114 results about "Time index" patented technology

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 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.

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.

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 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.

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 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.

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.

The invention belongs to the technical field of power systems, and relates to a reliability evaluation method and system for an extreme meteorological new energy high-permeability power system. The method comprises the following steps: judging extreme meteorology, and constructing a new energy output and meteorological factor coupling model under the extreme meteorology; establishing a system operation model considering extreme meteorology, and obtaining system operation state parameters and power distribution parameters; calculating a short-time index reliability score and an ultra-short-time index reliability score of the system under each extreme weather and different time length scales; and fusing the reliability scores to obtain a comprehensive reliability score. The method can accurately describe the instantaneous impact and short-term influence of the extreme weather on the operation of the new energy high-permeability power system, and realizes the quantitative analysis of the reliability of the system; the reliability indexes of the system are divided into a short-time type and an ultra-short-time type, the power shortage risks under different time scales can be captured respectively, and more refined and dynamic reliability evaluation is provided for system operation under extreme weather.

The application relates to a method and device for determining income, a computer device, a computer readable storage medium and a computer program product. The method comprises: obtaining response indexes of a plurality of building objects, the response indexes representing response capabilities of the building objects to grid instructions, and the response indexes comprising at least one of the following: a response timeliness index, a response effectiveness index, a historical response average time index, and a historical response average effectiveness index; determining comprehensive scores of the building objects according to the response indexes of the building objects; and determining target incomes of the building objects according to the comprehensive scores of the building objects. The method can more accurately distinguish the response performances of different building objects, and can more fairly, accurately and incentively allocate incomes to the building objects according to the response performances of the different building objects.

This invention discloses an optimization prediction method for software testing based on big data management, specifically including: Step 1, collecting test data and performing time index alignment and numerical processing to form a test feature set; Step 2, constructing a static Markov blanket and a dynamic Markov blanket, and determining the target feature subset by switching input boundaries; Step 3, constructing a neural prediction model based on the target feature subset and outputting predicted values; Step 4, dividing the target feature subset into relevant feature clusters and updating the model parameters using the decorrelation-based isoangular minimum angle regression rule; Step 5, outputting test results, defect risk, and resource consumption prediction values; Step 6, generating test optimization configuration results based on the predicted values. This invention completes the prediction through isoangular regression using a two-layer Markov blanket.

The invention discloses a charging load prediction method and device based on time sequence decomposition and electronic equipment, belongs to the technical field of load prediction, and aims to solve the problem that a traditional prediction method is difficult to effectively capture a complex seasonal rule of charging of an electric vehicle. The method comprises the following steps: acquiring historical load data, wherein the historical load data comprises a load value and meteorological environment information in a preset period; a prior feature and a basic feature are obtained based on the historical load data, the basic feature comprises at least one of a time index feature and a meteorological environment feature, and the prior feature is obtained by decomposing a time sequence feature of the historical load data by a Prophet model; fusing the prior features and the basic features to obtain an input feature vector; and inputting the input feature vector into a pre-trained prediction module for load prediction to obtain a prediction result. According to the embodiment of the invention, the accuracy of the charging load prediction result can be 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 application relates to the technical field of resource scheduling and discloses an intelligent resource scheduling supply method and system in a heterogeneous computing environment, which comprises the following steps: acquiring core heterogeneous data, performing exponential smoothing denoising to obtain a smooth state value, combining an HAT extended tuple, and analyzing to obtain a comprehensive capability score; automatically adjusting contribution degree by similarity of static characteristics and dynamic characteristics; constructing a coupling correction function, calculating a hybrid precision matching degree, combining an exponential product to reflect nonlinear correlation between targets; constructing a dynamic penalty function, optimizing a quantum genetic algorithm fitness function, and performing multi-target collaborative optimization; constructing a complexity coupling and dynamic adaptation mechanism to dynamically adjust a task segmentation ratio; designing a frequency and reconfiguration time index ratio to optimize FPGA bit stream preloading; constructing a synchronous delay coupling model to perform state synchronization optimization; balancing multidimensional feedback through a combined reward function; and improving explainability by quantifying the influence of decision factors through contribution degree entropy.

This disclosure describes schemes for synchronization block design and Time index indication. Considering some scenarios, like NTN, where there are lower workable SNR and larger CFO. However, to ensure quick cell search in such scenarios, novel schemes for synchronization block design and Time index indication are necessary. This disclosure proposes methods for enabling UE to quickly cell search and detect the synchronization signal and information, so that to ensure better communication system.

The invention relates to the technical field of machine learning, in particular to an enterprise-level AI application construction method, computer equipment and a medium, and the enterprise-level AI application construction method comprises the following steps: obtaining resource data and extracting calling records, separating types and states, comparing an action sequence with a resource number, and adjusting an action position. And dividing a time range and an area to which the action belongs, and positioning unmatched contents to obtain an AI application construction task configuration structure set. According to the method, time indexes of resource data are extracted, an action overlapping relation is established, action position deviation caused by resource conflicts is identified, sequence matching and difference separation of action contents in time slices are completed, non-intersection calling is eliminated, a scheduling structure is updated, and an action distribution range is delimited according to resource numbers and calling boundaries; the pushing action sequence and the resource use are coordinated and consistent in the timeline, and the connection continuity between the scheduling fragments and the adaptation capability between the task structures are enhanced.

The invention discloses a bill processing method and system based on a price comparison model and edge calculation, and belongs to the technical field of computers, and the method comprises the steps: 1, generating a composite identifier containing latitude, longitude, timestamps and bill feature point abstracts; 2, locally constructing a one-way state propulsion cache based on the composite identifier, binding the bill original data with the composite identifier, and marking the bill original data and the composite identifier as a pending state; step 3, packaging the pending state and the composite identifier into a data packet, generating an integrity check code based on the composite identifier, and storing an association pointer of the integrity check code and the data packet; 4, constructing a distributed state mirror image table by using physical coordinates and timestamps in the composite identifier, and storing state mirror image records through spatio-temporal indexes; according to the invention, bill data synchronization and state consistency under an unreliable network can be realized, loss, repeated processing, edge end efficient operation and generation of a reliable approximate result during network disconnection are avoided, and the processing real-time performance is improved.

For irradiating a target with a charged-particle beam, a plurality of stripes are written using the beam at respective stripe locations (y0-y15). In order to reduce charging effects during irradiation, the time sequence of the stripes, expressed as a function y(k) of a time index k, is different from a direct linear order. Based on a pre-determined integer number w ("stripe wing number") which is not larger than the number of stripes divided by at least 4, the time sequence for writing the stripe of location y(k) at time index k is rearranged so as to comply with the condition that the stripe at a location y(k) is at most w stripe locations before the stripe location yk and at most w stripe locations behind the stripe location yk, and / or between each pair of spatially directly neighboring stripes, at most 2w different stripes are written.

The invention discloses a double-lane outer side geomagnetic vehicle track continuous reconstruction method and system for multiple leak detection, and is suitable for a scene in which geomagnetic sensors are arranged on outer side lane lines of two sides of a one-way double lane and are not arranged in the middle. And taking the event-level geomagnetic detection result as input, establishing a time index cache sorted according to measurement timestamps in a data center, and processing out-of-order events in a fixed lag sliding window. Self-calibrating the omission ratio of each sensor group on line based on the output stable track segment and obtaining the reliability; constructing an event transfer graph allowing to cross a plurality of sensor groups, accumulating detection-lacking punishment according to the reliability of the intermediate sensor group, and forming connection cost by combining time-space and feature consistency; and solving the minimum total cost connection under one-to-one constraint to obtain a trajectory path. And connecting the cross-missing section to generate a virtual track point and outputting a stable track section in a fixed lag manner. According to the invention, waveform level processing or additional hardware is not needed, and track continuity and online output stability can be improved.