69 results about "Time table" patented technology

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention discloses an integrated optimization method based on an abnormal state urban rail transit operation end period timetable and a jump stop plan. The method comprises the following steps: acquiring passenger demand data and train operation parameters in an operation end period; according to the passenger demand data and the train operation parameters, a mixed integer multi-objective optimization model is constructed, and the model comprises an objective function and constraint conditions; solving the mixed integer multi-objective optimization model by adopting a multi-objective whale optimization algorithm to obtain a Pareto optimal solution set; and outputting the Pareto optimal solution set for generating a time table and a jump stop plan of the operation end period. According to the method, the time table and the jump stop plan are integrated and optimized, so that the total waiting time of the passengers is remarkably shortened in the operation ending period, meanwhile, the number of the passengers who are not served due to jump stop is effectively reduced, and the overall operation efficiency and the service fairness are improved; according to the method, various optimization schemes can be flexibly generated, and decision basis is provided for operating enterprises.

The invention relates to the technical field of multi-machine cooperative control, in particular to a multi-machine cooperative automatic operation method and system for train loading. Comprising the steps that position information and predicted arrival time fluctuation data of a train are collected in real time through a sensor network, and an arrival time uncertainty quantitative index is obtained through a time sequence prediction model; according to the index, judging and determining an equipment allocation priority sequence; adjusting equipment working parameters and generating a preliminary adjustment scheme by combining the equipment state data and an optimization algorithm; according to the optimization parameters and the multi-machine cooperation requirements, a resource utilization model is constructed, a multi-machine cooperation rotation time table draft is obtained, refining is carried out, and finally a refining time table is generated; and based on the time table and the real-time feedback, updating the equipment cooperation time to obtain a final multi-machine cooperation scheduling scheme. The operation state of the loading equipment can be dynamically adjusted, the collaborative operation efficiency of the equipment is improved, loading delay is reduced, and the resource utilization rate is maximized.

The invention relates to an intelligent control method and system for a double-table-board laser cutting machine in the field of new-generation information technology, and the method comprises the steps: collecting complex workpiece parameters and processing duration data of each task in the double-table-board laser cutting machine in real time, and carrying out the preliminary sorting of an initial task sequence through a genetic algorithm, thereby obtaining a preliminarily optimized task distribution sequence; for a low-power operation scheme, a cooperative operation time table is generated in combination with the balanced task allocation sequence, potential energy waste points are extracted from the time table, and a refined energy management sequence is obtained by fusing task duration data through an information processing link; monitoring a real-time equipment state, if an idle state is detected, switching to low-power operation, and determining a final task coordination and energy consumption control model by continuously updating sequence parameters; and for the final task cooperation and energy consumption control model, historical operation data is obtained for verification, and if a verification result shows that the production efficiency is improved, an optimized double-table-board laser cutting machine operation protocol is output.

A quantum computing system includes a quantum device configured to execute a quantum operation; instrumentation devices configured to transmit a control signal for executing the quantum operation; and a synchronization trigger distribution device configured to transmit a second trigger signal for instructing transmission of the control signal to at least one of the instrumentation devices based on a first trigger signal indicating a timing. The synchronization trigger distribution device includes a control sequence table for specifying a predetermined instrumentation device, to which the second trigger signal is to be output, based on the first trigger signal, and a delay time table for specifying a delay time corresponding to each of the instrumentation devices. The synchronization trigger distribution device outputs the second trigger signal to the instrumentation device specified based on the control sequence table, at a timing based on the delay time specified based on the delay time table.

The application discloses a multifunctional smoke fire early warning method and system and a readable storage medium, and relates to the technical field of safety warning. In the method, a current time window is determined according to the position of the current time in a preset time table; a plurality of types of sensor data are collected in the current time window; if the current time window is a second type of window, and any sensor data is greater than a corresponding upper limit threshold value, an early warning information is sent; if the current time window is a first type of window, a change value of the sensor data in the current time window is obtained, and change values of the sensor data in a preset number of continuous time windows before the current time window are obtained; in the case that the change value continuously increases with time and each increase is greater than an accumulated threshold value, an early warning information is sent, and if any sensor data is greater than a corresponding non-sensitive threshold value, an early warning information is sent, so that the false positive rate in a high smoke environment, such as a kitchen, is reduced.

The invention discloses a seismic data removal processing method, system and device and a medium, and the method comprises the following steps: S1, analyzing and processing seismic data, and generating a minimum offset seismic attribute; s2, determining a'full coverage 'boundary and a'primary coverage' boundary by using the minimum offset seismic attribute, giving a corresponding water bottom removal time, and calculating an unknown water bottom removal time by using an isoline interpolation method; s3, smoothing the calculated water bottom removal time; and S4, applying a removal time table: writing all the water bottom removal time into a seismic trace header in a trace-by-trace matching mode, and carrying out zero filling processing on the amplitude value of the part above the water bottom removal time. The method is high in adaptability to irregular boundary three-dimensional seismic data, and the purposes of shortening the data processing period, reducing the labor intensity of processing personnel and improving the project operation efficiency can be achieved.

The application discloses a signal frequency measurement method and device based on a single FPGA, an FPGA and a medium, and relates to the technical field of electronic measurement. The method comprises the following steps: when the rising edge of a start signal arrives, the first integer period time and the first residual time are measured; the first residual time represents the time from the first rising edge of the logic signal to the next rising edge of the clock signal; according to the first integer period time and the first residual time, the time parameter in the logic signal is determined; the time parameter represents the time from the rising edge of the start signal to the first rising edge of the logic signal; and according to the time parameter, the frequency of the logic signal is calculated. The method can use a single FPGA to perform all time measurements, realize high-precision frequency measurement, and does not need additional chips, thereby improving the integration of the system.

The application discloses an ice-making time determination method and an ice-making device, and belongs to the technical field of ice-making. The method is applied to the ice-making device and comprises the following steps: obtaining a target water temperature; obtaining a target environment temperature; determining a target ice-making time according to the target water temperature and the target environment temperature through an ice-making time table, wherein the ice-making time table comprises ice-making times required for manufacturing ice blocks of a preset size under different water temperatures and different environment temperatures; and displaying the target ice-making time. The target ice-making time required for manufacturing ice blocks of the preset size under the target water temperature and the target environment temperature is determined through the ice-making time table, and subsequently, ice blocks of the preset size can be directly manufactured by using the target ice-making time, so that the quality of the manufactured ice blocks is improved. Moreover, after the target ice-making time is determined, the target ice-making time can be displayed, so that the user can accurately know how long time is needed to obtain ice blocks of the preset size after starting ice-making, and therefore, the ice-making experience of the user is improved.

The present application relates to a kind of intelligent warehousing systems for latex matrix ground station applied to the technical field of intelligent warehousing, the system includes warehouse, multiple densely arranged shelves, multiple AGVs and central dispatch system. Formed between the shelves and the inner wall of the warehouse one-way traffic loop network by multiple channel segments, channel width and the minimum safe traffic width required for a single AGV match, central dispatch system plans path for AGV executing task, and generates reservation occupancy time window for channel segment passed by it, and then maintains global space-time table to detect space-time conflict between AGVs in real time;When detecting conflict, the system dynamically calculates the traffic priority of conflict AGV, and instructs low-priority AGV to stagger its time window through speed regulation, thereby fundamentally avoiding deadlock.The present application realizes efficient, continuous and reliable operation of the system while ensuring space utilization.

The invention discloses an energy consumption detection system based on a smart campus, and relates to the technical field of energy consumption monitoring and adjustment, and the system comprises a data collection module which is used for obtaining curriculum schedule data from an educational administration system, obtaining a work and rest time table from a dormitory management system, and collecting the water and electricity energy consumption data of each region in real time; the dynamic reference generation module is used for associating reference energy consumption according to a course type and dynamically correcting a reference value based on an inter-class rest period and a light-off period in a work and rest time table; the anomaly detection module is used for calculating the deviation between the actual energy consumption and the corrected reference value, and generating a grading alarm signal when the deviation exceeds a threshold value; and the control execution module responds to the alarm signal to execute equipment linkage control. Therefore, the monitoring real-time performance can be improved, the prediction precision can be improved, and the operation and maintenance cost can be reduced.

The invention relates to the technical field of energy distribution, and solves the technical problems that in the prior art, a traditional system often regulates and controls a whole building, neglects regional functions, easily causes supercooling and energy waste, controls start and stop according to a fixed time table, cannot respond to dynamic factor regulation and control lag and is poor in economical efficiency. The method comprises the following steps: S1, obtaining a planar graph of each floor of a target building, and deploying a plurality of data acquisition points for carrying out multi-position multi-dimensional data dispersion acquisition on the target building, according to the method, through the self-attention mechanism model and the one-dimensional convolutional network model, finer modeling is realized, the prediction precision is remarkably improved, the relevance of a modeling area is improved through the improved graph attention network model, optimization and updating are automatically performed, the universality and the adaptability of the method are enhanced, curve smoothing prediction is performed on a result, and the prediction accuracy is improved. And the stability of prediction results is improved.

The invention discloses an intelligent bus scheduling method based on big data and an mcp protocol. The intelligent bus scheduling method comprises the seven steps of S1, obtaining a natural language demand of a user; s2, performing cue word optimization on the demand input by the user by utilizing a prompt function of an mcp protocol; s3, calling a data acquisition tool in the mcp server by using the large model to acquire multi-source data required by scheduling optimization; s4, calling a prediction tool and a clustering tool in the mcp server by using the large model, predicting passenger flow and dividing time periods; s5, calling a time table generation tool in the mcp server by the large model, and generating an optimal departure time table; s6, calling a scheduling generation tool in the mcp server by the large model to generate an optimal scheduling plan; and S7, summarizing and outputting the whole scheduling plan. According to the invention, intellectualization and automation of bus scheduling are realized, and the scheduling efficiency and scientificity are greatly improved.

The invention relates to an urban green land landscape evaluation method and system based on a three-dimensional data point cloud. According to the method, a phenological time table containing key phenological events and an uncertain range is generated by constructing a vegetation phenological rhythm prediction model, an index sensitivity weight matrix calculated based on point cloud time sequence data is combined, a non-uniform acquisition strategy is generated by using a joint optimization model, point cloud data acquisition and feature extraction are performed on the basis, and a vegetation phenological rhythm prediction result is obtained. And finally, according to the phenological stage, dynamically selecting a high-sensitivity index for calculation, so that the vegetation morphology data with the most characterization is accurately acquired in the key phenological stage, and the contradiction between data redundancy and insufficient feature capture caused by a traditional fixed interval acquisition mode is effectively solved. And the data acquisition efficiency and the accuracy and pertinence of landscape evaluation are remarkably improved.

The invention relates to the technical field of furnace temperature control, in particular to a method and system for adjusting the furnace temperature of a steel rolling heating furnace, and the method comprises the steps: firstly responding to the input of a target steel billet, calculating the remaining in-furnace time of the target steel billet, then matching the remaining in-furnace time with a preset machining section machining time table to obtain a current machining section of the target steel billet, and finally, adjusting the furnace temperature of the target steel billet. The correction coefficient of the current machining section is called through a table look-up method, then the theoretical furnace temperature set value of the current machining section is corrected through the correction coefficient, an actual furnace temperature set value is obtained, finally, whether the monitored temperature of the current machining section is lower than the actual furnace temperature set value or not is judged, and if yes, the current machining section in the heating furnace is heated. Compared with the prior art, the method has the advantages that the furnace temperature can be reliably adjusted when infrared temperature feedback is not accurate, and safe and reliable production is maintained.

A power line communication method and a related apparatus, which relate to the technical field of communications. The method is applied to a head-end node. The method comprises: acquiring a windowing plan, wherein the windowing plan comprises the number of windows and the start time and duration of a first time window, the windowing plan is obtained on the basis of the number of windows and a windowing start time, the windowing start time comprises a zero-crossing point time of a zero-crossing point of an alternating-current voltage or a first reference time, and the first reference time represents a network time base (NTB) of a head-end node; and sending the windowing plan to a tail-end node, wherein the windowing plan is used by the tail-end node to obtain a coding modulation parameter corresponding to the first time window. The present application can improve the communication efficiency between communication nodes.

The application provides a multimodal collaborative distribution scheduling method, comprising: constructing a dynamic environment model according to terrain data and traffic condition data, extracting distribution demand features from real-time order information, adopting a task decomposition technology to split a complex order into a plurality of subtask units to obtain a decomposed subtask set; extracting subtask features of adjacent regions and similar time windows from the optimized subtask sequence, adopting an intelligent combination technology to perform clustering analysis on the subtasks, determining the spatiotemporal correlation between the subtasks, and generating a preliminary subtask aggregation group; calculating path cost and time cost of each distribution batch according to a final resource allocation scheme and the subtask aggregation group, and adopting a genetic algorithm to optimize and iterate the batch path to obtain a time table and route planning of an efficient distribution batch.

The application provides a long-term load prediction method combined with deep learning, relates to the technical field of load prediction, and comprises the following steps: obtaining date characteristics of each date and performing normalization processing to obtain date representation; obtaining all historical load sequences to obtain global mode information and compress the global mode information into the date representation of the corresponding date to generate global prediction; obtaining historical observation values of a backtracking window to subtract the global prediction of the corresponding date to obtain residual errors of the backtracking window; inputting the residual errors of the backtracking window, the date representation of each date and the date representation of the next date into a short-term prediction network to obtain prediction residual errors of the next date; adding the prediction residual errors to the residual errors of the backtracking window, and repeating the previous step to obtain residual errors of all dates to be predicted; and combining the global prediction and the residual errors of all dates to be predicted to obtain a final prediction result; the application grasps the global mode of the sequence from the global information of the time representation, increases the model throughput, and realizes accurate long-term load prediction.

The invention relates to an inventory real-time monitoring and intelligent loss reporting processing method and system, and relates to the technical field of warehouse management, and the method comprises the steps: obtaining a current region number and a corresponding temperature and humidity time table and weight time table; searching a storage temperature range, a storage humidity range and a storage weight from the regional information database; searching a temperature value and a humidity value from the temperature and humidity time table, and searching a weight value according to the weight time table; when the adjacent abnormal temperature and humidity numbers exist, outputting a temperature and humidity abnormal signal; a weight reduction proportion is calculated according to the weight value and the stored weight, and when the weight reduction proportion is larger than a preset proportion threshold value, a weight abnormal signal is output; when the temperature and humidity abnormal signal or the weight abnormal signal exists, generating loss reporting information; and obtaining a replenishment scheme according to the current area material information and the loss report information, and outputting the loss report information and the replenishment scheme together. The method has the effect of finding the deterioration condition of the materials in the storage process in time.

The invention discloses a text processing method and device and a storage medium. Relates to the technical field of civil aviation information, and comprises the following steps: receiving a to-be-processed first text in a natural language form, and extracting a time text segment in the first text, the time text segment comprising time information; determining a time expression category of the time text segment, the time expression category being a category of an expression mode of the time text segment for the time information; processing the time text segment according to the time expression category to obtain a target time text; and replacing the time text segment in the first text with the target time text to obtain a second text. Through the method and the device, the problem of relatively low efficiency of processing the time text in related technologies is solved.

The invention relates to a streaming and batching integrated data updating method and device, equipment, a storage medium and a product. The method comprises the steps that real-time data are obtained and written into a real-time table, the real-time table comprises a first time attribute field, and the first time attribute field is used for recording the writing time of the real-time data; the method comprises the following steps: acquiring offline data from a data source, and extracting, converting and loading the offline data to obtain offline data in a target format; adding a second time attribute field to the offline data in the target format, wherein the second time attribute field is used for recording the generation time of the offline data; and determining whether to update the data in the real-time table or not according to the write-in time of the real-time data and the generation time of the offline data. Therefore, whether the data in the real-time table needs to be updated or not can be determined through the time corresponding to the real-time data and the off-line data, the timeliness and accuracy of the data in the real-time table are ensured, and stream batch integration of the off-line data and the real-time data is realized.

The present application discloses a market data processing method and apparatus, a device, and a storage medium. The method comprises: in response to a running operation of a target quantitative strategy, acquiring a current running moment, an underlying asset in the target quantitative strategy, and a target time unit of subscribed market data in the target quantitative strategy; acquiring a resource category of the underlying asset, and on the basis of the target time unit, the resource category and a resource category trading schedule, determining whether the current running moment is a starting time point corresponding to the target time unit; if so, acquiring, from cache data, first market data corresponding to the underlying asset and the target time unit; if the first market data is not real-time market data of the underlying asset in the target time unit at the current running moment, acquiring, from the cache data, second market data corresponding to the underlying asset and a candidate time unit at the current running moment, and constructing real-time market data on the basis of the second market data; and executing the target quantitative strategy on the basis of the real-time market data.

The invention discloses a multi-user-priority charging pile server-side sequence-dividing management method and system, and relates to the technical field of charging service scheduling. Pile group states are collected, charging slot operation records are established based on interface occupation, session identifiers, output power, metering increments, heartbeat and fault codes, release moments are calculated, and an available time table is generated; according to the required electric quantity, the available power, the arrival deviation and the historical cancel information, calculating a predicted time length, credibility and a compensation item, forming a priority and generating a sorting table, generating an arrival interval for the in-transit reservation, and taking a conservative upper bound to participate in simulation; performing forward simulation by taking the available timetable as a time orbit, determining expected charging starting and ending moments, generating an execution plan packet containing power derating and a reserved window, and issuing the execution plan packet; and when a change occurs, triggering iteration, updating credibility and muzzle reliability according to arrival, waiting and power errors, dividing a frozen set and a rearranged set, only recalculating the rearranged set, and replacing the plan with a version number.

The application relates to the technical field of video understanding and intelligent analysis, and discloses a long video text time period retrieval method, which comprises the following steps: adopting a time sequence pre-screening network to perform feature extraction on all obtained video clips to obtain a clip-level visual representation; calculating a correlation score based on the clip-level visual representation and a global semantic representation of an input query text to generate a candidate time window; utilizing a multi-scale hierarchical time sequence positioning trunk to perform fine positioning on each candidate time window, constructing a multi-scale time representation set from fine to coarse, combining the query text to perform cross-modal interaction, and predicting a global time boundary of an optimal candidate time period; and selecting a time window that is most matched with the query text from multiple candidate time windows based on a unified rearrangement score, and outputting the global time boundary of the optimal candidate time period in the time window. The application takes into account training data expansion, long video calculation efficiency and boundary positioning accuracy, and can be more suitable for actual application requirements in a long video scene.

The present disclosure provides a data processing method, device, equipment and storage medium. The present disclosure relates to the technical field of computers, and particularly relates to the technical field of artificial intelligence such as data mining, data analysis, data query. The specific scheme is: obtaining to-be-processed data; obtaining a target ID pair based on the to-be-processed data; updating a real-time table based on the target ID pair, the real-time table including first pass-through records of a first type of ID; mounting the real-time table to offline tables of each cluster in a data pass-through system, the offline tables including second pass-through records of a second type of ID, the second pass-through records including at least a second corresponding relationship between the second type of ID and second attribute information of the second type of ID. According to the scheme of the present disclosure, the real-time table can be updated to supplement the missing part of the pass-through rate of the data pass-through system due to the poor timeliness of the offline table, thereby improving the efficiency of real-time ID query and the accuracy of ID identification.

The invention discloses a distributed database fragmentation key updating method and device and electronic equipment, and the method comprises the steps: carrying out the synchronous updating of a first fragmentation key field recorded in a real-time table when a transaction is detected, if the fragmentation key is judged to be changed, and carrying out the synchronous updating of a second fragmentation key field recorded in a real-time table after the synchronous updating is completed; and asynchronously updating the second fragment key field recorded in the non-real-time table. According to the method, the data consistency and the system performance are balanced through the differential updating strategy, the strong data consistency is ensured by adopting a synchronous updating mechanism for the real-time table with high real-time requirement, and the problem of data inconsistency possibly caused by asynchronous updating is effectively avoided; and aiming at the non-real-time table with relatively low real-time requirement, an asynchronous updating mode is adopted, so that the system performance bottleneck and main flow blockage caused by synchronous updating are effectively avoided, and the system throughput is improved. According to the method, through a collaborative mechanism of synchronously and asynchronously updating the fragmentation keys, the consistency of the data before and after and the high concurrent processing capability of the system are ensured in the fragmentation key updating process.

The application provides a setting method of a rail potential limiting device meeting human body tolerance, comprising the following steps: S1: obtaining a human body tolerance limit voltage and a corresponding time table; S2: adjusting the voltage value of the tolerance limit voltage corresponding to each tolerance time in the table of step S1 to obtain a delay time and voltage setting value corresponding table; S3: adjusting the table obtained in step S2 according to the contactor action time without delay, the step length and the setting value range of the thyristor trigger voltage to obtain a final delay time and voltage setting upper limit value corresponding table of the rail potential limiting device. The application uses the disclosed and recognized human body tolerance limit voltage and the corresponding time provided in the standard, combines the current equipment technical level, comprehensively considers the action logic process, the setting value setting range and the step length, the inherent time of the equipment and other factors, and proposes the setting voltage setting value and the time setting value upper limit meeting the human body tolerance limit, which is used for setting multiple groups of setting values.

The invention discloses a music playing method and device, a terminal, a storage medium and a computer program product, and the method comprises the steps that the terminal determines gait frequency and first time based on gait data, and the first time represents a time point of HS; and playing music based on the gait frequency and / or the first time.

The invention discloses a method and equipment for predicting runway occupation time in an aircraft landing stage, and relates to the technical field of aviation. Aircraft data are screened through Pearson correlation analysis, and strong correlation data and weak correlation data are obtained. Based on the strong correlation data of the multiple time points, an advanced feature sequence is obtained through a double-branch memory structure. The advanced feature sequence represents the extracted local time sequence features. And through a global association module, based on the advanced feature sequence, mining a global dependency relationship between the time point and the feature dimension, obtaining a predicted occupancy time length. The predicted occupation time represents the predicted duration of time occupied by the runway in the landing stage of the aircraft. The beneficial effects of significantly improving prediction precision, enhancing generalization and robustness, and directly improving operation efficiency and calculation efficiency and precision balance are achieved.

A system for time-controlled optimization and real-time control of dynamic systems under constraints, based on the time to loss of control, consisting of: a data acquisition module configured to receive real-time data from one or more technical sources representing the state of a monitored dynamic system; a preprocessing module connected to the data acquisition module and configured to process and filter the received real-time data to generate a structured representation of the current system state; a state prediction module connected to the preprocessing module and configured to receive the preprocessed data and, based on the structured representation of the current system state, calculate predicted future states of the monitored dynamic system over a defined time horizon;a Time to Loss of Control (TLC) module connected to the state prediction module and configured to compare predicted future states with predefined thresholds representing acceptable operating limits, determine a Time to Loss of Control (TLC) that represents an estimated time at which the monitored dynamic system is expected to reach an unstable or undesired state, and continuously updates the TLC as new real-time data becomes available;a constraint modeling module configured to identify operational constraints related to the monitored dynamic system, wherein the operational constraints include resource constraints, safety thresholds, and operating rules, and wherein the constraint modeling module is further configured to transform the operational constraints into a structured representation that defines a feasible set of permissible actions;An optimization module connected to the constraint modeling module, comprising a quantum processing unit configured to perform constrained optimization using energy minimization-based computational techniques to determine executable control actions within the remaining time until loss of control, wherein the optimization module is configured to determine one or more executable control actions that improve system stability based on the current system state, predicted future states, determined TLC, and structured representation of the operational constraints, wherein the one or more executable control actions are determined based on the remaining time until loss of control (TLC) such that only actions possible within this time window are considered;A control signal generation module connected to the optimization module is configured to convert the results of the optimization module into executable output signals, which are executed by a control execution module connected to the monitored dynamic system. After the signal is executed, control actions are automatically performed on the monitored dynamic system within the remaining time until loss of control.