102 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 provides an electric energy meter clock error calibration system and calibration method. The method comprises the steps of presetting a control time table of a high and low temperature box and at least two automatic trimming schemes; the upper computer adjusts the temperature of the high-low temperature box according to the control time table; switching the connected meter numbers through a clock signal relay, obtaining a daily timing error message of each meter number in each round of test, and analyzing to obtain PPM and S / D data; carrying out mean value calculation on all PPM and all S / D data obtained by each meter number in each round of test, and obtaining a clock error value comprising a PPM mean value and an S / D mean value; after each round of test is finished, according to a clock error value corresponding to the meter number, assembling to obtain an RTC trimming frame according to the selected automatic trimming scheme, and sending the RTC trimming frame; and verifying an RTC trimming result. According to the invention, full-automatic test trimming can be realized, and the efficiency and precision of test and calibration are significantly improved.

The invention provides a loss prediction method and system for a light building material. Wherein the long-term change trend of the environment where the light building material is located is analyzed and processed, and an analysis result is obtained; according to the analysis result, key environmental factors having significant influence on aging of the light building material are identified, and the potential loss risk level of the building material is obtained; based on the potential loss risk level, mining historical data laws extracted from maintenance records, user feedback and a historical case library by using a time sequence analysis technology, and generating a performance degradation estimation curve; and making a maintenance plan and a replacement time table of the light building material by comprehensively considering functional requirements, economic cost and safety standards of the building according to the performance degradation estimation curve. According to the technical scheme provided by the invention, the aging prediction precision and the maintenance efficiency of the light building material are remarkably improved.

The invention discloses an animal epidemic prevention processing method and system based on a block chain, and the method comprises the steps: building a farm basic database, formulating an epidemic prevention time table in combination with animal variety characteristics and geographic position climate conditions, and determining epidemic prevention time nodes of a farm; the method comprises the following steps: acquiring farm environment data, animal health data and epidemic prevention operation records in real time through Internet of Things equipment, and generating an epidemic prevention execution data stream; comparing the epidemic prevention execution data stream with a preset epidemic prevention measure execution list, and judging the implementation condition of epidemic prevention measures by setting a threshold value and a rule; according to the epidemic prevention execution data flow and the implementation condition of the epidemic prevention measures, a random forest algorithm is adopted to analyze the epidemic prevention effect, and an epidemic prevention effect evaluation report is generated; and an epidemic prevention effect evaluation report and epidemic situation early warning information are encrypted, stored and shared through a block chain, and data support is provided for epidemic prevention decision making. According to the invention, intelligence, precision and traceability of epidemic prevention management of the farm are realized.

The invention relates to the technical field of multivariable time series prediction, in particular to a time series prediction method and system based on dynamic hypergraph and multi-scale coding. The method comprises the steps of performing data preprocessing on acquired data; constructing a multivariable time series prediction model, wherein the multivariable time series prediction model comprises dynamic clustering hypergraph construction and correlation information spreading and variable output; performing multi-scale time representation learning on the output variable; carrying out iterative training on the multivariable time sequence prediction model; and performing data prediction by using the trained model. According to the method, through dynamic clustering hypergraph learning, precise modeling of the complex space-time dependency relationship is realized.

A wireless device drops sounding reference signal (SRS) transmissions, in SRS resources linked for bandwidth aggregation, that conflict in time with a transmission or a reception scheduled by downlink control information (DCI). A time period between a last symbol of the DCI and a starting symbol of the SRS transmissions is equal to, or greater than, a number of symbols plus a guard period of the bandwidth aggregation.

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 discloses a beacon system based on dual-mode communication, an area approaching detection method, electronic equipment and a storage medium. The system comprises a plurality of beacons, each beacon comprises a controller, a communication module and a low-frequency transmitting module, and the controller comprises a transceiver; the communication module is used for receiving beacon configuration parameters of configuration equipment; the beacon configuration parameters comprise transmitting time slices and transmitting power; the controller is used for performing timestamp synchronization among a plurality of beacons through a private protocol in an idle time window of the transceiver, and establishing a unified time reference; under a unified time reference, determining a synchronization time table based on the transmission time slices; controlling a low-frequency transmitting module to transmit a low-frequency signal based on the synchronization time table and the transmitting power to form a signal coverage area; according to the method, microsecond-level accurate synchronization is realized through a private protocol, the problems that a traditional scheme lacks a time synchronization mechanism and signal conflicts are easily generated in multi-beacon work are solved, and the reliability of multi-beacon cooperative work is ensured.

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.

An apparatus for processing a substrate, comprises a plurality of processing modules, each configured to perform processing of a substrate; a transfer machine configured to transfer the substrate; and a controller configured to create a transfer time table, which causes the substrate to be transferred between the plurality of processing modules and to be processed, and to control the transfer of the substrate by the transfer machine and control the processing of the substrate in each of the plurality of processing modules, based on the transfer time table, wherein when it is determined that a retry or an extension of a cleaning process is expected to occur or occurs in a first processing module out of the plurality of processing modules, the controller sets the first processing module to be non-usable and updates the transfer time table, such that a subsequent substrate does not pass through the first processing module.

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 invention belongs to the technical field of heat-supply pipe networks, and particularly relates to an intelligent monitoring method and system for a heat-supply pipe network, temperature and pressure signals of each node in the pipe network are collected, the signals are converted into digital information, and response characteristics of the heat-supply pipe network under different environment conditions are evaluated based on the digital information; according to the evaluated response characteristics, the information interaction frequency among the nodes is adjusted, and the pipe network state is periodically checked by using the set information interaction frequency; determining an abnormal point and generating a warning mark according to a check result, and planning a maintenance path and a time table on the basis of the generated warning mark; and according to the formulated time table, the maintenance personnel are dispatched to the site to execute tasks, maintenance feedback is collected, and a monitoring strategy is optimized. The method can automatically identify and adapt to environmental changes, and ensures that the efficient operation of the system can be maintained even under extreme weather conditions.

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 present disclosure provides a battery management system including a reception unit configured to receive a start signal and a completion signal of job from another battery management system, a transmission unit configured to transmit the start signal and the completion signal of job by broadcast, a storage unit configured to store a time table including a job code of a plurality of jobs to be executed, a priority of corresponding job, and an execution time and period of corresponding job, a synchronization unit configured to execute synchronization of a job execution time using the received start signal or the received completion signal of job and the time table, and a job execution unit configured to execute a job using the time table stored in the storage unit and a time calculated by the synchronization unit.

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 present application discloses an improved transportation matching system, and corresponding methods and computer-readable media. According to disclosed embodiments, a transportation matching system trains a predictive request model to generate a metric predicted to trigger an increase in transportation provider activity within the geographic area for a given time period. Furthermore, the system determines a predicted gap between expected request activity and expected transportation provider activity for the geographic area during a future time period, utilizes the predictive request model and the predicted gap to generate a metric for the geographic area, and generates an interactive map associated with a customized schedule for the geographic area and the future time period based on the generated metric.

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.

A computer-implemented method for thinning scalar measurement data to reduce storage space required in a second data storage device to store the scalar measurement data, wherein the scalar measurement data was initially recorded in a first data storage device during a measurement period, the method comprising: (a) accessing the scalar measurement data from the first data storage device; (b) defining an amplitude range with which a plurality of amplitude values ​​of the scalar measurement data are compared; (c) setting a first or subsequent one of the amplitude values ​​as a reference amplitude value and setting a time corresponding to the first or subsequent one of the amplitude values ​​as a reference time value; (d) storing the reference amplitude value and the reference time value in the second data storage device; (e) accessing a next amplitude value of the plurality of amplitude values ​​from the first data storage device; (f) Determine whether an absolute difference between the reference amplitude value and the nearest amplitude value is greater than the amplitude range, and if the absolute difference between the reference amplitude value and the nearest amplitude value is not greater than the amplitude range: - discarding the next amplitude value by not storing it in the second data storage device; - Calculating an average measurement value based on a sum of the discarded next amplitude value and the previously discarded amplitude values ​​that are temporally adjacent to the discarded next amplitude value; - calculating a time gap value indicating a total time duration associated with the discarded next amplitude value and the previously discarded amplitude values ​​that are temporally adjacent to the discarded amplitude value; and - storing the average measurement value and the time gap value in the second data storage device; and - Go to step (g) or if the absolute difference between the reference amplitude value and the nearest amplitude value is greater than the amplitude range: - storing the next amplitude value and a time at which the next amplitude value was recorded in the second data storage device; and - setting the next amplitude value as the reference amplitude value and setting the time at which the next amplitude value was recorded as the reference time value; and (g) repeating steps (e) and (f) until all scalar measurement data recorded during the measurement period have been processed; and (h) generating a graphical amplitude-time representation of the scalar measurement data stored in the second data storage device, the graphical representation including one or more data gaps caused by discarding amplitude values ​​in step (f), the one or more data gaps having a time duration corresponding to the time gap value, and the amplitude values ​​discarded in step (f) in the one or more data gaps being replaced by artificially generated amplitude values ​​having amplitudes randomly distributed around the average measurement value.

Apparatus, method, and graphical user interface (GUI) for presentation and navigation of code path data. Code path data samples, including temporal data, related to a plurality of code paths of program code are used to render an interactive GUI including a set of graphical elements representing a corresponding set of code paths. Each graphical element indicates resource utilization changes associated with a respective code path over a time period. The set of graphical elements are visually arranged in a first dimension and the period of time is indicated in a second dimension. Different techniques are used to represent resource utilization over time including extending the graphical elements in a third dimension or changing a width of the graphical elements within a plane defined by the first dimension and the second dimension.

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.