201 results about "Aggregate data" patented technology

The invention discloses a material supply and resource recovery collaborative scheduling method and system, and belongs to the technical field of logistics scheduling, and the method comprises the steps: obtaining a material supply instruction and a garbage recovery instruction, carrying out the image recognition of a building garbage picture, obtaining the garbage attribute, and generating a demand instruction library; the method comprises the following steps: establishing a feature knowledge base, setting a spatial clustering method, carrying out spatial clustering and time window screening, carrying out geographic coordinate analysis and time sequence sorting on material demand points and recovery points, generating an initial task pool, setting a loading matching method, and generating a material supply and recovery loading schematic diagram according to building material and garbage attributes; a circulation path is planned, a path planning method is set, an ant colony algorithm is used for initial path planning, a monitoring feedback method is set, data are returned in real time, the running state of the vehicle is monitored, and an alarm is given out immediately once abnormity is found; and the sorted and regenerated aggregate data is synchronized to a building material database, a resource feedback method is set, a reverse transportation task is identified, and a material supply and resource recovery closed loop is formed.

The invention discloses an enterprise digital operation real-time data analysis method based on big data, and particularly relates to the field of data analys.The enterprise digital operation real-time data analysis method comprises the steps that enterprise operation data are collected in real time through a multi-protocol adapter, abnormity is dynamically cleaned and detected, and data standardization is achieved in combination with statistical process control and BERT sentiment analysis; in the hierarchical stream processing architecture, edge nodes pre-aggregate data and filter anomalies, a center engine dynamically divides time windows to generate minute-level / second-level indexes, and a self-adaptive rolling window and dual-threshold session cutting support inventory monitoring and user behavior analysis; the incremental machine learning model updates prediction in real time based on a sliding window, screens a dynamic optimization decision tree through Hoeffering splitting conditions and feature importance, and triggers fine tuning or full training when data distribution drift is detected; and the cross-source data association engine fuses real-time and historical data, calculates a KPI anomaly score, and triggers hierarchical early warning.

The invention relates to the technical field of intelligent traffic, in particular to a traffic abnormal event dynamic detection method and system based on trajectory analysis, and the method comprises the steps: receiving original space-time trajectory data; preprocessing the original spatio-temporal trajectory data, and mapping the original spatio-temporal trajectory data to a road section sequence of a road network through a map matching algorithm; constructing a behavior model based on the track sequence after map matching, learning the track sequence of the normal behavior mode on the road section, and establishing an observation emission model and a state transition matrix; in the online stage, the log-likelihood value of an observation sequence and / or the similarity between the observation sequence and a normal behavior pattern cluster are / is calculated for a real-time track in a set sliding window, and when the log-likelihood value and / or the similarity exceed a set threshold value, the track is marked as abnormal; performing time-space aggregation on abnormal trajectories of the same road section or intersection in unit time according to single vehicle abnormality judgment, and triggering group abnormal event alarm when aggregation data exceed a preset value.

The invention relates to a photovoltaic panel control management system, and belongs to the technical field of photovoltaic power generation. The system comprises an edge sensing unit, a string optimization unit and a central control unit. The edge sensing unit is deployed on each photovoltaic panel and is used for acquiring electrical parameters, environmental parameters and physical states in real time; the string optimization unit is connected with the edge sensing unit in the same string and executes dynamic working point matching and fault isolation; and the central control unit is connected with the string optimization unit through a communication network to execute whole-station monitoring and strategy scheduling. The string optimization unit is divided into a plurality of communication areas, each area is provided with an area agent node for gathering data, and the data is transmitted to the central control unit in a random backoff mode so as to avoid channel conflicts. And when the regional agent node fails, the child node is automatically switched to the direct connection communication mode. The system improves the power generation efficiency and reliability of a photovoltaic system through a multi-layer architecture and an intelligent communication mechanism.

The invention relates to a segmented aggregated hash table read-write method and system based on a graph database, and the method comprises the steps that an original hash table is separated into a hash entry table and a data load block, the hash entry table is used for storing hash entry data, and the data load block is used for storing aggregated data. And in response to the received write-in data instruction, positioning the hash entries in the hash entry table according to the hash value of the write-in data. Based on the data storage state of the hash entry, the write data is stored in the data load block through linear detection, and the hash entry is updated. And in response to receiving a data reading instruction, obtaining target aggregation data corresponding to the read data through linear detection according to the hash value of the read data. Therefore, the read-write performance of the hash table is improved, and the problem of low performance in the conventional read-write process of the hash table in a large data volume scene is solved.

The invention relates to a terminal protection and baseline inspection method and device based on a complex asset network, and the method comprises the steps: constructing a multi-dimensional holographic network topological graph, and determining the position association of a terminal; the method comprises the steps of semantic analysis of data, risk feature mining and database establishment, generation of an intelligent protection strategy according to a terminal role, a data sensitive level and risk assessment, generation of a dynamically adjustable baseline template by summarizing data, real-time monitoring of a terminal during operation, intelligent check by comparing the template and the strategy, rapid early warning of abnormity, and finally feedback of monitoring data. And optimizing a risk library, a strategy and a template. According to the scheme, the risk can be accurately identified, efficient protection and inspection are realized, and the security of the complex asset network terminal is improved.

The invention discloses a city platform and method based on a national network identity authentication public service, and relates to the field of smart cities, and the method comprises the steps: firstly completing user identity authentication through the national network identity authentication public service, so as to establish a trusted access basis; and constructing a data request contract, and storing an evidence after a user confirms authorization, thereby forming a transparent and traceable authorization mechanism. And then generating a data token for limiting access based on a user authorization range, obtaining required cross-source data based on a unified data aggregation service, realizing on-demand aggregation and safe circulation of the data, automatically auditing the aggregated data through business logic to obtain an auditing result, and finally returning the auditing result to the user. In this way, the business process can be simplified, the problems of repeated authorization and data islands are solved, meanwhile, the user data control right is guaranteed, and the intelligence and efficiency of urban platform business handling are improved.

A distributed mobile network traffic data decomposition and forecasting computer-implemented method, comprising:using a geo-location preserving mobile network representation. Locations of mobile network elements are received and converted into a graph representation. Relative distances between adjacent network elements are preserved using respective weights on graph edges; input data is received comprising aggregate network traffic data from network elements corresponding to the network elements locations. The aggregate data includes traffic data corresponding to a plurality of services operating over the network. A graph-based neural network based on the geo-location is used, preserving mobile network representation and configured to capture spatial and temporal correlations in the input data, including at least a spatio-temporal concentration block (STCB) and a parallel prediction block (PPB); loss functions train the graph-based neural network using the input data, to provide network per-service traffic forecasting, the loss functions including an operator cost function configured to capture operator costs.

Some aspects relate to technologies for employing metadata identifiers for part item listings on a listing platform to generate inventory intelligence. In accordance with some aspects, textual analysis of listing data for each of a plurality of part item listings on a listing platform is performed to assign a metadata identifier to each part item listing. The metadata identifier for each part item listing comprises a combination of an inventory segment identifier, a category identifier, and fitment data. For a first metadata identifier, a set of part item listings having the first metadata identifier is identified. Demand data and / or supply data is aggregated for the set of part item listings to provide aggregated data. A user interface with one or more metrics is generated using the aggregated data, and the user interface is communicated over a network to a computing device for presentation.

The invention discloses a power equipment control method and device and a nonvolatile storage medium. The method comprises the following steps: collecting target power data and target environment parameters corresponding to power equipment in a target park; based on the edge computing terminal, aggregating the target power data and the target environment parameters to obtain aggregated data; based on the cloud, predicting future target power data of the power equipment according to the aggregated data; based on the cloud, generating an optimization instruction according to the future target power data and a preset power optimization target; and issuing the optimization instruction to the power equipment. The technical problems of energy waste and poor management effect caused by difficulty in overall management and control of multiple pieces of power equipment in the park at present are solved.

The embodiment of the invention provides a railway vehicle-mounted sensing system data acquisition system and method based on multi-source triggering, and the system comprises a satellite communication system which is used for obtaining the current position information of a train in real time; the vehicle-mounted ATO system is used for acquiring current state information and running condition information of the train; the ground trackside sensing unit is used for sensing the ground trackside environment through sensing equipment deployed along the railway and acquiring ground trackside environment sensing information; the vehicle-mounted sensing system is used for sensing a headroom area in front of running of a train through a vehicle-mounted sensor, acquiring sensing information of the headroom area in front of running, acquiring information of the satellite communication system, the vehicle-mounted ATO system and the ground trackside sensing unit, and sending the information to the data acquisition cloud platform; and the data acquisition cloud platform is used for acquiring the information sent by the vehicle-mounted sensing system, performing data summarization, and issuing data acquisition triggering information to each train according to data acquisition requirements based on the summarized data.

The invention discloses a batch data reconciliation method and device, equipment, a storage medium and a product, and relates to the technical field of data comparison, and the batch data reconciliation method comprises the steps of obtaining database batch data in response to a batch reconciliation instruction; summarizing the database batch data to obtain first to-be-checked summarized data; obtaining second to-be-checked summarized data, checking the first to-be-checked summarized data with the second to-be-checked summarized data, and determining a checking result; and if the checking result is inconsistent, performing detail checking, confirming difference data, updating and supplementing the first to-be-checked summarized data based on the difference data, and returning to the step of comparing the first to-be-checked summarized data with the second to-be-checked summarized data until the checking result is consistent, and confirming that the batch reconciliation result is a balance reconciliation result. According to the method and the device, the batch data of the database is summarized for checking, and the account checking data is aligned through detail checking and supplementing, so that the account checking efficiency is improved.

Techniques for optimized aggregation of a data source are described. A server device receives a request from a data source for aggregating thereof and compares each of a set of hardware metrics corresponding to hardware configuration of the server device with a corresponding threshold hardware metric. The server device is classified as a small-configuration device or a high-configuration device and complete aggregation of the data source or partial aggregation of the data source is allowed based on the classification. A set of utilized hardware metrics of the server device that corresponds to hardware resources of the server device that is being utilized is determined. One or more operations corresponding to the transmission of data of the data source that is to be performed by the server device is blocked if at least one of the set of utilized hardware metrics is higher than a corresponding threshold operational soft limit.

The invention discloses a safety early warning method, device and equipment, and the method comprises the steps: a lightweight model carries out the function abnormality detection according to a fusion result, and transmits a function abnormality detection result and collection data to a cloud server through a network transmission layer; the cloud server summarizes the collection data and the anomaly detection results sent by the edge nodes to obtain summarized data; and the cloud server carries out overall anomaly detection according to the summarized data through a deployed detection model, and carries out safety early warning according to an overall anomaly detection result. According to the method, initial anomaly detection is carried out by deploying a lightweight model at an edge node, and real-time response is carried out according to detection data, so that an industrial scene with a high real-time requirement is met, and multi-dimensional sensor data can be effectively fused to improve the accuracy of monitoring and early warning; the collected data and the detection result of each edge node are summarized through the cloud server so as to carry out anomaly detection on the whole, thereby improving the comprehensiveness and the accuracy of safety early warning.

The invention discloses a safety production performance evidence storage verification method based on ZKP and multi-dimensional verification. The method comprises the steps of daily data aggregation and commitment generation, zero knowledge proof generation, optimistic summarization and batch uplink, data availability guarantee, time trigger automatic presumption and multi-dimensional verification execution. According to the method, zero-knowledge proof, optimistic summary, data availability sampling, Vickers tree and other frontier cryptography and block chain capacity expansion technologies are fused, and the method has the advantages of low cost, high efficiency and verifiability.

An information processing device according to the present invention includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations include: generating, based on inference target data include a detection target object, aggregate data smaller than the inference target data; and applying the aggregate data to a trained model to infer a first region that is a region of the detection target object in the aggregate data, generating packed data that is data packed with the first region or a region of the inference target data, applying the packed data to a trained model to infer a second region that is a region of the detection target object that is included, and inferring the detection target object in the inference target data based on the second region.

A method for providing alarm-related information in a security system comprises obtaining alarm indication(s) associated with alarm(s) triggered at a monitored location, obtaining media data related to the alarm(s), the media data acquired during a period of interest by media device(s) deployed at the monitored location, executing machine learning model (s)to obtain a textual description of event(s) captured in the media data, and outputting the textual description. A method for summarizing data comprises obtaining a data summary request, determining a period of interest associated therewith, identifying events that occurred at the monitored location during the period of interest, obtaining media data related to the events, the media data acquired by the media device(s) during the period of interest, executing machine learning model(s) to obtain, based on the media data, textual descriptions of events, executing the machine learning model(s) to generate summary(ies) based on the textual descriptions, and outputting the summary(ies).

The invention relates to the technical field of vehicle part strength simulation analysis verification, and discloses a strength load processing method and system based on a virtual test field, and the system comprises a model construction module, a data configuration module, a simulation execution module, a load processing core module, and a finite element interface module. The method comprises the following steps: constructing a virtual test field pavement model and a multi-body dynamic model; calling a programming tool to process the file; further converting the sub-working-condition load into a standardized load header file required by finite element strength analysis, and finally driving a finite element solver to execute batch working condition calculation and output stress-strain summarized data of the parts. According to the method, intelligent normalization processing of load data, dynamic coverage of critical working conditions and multi-system seamless integration are achieved, the efficiency and precision of component strength verification are improved, the development period is shortened, and a unified comparability reference is provided for fatigue life evaluation.

The invention provides a periodic statistical data metering method and device, equipment, a medium and a program product. Relates to the technical field of computers. The method comprises the following steps: for each subject, acquiring configuration parameters configured by a user for each subject feature from a data system, and recording the configuration parameters in an accessible space; accessing the accessible space to determine a subject source corresponding to the subject and a corresponding statistical deadline, querying a target data value corresponding to the periodic statistical data from the subject source, and determining statistical results of the corresponding target data values under different statistical deadlines based on the target data value and the corresponding statistical deadline; and determining a metering result of the periodic statistical data based on the statistical results of the target data values under different statistical deadlines and pre-counted summarized data. According to the scheme, efficient and accurate periodic statistical data metering can be realized under the condition that the configuration parameters are consistent with the metering range and the rules are consistent, and the reliability of the periodic statistical data is evaluated.

This disclosure relates to knowledge generation and implementation. A knowledge graph system comprises at least one processor, at least one database communicatively connected to the at least one processor, and a memory storing executable instructions. When executed, the instructions cause the at least one processor to aggregate data associated with a plurality of entities, the aggregated data reflecting one or more relationships between two or more of the plurality of entities. Attribute data identifying loan amounts, property values, and appraisal sources may be extracted from the aggregated data. A knowledge graph data structure may be generated having a plurality of subject notes corresponding to the extracted attribute information. Statistical distributions of attributes associated with one or more appraisal sources may be generated and an anomaly in a first statistical distribution may be detected based on a comparison of the first statistical distribution with a second statistical distribution.

A control circuit determines a first period of time during which a jet turbine engine-powered aircraft was operating, then aggregates data from a plurality of data sources including an Aircraft Condition Monitoring System and an Electronic Engine Control that corresponds to that first period of time to provide aggregated data, and then determines whether the aggregated data meets a standard for quantitative sufficiency. By one approach, determining whether the aggregated data meets a standard for quantitative sufficiency can comprise determining whether the aggregated data meets a standard for quantitative sufficiency as a function of assessing the intermittency of data from at least some of the plurality of data sources. Assessing that intermittency of data from at least some of the plurality of data sources can comprise comparing received data to corresponding intermittency signatures.

The application discloses a data privacy protection method and a detection method for a privacy-enhanced semi-blind digital fingerprint, combines a differential privacy technology and a digital fingerprint technology, embeds carefully designed noise into specific positions of a numerical aggregate data set, can realize fingerprint embedding and noise interference operation on carrier data in one step, and ensures protection of private information in the carrier data set and tracking function on traitors.

Based on demographic information of the area being analyzed, the system estimates the distribution of people with user-specified attributes at a given location within that area. [Solution] The human flow information estimation device 2000' includes a calculation device 2100 that performs human flow information analysis based on predetermined demographic information, which includes the number of people located in each of a plurality of predetermined area meshes and information on the attributes of those people. The human flow information estimation unit 2103 identifies a set of unit sub-regions that cover the area to be analyzed without overlapping each other, and based on demographic information and predetermined lifestyle information, it distributes the number of people from the demographic information to the unit sub-regions at predetermined time intervals, based on the type of place and the size of the place corresponding to the classification of people's behavior in each area mesh, and estimates aggregated data for each unit sub-region. Based on the aggregated data, the human flow information estimation unit 2103 calculates a human absorption capacity value, which is the ratio of the number of people with the analyzed attributes for each unit sub-region to a predetermined value.

A system is provided.SOLUTION: The system includes means for receiving communication with the customer as voice data, means for converting the received voice data into text data using a voice recognition technology, means for analyzing the converted text data using a natural language processing technology and generating a summary of a matter, means for automatically counting the number of matters related to a specific keyword or phrase and generating aggregation data, and means for automatically creating a material for a report based on the generated aggregation data.SELECTED DRAWING: Figure 1

Example implementations relate to deduplication operations in a storage system. An example includes receiving a set of data units to be stored in persistent storage of a deduplication storage system, and recording the arrival order of the set of data units in a manifest. The example also includes, in response to determining that an average unit size of the set of data units is smaller than a minimum size threshold, identifying a first data unit that is smaller than the minimum size threshold. The example also includes generating an aggregated data unit including the first data unit and a second data unit, and, in response to determining that the size of the aggregated data unit is larger than a target size threshold, replacing, in the manifest, the first data unit and the second data unit with the aggregated data unit.

Methods, devices, and systems for false target detection for airport traffic control are described herein. One device includes a user interface, a memory, and a processor configured to execute executable instructions stored in the memory to receive one or more sensor reports from one or more sensors, aggregate data that corresponds to a particular target from the one or more sensor reports, determine the particular target is a false target responsive to only one of the sensor reports including data that corresponds to the particular target, and display the particular target as a false target on the user interface responsive to determining the particular target is a false target.

The present disclosure provides a system (108) and a method (300) for base grid creation for analyzing geographical locations The system (108) generates a base grid where an aggregation of data, specifically on international mobile subscriber identity (IMSI) levels provides a real time health status of each IMSI within the base grid. The system (108) generates IMSI level identification and plotting where a real time issue with a specific user may be identified. The system (108) summarizes data at different levels of aggregation and reduces a number of data points to be processed and displayed. The system (108) improves performance and scalability, allowing users to analyze and visualize large volumes of data efficiently.

The embodiment of the specification discloses a data aggregation method, device and medium for industry identification data, the method comprising: receiving a plurality of industry identification data and a data aggregation request for the plurality of industry identification data, wherein the industry identification data comprises node identification, identification registration data and identification analysis data of the industry identification data; generating a plurality of data aggregation sub-tasks according to the data aggregation request, judging whether there is a dependency relationship between the plurality of data aggregation sub-tasks; if it is determined that there is a dependency relationship between the plurality of data aggregation sub-tasks, generating an operation relationship between the data aggregation sub-tasks according to the dependency relationship between the data aggregation sub-tasks; aggregating the industry identification data to be aggregated in the plurality of data aggregation sub-tasks to generate aggregation sub-data corresponding to the plurality of data aggregation sub-tasks; and according to the operation relationship between the plurality of data aggregation sub-tasks, operating each aggregation sub-data to obtain aggregation data corresponding to the plurality of industry identification data.

Embodiments of the invention disclose a data aggregation method and apparatus, an electronic device and a storage medium, and can solve the problem of how to effectively reduce overhead for data pulling in a data aggregation process. The method comprises the steps of obtaining to-be-aggregated data and at least one to-be-aggregated data fragment corresponding to the to-be-aggregated data; according to the to-be-aggregated data, the original data stored in the at least one to-be-aggregated data fragment and the data stored in the at least one verification fragment, initial data pulling overhead is calculated; when it is detected that the to-be-aggregated data meets the aggregation mode conversion condition, target data pulling overhead corresponding to a target aggregation mode after aggregation mode conversion is calculated, and the target aggregation mode is full-amount aggregation or incremental aggregation; and when the target data pulling overhead is smaller than the initial data pulling overhead, performing data aggregation according to the target aggregation mode through the target verification fragment.

The present application relates to the field of data processing and discloses a data collection method, apparatus, computer equipment, and storage medium, including: when a setting operation for a data collection task is detected on a task configuration interface, generating a task configuration record and a task contact person record based on the setting operation; generating a task execution record based on the task configuration record, and generating a contact person execution record for each task contact person based on the task contact person record and the task execution record; if the current time reaches the task start time indicated in the task execution record, sending a task reminder message to the contact person terminal of each task contact person; receiving a reference data table sent by each contact person terminal of each task contact person, and merging the contents of each reference data table to obtain a summary data table. This can realize automated data collection and improve data collection efficiency. The present application relates to blockchain technology, and for example, the above data can be written into the blockchain for use in scenarios such as data collection.