11 results about "Statistical timing" patented technology

The application discloses a kind of dynamic congestion factor updating method and device of multiple physical paths, it is related to system on chip and request scheduling technical field, this method includes: multiple statistical time windows are independently allocated for each physical path, and only the first request sent in each window is round-trip delay timing, can accurately obtain the real transmission delay of each path with minimum cost;The average of the set number of single actual delay value can filter out transient fluctuation interference, and obtain stable and reliable delay average value;Based on the ratio of delay average value and physical delay value, dynamically update congestion factor, so that congestion factor can reflect the actual congestion degree of each physical path in real time, accurately, to realize the fine-grained, adaptive, early perception of multiple physical path congestion state, provide accurate basis for request injection control, avoid response congestion and anti-pressure propagation, improve the overall transmission efficiency and stability of system on chip.

Embodiments of the present application provide a fault analysis data storage method, a fault analysis method, a device and a storage medium. The method comprises: by an electronic device running a real-time operating system, every interval of a preset time length, obtaining first identification information of a task executed by the electronic device based on the real-time operating system at the current time, and storing the first identification information, updating the cumulative running number of a plurality of tasks within a statistical time according to the first identification information, and storing the updated cumulative running number, when the electronic device has a task timeout not executed, generating a data file comprising the stored first identification information and the cumulative running number, and storing the data file in the non-volatile storage unit of the electronic device, providing analysis data for subsequent fault analysis and positioning.

The application discloses a bearing fault diagnosis method and system based on statistical time rearrangement synchronous compression transformation, comprising the following steps: collecting bearing vibration and rotating speed signals, and intercepting vibration signal segments under stable working conditions; calculating a group delay estimation operator by using different window function short-time Fourier transformation; determining a group delay period center and calculating a group delay period and a group delay time width where the group delay period center is located based on the above operator; constructing a modified group delay estimation operator based on the unbiasedness of the group delay estimation operator at the center; statistically analyzing the group delay time width distribution difference between signals and noise, constructing an adaptive noise reduction threshold, and calculating a robust noise standard deviation estimation; constructing a statistical group delay estimation operator and calculating statistical time rearrangement synchronous compression transformation according to the threshold and the standard deviation; generating a time-frequency envelope spectrum based on the time-frequency representation, using a fault characteristic energy proportion index to extract an adaptive enhanced time-frequency envelope spectrum; finally, taking the index as a bearing state characteristic index to complete fault diagnosis.

It discloses a statistical timing analysis method of an integrated circuit under an advanced process and a low voltage. By simulating the fluctuation of process parameters of the integrated circuit under the advanced process, a statistical circuit timing model is built based on the relationship between the delay of the integrated circuit under the low voltage and the process parameters, and the maximum delay and the minimum delay under timing fluctuation of the integrated circuit are analyzed.

The application relates to the technical field of computer event statistics, in particular to a system event and application event statistics method and system, which comprises the following steps: presetting an event string based on an event definition rule and integrating the event string into an event list; receiving a local socket to collect event messages, and performing split processing on the event messages to obtain a plurality of event sub-strings; performing first and second mapping on the event sub-strings in the event list to obtain a matched main type segment and a sub-scene segment; performing event statistics after the mapping is matched to accumulate the number in the corresponding statistical group of different statistical time periods based on the event message segmentation; clearing and / or recording the count in the statistical group based on a timer in each statistical group; responding to a real-time data request, and outputting current statistical information according to the time period requirement of the real-time data request. The application has the effect of realizing high-speed statistics of event occurrence in a resource-limited embedded device.

The invention discloses a multi-dimensional dynamic load rate statistics and early warning method based on a CAN gateway. The method comprises the following steps that S1, message IDs, data lengths, message contents and transmission timestamps of a multi-channel CAN bus are collected in real time through the CAN gateway; s2, configuring an adjustable dynamic statistical time window, and recording the actual transmission time of the message in the window; s3, based on the collected data, classifying according to message ID, ECU, function requirements and data length dimensions, and calculating a dynamic load rate; s4, setting at least two levels of early warning thresholds and comparison mechanisms; s5, if any dimension exceeds the standard, triggering early warning of the corresponding level, and outputting information including the bus identifier, the ECU, the message ID, the load rate and the standard-exceeding dimension; s51, synchronously and automatically analyzing the message ID or ECU with the highest dimension proportion, and generating a correlation analysis report to position the source; and S6, updating and storing load rate data and early warning records in real time, supporting reading through a UDS protocol and the like, and exporting for bus optimization analysis.

The invention discloses a late data correction method and device, equipment and a medium, and the method comprises the steps: obtaining a plurality of pieces of initial arrival data in a current statistical time window, and determining a target event corresponding to a first data identifier of each piece of initial arrival data; determining a plurality of second data identifiers corresponding to the target event, matching the first data identifiers with the second data identifiers, and determining the amount of late data of the current statistical time window based on a matching result; determining statistical delay duration associated with the delay data quantity, and determining supplementary statistical time of the current statistical time window according to window closing time of the current statistical time window and the statistical delay duration; and obtaining supplementary arrival data in the supplementary statistical time, and carrying out aggregation processing on the supplementary arrival data and the initial arrival data to obtain a delayed data correction result of the current statistical time window. Through the technical scheme, the late data correction efficiency and the correction result accuracy can be improved.

Examples described herein provide a computer-implemented method that includes receiving a circuit design and a set of known process, voltage, and temperature (PVT) points for components of the circuit design. The method further includes determining parameter ratios for the set of known PVT points. The method further includes performing a statistical static timing analysis on the circuit design using the set of known PVT points and at least one additional PVT point generated during the statistical static timing analysis. The method further includes performing projections and root sum squaring for possible corners in a parameter space based on a canonical model generated during performing the statistical static timing analysis.

A contract rate intelligent calculation method, apparatus and device, and a medium, the method comprising: receiving a contract settlement rule configured by a user, the contract settlement rule comprising data source information, a statistical time range, a calculation period and a rate calculation formula; according to the data source information and the statistical time range, order data is pulled from a corresponding data source; dividing the order data according to the calculation period to generate corresponding calculation units; and executing the rate calculation formula to calculate the order data in each calculation unit to obtain the contract rate in the corresponding calculation period, thereby realizing automatic and intelligent rate settlement work, greatly improving the calculation efficiency and accuracy, and being capable of flexibly adapting to complex and changeable business rules.

The invention provides an IOT data processing method and electronic equipment. The method is applied to a communication system comprising a terminal, IOT equipment and a cloud. The method comprises the following steps that: a cloud periodically acquires a to-be-processed task, wherein the type of the to-be-processed task can be sampling, statistics or re-statistics; and if the task to be processed is a sampling task, locking the sampling task by adopting a distributed lock, normalizing the sampling time, and collecting and storing the IOT data when the sampling time is reached. If the to-be-processed task is a statistical task, locking the statistical task by adopting a distributed lock, normalizing statistical time, judging whether a preorder task of the statistical task is completed or not when the statistical time is up, and if so, counting and storing IOT data; otherwise, executing the re-statistics task. And if the to-be-processed task is a re-statistics task, locking the re-statistics task by adopting a distributed lock, normalizing the re-statistics time, and counting and storing the IOT data when the re-statistics time is reached. And finally, the cloud sends the stored data to the terminal for display.