21 results about "Data truncation" patented technology

The invention discloses an emotion recognition method based on an attention mechanism and a capsule network. The emotion recognition method specifically comprises the following steps: inputting multi-channel original EEG signals; performing data truncation, data filling and data standardization on the original EEG signal, unifying the format, and generating a preprocessed EEG signal; extracting space-time joint features from the preprocessed EEG signals by using a three-dimensional convolutional neural network; the construction of the graph structure comprises modeling a complex relationship between channels; introducing a graph attention mechanism to strengthen information expression of important channels and relationships; and high-dimensional vector modeling and final classification of the features are carried out through a capsule network, and capture of deeper space structure information is completed.

The invention discloses an offset scanning CBCT image reconstruction method. The method comprises the following steps: virtual detector conversion of projection data; carrying out geometric weighting on the data; carrying out data continuation and filtering; parker weighting is carried out on the data; and reconstructing bias back projection. According to the invention, aiming at the scanning mode of radiation source and detector bias, the FDK algorithm cannot be directly used for reconstruction, the virtual detector is introduced to convert the scanning mode into the scanning mode of detector bias, the data is subjected to continuation processing to solve the problem of data truncation, and the filtered data is subjected to Parker weighting aiming at the repeated scanning area to solve the problem of bias artifacts. And finally, carrying out three-dimensional reconstruction by adopting an FDK reconstruction method. The reconstruction method is high in speed, and the obtained cross-sectional image is high in definition.

The application relates to the technical field of data processing, and discloses a network public opinion hotspot evaluation system based on sentiment analysis, which comprises the following modules: a data analysis module, which is used for receiving an interactive flow extraction identifier and text and calculating a basic sentiment polarity value; a window control module, which outputs a current time window span based on a topological entropy gradient of a previous period; a topological layering module, which constructs a local directed graph to calculate a global topological entropy, and takes a shortest path hop count as a topological depth; a polarization rate calculation module, which allocates and calculates a variance of the polarity value according to the topological depth and outputs a sentiment polarization rate; and a comprehensive evaluation module, which combines a word frequency, the sentiment polarization rate and an activation function to output a comprehensive hotness evaluation value. The application adaptively adjusts data truncation according to network public opinion fluctuation, prevents feature dilution through spatial layering sentiment analysis, and automatically peels off abnormal concurrent flow based on a topological base, so that the hotspot evaluation is real and objective.

The present application relates to the field of spaceflight TT&C technology, and specifically discloses a method and system for implementing an extraterrestrial object blocking TT&C plan under deep space large time delay conditions. The present application determines extraterrestrial object blocking time windows and ground available tracking stations by using precise orbit data of a probe, calculates station prediction containing data truncation and one-way light time of uplink and downlink signal transmission, sets uplink tracking events in advance and downlink tracking events in arrears according to the one-way light time, merges adjacent TT&C arcs with a time interval less than a preset threshold into single-station continuous tracking arcs to set task preparation events, and decouples uplink and downlink under deep space TT&C mode, controls transmission of uplink carriers and remote control instructions according to the one-way light time, thereby solving the technical problems of difficulty in space-earth cooperation and poor timeliness of emergency retransmission during extraterrestrial object blocking under deep space large time delay conditions, and improving the reliability and timeliness of implementation of deep space exploration mission TT&C plans.

The invention discloses a differential dynamic microscopic image processing method based on a root-mean-square error. The differential dynamic microscopic image processing method comprises the following steps: a sequence image acquisition step: acquiring a time sequence microscopic image; a sequence image processing step: obtaining a difference image data set; performing one-dimensional Fourier transform in two directions on the differential image data set in sequence to obtain two-dimensional transform result data, and calculating to obtain Fourier amplitude spectrum image data; an obtained data processing step: carrying out dimension reduction processing on the Fourier amplitude spectrum image data; introducing structure function fitting, calculating a root-mean-square error between a fitting curve of the data after dimension reduction processing and the Fourier amplitude spectrum image data, and selecting a data truncation point according to the root-mean-square error; and an intermediate scattering function is introduced, and dynamic information of Brown particles is obtained based on the relationship between the data after selection of the truncation point and the diffusion coefficient. According to the method, the data truncation point is determined through the root-mean-square error, so that the processing time is remarkably shortened while the measurement precision is ensured.

The invention is suitable for the technical field of power spot market clearing, and provides a power delivery in-province power spot market clearing method, and the method comprises the steps: carrying out the calculation of the delivery superposed power of a target unit in a current transaction period based on the obtained delivery power data and technical parameter data; generating a delivery newly-added curve segment based on the delivery superposed power and the acquired market price lower limit; performing data translation processing on each section of the original declaration curve data based on the outgoing superposed power, and performing data truncation processing on the declaration curve data after data translation processing to obtain an effective curve section after data truncation processing; performing data splicing on the delivery newly-added curve segment and the effective curve segment to generate a reconstructed declaration curve of the target unit; based on the reconstructed declaration curve, clearing the current transaction period in the power spot market in the province; the accuracy of clearing calculation input data is ensured, and the reliability of clearing price signals in the provincial spot market is improved.

The invention discloses a building system self-adaptive control method based on data truncation and modular transfer learning, and relates to the technical field of intelligent building energy consumption optimization and control, modular modeling is performed on each component of a building system, a hybrid recurrent neural network is adopted as the architecture of a building system modular model, and the intelligent building energy consumption optimization and control is realized. The hybrid recurrent neural network comprises a plurality of recurrent neural network units, and each recurrent neural network unit corresponds to each component module in the building system; after sudden change of the building system is detected, only module parameters influenced by sudden change are unfrozen and finely adjusted, and module parameters not influenced by sudden change are kept frozen; meanwhile, data before mutation is abandoned to eliminate the influence of an old domain, and only the data after mutation is reserved to perform fine adjustment on unfrozen module parameters; in addition, in the model transfer learning process, only the prediction error output by the module influenced by sudden change is used as a loss function. The invention aims to solve the problems that the domain migration efficiency is low and old domain data interferes with new domain learning.

The invention discloses a verifiable outsourcing computing privacy protection method based on block data truncation, which adopts a three-party architecture of participants, an aggregator and a key generation center, and takes a non-collusion cloud node as a data block distribution and computing carrier. The key generation center constructs a distributed multi-trap-door public key cryptosystem based on security parameters, splits strong private keys and distributes the strong private keys to participants and aggregators. A participant splits original data into mutually exclusive blocks through binary segmented truncation and remainder operation according to the bit number of sensitive data, and distributes the blocks to different cloud nodes. And the cloud nodes complete data block addition, multiplication and equivalence verification by adopting a carry-aware mechanism to ensure that a calculation result is consistent with original data operation. And the aggregator collects a result and performs cross check, and the collaborative participant decrypts the result and then integrates the result to obtain a final weighted aggregation result. According to the method, the problem of unbalance among verifiability, privacy and practicability of the existing outsourcing computing technology can be solved.

The invention discloses a data synchronization storage space adjustment method and device, equipment and a storage medium. The method comprises the following steps: acquiring a source end storage unit of a storage unit used by a target source end and a standby end storage unit of a storage unit used by a target standby end; whether the source end storage unit and the standby end storage unit conform to preset storage unit types or not is determined, and the preset storage unit types comprise the first storage unit type and the second storage unit type. And determining a configuration item value corresponding to a matching type according to the source end storage unit, the standby end storage unit and the matching type of the preset storage unit type. According to the configuration item value and the target synchronization data requested by the target source end, the target storage space used for storing the target synchronization data in the target backup end is adjusted, so that the reliability of the data backup disaster recovery system is guaranteed, the data truncation and messy code risks in the data migration process are reduced, the data synchronization efficiency is remarkably improved, and the user experience is improved. And the data integrity and accuracy are ensured.

This invention discloses an adaptive data truncation method and system based on variance goodness-of-fit, relating to the field of natural language processing technology. The method includes acquiring multiple documents output by a re-ranking model and their corresponding relevance scores; arranging the relevance scores in descending order to obtain an ordered relevance score sequence; starting with a cluster size of 2, progressively increasing the cluster size; and classifying the ordered relevance score sequence using a natural breakpoint classification method for each cluster size. This invention performs natural breakpoint classification and variance goodness-of-fit calculation on the ordered relevance score sequence with multiple cluster sizes, then determines the optimal cluster size based on the variance goodness-of-fit gain value of adjacent cluster sizes, and adaptively determines the truncation threshold. This allows for dynamic adjustment of the filtering boundary based on the score distribution characteristics under different query scenarios, improving the adaptability of data truncation and reducing interference from low-relevance documents.

The invention relates to the technical field of seismic data processing, and particularly discloses a self-adaptive Hanning window construction method and device, and the method comprises the steps: giving an initial adjustment parameter of an initial window function, and calculating a flag value of the initial window function; setting an adjustment interval of the mark value and a correlation condition satisfied by the initial adjustment parameter and the mark value; obtaining a final adjustment parameter through iterative operation according to the adjustment interval and the association condition; and obtaining a calculation formula of the final window function according to the final adjustment parameter. The self-adaptive Hanning window constructed by the method changes more gently in starting, ending and intermediate function value, the truncation effects of data truncation, data filtering, data fusion and the like of actual data are obviously weakened, the practical requirements are better met, and the use adaptability and convenience in the practical process are improved.

A magnetic field speed sensor includes one or more magnetic field sensing elements to generate a magnetic field signal indicative of a magnetic field associated with a moveable target and a controller to generate a controller output signal including a plurality of output words, each including a speed pulse and a plurality of data pulses capable of having four different amplitudes. The time between the speed pulses of consecutive output words is indicative of the speed of movement of the target. Each data pulse corresponds to a data bit having a binary value represented by a transition direction of the data pulse and the amplitude of the data pulse. Features include a programmable data pulse width or an adaptively adjustable data pulse width to avoid truncation.

Systems and methods for data truncation are disclosed. A computing system may a storage medium and a processor. The processor may be configured to identify first data of a first data structure; identify a first portion of the first data and a second portion of the first data; store the first portion of the first data as a second data structure in the storage medium, and the second portion of the first data as a third data structure in the storage medium; identify a request for the first data; based on the request, retrieve from the storage medium the second data structure including the first portion of the first data; identify a value associated with the second portion; generate second data based on the first portion and the value; and perform a task based on the second data.

Methods for asynchronously determining relational data integrity using cryptographic data structures are performed by systems and devices. Changes in current tables of relational databases are reflected in associated history tables. Cryptographic hybrid blockchain ledgers are updated with transaction records, for entry changes in current and history tables, including transaction information and hash values of corresponding entry changes. Hybrid blockchain ledgers also include root hash values of Merkle trees of transaction records in current blocks, and hash values of prior blocks. A current block receipt is asynchronously generated and provided as a single hash value from which the validity states of the tables and ledger are able to be verified. Cryptographic receipts of specific transactions reflected in table entry changes are generated and provide immutable evidence of specific transaction existence for users. Ledger-enabled tables are provided for mixed database operations with ledger-disabled tables, and temporal history table database operations are enabled.

The invention discloses a sensor digital output data compression method, and relates to the technical field of sensor chip design, and the method comprises the steps: obtaining the original digital output data of a sensor; determining a data interception bit width N and a bit width K of a scaling factor SF based on the bit width M of the original digital output data in combination with an error requirement of a digital output dynamic range of the sensor and a data volume compression requirement; intercepting N-bit data from the original digital output data to obtain effective data segments; determining the value of a scaling factor (SF) according to the position of the effective data segment in the original digital output data; and combining the effective data segment and the scaling factor SF to obtain compressed data output by the sensor. The method maintains an extremely high dynamic range while significantly reducing the amount of data by compressing the bit width of the original digital output data.

The application discloses a kind of energy storage battery management devices based on AI computing power, it is related to energy storage battery management technical field.The device obtains the relaxation voltage signal after charging cut-off under long-term shallow charge-discharge condition of lithium iron phosphate energy storage cell by signal acquisition unit, risk assessment unit is according to the signal to assess the potential abnormal evolution risk of each cell under multiple cells concurrent scene and generate high-risk cell identification, simultaneously determine timing characteristic window, and power scheduling unit adopts timing characteristic window locking distribution algorithm to adjust the AI inference resource scheduling mode from fixed polling, average distribution or shared call to window locking distribution mode, and signal processing unit executes modal decomposition under locking distribution mode to separate abnormal fluctuation component to relaxation voltage signal.The application assesses risk and locks window, and converts computing power scheduling to continuous distribution in window, avoids time series data truncation, separates abnormal component under continuous computing power and executes inference, captures hidden abnormality and early warning.

This invention relates to the field of computer data processing technology and discloses a dynamic regularization and collaborative scheduling method for time-series information data. The method accesses asynchronous multi-source time-series data streams, initializes a regularization pool based on a global tracking identifier, and sets a basic survival time window. It scans the regularization pool to calculate the associated state feedback factor and collects execution layer load data to calculate the resource water level. When the feedback factor reaches the high readiness state locking threshold and the resource water level is below the congestion threshold, the regularization pool is locked, a dedicated thread is allocated, and a data block access descriptor is generated for reading and computation. If the regularization pool approaches the time window and the data is not fully ready, the execution state dimensionality reduction or time window compensation operation is dynamically selected based on the resource water level status. This invention avoids memory accumulation and effective data truncation problems caused by fixed time windows by establishing a joint determination mechanism of data readiness and underlying resources, thereby improving the utilization and throughput of computing resources in concurrent scenarios.

This invention relates to the field of computer data processing technology and discloses a dynamic regularization and collaborative scheduling method for time-series information data. This method accesses asynchronous multi-source time-series data streams, initializes a regularization pool based on a global tracking identifier, and sets a basic survival time window. It scans the regularization pool to calculate the associated state feedback factor and collects execution layer load data to calculate the resource water level. When the feedback factor reaches the high readiness state locking threshold and the resource water level is below the congestion threshold, the regularization pool is locked, a dedicated thread is allocated, and a data block access descriptor is generated for reading and computation. If the regularization pool approaches the time window and the data is not fully ready, the execution state dimensionality reduction or time window compensation operation is dynamically selected based on the resource water level status. This invention avoids memory accumulation and effective data truncation problems caused by fixed time windows by establishing a joint determination mechanism of data readiness and underlying resources, thereby improving the utilization and throughput of computing resources in concurrent scenarios.

This invention relates to the field of aerospace telemetry and control technology, specifically disclosing a method and system for implementing telemetry and control plans under deep-space conditions with large time delays due to extraterrestrial celestial body obstruction. This invention determines the time window for extraterrestrial celestial body obstruction and the available ground tracking stations by utilizing precise orbital data from the probe. It calculates the station forecast including data truncation and the one-way optical travel time of uplink and downlink signal transmission. Based on the one-way optical travel time, it pre-sets uplink tracking events and delays downlink tracking events. It merges adjacent telemetry and control arcs with time intervals less than a preset threshold into a single-station continuous tracking arc to set mission preparation events. In deep-space telemetry and control mode, it decouples the uplink and downlink, and controls the transmission of uplink carriers and remote control commands based on the one-way optical travel time. This solves the technical problems of difficult space-ground coordination and poor timeliness of emergency resending during extraterrestrial celestial body obstruction under deep-space conditions with large time delays, improving the reliability and timeliness of deep-space exploration mission telemetry and control plans.

The application discloses a large model hardware deployment processing method, a chip and an electronic device. The method comprises the following steps: acquiring the quantization sensitivity of each operation unit of a large model during deployment and running on a hardware device; identifying a quantization sensitive target which meets the set requirement for the influence on the hardware calculation error according to the quantization sensitivity; obtaining a first quantization coefficient for controlling data truncation in a manner independent of training based on the quantization sensitive target, so as to minimize the quantization output error of the hardware device; when the optimization result of improving the quantization precision based on the first quantization coefficient does not meet the requirement or the quantization precision needs to reach the set standard, performing layer-by-layer optimization based on the quantization sensitive target by using a training method to obtain a second quantization coefficient for controlling data truncation; and generating a large model hardware deployment processing file suitable for the hardware device to perform reasoning based on the first quantization coefficient or the second quantization coefficient. The application can improve the deployment efficiency of the large model.

Systems and methods for data truncation are disclosed. A computing system may include a storage medium and a processor. The processor may be configured to: identify first data of a first data structure; identifying a first portion of the first data and a second portion of the first data; storing a first portion of the first data as a second data structure in the storage medium, and storing a second portion of the first data as a third data structure in the storage medium; identifying a request for first data; retrieving, based on the request, a second data structure including a first portion of the first data from a storage medium; identifying a value associated with the second portion; generating second data based on the first portion and the value; and execute the task based on the second data.