阅读说明：本技术 一种时空数据序列的压缩方法、装置及收集系统 (Method, device and collection system for compressing space-time data sequence ) 是由 耿晓亮 于 2019-11-12 设计创作，主要内容包括：本发明公开了一种时空数据序列的压缩方法、装置及收集系统,涉及计算机技术领域。该方法的一具体实施方式包括：根据时空数据序列中的位置坐标信息构建平滑去噪模型,其中,平滑去噪模型包括噪音测量矩阵、状态转换矩阵以及卡尔曼滤波方程；根据平滑去噪模型对时空数据序列进行平滑去噪处理；根据有损压缩算法对经过平滑去噪处理的时空数据序列进行压缩处理,以实现对时空数据序列的压缩。该实施方式能够识别并剔除噪音数据,实现了在保证较高精确度的情况下对时空数据序列压缩。(The invention discloses a method, a device and a collection system for compressing a space-time data sequence, and relates to the technical field of computers. One embodiment of the method comprises: constructing a smooth denoising model according to position coordinate information in a space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation; carrying out smooth denoising processing on the time-space data sequence according to the smooth denoising model; and compressing the smooth and denoised space-time data sequence according to a lossy compression algorithm so as to compress the space-time data sequence. The embodiment can identify and reject noise data, and realizes the compression of the time-space data sequence under the condition of ensuring higher accuracy.)

1. A method for compressing a sequence of spatio-temporal data, comprising:

constructing a smooth denoising model according to position coordinate information in a space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation;

carrying out smooth denoising processing on the space-time data sequence according to the smooth denoising model;

and compressing the space-time data sequence subjected to the smooth denoising treatment according to a lossy compression algorithm so as to realize compression of the space-time data sequence.

2. The method for compressing spatio-temporal data sequence according to claim 1, wherein before the step of constructing a smooth denoising model according to the position coordinate information in the spatio-temporal data sequence, the method for compressing spatio-temporal data sequence further comprises: and arranging the plurality of spatiotemporal data according to the sequence of the timestamps to construct the spatiotemporal data sequence.

3. The method of compressing a spatiotemporal data sequence as defined in claim 1 wherein said noise measurement matrix is constructed in accordance therewith; the state transformation matrix is constructed according to a system matrix, a plurality of space-time data and noise items; the Kalman filtering equations comprise extrapolation equations and/or estimation equations derived from the extrapolation equations.

4. The method of compressing a spatiotemporal data sequence as defined in claim 1, wherein the step of performing a smooth denoising process on the spatiotemporal data sequence according to the smooth denoising model comprises: and identifying noise data in the spatio-temporal data sequence according to the smooth denoising model, calculating an estimation value corresponding to the noise data, and replacing the noise data with the estimation value to realize smooth denoising processing of the spatio-temporal data sequence.

5. The method for compressing spatiotemporal data sequences according to claim 1, wherein the step of compressing the spatiotemporal data sequences subjected to the smoothing and denoising process according to the lossy compression algorithm comprises:

step 1, connecting a head node and a tail node of a smooth and denoising processed space-time data sequence into a straight-line segment;

step 2, sequentially calculating the vertical Euclidean distance from the space-time data in the space-time data sequence to the straight-line segment, and judging whether the vertical Euclidean distance is smaller than or equal to a position error threshold value; if yes, executing step 3; if not, go to step 5;

step 3, judging whether the vertical Euclidean distances of all the space-time data are completely calculated; if not, executing the step 4; if yes, go to step 6;

step 4, substituting the next data in the spatio-temporal data sequence and returning to the step 2;

step 5, splitting the current spatio-temporal data sequence into two spatio-temporal data sequences at the position corresponding to the current spatio-temporal data, and then returning to the step 1;

and 6, finishing the compression processing step to obtain a space-time data sequence consisting of a plurality of sequentially connected straight line segments, and completing the compression of the space-time data sequence.

6. The method of compressing a spatiotemporal data sequence as defined in claim 5, wherein in the case where the vertical Euclidean distance is less than or equal to the position error threshold, before performing the step 3, the step 2 further comprises: judging whether the state dimension of the space-time data corresponding to the vertical Euclidean distance is smaller than or equal to a state threshold value, if so, executing the step 3; if not, go to step 5.

7. An apparatus for compressing a sequence of spatio-temporal data, comprising:

the smooth denoising model constructing module is used for constructing a smooth denoising model according to position coordinate information in a space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation;

the smooth denoising processing module is used for carrying out smooth denoising processing on the space-time data sequence according to the smooth denoising model;

and the compression processing module is used for compressing the space-time data sequence subjected to the smooth denoising processing according to a lossy compression algorithm so as to realize the compression of the space-time data sequence.

8. A system for spatio-temporal data sequence collection, comprising:

the data acquisition device is used for acquiring space-time data and uploading the space-time data to the data compression processing device;

the data compression processing device is used for constructing a space-time data sequence and constructing a smooth denoising model according to position coordinate information in the space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation; carrying out smooth denoising processing on the space-time data sequence according to the smooth denoising model; compressing the space-time data sequence subjected to smooth denoising processing according to a lossy compression algorithm to realize compression of the space-time data sequence;

and the data storage device is used for collecting the spatiotemporal data sequence subjected to the compression processing.

9. A terminal, comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.

10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-6.

Technical Field

The invention relates to the technical field of computers, in particular to a method, a device and a collection system for compressing a space-time data sequence.

Background

With the development of 5G (5th-Generation, abbreviated as 5G) and IoT (Internet of Things), a large number of sensors collect spatio-temporal data in real time. The generation of large amounts of spatiotemporal data requires a large amount of storage space and transmission bandwidth. The prior art controls the amount of uploaded spatiotemporal data by adjusting the uploading time interval of a sensor so as to save flow and transmission bandwidth.

In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:

although the quantity of the time-space data uploaded at the same moment can be reduced by adjusting the uploading time interval of the sensor, the accuracy of the data is reduced, and the noise data cannot be removed.

Disclosure of Invention

In view of this, embodiments of the present invention provide a method, an apparatus, and a collection system for compressing a spatio-temporal data sequence, which can identify and reject noise data, and implement compression of the spatio-temporal data sequence while ensuring higher accuracy.

To achieve the above object, according to a first aspect of embodiments of the present invention, there is provided a method for compressing a spatio-temporal data sequence, including:

constructing a smooth denoising model according to position coordinate information in a space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation;

carrying out smooth denoising processing on the time-space data sequence according to the smooth denoising model;

and compressing the smooth and denoised space-time data sequence according to a lossy compression algorithm so as to compress the space-time data sequence.

Further, before the step of constructing a smooth denoising model according to the position coordinate information in the spatio-temporal data sequence, the method for compressing the spatio-temporal data sequence further includes: and arranging the plurality of spatiotemporal data according to the sequence of the timestamps to construct a spatiotemporal data sequence.

Furthermore, a noise measurement matrix is constructed according to the measurement matrix, the true state vector and the zero mean value Gaussian noise vector; the state transformation matrix is constructed according to the system matrix, a plurality of space-time data and noise items; the kalman filtering equations include extrapolation equations and/or estimation equations derived from the extrapolation equations.

Further, the step of performing smooth denoising processing on the spatio-temporal data sequence according to the smooth denoising model comprises: and identifying noise data in the spatio-temporal data sequence according to the smooth denoising model, calculating an estimation value corresponding to the noise data, and replacing the noise data with the estimation value to realize smooth denoising processing on the spatio-temporal data sequence.

Further, the step of compressing the spatio-temporal data sequence subjected to the smoothing and denoising processing according to the lossy compression algorithm comprises:

step 1, connecting a head node and a tail node of a smooth and denoising processed space-time data sequence into a straight-line segment;

step 2, sequentially calculating the vertical Euclidean distance from the space-time data in the space-time data sequence to the straight-line segment, and judging whether the vertical Euclidean distance is smaller than or equal to a position error threshold value; if yes, executing step 3; if not, go to step 5;

step 3, judging whether the vertical Euclidean distances of all the space-time data are completely calculated; if not, executing the step 4; if yes, go to step 6;

step 4, substituting the next data in the spatio-temporal data sequence and returning to the step 2;

step 5, splitting the current spatio-temporal data sequence into two segments of spatio-temporal data sequences at the position corresponding to the current spatio-temporal data, and then returning to the step 1;

and 6, finishing the compression processing step to obtain a space-time data sequence consisting of a plurality of sequentially connected straight line segments, and completing the compression of the space-time data sequence.

Further, in the case that the vertical euclidean distance is less than or equal to the position error threshold, before performing step 3, step 2 further includes: judging whether the state dimension of the space-time data corresponding to the vertical Euclidean distance is smaller than or equal to a state threshold value, if so, executing the step 3; if not, go to step 5.

According to a second aspect of the embodiments of the present invention, there is provided an apparatus for compressing spatio-temporal data sequences, comprising:

the smooth denoising model constructing module is used for constructing a smooth denoising model according to position coordinate information in the space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation;

the smooth denoising processing module is used for carrying out smooth denoising processing on the spatio-temporal data sequence according to the smooth denoising model;

and the compression processing module is used for compressing the time-space data sequence subjected to the smooth denoising processing according to a lossy compression algorithm so as to realize the compression of the time-space data sequence.

According to a third aspect of embodiments of the present invention, there is provided a spatiotemporal data sequence collection system, comprising:

the data acquisition device is used for acquiring space-time data and uploading the space-time data to the data compression processing device;

the data compression processing device is used for constructing a space-time data sequence and constructing a smooth denoising model according to position coordinate information in the space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation; carrying out smooth denoising processing on the time-space data sequence according to the smooth denoising model; compressing the smooth and denoised space-time data sequence according to a lossy compression algorithm to realize the compression of the space-time data sequence;

and the data storage device is used for collecting the spatiotemporal data sequence subjected to the compression processing.

According to a fourth aspect of the embodiments of the present invention, there is provided a terminal, including:

one or more processors;

a storage device for storing one or more programs,

when executed by one or more processors, cause the one or more processors to implement any of the above methods of compressing spatiotemporal data sequences.

According to a fifth aspect of embodiments of the present invention, there is provided a computer readable medium, on which a computer program is stored, which when executed by a processor, implements any of the above-described methods of compressing spatiotemporal data sequences.

One embodiment of the above invention has the following advantages or benefits: because a smooth denoising model is constructed according to position coordinate information in a space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation; carrying out smooth denoising processing on the time-space data sequence according to the smooth denoising model; according to the technical means of compressing the spatio-temporal data sequence subjected to the smooth denoising processing by the lossy compression algorithm to realize the compression of the spatio-temporal data sequence, the technical problems of low accuracy of uploaded spatio-temporal data and noise data in the prior art are solved, and the technical effects of recognizing and eliminating the noise data and realizing the compression of the spatio-temporal data sequence under the condition of ensuring higher accuracy are achieved.

Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.

Drawings

The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:

FIG. 1 is a schematic diagram of a main flow of a method of compressing spatiotemporal data sequences provided in accordance with a first embodiment of the present invention;

FIG. 2a is a schematic diagram of the main flow of a method for compressing spatiotemporal data sequences according to a second embodiment of the present invention;

FIG. 2b is a sample spatio-temporal data sequence s in a method of compressing a spatio-temporal data sequence as provided in FIG. 2 a;

FIG. 2c is a flow chart of a smoothing and denoising process of a spatio-temporal data sequence s according to the method for compressing the spatio-temporal data sequence provided in FIG. 2 a;

FIG. 2d is a schematic diagram illustrating a process of compressing a spatiotemporal data sequence s according to the spatiotemporal data sequence compression method provided in FIG. 2 a;

FIG. 2e is a diagram illustrating the result of compressing the spatiotemporal data sequence s according to the spatiotemporal data sequence compression method provided in FIG. 2 a;

FIG. 2f is a graph comparing the output results of the spatio-temporal data sequence s without smoothing and with smoothing and de-noising in the method for compressing the spatio-temporal data sequence provided in FIG. 2 a;

FIG. 3 is a schematic diagram of the main blocks of an apparatus for compressing spatiotemporal data sequences provided in accordance with an embodiment of the present invention;

FIG. 4 is a block diagram of a spatiotemporal data sequence collection system provided in accordance with an embodiment of the present invention;

FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;

fig. 6 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.

Detailed Description

Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Fig. 1 is a schematic diagram illustrating a main flow of a method for compressing a spatiotemporal data sequence according to a first embodiment of the present invention, and as shown in fig. 1, the method for compressing a spatiotemporal data sequence according to the embodiment of the present invention includes:

step S101, a smooth denoising model is constructed according to position coordinate information in a space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation.

The smooth denoising model is constructed according to the position coordinates of the space-time data, so that noise data can be accurately identified according to the relation between coordinate information adjacent to each other (in a time stamp sequence) and can be smoothed (namely, the corresponding noise data is corrected by the calculation value of the smooth denoising model to be closer to a real value), and the accuracy of the space-time data is further improved.

Further, according to the embodiment of the present invention, before the step of constructing the smooth denoising model according to the position coordinate information in the spatio-temporal data sequence, the method for compressing the spatio-temporal data sequence further includes: and arranging the plurality of spatiotemporal data according to the sequence of the timestamps to construct a spatiotemporal data sequence.

Spatio-temporal data: the invention relates to data which simultaneously has time dimension, space dimension and attribute information, and is characterized by a time stamp for recording time, position coordinates for recording space and state dimension for recording attribute information.

The space-time data sequence is as follows: the time stamp is an ordered sequence formed by a group of space-time data arranged according to the sequence of the time stamps.

Specifically, the noise measurement matrix is constructed according to the measurement matrix, the true state vector and the zero mean value Gaussian noise vector; the state transformation matrix is constructed according to the system matrix, a plurality of space-time data and noise items; the kalman filtering equations include extrapolation equations and/or estimation equations derived from the extrapolation equations.

Noise data exists in observation (namely measurement) of the space-time data, corresponding noise data can be identified through a noise measurement matrix constructed according to a measurement matrix, a real state vector and a zero-mean Gaussian noise vector, and the noise data is favorably subjected to smooth denoising processing later so as to improve the accuracy of the space-time data in a space-time data sequence.

And S102, carrying out smooth denoising processing on the space-time data sequence according to the smooth denoising model.

Further, according to the embodiment of the present invention, the step of performing smooth denoising processing on the spatio-temporal data sequence according to the smooth denoising model includes: and identifying noise data in the spatio-temporal data sequence according to the smooth denoising model, calculating an estimation value corresponding to the noise data, and replacing the noise data with the estimation value to realize smooth denoising processing on the spatio-temporal data sequence.

Based on the smooth denoising model constructed above, noise data can be accurately identified through the noise measurement matrix, and then smooth denoising processing is carried out on the noise data through the corresponding relation between position coordinate information between adjacent space-time data through the state transition matrix and the Kalman filtering equation, namely, the corresponding noise data is corrected through the calculated value of the smooth denoising model, so that the noise data is closer to a true value, and the accuracy of the space-time data is further improved.

And S103, compressing the time-space data sequence subjected to the smooth denoising treatment according to a lossy compression algorithm so as to compress the time-space data sequence.

Further, according to the embodiment of the present invention, the step of compressing the spatio-temporal data sequence subjected to the smoothing and denoising processing according to the lossy compression algorithm includes:

step 1, connecting a head node and a tail node of a smooth and denoising processed space-time data sequence into a straight-line segment;

step 2, sequentially calculating the vertical Euclidean distance from the space-time data in the space-time data sequence to the straight-line segment, and judging whether the vertical Euclidean distance is smaller than or equal to a position error threshold value; if so, namely the vertical Euclidean distance is smaller than or equal to the position error threshold, executing the step 3; if not, namely the vertical Euclidean distance is larger than the position error threshold value, turning to the step 5;

step 3, judging whether the vertical Euclidean distances of all the space-time data are completely calculated; if not, executing the step 4; if yes, go to step 6;

step 4, substituting the next data in the spatio-temporal data sequence and returning to the step 2;

step 5, splitting the current spatio-temporal data sequence into two segments of spatio-temporal data sequences at the position corresponding to the current spatio-temporal data, and then returning to the step 1;

and 6, finishing the compression processing step to obtain a space-time data sequence consisting of a plurality of sequentially connected straight line segments, and completing the compression of the space-time data sequence.

The space-time data sequence subjected to smooth denoising processing is compressed by the lossy compression algorithm, so that the storage space of the space-time data is reduced (the compression effect is good), and meanwhile, higher accuracy is kept. According to a specific implementation manner of the embodiment of the present invention, the spatio-temporal data sequence may be compressed by a lossy compression algorithm such as the douglas-pock method, the vertical distance method, and/or the diaphragm method.

According to a specific implementation manner of the embodiment of the present invention, in a case that the vertical euclidean distance is less than or equal to the position error threshold, before performing step 3, step 2 further includes: judging whether the state dimension of the space-time data corresponding to the vertical Euclidean distance is smaller than or equal to a state threshold value, if so, executing the step 3; if not, go to step 5.

According to the setting, the compression of the space-time data sequence can be further refined by limiting the state threshold, the storage space of the space-time data is further reduced, and the compression effect of the space-time data sequence is improved.

According to the technical scheme of the embodiment of the invention, a smooth denoising model is constructed according to position coordinate information in a space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation; carrying out smooth denoising processing on the time-space data sequence according to the smooth denoising model; according to the technical means of compressing the spatio-temporal data sequence subjected to the smooth denoising processing by the lossy compression algorithm to realize the compression of the spatio-temporal data sequence, the technical problems of low accuracy of uploaded spatio-temporal data and noise data in the prior art are solved, and the technical effects of recognizing and eliminating the noise data and realizing the compression of the spatio-temporal data sequence under the condition of ensuring higher accuracy are achieved.

FIG. 2a is a schematic diagram of the main flow of a method for compressing spatiotemporal data sequences according to a second embodiment of the present invention; as shown in fig. 2a, the method for compressing spatio-temporal data sequence according to the embodiment of the present invention includes;

step S201, arranging a plurality of space-time data according to the sequence of the time stamps to construct a space-time data sequence.

Spatio-temporal data points p_i(x_i，y_i，z_i，t_i) Consisting of a time stamp t of the recording time, position coordinates x, y of the recording space, and a state dimension z of the recording attribute information. According to a specific implementation manner of the embodiment of the present invention, the state dimension may represent an instantaneous speed, but this is not a limitation of the present invention, and any state dimension may also be characterized.

Space-time data sequence s (p)₁，p₂，…，p_n) By a group in accordance with the chronological order of the time stampsThe spatio-temporal data points are arranged in a sequence, wherein n is the number of spatio-temporal data points in the spatio-temporal data sequence s. The data pattern of the spatio-temporal data sequence s is shown in fig. 2 b.

According to an embodiment of the present invention, the noisy moving object trace (spatio-temporal data sequence) is shown in table 1 below (where only the position coordinates x, y, and the time stamp t are shown):

x	y	t
			24.973902	102.864006	2019/4/21 9:00:02
29.747248	107.269174	2019/4/21 9:00:03
			36.211595	120.091359	2019/4/21 9:00:04
34.859158	114.507582	2019/4/21 9:00:05
			29.478616	106.707738	2019/4/21 9:00:06
22.804976	114.056507	2019/4/21 9:00:07
			30.435698	114.057233	2019/4/21 9:00:08
24.973244	102.864941	2019/4/21 9:00:09

step S202, a smooth denoising model is constructed according to position coordinate information in the space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation.

Due to the existence of noise data in the observation (i.e., measurement) of the spatio-temporal data, corresponding noise data can be identified through a noise measurement matrix constructed according to a measurement matrix, a true state vector and a zero-mean gaussian noise vector, so that the noise data needs to be subjected to smooth denoising processing later to improve the accuracy of the spatio-temporal data in the spatio-temporal data sequence.

Specifically, according to an embodiment of the present invention, the noise measurement matrix is:

Z_i＝H_i﹒X_i+V_i (1)

wherein Z is_iIs a noise measurement matrix, X, on position coordinate information_iIs a true state vector in which the element x_iAnd y_iIs the measured position coordinate (also called unknown coordinate) corresponding to the ith space-time data, which means that the point is an unknown point, the real coordinate position of which is unknown and can only be characterized by the measured value),andis the x coordinate component and the y coordinate component corresponding to the real position coordinate (also called the real unknown coordinate, representing the real coordinate of the unknown point) corresponding to the ith space-time data; h_iIs a measurement matrix, characterizes X_iAnd Z_iPrevious conversion relationships; v_iIs a zero mean gaussian noise vector.

The state transition matrix is:

X_i＝Φ_i-1*X_i-1+W_i-1 (2)

the true state vector X in equation (2)_iAs its previous time X_i-1Function of (1), system matrix phi_i-1Given the relationship between the two, according to a specific implementation of the embodiment of the present invention, in the iso-velocity motion model, the linear relationship between the real state vectors is represented as: x_iTime X speed X_i-1；W_i-1The noise term is an average value of a certain part, particularly which part, and needs to be obtained by fitting according to historical spatio-temporal data measurement values.

The Kalman filtering equation comprises an extrapolation equation and an estimation equation obtained according to the extrapolation equation, wherein the extrapolation equation is as follows:

wherein, the formula (3) is an estimation value equation of the finger state vector, the formula (4) is a covariance equation corresponding to the system matrix phi, phi represents the system matrix, and Q represents the system matrixA system noise covariance matrix. Specifically, the (-) superscript (-) refers to the extrapolated estimate of the state vector, and the (+) superscript refers to the estimate of the state vector. Due to errors in the measured values (acquired spatio-temporal data), the optimization is performed by the above-mentioned extrapolation equation, in particular, at the first moment (p), in order to reduce the errors and to improve the accuracy of the spatio-temporal data_i) The observed value is used, and then the value corresponding to each subsequent time (each timestamp) is calculated through recursion.

The estimation equation is:

according to the extrapolation equations (3) and (4), the measured space-time data are combined to obtain the estimation equations (5) and (6). K denotes a kalman gain matrix, and R denotes a covariance matrix of measurement noise. Wherein, calculated according to the formulas (5) and (6)The method is an optimal estimation value for the state vector X, and can further improve the smoothing effect and the accuracy of the space-time data.

And S203, carrying out smooth denoising processing on the space-time data sequence according to the smooth denoising model.

According to the smooth denoising model constructed by the noise measurement matrix, the state transition matrix and the Kalman filtering equation, after noise data are accurately identified, an optimized estimation value is obtained through calculation and optimization, and then the optimized estimation value is replaced by an observation value in the noise data, so that smooth optimization of the noise data in the space-time data sequence is realized.

According to a specific implementation manner of the embodiment of the present invention, a flow of performing a smoothing and denoising process on a spatio-temporal data sequence by using a smoothing and denoising model is shown in fig. 2 c.

First, the spatio-temporal dataInputting the sequence s into a smooth denoising model, and performing initial estimation on the state error covariance of an initial state, namely ordering p₁＝Var(p₁) And then, an optimal estimation value is obtained through the formulas 4, 5, 3 and 6, the noise data is replaced, and all space-time data in the space-time data sequence are sequentially calculated so as to complete the smooth optimization of the space-time state sequence s.

And S204, connecting the head node and the tail node of the smooth denoising processed space-time data sequence into a straight-line segment. According to an embodiment of the present invention, as shown in FIG. 2d, P is₀And P₁₆Connected to form a straight line segment.

Step S205, sequentially calculating the vertical Euclidean distance from the space-time data in the space-time data sequence to the straight line segment, and judging whether the vertical Euclidean distance is less than or equal to a position error threshold value; if yes, go to step S206; if not, go to step S208. Specifically, the setting of the position error threshold may be set according to actual conditions, and generally, the unit of the position error threshold is consistent with the space-time data.

Step S206, judging whether the state dimension of the space-time data corresponding to the vertical Euclidean distance is less than or equal to a state threshold value, if so, executing step S207; if not, go to step S208.

Step S207, judging whether the vertical Euclidean distances of all the space-time data are calculated, if not, executing step S208; if yes, go to step S210.

In step S208, the next data in the spatio-temporal data sequence is substituted, and the process returns to step S205.

Step S209 is to split the current spatio-temporal data sequence into two segments of spatio-temporal data sequences at the positions corresponding to the current spatio-temporal data, and then to return to step S204.

Step S210, ending the compression processing step, obtaining a spatio-temporal data sequence (as shown in fig. 2 e) composed of a plurality of sequentially connected straight line segments, and completing the compression of the spatio-temporal data sequence.

According to an embodiment of the present invention, the output result of the moving object trace (spatio-temporal data sequence) obtained according to the data shown in table 1 above and the output result of the compressed moving object trace (spatio-temporal data sequence) after the smoothing and denoising processing are shown in fig. 2 f.

According to the technical scheme of the embodiment of the invention, a smooth denoising model is constructed according to position coordinate information in a space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation; carrying out smooth denoising processing on the time-space data sequence according to the smooth denoising model; according to the technical means of compressing the spatio-temporal data sequence subjected to the smooth denoising processing by the lossy compression algorithm to realize the compression of the spatio-temporal data sequence, the technical problems of low accuracy of uploaded spatio-temporal data and noise data in the prior art are solved, and the technical effects of recognizing and eliminating the noise data and realizing the compression of the spatio-temporal data sequence under the condition of ensuring higher accuracy are achieved.

FIG. 3 is a schematic diagram of the main blocks of an apparatus for compressing spatiotemporal data sequences provided in accordance with an embodiment of the present invention; as shown in fig. 3, an apparatus 300 for compressing spatio-temporal data sequences according to an embodiment of the present invention includes:

the smooth denoising model constructing module 301 is configured to construct a smooth denoising model according to the position coordinate information in the spatio-temporal data sequence, where the smooth denoising model includes a noise measurement matrix, a state transition matrix, and a kalman filter equation.

The smooth denoising model is constructed according to the position coordinates of the space-time data, so that noise data can be accurately identified according to the relation between coordinate information adjacent to each other (in a time stamp sequence) and can be smoothed (namely, the corresponding noise data is corrected by the calculation value of the smooth denoising model to be closer to a real value), and the accuracy of the space-time data is further improved.

Further, according to the embodiment of the present invention, the apparatus 300 for compressing a spatio-temporal data sequence further includes a spatio-temporal data sequence construction module, and before the step of constructing the smooth denoising model according to the position coordinate information in the spatio-temporal data sequence, the spatio-temporal data sequence construction module is configured to arrange the plurality of spatio-temporal data according to the sequence of the time stamps to construct the spatio-temporal data sequence.

Spatio-temporal data: the invention relates to data which simultaneously has time dimension, space dimension and attribute information, and is characterized by a time stamp for recording time, position coordinates for recording space and state dimension for recording attribute information.

The space-time data sequence is as follows: the time stamp is an ordered sequence formed by a group of space-time data arranged according to the sequence of the time stamps.

Specifically, the noise measurement matrix is constructed according to the measurement matrix, the true state vector and the zero mean value Gaussian noise vector; the state transformation matrix is constructed according to the system matrix, a plurality of space-time data and noise items; the kalman filtering equations include extrapolation equations and/or estimation equations derived from the extrapolation equations.

Noise data exists in observation (namely measurement) of the space-time data, corresponding noise data can be identified through a noise measurement matrix constructed according to a measurement matrix, a real state vector and a zero-mean Gaussian noise vector, and the noise data is favorably subjected to smooth denoising processing later so as to improve the accuracy of the space-time data in a space-time data sequence.

And a smoothing and denoising processing module 302, configured to perform smoothing and denoising processing on the spatio-temporal data sequence according to the smoothing and denoising model.

Further, according to the embodiment of the present invention, the smooth denoising processing module 302 is further configured to: and identifying noise data in the spatio-temporal data sequence according to the smooth denoising model, calculating an estimation value corresponding to the noise data, and replacing the noise data with the estimation value to realize smooth denoising processing on the spatio-temporal data sequence.

Based on the smooth denoising model constructed above, noise data can be accurately identified through the noise measurement matrix, and then smooth denoising processing is carried out on the noise data through the corresponding relation between position coordinate information between adjacent space-time data through the state transition matrix and the Kalman filtering equation, namely, the corresponding noise data is corrected through the calculated value of the smooth denoising model, so that the noise data is closer to a true value, and the accuracy of the space-time data is further improved.

And the compression processing module 303 is configured to perform compression processing on the smooth and denoising processed spatio-temporal data sequence according to a lossy compression algorithm, so as to implement compression on the spatio-temporal data sequence.

According to an embodiment of the present invention, the compression processing module 303 is further configured to perform the following steps:

step 1, connecting a head node and a tail node of a smooth and denoising processed space-time data sequence into a straight-line segment;

step 2, sequentially calculating the vertical Euclidean distance from the space-time data in the space-time data sequence to the straight-line segment, and judging whether the vertical Euclidean distance is smaller than or equal to a position error threshold value; if so, namely the vertical Euclidean distance is smaller than or equal to the position error threshold, executing the step 3; if not, namely the vertical Euclidean distance is larger than the position error threshold value, turning to the step 5;

step 3, judging whether the vertical Euclidean distances of all the space-time data are completely calculated; if not, executing the step 4; if yes, go to step 6;

step 4, substituting the next data in the spatio-temporal data sequence and returning to the step 2;

step 5, splitting the current spatio-temporal data sequence into two segments of spatio-temporal data sequences at the position corresponding to the current spatio-temporal data, and then returning to the step 1;

and 6, finishing the compression processing step to obtain a space-time data sequence consisting of a plurality of sequentially connected straight line segments, and completing the compression of the space-time data sequence.

The space-time data sequence subjected to smooth denoising processing is compressed by the lossy compression algorithm, so that the storage space of the space-time data is reduced (the compression effect is good), and meanwhile, higher accuracy is kept. According to a specific implementation manner of the embodiment of the present invention, the spatio-temporal data sequence may be compressed by a lossy compression algorithm such as the douglas-pock method, the vertical distance method, and/or the diaphragm method.

Further, according to a specific implementation manner of the embodiment of the present invention, in a case that the vertical euclidean distance is less than or equal to the position error threshold, before performing step 3, the compression processing module 303 is further configured to: judging whether the state dimension of the space-time data corresponding to the vertical Euclidean distance is smaller than or equal to a state threshold value, if so, executing the step 3; if not, go to step 5.

According to the setting, the compression of the space-time data sequence can be further refined by limiting the state threshold, the storage space of the space-time data is further reduced, and the compression effect of the space-time data sequence is improved.

According to the technical scheme of the embodiment of the invention, a smooth denoising model is constructed according to position coordinate information in a space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation; carrying out smooth denoising processing on the time-space data sequence according to the smooth denoising model; according to the technical means of compressing the spatio-temporal data sequence subjected to the smooth denoising processing by the lossy compression algorithm to realize the compression of the spatio-temporal data sequence, the technical problems of low accuracy of uploaded spatio-temporal data and noise data in the prior art are solved, and the technical effects of recognizing and eliminating the noise data and realizing the compression of the spatio-temporal data sequence under the condition of ensuring higher accuracy are achieved.

It can be understood that, since the method embodiment and the apparatus embodiment are different presentation forms of the same technical concept, the content of the method embodiment portion in the present application should be synchronously adapted to the apparatus embodiment portion, and is not described herein again.

FIG. 4 is a block diagram of a spatiotemporal data sequence collection system provided in accordance with an embodiment of the present invention; as shown in fig. 4, the spatio-temporal data sequence collection system provided by the embodiment of the present invention includes:

and the data acquisition device 401 is used for acquiring the space-time data and uploading the space-time data to the data compression processing device.

Specifically, the data acquisition device 401 acquires spatio-temporal data through a plurality of sensor devices, and may upload the acquired spatio-temporal data to the data compression processing device in real time or in a delayed batch.

The data compression processing device 402 is used for constructing a space-time data sequence and a smooth denoising model according to position coordinate information in the space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation; carrying out smooth denoising processing on the time-space data sequence according to the smooth denoising model; and compressing the smooth and denoised space-time data sequence according to a lossy compression algorithm so as to compress the space-time data sequence.

According to an embodiment of the present invention, the cache data module of the data compression processing apparatus 402 receives the spatiotemporal data uploaded by the data acquisition apparatus 401, and then arranges the plurality of spatiotemporal data according to the sequence of the timestamps to construct a spatiotemporal data sequence.

Then, the smoothing module of the data compression processing device 402 constructs a smoothing and denoising model according to the position coordinate information in the space-time data sequence, and performs smoothing and denoising processing on the space-time data sequence according to the smoothing and denoising model. The smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation.

Further, according to the embodiment of the present invention, the step of performing smooth denoising processing on the spatio-temporal data sequence according to the smooth denoising model includes: and identifying noise data in the spatio-temporal data sequence according to the smooth denoising model, calculating an estimation value corresponding to the noise data, and replacing the noise data with the estimation value to realize smooth denoising processing on the spatio-temporal data sequence.

Based on the smooth denoising model constructed above, noise data can be accurately identified through the noise measurement matrix, and then smooth denoising processing is carried out on the noise data through the corresponding relation between position coordinate information between adjacent space-time data through the state transition matrix and the Kalman filtering equation, namely, the corresponding noise data is corrected through the calculated value of the smooth denoising model, so that the noise data is closer to a true value, and the accuracy of the space-time data is further improved.

Finally, the compression module of the data compression processing device 402 compresses the spatio-temporal data sequence subjected to the smoothing and denoising processing according to a lossy compression algorithm, so as to compress the spatio-temporal data sequence.

Further, according to the embodiment of the present invention, the step of compressing the spatio-temporal data sequence subjected to the smoothing and denoising processing according to the lossy compression algorithm includes:

step 1, connecting a head node and a tail node of a smooth and denoising processed space-time data sequence into a straight-line segment;

step 2, sequentially calculating the vertical Euclidean distance from the space-time data in the space-time data sequence to the straight-line segment, and judging whether the vertical Euclidean distance is smaller than or equal to a position error threshold value; if so, namely the vertical Euclidean distance is smaller than or equal to the position error threshold, executing the step 3; if not, namely the vertical Euclidean distance is larger than the position error threshold value, turning to the step 5;

step 3, judging whether the vertical Euclidean distances of all the space-time data are completely calculated; if not, executing the step 4; if yes, go to step 6;

step 4, substituting the next data in the spatio-temporal data sequence and returning to the step 2;

step 5, splitting the current spatio-temporal data sequence into two segments of spatio-temporal data sequences at the position corresponding to the current spatio-temporal data, and then returning to the step 1;

and 6, finishing the compression processing step to obtain a space-time data sequence consisting of a plurality of sequentially connected straight line segments, and completing the compression of the space-time data sequence.

According to a specific implementation manner of the embodiment of the present invention, in a case that the vertical euclidean distance is less than or equal to the position error threshold, before performing step 3, step 2 further includes: judging whether the state dimension of the space-time data corresponding to the vertical Euclidean distance is smaller than or equal to a state threshold value, if so, executing the step 3; if not, go to step 5.

A data storage 403 for collecting the compressed spatiotemporal data sequence.

According to the technical scheme of the embodiment of the invention, a smooth denoising model is constructed according to position coordinate information in a space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation; carrying out smooth denoising processing on the time-space data sequence according to the smooth denoising model; according to the technical means of compressing the spatio-temporal data sequence subjected to the smooth denoising processing by the lossy compression algorithm to realize the compression of the spatio-temporal data sequence, the technical problems of low accuracy of uploaded spatio-temporal data and noise data in the prior art are solved, and the technical effects of recognizing and eliminating the noise data and realizing the compression of the spatio-temporal data sequence under the condition of ensuring higher accuracy are achieved.

FIG. 5 illustrates an exemplary system architecture 500 of a method or apparatus for compressing spatiotemporal data sequences to which embodiments of the present invention may be applied.

As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505 (this architecture is merely an example, and the components included in a particular architecture may be adapted according to application specific circumstances). The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. The terminal devices 501, 502, 503 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The terminal devices 501, 502, 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 505 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 501, 502, 503. The background management server may analyze and perform other processing on data such as position coordinate information in the received spatio-temporal data sequence, and feed back a processing result (e.g., the spatio-temporal data sequence subjected to the smoothing and denoising processing and the compressed spatio-temporal data sequence) to the terminal device.

It should be noted that the method for compressing spatio-temporal data sequences provided by the embodiment of the present invention is generally performed by the server 505, and accordingly, the means for compressing spatio-temporal data sequences is generally disposed in the server 505.

It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to FIG. 6, a block diagram of a computer system 600 suitable for use with a terminal device implementing an embodiment of the invention is shown. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.

As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.

In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 601.

It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a smooth denoising model construction module, a smooth denoising processing module, and a compression processing module. The names of these modules do not limit the modules themselves in some cases, for example, the compression processing module may also be described as a "module for compressing the spatio-temporal data sequence subjected to the smoothing and denoising processing according to the lossy compression algorithm".

As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: constructing a smooth denoising model according to position coordinate information in a space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation; carrying out smooth denoising processing on the time-space data sequence according to the smooth denoising model; and compressing the smooth and denoised space-time data sequence according to a lossy compression algorithm so as to compress the space-time data sequence.

According to the technical scheme of the embodiment of the invention, a smooth denoising model is constructed according to position coordinate information in a space-time data sequence, wherein the smooth denoising model comprises a noise measurement matrix, a state transition matrix and a Kalman filtering equation; carrying out smooth denoising processing on the time-space data sequence according to the smooth denoising model; according to the technical means of compressing the spatio-temporal data sequence subjected to the smooth denoising processing by the lossy compression algorithm to realize the compression of the spatio-temporal data sequence, the technical problems of low accuracy of uploaded spatio-temporal data and noise data in the prior art are solved, and the technical effects of recognizing and eliminating the noise data and realizing the compression of the spatio-temporal data sequence under the condition of ensuring higher accuracy are achieved.

The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.