阅读说明：本技术 一种北斗定位长期监测数据的处理方法及系统 (Processing method and system for Beidou positioning long-term monitoring data ) 是由 刘翰林 李林超 杜博文 杜彦良 于 2020-11-30 设计创作，主要内容包括：本发明提供一种北斗定位长期监测数据的处理方法,包括以下步骤：S1：获得变形监测时间序列,并进行单历元异常值分析和剔除；S2：进行数据矩阵化处理；S3：进行周期性、半周期性波动及噪音分析,并移除周期性波动与噪音；S4：进行固定时间段的分段,并剔除非周期性共模误差；S5：进行数据重构拟合。本发明还提供一种北斗定位长期监测数据的处理系统,应用于实现所述的一种北斗定位长期监测数据的处理方法。本发明提供一种北斗定位长期监测数据的处理方法及系统,解决了目前的监测数据处理方法并没有考虑到监测时间序列内部的非周期位移信号实质为外部环境影响下的被监测物真实位移信号的问题。(The invention provides a processing method of Beidou positioning long-term monitoring data, which comprises the following steps: s1: acquiring a deformation monitoring time sequence, and analyzing and removing single epoch abnormal values; s2: carrying out data matrixing processing; s3: carrying out periodic and semi-periodic fluctuation and noise analysis, and removing periodic fluctuation and noise; s4: segmenting a fixed time period, and eliminating aperiodic common mode errors; s5: and performing data reconstruction fitting. The invention also provides a processing system of the Beidou positioning long-term monitoring data, which is applied to the processing method of the Beidou positioning long-term monitoring data. The invention provides a processing method and a processing system for Beidou positioning long-term monitoring data, and solves the problem that the current monitoring data processing method does not consider that a non-periodic displacement signal inside a monitoring time sequence is a real displacement signal of a monitored object under the influence of an external environment.)

1. A processing method of Beidou positioning long-term monitoring data is characterized by comprising the following steps:

s1: performing double-difference integer ambiguity resolution based on a single epoch on a single-epoch observation value obtained by monitoring a Beidou satellite to obtain a deformation monitoring time sequence, and performing single-epoch abnormal value analysis and elimination on the deformation monitoring time sequence to preliminarily obtain a deformation monitoring time sequence based on an hour solution of a Beidou monitoring station;

s2: performing data matrixing processing on the deformation monitoring time sequence based on the Beidou monitoring station hourly solution to obtain a matrixed deformation monitoring time sequence;

s3: carrying out periodic and semi-periodic fluctuation and noise analysis on the matrixed deformation monitoring time sequence, and removing the periodic fluctuation and the noise to obtain a corrected coordinate time sequence;

s4: segmenting the corrected coordinate time sequence for a fixed time period to obtain a multi-segment coordinate time sequence; removing aperiodic common mode errors from the multi-segment coordinate time sequence to obtain a standard coordinate time sequence;

s5: and performing data reconstruction fitting on the standard coordinate time sequence to obtain a deformation time sequence curve, and finishing the processing of the monitoring data.

2. According toThe processing method of Beidou positioning long-term monitoring data as set forth in claim 1, characterized in that n monitoring stations are provided, and the number of the monitoring stations is i, i-1, 2,3, …, n, x_jIs the relative solution of j-th epoch, and has N epochs, j is 2,3,4, …, N-1;

in step S1, the single epoch outlier analysis and elimination of the deformation monitoring time series includes the following steps:

s1.1: according to the relative solution x_jSequentially calculating corresponding judgment values y_m：

y_m＝2x_j-(x_j+1+x_j-1)

Wherein, the judgment value serial number m is 1,2, …, N-2;

s1.2: calculating the mean of the relative solutionsAnd a variance σ;

s1.3: calculating a standard value z_mAccording to the standard value z_mAnd judging whether the corresponding deformation monitoring time sequence is an abnormal value or not, and removing the abnormal value.

3. The processing method of Beidou positioning long-term monitoring data according to claim 2, characterized in that in step S1.3, the standard value z is_mThe calculation formula of (2) is as follows:

when z is_m>And 3, judging that the corresponding deformation monitoring time sequence is an abnormal value.

4. The processing method of Beidou positioning long-term monitoring data according to claim 2, characterized in that in step S1.3, the standard value z is_mThe calculation formula of (2) is as follows:

when z is_m>1.7, judging that the corresponding deformation monitoring time sequence is an abnormal value.

5. The processing method of Beidou positioning long-term monitoring data as set forth in claim 1, wherein in step S3, the matrixed deformation monitoring time series is analyzed for periodicity, semiperiodicity fluctuation and noise based on the complete empirical mode decomposition method of white noise with adaptive corresponding modal order.

6. The processing method of Beidou positioning long-term monitoring data according to claim 1, wherein in step S4,

let n_iRepresenting monitoring stations, where n_i＝(x₁,x₂,…x_m) Representing the relative displacement processed by each monitoring station, setting a segmentation numerical value reference and connecting the monitoring stations n_iThe time sequence of coordinates in (1) is divided into C segments, i.e. there is a segmented first-order functionThe whole monitoring network processes to obtain a first-order function matrix of each monitoring station in sections of

Selecting a piecewise first-order function mean value of a central monitoring station in a monitoring networkAs a reference value whenDetermining the corresponding piecewise first-order function as a non-periodic common mode error, and removing;

wherein, the segment sequence number C is 1,2, …, C.

7. The processing method of Beidou positioning long-term monitoring data according to claim 6, wherein the reference range of segmented numerical values of the coordinate time series is 24 to 168.

8. The processing method of Beidou positioning long-term monitoring data according to claim 6, wherein in step S5,

when the external influence is large, a first-order function matrix after segmentation is selected according to 1/2, 1/4 or 1/8 of the segmentation value reference in the segmentation first-order function in step S4And calculating adjacent slopes, and performing reconstruction fitting based on a least square method to obtain a deformation time series curve.

9. The processing method of the Beidou positioning long-term monitoring data according to claim 1, further comprising the step of judging the accuracy of a deformation time series curve by using a signal-to-noise ratio and a correlation coefficient, and specifically comprises the following steps:

taking correlation coefficient

Cov is the covariance of the data, and σ is the variance;

get signal to noise ratio

Wherein m is_i(t) raw data, Ri, representing each monitoring station with noise_nAnd (t) represents the data of each monitoring station after being processed and reconstructed, N represents the time series length, i is the monitoring station, and i is 1,2,3, … and N.

10. A processing system for Beidou positioning long-term monitoring data is characterized by comprising a Beidou data storage module (1), a data matching module (2), a Beidou data processing module (3), a time sequence data processing module (4) and a monitoring identification module (5);

the output end of the Beidou data storage module (1) is connected with the input end of the data matching module (2), the output end of the data matching module (2) is connected with the input end of the Beidou data processing module (3), the output end of the Beidou data processing module (3) is connected with the input end of the time sequence data processing module (4), and the output end of the time sequence data processing module (4) is connected with the input end of the monitoring and identifying module (5);

the Beidou data storage module (1) stores a positioning single epoch observation value and base station data acquired by Beidou equipment;

the data matching module (2) is used for performing data matching based on time sequences from each Beidou monitoring station to each Beidou base station and matching preliminary data results among the monitoring stations;

the Beidou data processing module (3) is used for carrying out short-baseline double-difference single epoch Beidou solution based on a fusion lambda algorithm on the Beidou data and removing abnormal values;

the time sequence data processing module (4) is used for analyzing periodic and semi-periodic fluctuation and noise based on improved empirical mode decomposition and removing the periodic fluctuation and the noise;

the monitoring and identifying module (5) is used for performing traversal processing on data of each monitoring station influenced by the external environment, judging and eliminating the data, then performing piecewise function reconstruction and finally outputting a deformation time series curve.

Technical Field

The invention relates to the technical field of satellite navigation measurement, in particular to a processing method and system for Beidou positioning long-term monitoring data.

Background

In recent years, China satellite monitoring systems are greatly developed on the basis of original application, and particularly, a Beidou satellite navigation system independently researched and developed in China performs global networking in 7 months in 2020. However, the Beidou satellite navigation technology is greatly influenced by various factors in different applications in the monitoring field, so that the measurement accuracy is different in stability under different conditions in long-term monitoring, and the trust of non-measurement professionals on the Beidou satellite long-term monitoring technology and the further popularization of the application are influenced.

The long-term slow deformation monitoring before the disaster deformation is a mainly centralized monitoring direction in the monitoring field, the method based on engineering application at present mainly adopts a Beidou satellite high-precision monitoring and resolving within a 15km range with a base station as a reference point, and because of resolving real-time property and precision requirements required by many fields, the short-baseline resolving long-term monitoring method based on Beidou is an appropriate measurement technical method, the main method mainly focuses on resolving the ambiguity in the whole cycle, the common methods are an ambiguity function method, a wide-narrow lane technology, an ambiguity least square search method, an ambiguity fast solution algorithm, an ambiguity fast search filtering method and a least square ambiguity based correlation adjustment method (LAMBDA algorithm), the ambiguity is fixed according to a short epoch, the solution under a sequential condition is resolved, and a target function is constructed and an ambiguity integer value is solved based on ambiguity decorrelation operation and whole cycle ambiguity search, thereby further obtaining the accuracy of centimeter or millimeter.

However, the current monitoring data processing method does not consider that the non-periodic displacement signal inside the monitoring time sequence is substantially the real displacement signal of the monitored object under the influence of the external environment, and directly influences the monitoring precision. For example, the monitored objects such as high-speed rail side monitoring equipment, long-span bridge monitoring equipment and high-rise monitoring equipment are influenced by wind speed, train load, cargo load and the like, and have real displacement in the aspect of measurement.

In the prior art, as a chinese patent disclosed in 2015, 2/25, a beidou single-frequency ambiguity resolution method assisted by inertial navigation under a short baseline, with a publication number of CN104375157A, can effectively improve resolution rate and accuracy of ambiguity of the whole cycle, and is suitable for positioning and attitude determination of a high dynamic carrier under the condition of a single-frequency beidou satellite system, but does not consider that a non-periodic displacement signal inside a monitoring time sequence is substantially a real displacement signal of a monitored object under the influence of an external environment.

Disclosure of Invention

The invention provides a processing method and a processing system for Beidou positioning long-term monitoring data, aiming at overcoming the technical defect that the current monitoring data processing method does not consider that the non-periodic displacement signals in the monitoring time sequence are actually real displacement signals of a monitored object under the influence of an external environment.

In order to solve the technical problems, the technical scheme of the invention is as follows:

a processing method of Beidou positioning long-term monitoring data comprises the following steps:

s1: performing double-difference integer ambiguity resolution based on a single epoch on a single-epoch observation value obtained by monitoring a Beidou satellite to obtain a deformation monitoring time sequence, and performing single-epoch abnormal value analysis and elimination on the deformation monitoring time sequence to preliminarily obtain a deformation monitoring time sequence based on an hour solution of a Beidou monitoring station;

s2: performing data matrixing processing on the deformation monitoring time sequence based on the Beidou monitoring station hourly solution to obtain a matrixed deformation monitoring time sequence;

s3: carrying out periodic and semi-periodic fluctuation and noise analysis on the matrixed deformation monitoring time sequence, and removing the periodic fluctuation and the noise to obtain a corrected coordinate time sequence;

s4: segmenting the corrected coordinate time sequence for a fixed time period to obtain a multi-segment coordinate time sequence; removing aperiodic common mode errors from the multi-segment coordinate time sequence to obtain a standard coordinate time sequence;

s5: and performing data reconstruction fitting on the standard coordinate time sequence to obtain a deformation time sequence curve, and finishing the processing of the monitoring data.

Preferably, n monitoring stations are arrangedThe monitoring station is numbered i, i-1, 2,3, …, n, x_jIs the relative solution of j-th epoch, and has N epochs, j is 2,3,4, …, N-1;

in step S1, the single epoch outlier analysis and elimination of the deformation monitoring time series includes the following steps:

s1.1: according to the relative solution x_jSequentially calculating corresponding judgment values y_m：

y_m＝2x_j-(x_j+1+x_j-1)

Wherein, the judgment value serial number m is 1,2, …, N-2;

s1.2: calculating the mean of the relative solutionsAnd a variance σ;

s1.3: calculating a standard value z_mAccording to the standard value z_mAnd judging whether the corresponding deformation monitoring time sequence is an abnormal value or not, and removing the abnormal value.

Preferably, in step S1.3, the standard value z_mThe calculation formula of (2) is as follows:

when z is_mAnd if the time sequence is more than 3, judging that the corresponding deformation monitoring time sequence is an abnormal value.

Preferably, in step S1.3, the standard value z_mThe calculation formula of (2) is as follows:

when z is_mAnd if the time sequence is more than 1.7, judging that the corresponding deformation monitoring time sequence is an abnormal value.

Preferably, in step S3, the matrixed deformation monitoring time series is analyzed for periodicity, semiperiodicity fluctuation and noise based on a complete empirical mode decomposition method for white noise with adaptively corresponding modal order.

Preferably, in step S4,

let n_iRepresenting monitoring stations, where n_i＝(x₁，x₂，…x_m) Representing the relative displacement processed by each monitoring station, setting a segmentation numerical value reference and connecting the monitoring stations n_iThe time sequence of coordinates in (1) is divided into C segments, i.e. there is a segmented first-order functionThe whole monitoring network processes to obtain a first-order function matrix of each monitoring station in sections of

Selecting a piecewise first-order function mean value of a central monitoring station in a monitoring networkAs a reference value whenDetermining the corresponding piecewise first-order function as a non-periodic common mode error, and removing;

wherein, the segment sequence number C is 1,2, …, C.

Preferably, the reference range of the segmented values of the coordinate time series is 24 to 168.

Preferably, in step S5,

when the external influence is large, a first-order function matrix after segmentation is selected according to 1/2, 1/4 or 1/8 of the segmentation value reference in the segmentation first-order function in step S4And calculating adjacent slopes, and performing reconstruction fitting based on a least square method to obtain a deformation time series curve.

Preferably, the method further comprises the step of judging the accuracy of the deformation time series curve by using the signal-to-noise ratio and the correlation coefficient, and specifically comprises the following steps:

taking correlation coefficient

Cov is the covariance of the data, and σ is the variance;

get signal to noise ratio

Wherein m is_i(t) raw data, Ri, representing each monitoring station with noise_nAnd (t) represents the data of each monitoring station after being processed and reconstructed, N represents the time series length, i is the monitoring station, and i is 1,2,3, … and N.

A processing system of Beidou positioning long-term monitoring data is applied to a processing method for realizing the Beidou positioning long-term monitoring data, and comprises a Beidou data storage module, a data matching module, a Beidou data processing module, a time sequence data processing module and a monitoring identification module;

the output end of the Beidou data storage module is connected with the input end of the data matching module, the output end of the data matching module is connected with the input end of the Beidou data processing module, the output end of the Beidou data processing module is connected with the input end of the time sequence data processing module, and the output end of the time sequence data processing module is connected with the input end of the monitoring identification module;

the Beidou data storage module stores a positioning single epoch observation value and base station data acquired by Beidou equipment;

the data matching module is used for performing data matching based on time sequences from each Beidou monitoring station to each Beidou base station and matching preliminary data results among the monitoring stations;

the Beidou data processing module is used for carrying out short baseline double-difference single epoch Beidou solution based on a fusion lambda algorithm on the Beidou data and carrying out abnormal value elimination;

the time sequence data processing module is used for analyzing periodic and semi-periodic fluctuation and noise based on improved empirical mode decomposition and removing the periodic fluctuation and the noise;

the monitoring and identifying module is used for performing traversal processing on data of each monitoring station influenced by the external environment, judging and eliminating the data, then performing piecewise function reconstruction and finally outputting a deformation time series curve.

Compared with the prior art, the technical scheme of the invention has the beneficial effects that:

the invention provides a processing method and a processing system for Beidou positioning long-term monitoring data, which are used for eliminating non-deformation periodic fluctuation and noise of a deformation monitoring time sequence, analyzing by adopting a piecewise function, eliminating non-periodic common mode errors, and finally obtaining a deformation time sequence curve through data reconstruction fitting.

Drawings

FIG. 1 is a flow chart of the steps for implementing the technical solution of the present invention;

FIG. 2 is a schematic diagram of the module connection of the present invention;

wherein: 1. a Beidou data storage module; 2. a data matching module; 3. the Beidou data processing module; 4. a time series data processing module; 5. and monitoring the identification module.

Detailed Description

The drawings are for illustrative purposes only and are not to be construed as limiting the patent;

for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;

it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.

The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.

Example 1

As shown in fig. 1, a processing method of Beidou positioning long-term monitoring data includes the following steps:

s1: aiming at an original short baseline based on a Beidou satellite monitoring single-epoch observation value, most of the monitoring area application of a base station is less than 15km and at most not more than 20km, a single-epoch solution is adopted, a LAMBDA algorithm is combined to realize that a single-epoch ambiguity fixed base station carries out double-difference integer ambiguity resolution based on a single epoch to obtain north-south, east-west and height coordinate values of a monitoring station relative to the base station as a deformation monitoring time sequence, single-epoch abnormal value analysis and elimination are carried out on the deformation monitoring time sequence by taking hours as a unit, and the deformation monitoring time sequence based on the Beidou satellite monitoring station hour solution is obtained preliminarily;

s2: performing data matrixing processing on the deformation monitoring time sequence based on the Beidou monitoring station hourly solution to obtain a matrixed deformation monitoring time sequence so as to facilitate analysis;

s3: carrying out periodic and semi-periodic fluctuation and noise analysis on the matrixed deformation monitoring time sequence, and removing the periodic fluctuation and the noise to obtain a corrected coordinate time sequence;

s4: segmenting the corrected coordinate time sequence for a fixed time period to obtain a multi-segment coordinate time sequence; removing aperiodic common mode errors from the multi-segment coordinate time sequence to obtain a standard coordinate time sequence;

s5: and performing data reconstruction fitting on the standard coordinate time sequence to obtain a deformation time sequence curve, and finishing the processing of the monitoring data.

In the specific implementation process, the double difference integer ambiguity resolution result based on the single epoch can be obtained by different mainstream methods. Due to influences of ionosphere interference, data jumping and the like, a time sequence during deformation monitoring is obviously abnormal, and analysis and elimination are needed.

More specifically, n monitoring stations are provided, and the numbers of the monitoring stations are i, i-1, 2,3, …, n, x_jIs the relative solution of j-th epoch, and has N epochs, j is 2,3,4, …, N-1;

in step S1, the single epoch outlier analysis and elimination of the deformation monitoring time series includes the following steps:

s1.1: according to the relative solution x_jSequentially calculating corresponding judgment values y_m：

y_m＝2x_j-(x_j+1+x_j-1)

Wherein, the judgment value serial number m is 1,2, …, N-2;

s1.2: calculating the mean of the relative solutionsAnd a variance σ;

s1.3: calculating a standard value z_mAccording to the standard value z_mAnd judging whether the corresponding deformation monitoring time sequence is an abnormal value or not, and removing the abnormal value.

More specifically, in step S1.3, the standard value z_mThe calculation formula of (2) is as follows:

when z is_mAnd if the time sequence is more than 3, judging that the corresponding deformation monitoring time sequence is an abnormal value.

More specifically, in step S1.3, the standard value z_mThe calculation formula of (2) is as follows:

when z is_mAnd if the time sequence is more than 1.7, judging that the corresponding deformation monitoring time sequence is an abnormal value.

More specifically, in step S3, the matrixed deformation monitoring time series is analyzed for periodicity, semiperiodicity fluctuation and noise based on the perfect empirical mode decomposition method for white noise with adaptively corresponding modal order.

In the specific implementation process, the time sequence after noise reduction is obtained by a complete empirical mode decomposition method based on white noise of a self-adaptive corresponding modal order, the method mainly removes periodic signals and reduces noise, wherein the k-th order modal is solvedTaking n (t) as white noise with constant variance at t moment, and repeatedly solving the first-order residual value r of the signal₁And a first order modal component c₁Adding noise, r, at the first order residual value_kFor the k-th order residual value, the k-th order additive noise is gamma_k＝β_kn_k+1(t); wherein, beta_kSelecting Gaussian white noise k-th order SNR, n, for modal decomposition_k+1(t) white noise having a constant variance at time t of the k +1 th order; recombination function is R_k(n)＝r_k+γ_kRepeating the steps to obtain Re (R)_k(n)) establishing a k-order modal function for the operation of the residual signal after empirical mode decomposition. The complete empirical mode decomposition method based on the white noise of the self-adaptive corresponding modal order solves the problems of modal aliasing and unreal modal components, and incomplete pseudo components of the same modality appear under the same modal component, and is proved to be a suitable modal decomposition analysis method in a scene with larger short baseline disturbance in practical application.

More specifically, in step S4,

let n_iRepresenting monitoring stations, where n_i＝(x₁，x₂，…x_m) Representing the relative displacement of each monitoring station after processing, setting the reference of the segmentation numerical value as 24 and setting the n monitoring stations as the relative areas of the short base lines to be smaller, wherein each monitoring station can record the displacement of the disturbance thereof more similarly in view of the fact that the monitoring area is a relative area with a shorter base line, and the n monitoring stations are connected with each other_iThe time sequence of coordinates in (1) is divided into C segments, i.e. there is a segmented first-order functionThe whole monitoring network processes to obtain a first-order function matrix of each monitoring station in sections of

Selecting a piecewise first-order function mean value of a central monitoring station in a monitoring networkAs a reference value whenDetermining the corresponding piecewise first-order function as a non-periodic common mode error, and removing;

wherein, the segment sequence number C is 1,2, …, C.

In the specific implementation process, the common-mode error processing is based on the object transfer rule and the real motion calculation, time segmentation needs to be carried out on each monitoring station, and the method mainly aims at solving the external real influence. The external real influence has the transmission property of physical displacement influence on each monitoring station in a small area, and has an obvious time transmission rule.

More specifically, the reference range of the segmented numerical values of the coordinate time series is 24 to 168.

In the specific implementation process, the influence time of the regular disturbance is shorter by 2 hours, the influence time of the regular disturbance is longer by not more than 2 days, and the displacement caused by the regular disturbance has a certain rule. Therefore, on the basis of the monitoring segment with the segment value reference of 24, larger 168 monitoring data can be selected, and the actual operation preferably does not exceed 168.

More specifically, in step S5,

when the external influence is large, a first-order function matrix after segmentation is selected according to 1/2, 1/4 or 1/8 of the segmentation value reference in the segmentation first-order function in step S4And calculating adjacent slopes, and performing reconstruction fitting based on a least square method to obtain a deformation time series curve.

More specifically, the method further comprises the step of judging the accuracy of the deformation time series curve by using the signal-to-noise ratio and the correlation coefficient, and specifically comprises the following steps:

taking correlation coefficient

Cov is the covariance of the data, and σ is the variance;

get signal to noise ratio

Wherein m is_i(t) raw data, Ri, representing each monitoring station with noise_nAnd (t) represents the data of each monitoring station after being processed and reconstructed, N represents the time series length, i is the monitoring station, and i is 1,2,3, … and N.

In the specific implementation process, the higher the r and the SNR value is, the better the noise reduction effect is.

Example 2

As shown in fig. 2, a processing system for Beidou positioning long-term monitoring data is applied to a processing method for realizing the Beidou positioning long-term monitoring data, and comprises a Beidou data storage module 1, a data matching module 2, a Beidou data processing module 3, a time sequence data processing module 4 and a monitoring identification module 5;

the output end of the Beidou data storage module 1 is connected with the input end of the data matching module 2, the output end of the data matching module 2 is connected with the input end of the Beidou data processing module 3, the output end of the Beidou data processing module 3 is connected with the input end of the time sequence data processing module 4, and the output end of the time sequence data processing module 4 is connected with the input end of the monitoring identification module 5;

the Beidou data storage module 1 stores a positioning single epoch observation value and base station data acquired by Beidou equipment;

the data matching module 2 is used for performing data matching based on time sequences from each Beidou monitoring station to each Beidou base station and matching preliminary data results among the monitoring stations;

the Beidou data processing module 3 is used for carrying out short baseline double-difference single epoch Beidou solution based on a fusion lambda algorithm on the Beidou data and removing abnormal values;

the time sequence data processing module 4 is used for analyzing periodic and semi-periodic fluctuation and noise based on improved empirical mode decomposition and removing the periodic fluctuation and the noise;

the monitoring and identifying module 5 is used for performing traversal processing on data of each monitoring station influenced by the external environment, judging and eliminating the data, then performing piecewise function reconstruction, and finally outputting a deformation time series curve.

It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. 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 claims of the present invention.