CN118890031B

CN118890031B - Data processing method, device, equipment and medium

Info

Publication number: CN118890031B
Application number: CN202411379626.8A
Authority: CN
Inventors: 吴冬升; 欧阳航; 郑廷钊; 杨杰; 袁爱平
Original assignee: Guangzhou Gaoxing Internet Connection Technology Co ltd
Current assignee: Guangzhou Gaoxing Internet Connection Technology Co ltd
Priority date: 2024-09-30
Filing date: 2024-09-30
Publication date: 2025-02-21
Anticipated expiration: 2044-09-30
Also published as: CN118890031A

Abstract

The application provides a data processing method, a device, equipment and a medium, wherein the method comprises the steps of obtaining input frequency of input data, frame number of the input data and arrival time of each input frame in the input data, determining output phases according to the input frequency of the input data, the frame number of the input data and the arrival time of each input frame in the input data, determining Kalman filtering starting time of each input frame according to the output phases, and determining and outputting target output frames corresponding to each input frame according to the arrival time of each input frame, the input frequency of the input data and the Kalman filtering starting time of each input frame, wherein the target output frames are predicted frames or corrected frames so as to improve quality and output efficiency of the target output frames.

Description

Data processing method, device, equipment and medium

Technical Field

The present application relates to the field of data processing, and in particular, to a data processing method, apparatus, device, and medium.

Background

In the technical field of intelligent transportation, vehicle-road cooperation is a mainstream development direction, and a road side sensing system needs to fuse input data of a sensor, track a track of a sensing object and output the track of the sensing object at a fixed frequency. In the track tracking process, data processing is performed through a Kalman filtering algorithm.

At present, the track tracking method based on the Kalman filtering algorithm needs input data with fixed frequency, and Kalman filtering calculation is started immediately to obtain fixed output when the input data arrives, but unstable network transmission or time-consuming unfixed performance of the sensor for processing the original data cause unfixed time delay and even data loss of the acquired data of the sensor. The input data delay has fluctuation or the input data is lost during the Kalman filtering, so that the input data can not arrive at equal intervals during the Kalman filtering, and the output with fixed frequency can not be obtained by adopting the Kalman filtering algorithm.

In the existing Kalman filtering algorithm, the input data is assumed to be ideal fixed frequency, the mode of outputting the data by using the Kalman filtering algorithm does not consider the condition of losing or lagging of the input data, and the defect of lower quality of the output data exists. In addition, some existing Kalman filtering algorithms adopt complex neural network algorithms to predict and complement lost data, and then output data by using the Kalman filtering algorithm, so that the operation amount is large, and the disadvantage of low data output efficiency exists.

Therefore, the existing kalman filtering algorithm has a certain limitation, and how to improve the kalman filtering algorithm to improve the quality and output efficiency of the output data is a problem to be solved.

Disclosure of Invention

The application aims to provide a data processing method, a device, equipment and a medium for solving the problem of the actual need that the Kalman filtering algorithm in the prior art has certain limitation.

In order to achieve the above purpose, the technical scheme adopted by the embodiment of the application is as follows:

In a first aspect, an embodiment of the present application provides a data processing method, where the method includes obtaining an input frequency of input data, a frame number of the input data, and an arrival time of each input frame in the input data;

determining an output phase according to the input frequency of the input data, the frame number of the input data and the arrival time of each input frame in the input data;

determining the Kalman filtering starting time of each input frame according to the output phase;

And determining and outputting a target output frame corresponding to each input frame according to the arrival time of each input frame, the input frequency of the input data and the Kalman filtering starting time of each input frame, wherein the target output frame is a predicted frame or a corrected frame.

As an optional implementation manner, the determining the output phase according to the input frequency of the input data, the frame number of the input data, and the arrival time of each input frame in the input data includes:

Determining the input phase of each input frame according to the input frequency of the input data and the arrival time of each input frame in the input data;

sequencing the input phases of the input frames to obtain a target sequence, and determining a target frame number according to a preset filtering threshold and the frame number of the input data;

and determining the output phase according to the target sequence and the target frame number.

As an optional implementation manner, the determining the input phase of each input frame according to the input frequency of the input data and the arrival time of each input frame in the input data includes:

Determining the period of the input data according to the input frequency of the input data;

Determining a reference time origin according to the arrival time of the first frame input frame in the arrival time of each input frame and the period of the input data;

determining the time origin of each input frame according to the reference time origin and the period of the input data;

And determining the input phase of each input frame according to the arrival time of each input frame and the time origin of each input frame.

As an optional implementation manner, the determining a kalman filter start time of each input frame according to the output phase includes:

the sum of the time origin of the input frame and the output phase is taken as the Kalman filtering starting time of the input frame.

As an optional implementation manner, the determining and outputting the target output frame corresponding to each input frame according to the arrival time of each input frame, the input frequency of the input data, and the kalman filtering start time of each input frame includes:

determining a standby output frame corresponding to a last input frame of a current input frame, wherein the standby output frame corresponding to the last input frame is a predicted frame corresponding to the last input frame or a corrected frame corresponding to the last input frame;

predicting to obtain a predicted frame corresponding to the current input frame according to the standby output frame of the previous input frame;

And determining and outputting a target output frame corresponding to the current input frame according to the arrival time of the current input frame, the Kalman filtering starting time of the current input frame and the predicted frame corresponding to the current input frame.

As an optional implementation manner, the determining and outputting the target output frame corresponding to the current input frame according to the arrival time of the current input frame, the kalman filtering start time of the current input frame, and the predicted frame corresponding to the current input frame includes:

determining whether the current input frame arrives overtime or not according to the arrival time of the current input frame and the Kalman filtering starting time of the current input frame;

If not, correcting to obtain a correction frame corresponding to the current input frame according to the current input frame, and taking the correction frame corresponding to the current input frame as a target output frame corresponding to the current input frame;

Otherwise, taking the predicted frame corresponding to the current input frame as a target output frame corresponding to the current input frame.

As an optional implementation manner, the determining the inactive output frame corresponding to the previous input frame of the current input frame includes:

Determining the arrival time of a last input frame of a current input frame, and determining whether the last input frame arrives overtime or not according to the arrival time of the last input frame and the Kalman filtering starting time of the last input frame;

If the last input frame arrives overtime and the arrival time of the last input frame is not later than the Kalman filtering starting time of the current input frame, correcting to obtain a correction frame corresponding to the last input frame according to the last input frame, and taking the correction frame corresponding to the last input frame as a standby output frame corresponding to the last input frame;

If the last input frame arrives overtime and the arrival time of the last input frame is later than the Kalman filtering starting time of the current input frame, taking a predicted frame corresponding to the last input frame as a standby output frame corresponding to the last input frame;

And if the last input frame arrives on time, taking the correction frame corresponding to the last input frame as a standby output frame corresponding to the last input frame.

In a second aspect, an embodiment of the present application provides a data processing apparatus, the apparatus including:

the acquisition module is used for acquiring the input frequency of the input data, the frame number of the input data and the arrival time of each input frame in the input data;

The determining module is used for determining an output phase according to the input frequency of the input data, the frame number of the input data and the arrival time of each input frame in the input data;

the determining module is further configured to determine a kalman filtering start time of each input frame according to the output phase;

the determining module is further configured to determine and output a target output frame corresponding to each input frame according to the arrival time of each input frame, the input frequency of the input data, and the kalman filtering start time of each input frame, where the target output frame is a predicted frame or a corrected frame.

As an alternative implementation manner, the determining module is specifically configured to:

determining whether a current input frame arrives overtime, if not, correcting to obtain a correction frame corresponding to the current input frame according to the current input frame, and taking the correction frame corresponding to the current input frame as a target output frame corresponding to the current input frame;

Determining the arrival time of the last input frame of the current input frame according to the arrival time of the current input frame and the input frequency of the input data;

Determining whether the last input frame arrives overtime or not according to the arrival time of the last input frame and the Kalman filtering starting time of the last input frame;

In a third aspect, an embodiment of the present application provides a computer device, including a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the computer device is running, the processor executing the machine-readable instructions to perform the steps of the data processing method as described in the first aspect.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the data processing method as described in the first aspect above.

The beneficial effects of the application are as follows:

The application provides a data processing method, a device, equipment and a medium, which are used for determining an output phase according to the input frequency, the frame number and the arrival time of each input frame of acquired input data through phase alignment during Kalman filtering, and determining the Kalman filtering starting time of each input frame according to the delay time indicated by the output phase. According to the input frequency of the input data, determining an output period, separating prediction correction during Kalman filtering, determining the Kalman filtering starting time of each input frame according to the arrival time, the output period and the Kalman filtering starting time of each input frame, predicting or predicting and correcting each input frame, further determining that a target output frame corresponding to each input frame is a predicted frame or a corrected frame, and respectively outputting the target output frame corresponding to each input frame at the Kalman filtering starting time of each input frame. The Kalman filtering is enabled to output the target output frames corresponding to the input frames at a fixed output frequency, and the target output frames corresponding to the input frames can be output under the condition that the input frames reach or even lose frames in a time-out manner through phase alignment and prediction correction separation during the Kalman filtering, so that the quality and the output efficiency of the target output frames are improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart of a data processing method according to an embodiment of the present application;

FIG. 2 is a schematic diagram of each input frame and an output frame corresponding to each input frame of Kalman filtering in a data processing method according to an embodiment of the present application;

FIG. 3 is a schematic flow chart of determining an output phase of a data processing method according to an embodiment of the present application;

FIG. 4 is a schematic flow chart of determining an input phase of each input frame according to the data processing method provided by the embodiment of the application;

FIG. 5 is a flowchart illustrating a method for determining and outputting a target output frame corresponding to each input frame according to the data processing method provided by the embodiment of the present application;

FIG. 6 is a flowchart illustrating a method for determining and outputting a target output frame corresponding to a current input frame according to the data processing method provided by the embodiment of the present application;

FIG. 7 is a schematic flow chart of determining a standby output frame corresponding to a previous input frame of a current input frame according to the data processing method provided by the embodiment of the present application;

FIG. 8 is a block diagram of a data processing apparatus according to an embodiment of the present application;

fig. 9 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for the purpose of illustration and description only and are not intended to limit the scope of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. A flowchart, as used in this disclosure, illustrates operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be implemented out of order and that steps without logical context may be performed in reverse order or concurrently. Moreover, one or more other operations may be added to or removed from the flow diagrams by those skilled in the art under the direction of the present disclosure.

In addition, the described embodiments are only some, but not all, embodiments of the application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.

It should be noted that the term "comprising" will be used in embodiments of the application to indicate the presence of the features stated hereafter, but not to exclude the addition of other features.

In the field of data processing, data processing is performed by a kalman filter algorithm, input data with a fixed frequency is required, and when the input data arrives, kalman filter calculation is started immediately to obtain a fixed output. The existing Kalman filtering algorithm needs to assume that input data is ideal fixed frequency, and the condition of input data loss or hysteresis is not considered, so that output data quality is lower. And the other part of the existing Kalman filtering algorithm adopts a complex neural network algorithm to predict and complement the lost data, and then outputs the data by using the Kalman filtering algorithm, so that the operation amount is larger, and the output data efficiency is lower.

Based on the above problems, the embodiment of the application provides a data processing method, which is used for fixing and outputting target output frames of each input frame through phase alignment, prediction correction separation and delay correction when Kalman filtering is carried out on each input frame in input data, so that the data can be processed under the condition that the arrival time of each input frame in the input data fluctuates and even the frame is lost, and the quality and the output efficiency of the target output frame are improved. The phase alignment is to determine an output phase according to input data, determine a Kalman filtering starting time of each input frame according to the output phase, and realize fixed frequency output. The prediction correction separation is to separate the continuous prediction correction step in the Kalman filtering process into an independent prediction step and a correction step, only execute the prediction step when the input frame is lost or arrives overtime, output the predicted frame as a target output frame, execute the prediction step and the correction step when the input frame arrives on time, and output the correction frame as the target output frame. The delay correction is to delay the correction step of the current input frame to the next frame correction of the current input frame when the current input frame arrives overtime, so as to eliminate the accumulated error.

Fig. 1 is a schematic flow chart of a data processing method according to an embodiment of the present application, and an execution subject of the method may be any computer device having computing processing capability. As shown in fig. 1, the method includes:

S101, acquiring input frequency of input data, frame number of the input data and arrival time of each input frame in the input data.

Optionally, the sensor collects data in a period of time, processes the data in the period of time, and uses the processed data as input data of Kalman filtering, wherein the input data comprises a plurality of input frames. Each input frame in the input data is respectively input into a Kalman filtering algorithm at a fixed input frequency, and the arrival time intervals of each input frame are unequal due to instability of network transmission and time consuming instability of sensor preprocessing data.

Reading input data input into a Kalman filtering algorithm, and obtaining input frequency of the input dataFrame number N and time of arrival of each input frameWherein the input frequencyAnd output frequencyIdentical input periodWith input frequencyInversely proportional, output periodAnd output frequencyInversely proportional, the period of inputAnd output periodThe same applies. Fig. 2 is a schematic diagram of each input frame of kalman filtering and an output frame corresponding to each input frame of the data processing method according to the embodiment of the present applicationAnd output frequencyAll 10Hz, as shown in FIG. 2, the number of frames of the input dataTime of arrival of 1 st input frameTime of arrival of the 2 nd input frame of 100 millisecondsFor 213 ms, the 3 rd input frame is lost, the arrival time of the 3 rd input frameCan be regarded asTime of arrival of 4 th input frameTime of arrival of the 5 th input frame at 412 millisecondsFor 507 milliseconds.

S102, determining an output phase according to the input frequency of the input data, the frame number of the input data and the arrival time of each input frame in the input data.

Optionally, according to the input frequency of the input dataFrame number N and time of arrival of each input frameDetermination of output phase by phase alignment at Kalman filtering. Wherein the output phaseFor indicating the delay time. Referring to fig. 2, the output phaseThe delay time was 10 milliseconds.

S103, determining the Kalman filtering starting time of each input frame according to the output phase.

Optionally according to the output phaseIndicated delay time, kalman filtering start time of each input frame is obtained by phase alignment during Kalman filtering. Wherein the Kalman filtering start time of each input frameIs fixed.

Illustratively, referring to FIG. 2, the Kalman filtering start time of the 1 st input frameKalman filtering start time for input frame 2 of 110 millisecondsKalman filtering start time for the 3 rd input frame of 210 millisecondsStart time of Kalman filtering for the 4 th input frame of 310 msKalman filtering start time for the 5 th input frame of 410 milliseconds510 Milliseconds. Kalman filtering start time for each input frameIs a fixed 100 ms time interval.

S104, determining and outputting a target output frame corresponding to each input frame according to the arrival time of each input frame, the input frequency of the input data and the Kalman filtering starting time of each input frame, wherein the target output frame is a predicted frame or a corrected frame.

Optionally, according to the input frequency with the input dataThe same output frequencyDetermining output period. According to the arrival time of each input frameOutput periodKalman filtering start time for each input frameBy separating prediction correction at the time of Kalman filtering, the prediction and correction are separated into two independent steps, and the Kalman filtering start time of each input frame is determinedPredicting or predicting and correcting each input frame, further determining the target output frame corresponding to each input frame as a predicted frame or a corrected frame, and starting time of Kalman filtering of each input frameAnd outputting target output frames corresponding to the input frames respectively. If the input frame is predicted and corrected at the Kalman filtering starting time of the input frame to obtain a predicted frame and a corrected frame corresponding to the input frame, the corrected frame is used as a target output frame corresponding to the input frame. If only one input frame is predicted without correction at the Kalman filtering start time of the input frame, only the predicted frame corresponding to the input frame is obtained, and the predicted frame is used as the target output frame corresponding to the input frame.

Illustratively, with continued reference to FIG. 2, at the Kalman filter start time of the 1 st input frameThat is, when the 1 st input frame is predicted and corrected at 110 ms, the corrected frame corresponding to the 1 st input frame is outputted as the target output frame corresponding to the first input frame. Kalman filtering start time at 2 nd input frameThat is, if only the 2 nd input frame is predicted at 210 ms, the predicted frame corresponding to the 2 nd input frame is outputted as the target output frame corresponding to the 2 nd input frame. Correspondingly, the target output frame corresponding to the 3 rd input frame is the predicted frame corresponding to the 3 rd input frame, the target output frame corresponding to the 4 th input frame is the predicted frame corresponding to the 4 th input frame, and the target output frame corresponding to the 5 th input frame is the corrected frame corresponding to the 5 th input frame.

In this embodiment, through phase alignment during kalman filtering, an output phase is determined according to the input frequency, the frame number and the arrival time of each input frame of the acquired input data, and a kalman filtering start time of each input frame is determined according to a delay time indicated by the output phase. According to the input frequency of the input data, determining an output period, separating prediction correction during Kalman filtering, determining the Kalman filtering starting time of each input frame according to the arrival time, the output period and the Kalman filtering starting time of each input frame, predicting or predicting and correcting each input frame, further determining that a target output frame corresponding to each input frame is a predicted frame or a corrected frame, and respectively outputting the target output frame corresponding to each input frame at the Kalman filtering starting time of each input frame. The Kalman filtering is enabled to output the target output frames corresponding to the input frames at a fixed output frequency, and the target output frames corresponding to the input frames can be output under the condition that the input frames reach or even lose frames in a time-out manner through phase alignment and prediction correction separation during the Kalman filtering, so that the quality and the output efficiency of the target output frames are improved.

Hereinafter, a process of determining an output phase based on an input frequency of input data, a frame number of input data, and an arrival time of each input frame in the input data will be described in detail.

Fig. 3 is a flowchart illustrating a method for determining an output phase according to the data processing method according to the embodiment of the present application, as shown in fig. 3, the step of determining the output phase in step S102 according to an input frequency of input data, a frame number of the input data, and an arrival time of each input frame in the input data includes:

S301, determining the input phase of each input frame according to the input frequency of the input data and the arrival time of each input frame in the input data.

Optionally, according to the input frequency of the input dataArrival time of each input frameDetermining the time origin of each input frameTime origin of each input frameCan represent ideal arrival time of each input frame in ideal state according to actual arrival time of each input frameCalculating the input phase of each input frame with the ideal arrival time of each input frame。

S302, sorting the input phases of the input frames to obtain a target sequence, and determining a target frame number according to a preset filtering threshold and the frame number of the input data.

Optionally, the input phase for each input frameSorting is carried out according to the order from big to small, and a sorted target sequence { is obtained,,...,}. According to the sensing precision of the sensor, a filtering threshold A is preset, and the filtering threshold A is positioned between the intervals [0,1]. The value of the filtering threshold A can be adjusted according to sensors with different sensing precision, and the smaller the fluctuation of input data is, the smaller the value of the preset filtering threshold A is.

According to a preset filtering threshold A and the frame number N of input data, determining a target frame number M based on the following formula:

Wherein M is the target frame number, A is a preset filtering threshold value,N is the number of frames of the input data,The product of the filtering threshold a and the number of frames N of the input data is represented as a downward rounding.

S303, determining an output phase according to the target sequence and the target frame number.

Optionally, the target sequence {,,...,Input phase corresponding to the target frame number M frame in the sequence }As output phaseI.e.. Illustratively, if the target frame number m=2, the target sequence {,,...,Input phase corresponding to the target frame number M frame in the sequence }As output phase。

In this embodiment, the ideal arrival time of each input frame in the ideal state is determined according to the input frequency of the input data and the arrival time of each input frame, and the input phase of each input frame is determined according to the arrival time of each input frame and the ideal arrival time of each input frame. The input phases of all the input frames are sequenced according to the sequence from big to small to obtain a target sequence, the target frame number is obtained according to a preset filtering threshold value and the frame number of the input data, and then the input phase corresponding to the target frame number in the target sequence is used as the output phase according to the target frame number and the sequenced target sequence. And determining an output phase according to a preset filtering threshold and the ordered target sequence, so that the output phase is more suitable for fluctuation of input data.

Hereinafter, a process of determining the input phase of each input frame based on the input frequency of the input data and the arrival time of each input frame in the input data will be described in detail.

Fig. 4 is a flowchart of determining an input phase of each input frame according to the data processing method provided by the embodiment of the present application, as shown in fig. 4, the step of determining the input phase of each input frame according to the input frequency of the input data and the arrival time of each input frame in the input data in step S301 includes:

S401, determining the period of the input data according to the input frequency of the input data.

Optionally, the input frequency of the input dataWith period of input dataInversely proportional, according to the input frequency of the input dataDetermining a period of the input data based on the following formula:

Wherein, In order to input a period of data,Is the frequency of the input data.

Referring to fig. 2, an input frequency of input dataInput period of input data at 10HzFor 0.1 seconds, i.e. 100 milliseconds.

S402, determining a reference time origin according to the arrival time of the first frame input frame in the arrival time of each input frame and the period of the input data.

Optionally, the arrival time of each input frameThe arrival time of the input frame of the first frame is the arrival time of the 1 st input frameArrival time of 1 st input frameDivided by period of input dataObtaining remainderAccording to the arrival time of the 1 st input framePeriod of input dataRemainder of the processThe reference time origin Z is determined based on the following formula:

Wherein Z is a reference time origin, representing the time origin of the 0 th input frame, For the arrival time of the 1 st input frame,Is used as a remainder of the product,Is the period of the input data.

S403, determining the time origin of each input frame according to the reference time origin and the period of the input data.

Optionally, according to the input frame number of each input framePeriod of input dataOrigin of reference timeDetermining the time origin of each input frame based on the following formula:

()

Wherein, For the input frame number of each input frame,As the origin of the reference time,Is the period of the input data.

S404, determining the input phase of each input frame according to the arrival time of each input frame and the time origin of each input frame.

Optionally, the arrival time of each input frameWith the time origin of each input frameAs the input phase of each input frameI.e. the actual arrival time of each input frameThe difference from the ideal arrival time of each input frame is taken as the input phase of each input frame. In particular, according to the arrival time of each input frameAnd the time origin of each input frameDetermining the input phase of each input frame based on the following formula:

Wherein, For the input phase of each input frame,For the arrival time of each input frame,For the time origin of each input frame,As the origin of the reference time,Input frame number for each input frame, and,Is the period of the input data.

In this embodiment, the input frequency of the input data is inversely proportional to the period of the input data, the period of the input data is determined according to the input frequency of the input data, and the reference time origin is determined according to the arrival time of the input frame of the first frame, the period of the input data, and the remainder obtained by dividing the arrival time of the input frame of the first frame by the period of the input data. And determining the time origin of each input frame according to the input frame number, the period of the input data and the reference time origin of each input frame, and taking the difference value between the arrival time of each input frame and the time origin of each input frame as the input phase of each input frame. The input phase of each input frame in the input data is precisely determined so as to determine the output phase from the input phase of each input frame.

As an alternative embodiment, the step of determining the kalman filter start time of each input frame according to the output phase in the step S103 includes:

The sum of the time origin and the output phase of the input frame is taken as the Kalman filtering start time of the input frame.

Optionally, the temporal origin of each input frameRepresenting the ideal arrival time of each input frame, which in an ideal state is equivalent to the ideal kalman filter start time of each input frame. Phase alignment by Kalman filtering, according to output phaseThe indicated delay time delays the ideal Kalman filtering start time of each input frame to obtain the Kalman filtering start time of each input frame in the actual state. I.e. the kalman filter start time for each input frame is determined based on the following formula:

Wherein, For the kalman filter start time for each input frame,For the time origin of each input frame,In order to output the phase of the signal,As the origin of the reference time,For the input frame number of each input frame,,In order to input a period of data,Is a target sequence {,,...,Input phase corresponding to the target frame number M frame.

In this embodiment, by aligning phases during the kalman filtering, an ideal kalman filtering start time of an input frame is delayed according to a delay time indicated by an output phase, so as to obtain a kalman filtering start time of the input frame in an actual state. Wherein the time origin of the input frame represents the ideal arrival time of the input frame, which is equal to the ideal start filter time of the input frame in an ideal state. The Kalman filter start time of each input frame is obtained by delaying the Kalman filter start time of each input frame based on the input phase and the time origin of each input frame by taking the sum of the time origin and the output phase of the input frame as the Kalman filter start time of the input frame.

Hereinafter, a process of determining and outputting a target output frame corresponding to each input frame based on the arrival time of each input frame, the input frequency of the input data, and the kalman filter start time of each input frame will be described in detail.

Fig. 5 is a flowchart of determining and outputting a target output frame corresponding to each input frame according to the data processing method provided by the embodiment of the present application, as shown in fig. 5, in the step S104, determining and outputting a target output frame corresponding to each input frame according to the arrival time of each input frame, the input frequency of the input data, and the kalman filter start time of each input frame, which includes:

S501, determining a standby output frame corresponding to a last input frame of the current input frame, wherein the standby output frame corresponding to the last input frame is a predicted frame corresponding to the last input frame or a corrected frame corresponding to the last input frame.

Alternatively, assume that the current input frame is the firstAn input frame, the last input frame of the current input frame is the firstThe Kalman filtering start time of the last input frame of the current input frame is the firstKalman filtering start time of input frame. And determining a standby output frame corresponding to the last input frame of the current input frame through prediction correction separation and delay correction during Kalman filtering, wherein the standby output frame corresponding to the last input frame is a prediction frame corresponding to the last input frame or a correction frame corresponding to the last input frame.

Specifically, if the Kalman filtering start time of the last input frameAnd predicting and correcting the last input frame to obtain a predicted frame and a corrected frame corresponding to the last input frame, wherein the standby output frame corresponding to the last input frame is the corrected frame corresponding to the last input frame. If the Kalman filtering start time of the last input frameThe last input frame is only predicted without correction, only the predicted frame corresponding to the last input frame is obtained, and at the Kalman filtering starting time of the current input frameAnd performing delay correction on the previous input frame to obtain a correction frame corresponding to the previous input frame, wherein the standby output frame corresponding to the previous input frame is the correction frame corresponding to the previous input frame. If the Kalman filtering start time of the last input frameThe previous input frame is only predicted without correction, only the predicted frame corresponding to the previous input frame is obtained, and the Kalman filtering starting time of the current input frame is not reachedAnd performing delay correction on the previous input frame, wherein the standby output frame corresponding to the previous input frame is a predicted frame corresponding to the previous input frame. Wherein the standby output frame corresponding to the last input frame of the current input frame is used for the Kalman filtering starting time of the current input frameAnd predicting to obtain a predicted frame corresponding to the current input frame.

Illustratively, referring to fig. 2, the inactive output frame corresponding to the 1 st input frame is the correction frame corresponding to the 1 st input frame, the inactive output frame corresponding to the 2 nd input frame is the correction frame corresponding to the 2 nd input frame, the inactive output frame corresponding to the 3rd input frame is the prediction frame corresponding to the 3rd input frame, the inactive output frame corresponding to the 4 th input frame is the correction frame corresponding to the 4 th input frame, and the inactive output frame corresponding to the 5 th input frame is the correction frame corresponding to the 5 th input frame.

S502, according to the standby output frame of the last input frame, predicting to obtain a predicted frame corresponding to the current input frame.

Optionally, at the Kalman filtering start time of the current input frameAnd predicting to obtain a predicted frame corresponding to the current input frame according to the standby output frame corresponding to the last input frame. Illustratively, with continued reference to FIG. 2, if the current input frame is the 2 nd input frame, then the Kalman filtering start time for the 2 nd input frameThat is, 210 ms, a predicted frame corresponding to the 2 nd input frame is predicted from the corrected frame corresponding to the 1st input frame. If the current input frame is the 4 th input frame, the Kalman filtering starting time of the 4 th input frameThat is, 410 ms, a predicted frame corresponding to the 4 th input frame is predicted from a predicted frame corresponding to the 3 rd input frame.

S503, determining and outputting a target output frame corresponding to the current input frame according to the arrival time of the current input frame, the Kalman filtering starting time of the current input frame and the predicted frame corresponding to the current input frame.

Optionally, according to the arrival time of the current input frameKalman filtering start time with current input frameDetermining Kalman filtering start time of current input frame by prediction correction separation during Kalman filteringIf the correction frame corresponding to the current input frame exists, the correction frame corresponding to the current input frame is output as a target output frame corresponding to the current input frame, and if the correction frame corresponding to the current input frame does not exist, the prediction frame corresponding to the current input frame is output as the target output frame corresponding to the current input frame.

Illustratively, with continued reference to FIG. 2, if the current input frame is the 5 th input frame, the Kalman filtering start time for the 5 th input frameThat is, when there is a correction frame corresponding to the 5 th input frame at 510 ms, the correction frame corresponding to the 5 th input frame is outputted as the target output frame corresponding to the 5 th input frame. If the current input frame is the 2 nd input frame, the Kalman filtering starting time of the 2 nd input frameThat is, when the correction frame corresponding to the 2 nd input frame does not exist in 210 ms, the predicted frame corresponding to the 2 nd input frame is outputted as the target output frame corresponding to the 2 nd input frame.

In this embodiment, a standby output frame corresponding to a previous input frame of the current input frame is determined, and the standby output frame corresponding to the previous input frame is determined by prediction correction separation and delay correction during kalman filtering, where the standby output frame corresponding to the previous input frame is a prediction frame corresponding to the previous input frame or a correction frame corresponding to the previous input frame. And predicting to obtain a predicted frame corresponding to the current input frame according to the standby output frame corresponding to the previous input frame at the Kalman filtering starting time of the current input frame, and determining and outputting a target output frame corresponding to the current input frame according to the arrival time of the current input frame and the Kalman filtering starting time of the current input frame by prediction correction separation during Kalman filtering. And through prediction correction separation and delay correction during Kalman filtering, accumulated errors brought by the predicted frames corresponding to the input frames are eliminated, and the quality of the target output frames corresponding to the input frames is improved.

Hereinafter, a process of determining and outputting a target output frame corresponding to a current input frame according to an arrival time of the current input frame, a kalman filter start time of the current input frame, and a predicted frame corresponding to the current input frame will be described in detail.

Fig. 6 is a flowchart of determining and outputting a target output frame corresponding to a current input frame according to the data processing method provided by the embodiment of the present application, as shown in fig. 6, in the step S503, determining and outputting a target output frame corresponding to a current input frame according to an arrival time of the current input frame, a kalman filtering start time of the current input frame, and a predicted frame corresponding to the current input frame, where the steps include:

S601, determining whether the current input frame arrives overtime according to the arrival time of the current input frame and the Kalman filtering starting time of the current input frame.

Optionally, according to the arrival time of the current input frameKalman filtering start time with current input frameDetermining whether the current input frame arrives overtime, and further determining whether the Kalman filtering starting time of the current input frame can be reachedThe current input frame is corrected. In particular, if the arrival time of the current input frameKalman filtering start time no later than current input frameI.e.Determining that the current input frame has not arrived overtime, and determining that the current input frame does not arrive overtime at the Kalman filtering start time of the current input frameThe current input frame is corrected. If the arrival time of the current input frameKalman filtering start time later than current input frameI.e.Determining that the current input frame arrives overtime and cannot be at the Kalman filtering starting time of the current input frameThe current input frame is corrected.

Illustratively, referring to fig. 2, the 1 st input frame and the 5 th input frame arrive without a timeout, and the 2 nd input frame, the 3 rd input frame, and the 4 th input frame arrive with a timeout.

S602, if not, correcting to obtain a correction frame corresponding to the current input frame according to the current input frame, and taking the correction frame corresponding to the current input frame as a target output frame corresponding to the current input frame.

Optionally, if it is determined that the current input frame has not arrived timeout, then at the Kalman filtering start time of the current input frameCorrecting the current input frame to obtain a correction frame corresponding to the current input frame, and outputting the correction frame corresponding to the current input frame as a target output frame corresponding to the current input frame. Exemplary, if the current input frame is the 5 th input frame, the Kalman filtering start time of the 5 th input frameThat is, 510 ms, the 5 th input frame is corrected to obtain a corrected frame corresponding to the 5 th input frame, and the corrected frame corresponding to the 5 th input frame is outputted as a target output frame corresponding to the 5 th input frame.

S603, otherwise, taking the predicted frame corresponding to the current input frame as a target output frame corresponding to the current input frame.

Alternatively, if it is determined that the current input frame arrives overtime, the Kalman filtering start time of the current input frameThe correction frame corresponding to the current input frame does not exist, the prediction frame corresponding to the current input frame is taken as the target output frame corresponding to the current input frame, and delay correction is carried out when Kalman filtering is carried out, so that the arrival time of the current input frame is obtainedKalman filtering start time of next input frame to current input frameDetermining whether to start time of Kalman filtering of next input frame of current input frameDelay correction is performed on the current input frame.

In this embodiment, according to the arrival time of the current input frame and the kalman filter start time of the current input frame, it is determined whether the current input frame arrives overtime, and further it is determined whether the current input frame can be corrected at the kalman filter start time of the current input frame. If the current input frame is determined not to arrive overtime, correcting the current input frame at the Kalman filtering starting time of the current input frame to obtain a correction frame corresponding to the current input frame, and outputting the correction frame corresponding to the current input frame as a target output frame corresponding to the current input frame. If the current input frame is determined to arrive overtime, taking a predicted frame corresponding to the current input frame as a target output frame corresponding to the current input frame, and determining whether to delay-correct the current input frame at the Kalman filtering starting time of the next input frame of the current input frame according to the arrival time of the current input frame and the Kalman filtering starting time of the next input frame of the current input frame through delay correction during Kalman filtering. The accumulated error generated by prediction is eliminated, and the output quality of the target output frame corresponding to the current input frame is improved.

Hereinafter, a process of determining a standby output frame corresponding to a previous input frame of the current input frame will be described in detail.

Fig. 7 is a schematic flow chart of determining a standby output frame corresponding to a last input frame of a current input frame according to the data processing method provided by the embodiment of the present application, as shown in fig. 7, the step of determining a standby output frame corresponding to a last input frame of a current input frame in the step S501 includes:

s701, determining the arrival time of the last input frame of the current input frame, and determining whether the last input frame arrives overtime according to the arrival time of the last input frame and the Kalman filtering starting time of the last input frame.

Optionally, the arrival time of the last input frame of the current input frame is the firstArrival time of input frameAccording to the arrival time of the last input frameKalman filtering start time with last input frameIt is determined whether the last input frame arrived over time.

Specifically, if the arrival time of the last input frameKalman filtering start time no later than last input frameI.e.Determining that the last input frame arrives at a time, i.e. the last input frame does not arrive at a time-out, and separating by prediction correction during Kalman filtering, and starting Kalman filtering of the last input frameAnd predicting and correcting the previous input frame to obtain a predicted frame and a corrected frame corresponding to the previous input frame. If the arrival time of the last input frameKalman filtering start time later than last input frameI.e.Determining that the last input frame arrives overtime, i.e. the last input frame does not arrive at the Kalman filtering start time of the last input frame, and separating the prediction correction during Kalman filtering to obtain the Kalman filtering start time of the last input frameAnd only predicting the previous input frame to obtain a predicted frame corresponding to the previous input frame.

With continued reference to FIG. 2, if the current input frame is the 5 th input frame, the last input frame of the current input frame is the 4 th input frame, the arrival time of the 4 th input frameKalman filtering start time for the 4 th input frame of 412 millisecondsTime of arrival of the 4 th input frame at 410 msKalman filtering start time later than input frame 4Then the 4 th input frame arrives overtime at the Kalman filtering start time of the 4 th input frameOnly the 4 th input frame is predicted, and a predicted frame corresponding to the 4 th input frame is obtained. If the current input frame is the 2 nd input frame, the last input frame of the current input frame is the 1 st input frame, and the arrival time of the 1 st input frameKalman filtering start time for the 1 st input frame of 100 millisecondsTime of arrival of the 1 st input frame of 110 millisecondsKalman filtering start time no later than 1 st input frameThe 1 st input frame does not arrive overtime at the Kalman filtering start time of the 1 st input frameAnd predicting and correcting the 1 st input frame to obtain a predicted frame and a corrected frame corresponding to the 1 st input frame.

S702, if the last input frame arrives overtime and the arrival time of the last input frame is not later than the Kalman filtering start time of the current input frame, correcting to obtain a correction frame corresponding to the last input frame according to the last input frame, and taking the correction frame corresponding to the last input frame as a standby output frame corresponding to the last input frame.

Alternatively, if the last input frame arrives overtime, the Kalman filtering start time of the last input frame is determinedThe last input frame is only predicted, but not corrected, and the delay correction by Kalman filtering is needed according to the arrival time of the last input frameKalman filtering start time with current input frameDetermining whether Kalman filtering start time of current input frameAnd performing delay correction on the last input frame.

Specifically, if the arrival time of the last input frameKalman filtering start time no later than current input frameI.e.Then determine the Kalman filter start time that can be at the current input frameDelay correction is carried out on the last input frame, and the Kalman filtering starting time of the current input frame is carried outAnd correcting the last input frame to obtain a correction frame corresponding to the last input frame, and taking the correction frame corresponding to the last input frame as a standby output frame corresponding to the last input frame.

With continued reference to FIG. 2, if the current input frame is the 5 th input frame, the last input frame of the current input frame is the 4 th input frame, the arrival time of the 4 th input frameKalman filtering start time for the 5 th input frame at 412 millisecondsTime of arrival of the 4 th input frame at 510 msKalman filtering start time no later than 5 th input frameThe Kalman filtering start time at the 5 th input frame is corrected by the delay in Kalman filteringAnd carrying out delay correction on the 4 th input frame to obtain a correction frame corresponding to the 4 th input frame, and taking the correction frame corresponding to the 4 th input frame as a standby output frame corresponding to the 4 th input frame.

S703, if the last input frame arrives overtime and the arrival time of the last input frame is later than the Kalman filtering start time of the current input frame, taking the predicted frame corresponding to the last input frame as the standby output frame corresponding to the last input frame.

Alternatively, if the last input frame arrives overtime, and the arrival time of the last input frameKalman filtering start time later than current input frameI.e.Representing the Kalman filtering start time of the last input frame at the current input frameIf not already reached, determining the Kalman filtering start time of the current input frameDelay correction is performed on the last input frame, and the Kalman filtering starting time of the last input frame is used forAnd predicting the previous input frame to obtain a predicted frame corresponding to the previous input frame, and taking the predicted frame corresponding to the previous input frame as a standby output frame corresponding to the previous input frame.

With continued reference to FIG. 2, if the current input frame is the 4 th input frame, the last input frame of the current input frame is the 3 rd input frame, the 3 rd input frame is lost, and the arrival time of the 3 rd input frameRegarded asKalman filtering start time for 4 th input frameTime of arrival of the 3 rd input frame at 410 msKalman filtering start time later than input frame 4The delay correction during the Kalman filtering cannot be performed at the Kalman filtering start time of the 4 th input frameDelay correction is performed on the 3 rd input frame, and Kalman filtering start time of the 3 rd input frame is performedAnd predicting the 3 rd input frame to obtain a predicted frame corresponding to the 3 rd input frame, and taking the predicted frame as a standby output frame corresponding to the 3 rd input frame.

S704, if the last input frame arrives on time, the correction frame corresponding to the last input frame is used as the standby output frame corresponding to the last input frame.

Alternatively, if the last input frame arrives on time, the Kalman filtering start time of the last input frame is determinedPredicting and correcting the last input frame, and determining the Kalman filtering start time of the last input frameAnd correcting the last input frame to obtain a corrected frame corresponding to the last input frame, and taking the corrected frame as a standby output frame corresponding to the last input frame.

With continued reference to FIG. 2, if the current input frame is the 2 nd input frame, the last input frame of the current input frame is the 1 st input frame, the 1 st input frame arrives on time, and the Kalman filtering start time of the 1 st input frame is determinedAnd correcting the 1 st input frame to obtain a correction frame corresponding to the 1 st input frame, and taking the correction frame as a standby output frame corresponding to the 1 st input frame.

In this embodiment, whether the last input frame arrives overtime is determined according to the arrival time of the last input frame and the kalman filtering start time of the last input frame, if the last input frame arrives overtime is determined, the delay correction is performed during the kalman filtering, and whether the last input frame is subjected to the delay correction at the kalman filtering start time of the current input frame is determined according to the arrival time of the last input frame and the kalman filtering start time of the current input frame. And if the arrival time of the last input frame is not later than the Kalman filtering starting time of the current input frame, correcting the last input frame at the Kalman filtering starting time of the current input frame to obtain a correction frame corresponding to the last input frame, and taking the correction frame corresponding to the last input frame as a standby output frame corresponding to the last input frame. If the last input frame arrives overtime and the arrival time of the last input frame is later than the Kalman filtering starting time of the current input frame, which means that the Kalman filtering starting time of the last input frame at the current input frame is not yet arrived, determining that delay correction cannot be carried out on the last input frame at the Kalman filtering starting time of the current input frame, and taking a predicted frame corresponding to the last input frame obtained by predicting the last input frame at the Kalman filtering starting time of the last input frame as a standby output frame corresponding to the last input frame. If the last input frame arrives on time, determining that the last input frame is predicted and corrected at the Kalman filtering starting time of the last input frame, and directly using a corrected frame corresponding to the last input frame obtained by correcting the last input frame at the Kalman filtering starting time of the last input frame as a standby output frame corresponding to the last input frame. And determining a standby output frame corresponding to the last input frame through prediction correction separation and delay correction during Kalman filtering, so that accumulated errors are eliminated.

Based on the same inventive concept, the embodiment of the present application further provides a data processing device corresponding to the data processing method, and since the principle of the device in the embodiment of the present application for solving the problem is similar to that of the data processing method in the embodiment of the present application, the implementation of the device may refer to the implementation of the method, and the repetition is omitted.

Fig. 8 is a block diagram of a data processing apparatus according to an embodiment of the present application, and as shown in fig. 8, the apparatus includes:

the acquiring module 801 is configured to acquire an input frequency of input data, a frame number of the input data, and an arrival time of each input frame in the input data.

A determining module 802, configured to determine an output phase according to an input frequency of the input data, a frame number of the input data, and an arrival time of each input frame in the input data.

The determining module 802 is further configured to determine a kalman filter start time of each input frame according to the output phase.

The determining module 802 is further configured to determine and output a target output value frame corresponding to each input frame according to the arrival time of each input frame, the input frequency of the input data, and the kalman filtering start time of each input frame, where the target output frame is a predicted frame or a corrected frame.

As an alternative embodiment, the determining module 802 is specifically configured to:

The input phase of each input frame is determined according to the input frequency of the input data and the arrival time of each input frame in the input data.

And sequencing the input phases of the input frames to obtain a target sequence, and determining a target frame number according to a preset filtering threshold and the frame number of the input data.

the period of the input data is determined according to the input frequency of the input data.

And determining a reference time origin according to the arrival time of the first frame input frame in the arrival time of each input frame and the period of the input data.

And determining the time origin of each input frame according to the reference time origin and the period of the input data.

The input phase of each input frame is determined according to the arrival time of each input frame and the time origin of each input frame.

And determining a standby output frame corresponding to a last input frame of the current input frame, wherein the standby output frame corresponding to the last input frame is a predicted frame corresponding to the last input frame or a corrected frame corresponding to the last input frame.

And predicting to obtain a predicted frame corresponding to the current input frame according to the standby output frame of the previous input frame.

And determining whether the current input frame arrives overtime or not according to the arrival time of the current input frame and the Kalman filtering starting time of the current input frame.

If not, correcting to obtain a correction frame corresponding to the current input frame according to the current input frame, and taking the correction frame corresponding to the current input frame as a target output frame corresponding to the current input frame.

Otherwise, taking the predicted frame corresponding to the current input frame as the target output frame corresponding to the current input frame.

determining the arrival time of the last input frame of the current input frame, and determining whether the last input frame arrives overtime or not according to the arrival time of the last input frame and the Kalman filtering starting time of the last input frame.

And if the last input frame arrives overtime and the arrival time of the last input frame is not later than the Kalman filtering starting time of the current input frame, correcting to obtain a correction frame corresponding to the last input frame according to the last input frame, and taking the correction frame corresponding to the last input frame as a standby output frame corresponding to the last input frame.

If the last input frame arrives overtime and the arrival time of the last input frame is later than the Kalman filtering starting time of the current input frame, taking the predicted frame corresponding to the last input frame as the standby output frame corresponding to the last input frame.

And if the last input frame arrives on time, taking the correction frame corresponding to the last input frame as the standby output frame corresponding to the last input frame.

The embodiment of the application also provides a computer device, as shown in fig. 9, which is a schematic structural diagram of the computer device according to the embodiment of the application, and includes a processor 91, a memory 92 and a bus 93. The memory 92 stores machine-readable instructions executable by the processor 91 (e.g., execution instructions corresponding to the acquisition module 801 and the determination module 802 in the apparatus of fig. 9), and when the computer device is running, the processor 91 and the memory 92 communicate through the bus 93, the machine-readable instructions are executed by the processor 91 to perform the steps of the data processing method in the above embodiment.

The embodiment of the present application also provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of the data processing method in the above embodiment.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system and apparatus may refer to corresponding procedures in the method embodiments, and are not repeated in the present disclosure. In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, and the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, and for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, indirect coupling or communication connection of devices or modules, electrical, mechanical, or other form.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. The storage medium includes a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, an optical disk, or other various media capable of storing program codes.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily appreciate variations or alternatives within the scope of the present application.

Claims

1. A method of data processing, the method comprising:

acquiring input frequency of input data, frame number of the input data and arrival time of each input frame in the input data;

determining an output phase according to the input frequency of the input data, the frame number of the input data and the arrival time of each input frame in the input data, wherein the output phase is used for indicating delay time;

determining and outputting a target output frame corresponding to each input frame according to the arrival time of each input frame, the input frequency of the input data and the Kalman filtering starting time of each input frame, wherein the target output frame is a predicted frame or a corrected frame;

The determining an output phase according to the input frequency of the input data, the frame number of the input data and the arrival time of each input frame in the input data includes:

Determining the output phase according to the target sequence and the target frame number;

the determining the kalman filter starting time of each input frame according to the output phase comprises the following steps:

Determining a Kalman filtering starting time of each input frame through phase alignment according to the delay time indicated by the output phase;

the determining and outputting the target output frame corresponding to each input frame according to the arrival time of each input frame, the input frequency of the input data and the kalman filtering start time of each input frame includes:

2. The method of claim 1, wherein determining the input phase of each input frame based on the input frequency of the input data and the arrival time of each input frame in the input data comprises:

3. The method of claim 1, wherein determining a kalman filter start time for each input frame based on the output phase, further comprises:

4. The method according to claim 1, wherein the determining and outputting the target output frame corresponding to the current input frame according to the arrival time of the current input frame, the kalman filter start time of the current input frame, and the predicted frame corresponding to the current input frame includes:

5. The method of claim 1, wherein determining a dormant output frame corresponding to a previous input frame to a current input frame comprises:

6. A data processing apparatus, the apparatus comprising:

The device comprises a determining module, a delay module and a control module, wherein the determining module is used for determining an output phase according to the input frequency of the input data, the frame number of the input data and the arrival time of each input frame in the input data, wherein the output phase is used for indicating the delay time;

The determining module is further configured to determine and output a target output value frame corresponding to each input frame according to the arrival time of each input frame, the input frequency of the input data, and the kalman filtering start time of each input frame, where the target output frame is a predicted frame or a corrected frame;

The determining module is specifically configured to determine an input phase of each input frame according to an input frequency of the input data and an arrival time of each input frame in the input data, sort the input phases of the input frames to obtain a target sequence, and determine a target frame number according to a preset filtering threshold and a frame number of the input data;

The determining module is specifically configured to determine a kalman filtering start time of each input frame through phase alignment according to the delay time indicated by the output phase;

The determining module is specifically configured to determine a standby output frame corresponding to a previous input frame of a current input frame, where the standby output frame corresponding to the previous input frame is a predicted frame corresponding to the previous input frame or a corrected frame corresponding to the previous input frame, predict the standby output frame of the previous input frame to obtain a predicted frame corresponding to the current input frame, and determine and output a target output frame corresponding to the current input frame according to an arrival time of the current input frame, a kalman filtering start time of the current input frame, and the predicted frame corresponding to the current input frame.

7. A computer device comprising a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory in communication via the bus when the computer device is in operation, the processor executing the machine readable instructions to perform the steps of the data processing method according to any one of claims 1 to 5.

8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, performs the steps of the data processing method according to any one of claims 1 to 5.