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CN-122029862-A - Data processing method and related device

CN122029862ACN 122029862 ACN122029862 ACN 122029862ACN-122029862-A

Abstract

The application provides a data processing method and a related device, wherein in the method, data sending equipment can acquire N t groups of first data based on N t groups of observed data in S t groups of observed data corresponding to a t time unit, and send the N t groups of first data so as to be used for compensating predicted data corresponding to the t time unit by data receiving equipment, wherein the data in the S t groups of observed data is more than the data in the N t groups of observed data, namely, the N t groups of first data are obtained based on part of data in the S t groups of observed data, and the predicted data corresponding to the t time unit is obtained by p predicted data corresponding to p previous time units. The scheme can improve the prediction accuracy and save the transmission overhead.

Inventors

  • MA HE
  • LI JIAHUI
  • MA MENGYAO
  • YAN MIN

Assignees

  • 华为技术有限公司

Dates

Publication Date
20260512
Application Date
20231013

Claims (20)

  1. A method of data processing, comprising: Acquiring N t sets of first data, wherein the N t sets of first data are determined based on N t sets of observed data in the S t sets of observed data corresponding to the t-th time unit; And transmitting the N t groups of first data, wherein the N t groups of first data are used for compensating the predicted data corresponding to the t-th time unit, the predicted data are obtained based on p predicted data corresponding to the (t-p-a+1) -th time unit to the (t-a) -th time unit, and p and a are constants.
  2. The method of claim 1, wherein a is 1.
  3. The method according to claim 1 or2, wherein the N t sets of first data are used for compensating the prediction data corresponding to the t-th time unit, and include: The N t groups of first data are used for determining the compensation data, and the compensation data are used for compensating the prediction data corresponding to the t-th time unit so as to obtain compensated data.
  4. The method of claim 3, wherein, The compensated data is the sum of the predicted data and the compensated data corresponding to the t-th time unit.
  5. The method according to claim 3 or 4, wherein the compensation data Δv (t) satisfies: Wherein the K (t) is a Kalman gain matrix corresponding to the t-th time unit, the For the N t sets of first data, the For the prediction data corresponding to the t-th time unit, the O (t) is determined based on the position of the N t sets of observation data in the S t sets of observation data.
  6. The method of any one of claims 1 to 5, wherein the position of the N t sets of observations in the S t sets of observations is determined based on a predefined first pattern, or The position of the N t sets of observations in the S t sets of observations is determined based on a predefined first selection rule.
  7. The method of claim 6, wherein the location of the N t set of observations in the S t set of observations is determined based on the first selection rule; the method further comprises the steps of: And sending first information, wherein the first information is used for indicating the position of the N t groups of observation data in the S t groups of observation data.
  8. The method of claim 6 or7, wherein the method further comprises: And receiving second information, wherein the second information is used for indicating the selection mode of the N t groups of observation data, and the selection mode of the N t groups of observation data comprises selection based on a predefined pattern or selection based on a predefined selection rule.
  9. The method of any one of claims 1 to 8, wherein the N t sets of first data are the N t sets of observations, or The N t groups of first data are N t groups of residual data, and the data in the N t groups of residual data Data for the N t sets of observations With data in N t sets of predicted data Wherein, An ith data in an nth set of residual data representing the N t sets of residual data, An ith data in an nth set of observations representing the N t sets of observations, The method comprises the steps of representing the ith data in the nth group of predicted data of the N t group of predicted data, wherein the N t group of predicted data is part of the data in the S t group of predicted data, the position of the nth group of predicted data in the N t group of predicted data is the same as the position of the nth group of observed data in the N t group of observed data, the S t group of predicted data comprises the predicted data corresponding to the t time unit, I n ,1≤n≤N t ,I n is more than or equal to 1, the number of the data included in the nth group of residual data is represented, and I, I n and N are all positive integers.
  10. The method of any one of claims 1 to 9, wherein any two of the sets of observations in S t include equal amounts of data, or The S t -group observation data is obtained by dividing an observation data set based on at least two preconfigured parameters, namely a starting position, an ending position or the number of included data of each group of observation data in the S t -group observation data in the observation data set, wherein the observation data set is a set of observation data corresponding to the t-th time unit.
  11. The method of claim 10, wherein the method further comprises: Transmitting or receiving third information indicating at least two parameters of a start position, an end position or an amount of data included in the observation set of each of the S t sets of observations, or The number of data included in any two groups of observation data in the S t groups of observation data is equal, and the method further comprises: Fourth information is transmitted or received, the fourth information indicating one or more of the number of data included in each of the S t or S t sets of observations.
  12. The method of any one of claims 1 to 11, wherein the T-th time unit is one of T time units, wherein T is an integer greater than 1 and the T time units are periodic, or the T is 1; the method further comprises the steps of: Sixth information is received, the sixth information being used to indicate the T time units.
  13. The method of any one of claims 1 to 12, wherein the predictive data is obtained by autoregressive AR prediction.
  14. A method of data processing, comprising: Receiving N t sets of first data, the N t sets of first data determined based on N t sets of observation data in the set of S t sets of observation data corresponding to the t-th time unit; And compensating the predicted data corresponding to the t-th time unit based on the N t groups of first data to obtain compensated data, wherein the predicted data is obtained based on p predicted data corresponding to the (t-p-a+1) -th time unit to the (t-a) -th time unit, and p and a are constants.
  15. The method of claim 14, wherein a is 1.
  16. The method according to claim 14 or 15, wherein compensating the predicted data corresponding to the t-th time unit based on the N t sets of first data to obtain compensated data includes: The N t groups of first data determine compensation data; And obtaining compensated data based on the compensation data and the predicted data corresponding to the t-th time unit.
  17. The method of claim 16, wherein the compensated data is a sum of predicted data corresponding to the t-th time unit and the compensated data.
  18. The method according to claim 16 or 17, wherein the compensation data Δv (t) satisfies: Wherein the K (t) is a Kalman gain matrix corresponding to the t-th time unit, the For the N t sets of first data, the For the prediction data corresponding to the t-th time unit, the O (t) is determined based on the position of the N t sets of observation data in the S t sets of observation data.
  19. The method of any one of claims 14 to 18, wherein the method further comprises: First information is received, the first information being used to indicate a position of the N t sets of observations in the S t sets of observations.
  20. The method of claim 19, wherein the method further comprises: And sending second information, wherein the second information is used for indicating the selection mode of the N t groups of observation data, and the selection mode of the N t groups of observation data comprises selection based on a predefined pattern or selection based on a predefined selection rule.

Description

Data processing method and related device Technical Field The present application relates to the field of wireless communications, and in particular, to a data processing method and related apparatus. Background The application scenes of the wireless communication technology are increasingly abundant, but new scenes also bring more and more transmission overhead. At present, it has been proposed to utilize data at a historical time to predict data at a future time, so as to reduce data transmission and save transmission overhead. For example, the time correlation of the data may be used to predict the data at a future time by means of time series prediction, such as autoregressive (autoregressive, AR) prediction. However, in the process of predicting data, errors are accumulated over time, resulting in distortion of the predicted data. Thus, the prediction data can be compensated. However, compensating the predicted data often requires feeding back the complete observed data, which in turn results in a large transmission overhead. Disclosure of Invention The application provides a data processing method and a related device, which are used for improving the accuracy of predicted data and avoiding huge transmission expenditure. In a first aspect, the present application provides a data processing method, which may be performed by a transmitting device of data (abbreviated as a transmitting device), where, unless specifically stated otherwise, the transmitting device in the present application may refer to the transmitting device itself (e.g., a network device, a terminal device), a component in the transmitting device (e.g., a chip system, or a processor, etc.), or may refer to a logic module or software capable of implementing all or part of the functions of the transmitting device. The method comprises the steps of obtaining N t groups of first data, wherein the N t groups of first data are determined based on N t groups of observed data in S t groups of observed data corresponding to the t time unit, sending the N t groups of first data, wherein the N t groups of first data are used for compensating predicted data corresponding to the t time unit, the predicted data are obtained based on p predicted data corresponding to the (t-p-a+1) th time unit to the (t-a) th time unit, and p and a are constants. In a second aspect, a data processing method is provided, where the method may be performed by a receiving device of data (abbreviated as receiving device), where the receiving device in the present application may refer to the receiving device itself (e.g. a network device, a terminal device), a component in the receiving device (e.g. a chip, a chip system, or a processor, etc.), or may be a logic module or software capable of implementing all or part of the functions of the receiving device, where no special description is made. The method comprises the steps of receiving N t groups of first data, wherein the N t groups of first data are determined based on N t groups of observed data of S t groups of observed data corresponding to the t time unit, compensating predicted data corresponding to the t time unit based on the N t groups of first data to obtain compensated data, the predicted data are obtained based on p predicted data corresponding to the (t-p-a+1) th time unit to the (t-a) th time unit, and p and a are constants. The set of observation data S t may be obtained by grouping a set of observation data corresponding to the t-th time unit (hereinafter referred to as an observation data set), and the observation data corresponding to the t-th time unit may be data obtained by observing the t-th time unit. Each set of observations may include one or more observations. N t may be a value less than S t. The N t group of observations is a part of the S t group of observations, or the N t group of observations is a subset of the S t group of observations. In other words, the number of data in the S t set of observations is greater than the number of data in the N t set of observations, or the number of data in the S t set of observations is greater than the number of data in the N t set of observations. If each set of observations includes one observation, it may be said that the number of sets of observations in the S t set is greater than the number of sets of observations in the N t set, and if each set of observations includes a plurality of observations, it may be said that the total data size of the data in the S t set is greater than the total data size of the data in the N t set. The N t set of first data is data to be transmitted determined based on the N t set of observation data, and the N t set of first data may be N t set of observation data itself, or may be data determined based on the N t set of observation data. The application is not limited in this regard. The transmitting device may transmit the N t sets of first data to another device in communication therewith, for example, a receivin