simulation based on the applicable measures and the VOCs information model to obtain simulated governance expected data; displaying the simulated governance expected data in the VOCs information model; acquiring governance needs in real time; analyzing the governance needs based on the VOCs information model; and determining the recommended governance method to meet the governance needs. This invention not only solves the self-governing VOCs quality control problem of a single enterprise, but also achieves data interoperability, resource collaboration, and intelligent decision-making for multiple users by constructing a regional / industry-level comprehensive VOCs governance platform.

The invention provides a file processing method and computer equipment, and relates to the technical field of data processing, in response to a file processing request for a monitoring task, recording index expected data corresponding to at least one target file needing to be processed for executing the monitoring task; and in response to execution of the monitoring task, performing index monitoring on the correspondingly processed target file to obtain index monitoring data of the target file, and then comparing the index monitoring data and the index expected data corresponding to the same target file to obtain an index monitoring result of the corresponding target file. Therefore, the abnormal indexes in the target file processing process are obtained in time, operation and maintenance personnel can be timely warned to perform operation and maintenance, the index expected data can be updated based on the historical index monitoring data, and the index expected data can be timely updated along with the development change of the bank business environment. It is ensured that the obtained index monitoring data can reflect the actual processing performance of the target file, and the reliability and effectiveness of a monitoring system are improved.

The invention discloses a deep endometriosis postoperative pregnancy outcome prediction and model construction method and device, and belongs to the field of gynaecology and obstetrics data processing, and the method comprises the steps: collecting and dividing retrospective collection data of a first target group before a first time point, and obtaining a first training set and a first verification set; after a plurality of different types of preset prediction models are trained through the first training set, screening a first prediction model from the trained preset prediction models through the first verification set; after the first time point, collecting and dividing prospective collection data of a second target population, and obtaining a second training set and a second verification set; when it is judged that the first prediction model needs to be calibrated according to the second verification set, the first prediction model is finely adjusted according to the second training set; and taking the finally obtained first prediction model as a deep endometriosis postoperative pregnancy outcome model. According to the method, the postoperative pregnancy probability of the deep endometriosis patient can be predicted through various clinical risk factors.

The application relates to the technical field of data processing, in particular to a data desensitization method and system capable of keeping expected data characteristics, which comprises the following steps: firstly, determining a key data column and a characteristic set of an original data set; secondly, performing standardization processing on the original data set; thirdly, dividing the original data set into a plurality of data clusters with similar characteristics by using a clustering analysis technology; fourthly, replacing data points in the data clusters; fifthly, calculating characteristic differences between the original data set and a desensitized data set; and finally, performing k-anonymity and l-diversity tests on the desensitized data set. While ensuring data privacy security, the application effectively keeps key characteristics of the original data, significantly improves the usability of the desensitized data, accurately controls desensitization intensity through a dynamic clustering and characteristic weighting mechanism, avoids data distortion problems caused by excessive desensitization in traditional methods, and ensures that the data meets strict privacy protection requirements through the k-anonymity and l-diversity tests.

A computer-implemented system and method can be used to identify policy information applicable to a compute resource within a specified time range, the policy information related to data management for the compute resource. Based on the identified policy information, a set of expected data management events for the compute resource is generated within the specified time range. The set of expected data protection events is modified based on information related to exceptions to the data management indicated by the policy information. The modified set of data protection events provides lineage information for the compute resource.