151 results about "Time index" patented technology

The invention relates to a task scheduling optimization method and device based on reinforcement learning, equipment and a medium. The method comprises the steps that firstly, system resource state data are collected in real time, dynamic environment characteristics are determined through preprocessing and time sequence analysis, task characteristic data are analyzed at the same time, and a task priority sequence and a resource demand vector are generated through a priority ranking algorithm and a resource evaluation model; and then a state space and an action space are constructed by adopting a reinforcement learning algorithm, an optimal task allocation scheme is generated through strategy iteration and reward function optimization, and if the scheme meets a resource balance threshold, scheduling is executed, and performance indexes are collected. And finally, fusing real-time indexes with historical data, and updating parameters of the reinforcement learning model through experience playback and gradient descent to form a closed-loop optimized improved scheduling strategy. By adopting the method, the accurate mapping of the resource state and the task requirement can be realized, and the problem of insufficient adaptability of the traditional static scheduling to a complex scene is solved.

The invention discloses a micro-service dynamic weight load balancing method and related equipment. The method comprises the following steps: asynchronously acquiring a multi-dimensional real-time load index of a micro-service instance, obtaining a load prediction value by virtue of a model, fusing the real-time index, the prediction value and a service health degree, calculating a dynamic weight by depending on service load data, distributing a request according to the weight, and feeding back an adjustment coefficient. According to the method, by asynchronously collecting the multi-dimensional real-time indexes, an accurate basis is provided for weight calculation, so that the request allocates the actual load of the matched instance, and the situation that the response of a high-load instance is slow due to too many requests is avoided; by introducing a load prediction value, load change can be pre-judged in advance, a weight strategy is adjusted in advance, overload when an instance load suddenly increases is prevented, and the fluctuation resistance of the system is improved; the weight is calculated by fusing multi-dimensional data, so that the comprehensive load of the instance can be comprehensively reflected, and evaluation deviation caused by a single index is avoided; a feedback mechanism can enable weight calculation to continuously adapt to a system state, and load unevenness caused by weight fixing or adjustment lag is avoided.

The invention provides a data exception early warning method and device based on a dynamic threshold model. The method comprises the following steps: firstly, constructing an index time sequence library and an event library for storing index and event information; fusing the historical same-period base line, the trend term and the event influence value and generating a base line through a dynamic weight formula; calculating upper and lower limits of a dynamic threshold by combining the fluctuation acceleration and a basic fluctuation coefficient; and finally, comparing the real-time index value with a threshold range to judge whether to give an alarm, and optimizing the model or updating the event library according to a manual labeling result. According to the method, dynamic and accurate early warning of data exception can be realized, service scene changes can be adapted, the early warning accuracy is continuously improved through feedback optimization, and powerful support is provided for stable service operation and efficient exception disposal.

The invention relates to the technical field, in particular to an unmanned inspection method and system for a switch cabinet, and the method comprises the following steps: building an incidence relation between signal nodes through a graph neural network, improving the time sequence consistency of multi-source signals, enabling instrument reading and temperature and humidity signals to form a hierarchical structure under a unified time frame, and improving the accuracy of inspection. The logic association of state layering is enhanced, continuous sampling sequence features are extracted through a long-short-term memory network, the response sensitivity and time prediction capability to abnormal signal abrupt change are improved, amplitude matching of temperature difference and current signals is achieved under the same time index, early abnormal nodes are accurately recognized, and a dynamic positioning sequence is established. Through joint tradeoff of distance and energy consumption, path lengthens and energy peak values are reduced, task throughput and equipment availability are improved, timing sequence capture and trend judgment capabilities are enhanced in an anomaly recognition layer, and accuracy and continuity of inspection decision and reliability of safety assessment are integrally improved.

The invention relates to the field of data query, and discloses a data aggregation query method and system based on time dimensionalities, the method comprises the following steps: obtaining a data aggregation query request, the data aggregation query request at least comprising a time index table of the time dimensionalities, the time dimensionalities being represented by at least one of year, month, day, hour and minute; analyzing the data aggregation query request, and determining an aggregation granularity, a date index value and an aggregation step length in a time index table; performing aggregation calculation according to the aggregation granularity, the date index value and the aggregation step length, and determining an aggregation date index; and performing field matching in a database through the aggregation date index, and performing grouping aggregation on matching results to determine a data query result. Indexes are added for different aggregation dimensions by creating a date index table, and an index calculation algorithm is combined, so that multiple time dimension query requirements are met, the data query speed can be remarkably increased, consumption of calculation resources is reduced, and the query flexibility is improved.

The invention provides a remote crusher cluster intelligent scheduling method and system based on 5G communication, and relates to the technical field of wireless communication, and the method comprises the steps: collecting the real-time load, feeding intensity, yield, conveying equipment flow and other data of crushers through a 5G network; a fluid dynamic model containing upstream-downstream material transportation time delay parameters is adopted in the central dispatching server, and downstream material input is predicted in combination with the material attribute tags; and generating a target production rhythm curve with a time index, forming a time-related cooperative control instruction, and issuing the time-related cooperative control instruction to an equipment end controller for synchronous execution. According to the method, operation parameters are adjusted in a self-adaptive mode according to feeding strength differences of different cavity types, the yield and mechanical loads are balanced, and the instantaneous accumulation and overload risks are reduced; the execution precision and robustness under network jitter are improved through mechanisms such as time slot alignment, time delay compensation and sub-lattice point protection; and the productivity utilization rate is increased, equipment abrasion is reduced, and production continuity and safety are guaranteed.

The invention provides a computing power center computing power scheduling method based on a multi-dimensional weight algorithm and a storage medium, and the scheduling method comprises the following steps: S1, obtaining a submitted task of a user through a scheduling portal, forwarding the task to a scheduling management service, and obtaining metadata information such as a resource demand, a priority and deadline of the task by the scheduling management service; s2, the resource management service system obtains the newest NodeMetric of each computing node through a unified monitoring service, task types are divided according to the task resource demand characteristics, and the NodeMetric is a node real-time index; and S3, the scheduling management service system generates Wfinal according to a multi-dimensional index based on the task type through a weight calculator, and sorts the candidate nodes from high to low according to the Wfinal.

The invention discloses a regional meteorological model error control method based on a neural network, and particularly relates to the field of regional meteorological model error control, and the method comprises the steps: obtaining reanalysis data of a first data source and station observation data of a second data source, and forming an input matrix containing space coordinates, time indexes and meteorological element values; preprocessing operation is executed on the input matrix, and preprocessing comprises the steps that interpolation correction is conducted on missing values, normalization processing is conducted on data of different dimensions, sequence alignment is executed based on a unified time index, and a first input matrix and a second input matrix which are consistent in structure are formed. By constructing a double-residual channel and executing residual reciprocal correction in a training process, dynamic balance fusion of reanalysis data and station observation data is realized on a spatial-temporal scale, so that the problem of cross-source data fusion imbalance in the prior art is solved.

An audio description system includes a memory and a processor. The memory stores source media comprising frames positioned within the source media according to a time index. The processor is configured to generate, using an image-to-text model, a textual description of each frame; identify intervals within the time index, each interval encompassing one or more positions of one or more frames; identify placement periods within the time index, each placement period being temporally proximal to an interval; generate a summary description based on at least one textual description of at least one frame positioned within a selected interval temporally proximal to a placement period; and associate the summary description with the placement period.

The invention discloses a holographic communication system-oriented multi-dimensional interaction quality perception and resource optimization scheduling method, which comprises the following steps of: firstly, constructing a system containing objective performance and subjective experience indexes according to a scene, acquiring network, holographic and user layer data, training a subjective experience representation model, and estimating the subjective experience indexes in real time; estimating indexes at the current moment and the next moment, constructing a design vector, and fusing physical, semantic and cognitive layer multi-source knowledge to obtain an enhanced input vector; reasoning the dynamic weight of the index through a utility function and a fuzzy rule, and calculating an interaction quality comprehensive score; screening indexes to be optimized, converting the indexes to be optimized into a multi-objective optimization problem by taking a comprehensive score reaching the standard as a constraint, and searching a compromise optimal scheme; and finally, converting the scheme into a resource configuration scheme to realize self-adaptive scheduling of network and terminal resources. Accurate mapping of objective indexes and subjective experience is realized, scenes and user requirements are dynamically adapted, and immersion experience and communication performance of holographic communication are improved.

A method for generating soft information for a digital signal decoder. The method includes receiving a first plurality of signals, each corresponding to a time index, each time index being associated with two states including state 1 and state 0. The method also includes processing the first plurality of signals to calculate path metric values of state 0 and 1 for generating each of a second plurality of most-likely symbols by hard decisions and intermediate information for a most-likely path and a second-likely path at each time index. The method includes computing a difference between the most-likely path and a second-likely path for a given one of the two states at each time index. The method also includes determining a soft information corresponding to either one of the two states at the time index per one of the second plurality of most-likely symbols.

The invention provides a real-time incremental RAG method and system based on a dual-tense knowledge graph, and belongs to the technical field of artificial intelligence and knowledge graphs. The method comprises the following steps: through dual-tense decoupling modeling of event effective time and system input time, constructing a versioned knowledge graph and establishing a tense index; efficient retrieval is carried out by adopting a time sequence pruning algorithm based on indexes, and dynamic expansion of a graph pattern is realized through a declarative compiler; realizing abstract-free high-precision result sorting by utilizing a multi-modal concurrent retrieval and attention fusion mechanism; incremental updating of the knowledge graph is realized through streaming data processing and a distributed engine; and closed-loop optimization is formed according to user feedback. The system effectively solves the problems that a traditional RAG scheme is high in data updating delay, tense information is lost, retrieval efficiency is low and expansibility is poor, tense query accuracy, retrieval efficiency and system throughput are remarkably improved, and meanwhile operation and maintenance complexity is reduced.

The invention discloses an equipment network offline event statistical analysis and batch-level stability evaluation method, relates to the technical field of equipment monitoring, and realizes multi-source data time alignment and credibility fusion by constructing a multi-source observation set and generating an equipment context vector and a reference time index. The CUSUM and the hysteretic threshold are combined to identify the offline event, and the real offline and false alarms are accurately distinguished in combination with the characteristics such as the probability type offline duration of the event, so that the event identification precision is improved. Through common cause event cluster clustering and batch multi-dimensional feature extraction, batch-level exposure normalization evaluation is realized. The stability grade and the index contribution degree can be accurately output based on the stability score of the robust normalization and trend degradation item, data support is provided for operation and maintenance decision, and the equipment network operation and maintenance efficiency and reliability are improved. The problems of heterogeneous multi-source data, inconsistent timestamps and missing conflicts in a large-scale equipment network are effectively solved, and the offline event recognition accuracy and the batch-level stability evaluation reliability are improved.

The invention discloses a crane operation controllability health state evaluation method and device in the technical field of engineering machinery, and aims to solve the problem that in the prior art, judgment is performed only by depending on a single type of signals, and collaborative analysis of multi-dimensional indexes is lacked. The method comprises the following steps: in response to a collected sensor signal, carrying out data processing to obtain sensor data; calculating a real-time index according to the sensor data; acquiring a real-time working condition, and dynamically adjusting a scoring weight according to the real-time working condition; calculating a health score according to the real-time index and the score weight; according to the method, various real-time indexes are acquired by adopting various sensor signals, and meanwhile, the score weight is adjusted according to the real-time working condition to acquire the health score, so that the health state evaluation work is cooperatively completed through different indexes, and the method has the adaptive adjustment capability for different working conditions, and is suitable for popularization and application. And the health score in a complex scene is prevented from deviating from an actual state.

The invention provides a dynamic extensible time wheel implementation method, assembly, device and medium, and belongs to the technical field of timed task management.The method comprises the steps that a target object is packaged into a time wheel processing object, and the time wheel processing object comprises an object identifier, a polling interval and an optional processing event; storing an object index and a polling time index of a time wheel processing object through a double-hash table structure, constructing a time wheel slot position through an annular time wheel queue, and filling an object identifier of the time wheel processing object into the time wheel slot position; after the time wheel is started, the annular time wheel queue is scanned according to the polling interval of the time wheel, and when the object identifier exists in the time wheel slot position, task operation is executed for the corresponding time wheel processing object; and in the running process of the time wheel, in response to the change request of the time wheel processing object, executing differentiated time wheel updating. Dynamic expansion of a large number of timed tasks is achieved, and the management efficiency is improved.

The invention discloses a data processing method, electronic equipment and a storage medium, the electronic equipment responds to a query request of an index, and determines a target cache region based on timestamp information of the query request of the index; wherein the target cache region comprises at least one of a local cache region and a server cache region; querying in the index data cached in the target cache region to obtain target data; wherein the index data cached in the target cache region is obtained by monitoring operation data sent to the message queue by at least one operation device, and performing real-time index calculation based on index configuration information and the operation data corresponding to the at least one operation device; the index configuration information is used for indicating an index acquisition mode; based on the scheme, the efficiency and the stability of processing the index data can be effectively improved.

The invention provides an alarm processing method based on a rule engine, an enhanced RAG and multi-source data fusion, which comprises the following steps: after responding to a monitoring alarm, extracting a grade, an IP and a trigger text, and carrying out three-level rule verification: if all passes, carrying out automatic self-healing and notifying a person in charge; if not, selecting an optimal case from the RAG library by using a vector similarity + multi-dimensional weighting algorithm, synchronously pulling real-time indexes in a preset time window of the host, fusing the optimal case and the real-time indexes, and inputting the two into a large model to generate a disposal suggestion; if it is suggested that no high-similarity case exists, a multi-role session group collaborative generation scheme is established and stored, the disposal result is used for dynamically updating the knowledge base, the plan is fused when the failure reaches the threshold value, and the successful scheme is written back to the RAG base. According to the invention, efficient automation and intelligentization of alarm processing can be realized, the operation and maintenance efficiency, accuracy and knowledge management capability are improved, and the defects in a complex alarm scene in the prior art are overcome.

The invention discloses an index data analysis method and device, equipment and a program product, and relates to the technical field of data processing, and the method comprises the steps: dividing index data into different hierarchies based on a preset layering strategy, and obtaining a hierarchy label of the index data; obtaining business scene characteristics of the index data, performing scene classification on the index data in combination with a preset index coding rule, and performing physical isolation on a storage result of the index data in a distributed column database physical table of the scene classification based on the hierarchical label; and selecting target index data and data display parameters based on a user interface, and querying the target index data in the physically isolated storage result to generate a real-time index billboard. Through combination of layered architecture design, a scene-based storage strategy and dynamic interaction capability, data islands are eliminated, timeliness is improved, analysis capability is flexible, the requirement of a traditional BI tool for manually configuring a data set is cancelled, and query efficiency is remarkably improved.

Devices and methods for media alignment are disclosed herein. An example device for media alignment includes a media processing device defining a media process path, a drive train, a sensor and a processing platform. The device is configured to receive a waveform signal, identify a first time index of the waveform signal, identify a second time index of the waveform signal, determine a region midpoint between the first time index and the second time index, correlate the region midpoint to a predetermined location index on a web of media, and control the drive train to advance the web of media along the media process path.

The invention discloses an economic early warning identification method based on big data analysis. The economic monitoring precision and multi-dimensional control are remarkably improved. In the financial dimension, the accumulated income and expenditure difference value and the cost monitoring value are accurately compared, the monitoring threshold value is flexibly calibrated by means of the adaptive cost index, and the economic dilemma is sensitively informed. In the age level, the average age of the group is considered, different age indexes are matched according to age monitoring values to optimize a threshold value, and the economic pressure bearing difference of each age group is considered. And time dimension: locking one-quarter and four-quarter special time periods, finely regulating and controlling by virtue of corresponding time indexes, and conforming to seasonal income fluctuation characteristics. According to the technical scheme, the method comprises the following steps of: selecting a group number, calling a proper personnel index according to the group number, accurately measuring the family scale influence, performing multi-dimensional collaborative operation, breaking through the previous single-dimensional limitation, practically enhancing the accuracy and reliability of economic early warning monitoring, starting from big data and multiple dimensions, and bringing remarkable advantages for an economic early warning monitoring model.

The application discloses a real-time index updating method and an updating device thereof, belongs to the technical field of real-time calculation, can effectively solve the data jitter problem in the real-time index updating process, can effectively solve the data delay problem in the real-time index updating process, is completely transparent to the end user in the process of real-time index updating, and realizes decoupling of the index updating and index use processes; a metadata storage unit is used for maintaining a registration information table of an index set, the registration information table comprises the following fields: a master unit is used for receiving an index set updating request, registering the index set to the metadata storage unit, and submitting a report data calculation job to a Flink cluster.

The invention discloses a time series data analysis method and device, a storage medium and electronic equipment, and relates to the technical field of the Internet of Things. By establishing a composite index structure combining a time index and an equipment index, under the composite index structure, corresponding index entries are dynamically created for a target data flow, the framework of the composite index structure is not changed, and the time series data analysis efficiency is improved. The content is dynamically filled along with the data, and finally a complete composite index result capable of being efficiently retrieved is formed. And the time index adopts a B-Tree structure, so that data in a specific time range can be quickly positioned. Equipment indexing is realized based on a hash table, data generated by specific equipment can be efficiently retrieved, the throughput bottleneck of mass data storage is broken through, and second-level writing and aggregation query of ten millions of data are realized. And in combination with artificial intelligence AI, distributed time series data management and an Internet of Things multi-source data fusion technology, in the face of massive time series data, the analysis processing capability of the time series data is improved to meet the demand of time series data analysis.

The invention discloses an event prediction method and system based on a time sequence hypergraph, and belongs to the technical field of event prediction. The method comprises the following steps: acquiring multi-source heterogeneous data of a target region, defining a unified time index, and generating a time sequence feature sequence of each variable of the region through preprocessing; then, identifying a variable causal relationship in the region based on a frequency domain anti-fact condition mutual information algorithm, and fusing the variable causal relationship with a time sequence evolution relationship to construct a time sequence hypergraph structure representing the interior of the region; a dynamic filter is used for filtering the graph, and deep features of the graph are learned by means of a multi-band spectrum gating mechanism, so that internal complex causal and time sequence modes are effectively captured; and finally, performing dichotomy prediction based on the learned graph representation, and outputting the occurrence probability of future events in the region. According to the method, accurate and explainable event prediction is realized, training and prediction do not need to cross regions, data privacy and calculation efficiency are guaranteed, and stronger robustness is shown for specific data distribution change of the regions.

The embodiment of the invention provides a strategy determination method and device. The method comprises the following steps: acquiring real-time index data and a real-time prediction load of a Serverless function; determining a plurality of target optimization functions according to the real-time index data and the real-time prediction load; the multiple target optimization functions comprise a first target function and a second target function, the first target function is used for minimizing the cold start delay of the function, and the second target function is used for minimizing the resource cost of the function; the candidate solution of each target optimization function indicates a combined strategy of a resource allocation strategy and a cold start strategy; a plurality of target optimization functions are processed by utilizing a multi-target evolutionary algorithm, and a target combination strategy is screened out from numerous candidate solutions by simulating the principle of survival of victims and inferior and continuous optimization of populations in a biological evolution process, so that the resource cost of the functions is reduced on the basis of ensuring cold start low delay of the functions; and the resource allocation efficiency is improved.

The application relates to the technical field of data processing of multi-source data fusion, and discloses a survey data integrated analysis method and system based on multi-source data fusion. The scheme deploys a multi-source sensor and collects original voltage, and normalizes in the range of each physical quantity; temperature, pressure and vibration three-dimensional observation and state are constructed based on a unified time index, and initial values are set; a constant value is kept to recursively predict; the variances of multiple measuring points are statistically calculated in a static window, and the observation noise is determined by combining the minimum quantization step to calculate the fusion gain; the covariance is linearly updated and corrected, and then the amplitude of each dimension is limited according to the abnormal upper limit; the three-dimensional results of multiple measuring points at each sampling time are spatially averaged, and if necessary, the results are rolled back; a fusion sequence and a statistical report are formed in the whole cycle. The method unifies the data scale, reduces subjective parameters and algorithm burden, models the noise adaptively according to the environment, stably suppresses noise and prevents faults, and guarantees continuous output and comparable analysis.

This invention discloses a method for processing multimodal data acquisition in intelligent manufacturing systems, relating to the field of industrial data governance technology. The method includes: acquiring multimodal data streams; segmenting raw data blocks and standardizing and encapsulating them to generate metadata records; establishing time indexes, batch indexes, and process indexes; hashing the metadata records to generate content addresses and writing them to object storage; recalculating the hash and comparing the content addresses to complete consistency verification and writing the results to a commit log table; generating operator records for successfully committed data blocks and hashing them to obtain operator addresses; registering lineage edges to form directed acyclic lineage relationships; generating leaf lists according to a preset range and aggregating them to obtain root digests; verifying content addresses object by object based on the leaf lists and root digests; replaying and recalculating to generate replay-derived data blocks; comparing them to generate audit records and storing them. This invention ensures continuous monitoring of data quality during big data acquisition through the replay and recalculation processing of directed acyclic lineage relationships.

The application discloses a kind of dynamic security evaluation methods, device, medium and product for underwater system, it is related to underwater system dynamic evaluation technical field, including: three-level evaluation trigger mechanism, gradually refine security risk perception accuracy, propose the real-time index weight of whole process of security risk perception, dynamic adjustment method of evaluation time window, and if evaluation result does not converge, there is design based on post-evaluation closed-loop feedback mechanism, risk disposal result is evaluated again, guarantee that evaluation mechanism responds to the optimization adaptive of risk situation, proposed multi-level perception trigger containing three-level evaluation trigger, different levels of security risk grading determination is realized to underwater system, and it has feedback reprocessing capability to risk, proposed dynamic evaluation adjustment containing abnormal index coupling potential risk and dynamic reevaluation based on risk degree information, has a certain predictive processing capability to risk, and weight and time window will be dynamically adjusted according to risk condition.

The application discloses a multi-dimensional demonstration method and device based on time domain technology and a storage medium, and relates to the technical field of data interaction. The application first acquires a time domain file containing multiple perspectives and / or multiple time points, and encapsulates multi-dimensional index information containing a space index and a time index. Then, a demonstration narrative stream including a key frame sequence of the demonstration and a camera motion path of the key frame is constructed based on the multi-dimensional index information. In the process of playing and rendering, the current camera state is acquired in real time, the prediction information of the camera motion path is combined, and the target display area in the current view cone is calculated. Finally, the corresponding target pyramid tile data is called from the multi-dimensional index information, and progressive decoding and rendering are performed in the order from low to high resolution, so as to realize the non-susceptible loading of the demonstration scene from the overall overview to the local micro details. In this way, the loading efficiency of the multi-dimensional demonstration scene is improved, and the resource consumption is reduced.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating video anchors for a video. In one aspect, a method includes obtaining key moment identifiers for a video, where each key moment identifier includes a time index value specifying a playback time in the video, and is indicative subject matter of the video that has been determined to meet one or more interest criteria that define salient topics within the video. For each key moment identifier, a video anchor is generated, where each video anchor indicates a playback time for the video, and may include an image of a frame that occurs near the playback time. Upon a selection of the video anchor by the user, an instruction in the video anchor causes a video player to begin playback of the video at the playback time specified by the video anchor.

The invention discloses an intelligent AI pressure test method, device and platform for a financial service system, and the method comprises the steps: obtaining a pressure test instruction inputted by a user, analyzing the pressure test instruction, and determining a target service system, a service scene and a pressure mode; creating a business scene map according to the target business system and the business scene, and creating a pressure generation model according to the business scene map and the pressure mode; controlling the pressure generation model to generate pressure data, and generating a simulation request according to the pressure data; sending the simulation request to a target service system, and obtaining real-time index data when the target service system executes the simulation request; and detecting the real-time index data to obtain a detection result, and generating a pressure test result according to the detection result. According to the invention, the business scene atlas is created through the target business system and the business scene, and the pressure generation model is created according to the business scene atlas and the pressure mode, so that the pressure test can fit the business scene, and the accuracy of the pressure test result is improved.