1. A pixel scale-based ecological quality index construction method is characterized by comprising the following steps:

acquiring initial data of a preset time sequence of a target area, wherein the initial data comprises a plurality of index values for constructing an ecological quality index;

respectively preprocessing and standardizing the initial data to obtain standard data;

performing correlation analysis on all the index values according to the standard data to generate a correlation matrix, and screening all the index values according to a preset threshold;

calculating the weight of each index value pixel by pixel of the screened standard data according to a preset optimization searching algorithm;

and weighting each index value according to the weight to construct the ecological quality index of the target area.

2. The pixel-scale-based ecological quality index construction method according to claim 1, wherein the preprocessing of the initial data specifically comprises:

and cutting, resampling and re-projecting the initial data to unify the spatial resolution, the spatial position, the spatial coordinate reference and the projection of all the initial data.

3. The pixel-scale-based ecological quality index construction method according to claim 1, wherein the normalizing of the initial data specifically comprises:

judging the positive and negative direction of each index value in the initial data;

for forward data, a standard normalization process is performed using the following formula:

for negative going data, a standard normalization process was performed using the following formula:

wherein, F_iIs the normalized value of the ith pixel point, I_iIs the value of the ith pixel point, I_minIs the minimum value of the ith pixel point in time sequence, I_maxThe value range of i is the product of the row number and the column number of the data matrix, which is the maximum value of the ith pixel point in time sequence.

4. The pixel-scale-based ecological quality index construction method according to claim 1, wherein all the index values are screened according to a preset threshold, and the method specifically comprises the following steps:

respectively calculating the correlation between every two index values, selecting the index value pairs with the correlation larger than a preset threshold value, and respectively recording the index values in each index value pair as a first index value and a second index value;

calculating the average value of the correlation of the first index value and other index values except the second index value to obtain a first correlation value;

calculating the average value of the correlation between the second index value and other index values except the first index value to obtain a second correlation value;

and judging the sizes of the first correlation value and the second correlation value, and eliminating the index value corresponding to the larger one.

5. The pixel-scale-based ecological quality index construction method according to any one of claims 1 to 4, wherein the ecological quality index is calculated according to the following formula:

where i is 1,2,3, …, n, j is 1,2,3, …, m, n is the number of samples, m is the spatial dimension, x_ijIs a pixel value of_jIs a weight value, G_jIs an ecological quality index.

6. A pixel scale-based ecological quality index construction system is characterized by comprising:

the data acquisition unit is used for acquiring initial data of a preset time sequence of a target area, wherein the initial data comprises a plurality of index values for constructing an ecological quality index;

the standardization processing unit is used for respectively carrying out preprocessing and standardization processing on the initial data to obtain standard data;

the correlation analysis unit is used for performing correlation analysis on all the index values according to the standard data to generate a correlation matrix and screening all the index values according to a preset threshold;

the weight calculation unit is used for calculating the weight of each index value of the screened standard data pixel by pixel according to a preset optimization search algorithm;

and the index calculation unit is used for weighting each index value according to the weight and constructing the ecological quality index of the target area.

7. The system for constructing an ecological quality index based on a pixel scale according to claim 6, wherein the normalization processing unit is specifically configured to crop, resample, and re-project the initial data, so that spatial resolution, spatial position, spatial coordinate reference, and projection of all initial data are uniform.

8. The pixel-scale-based ecological quality index construction system according to claim 6, wherein the normalization processing unit is configured to determine a positive direction and a negative direction of each index value in the initial data;

for forward data, a standard normalization process is performed using the following formula:

for negative going data, a standard normalization process was performed using the following formula:

wherein, F_iIs the normalized value of the ith pixel point, I_iIs the value of the ith pixel point, I_minIs the minimum value of the ith pixel point in time sequence, I_maxThe value range of i is the product of the row number and the column number of the data matrix, which is the maximum value of the ith pixel point in time sequence.

9. The pixel-scale-based ecological quality index construction system according to claim 6, wherein the correlation analysis unit is specifically configured to calculate correlations between each two index values, select an index value pair whose correlation is greater than a preset threshold, and record the index value in each index value pair as a first index value and a second index value; calculating the average value of the correlation of the first index value and other index values except the second index value to obtain a first correlation value; calculating the average value of the correlation between the second index value and other index values except the first index value to obtain a second correlation value; and judging the sizes of the first correlation value and the second correlation value, and eliminating the index value corresponding to the larger one.

10. The pixel-scale-based ecological quality index construction system according to any one of claims 6 to 9, wherein the index calculation unit is specifically configured to calculate the ecological quality index according to the following formula:

where i is 1,2,3, …, n, j is 1,2,3, …, m, n is the number of samples, m is the spatial dimension, x_ijIs a pixel value of_jIs a weight value, G_jIs an ecological quality index.

Background

The ecological quality refers to the comprehensive characteristics of elements, structures and functions of the ecological system in a certain space-time range, and is specifically expressed by the condition, the production capacity, the stability of the structures/functions, the anti-interference capability and the recovery capability of the ecological system. The ecological quality monitoring is to comprehensively use scientific, comparable and mature technical methods to monitor the ecological systems with different scales and acquire multi-level and high-precision information so as to evaluate the quality condition and the change of the ecological systems.

The existing ecological quality evaluation method mainly comprises a remote sensing ecological index, a pressure state response model, an ecological environment index and the like. On the one hand, however, most of these evaluation methods apply a set of criteria to different ecosystems, ignoring the incompatibilities between ecosystems. This often results in a large gap in ecological quality scores for different ecosystems in the result. When the ecological quality evaluation with long time sequence is carried out, the quality scores of all the ecological systems are respectively fixed and fluctuate in a specific cell, and the characteristic change of the ecological systems on time cannot be well shown. On the other hand, the mainstream ecological quality evaluation method is often used to evaluate the quality of the ecosystem from the perspective of human society, and the quality grade of the ecosystem is evaluated according to the amount of energy contribution and material provided by the ecosystem for human society. This concept has certain limitations because the ecosystem can provide human welfare, some of which are directly quantifiable; however, each ecosystem has its own unique natural attributes, providing different profits for humans that are difficult to quantify directly, so that human-centered evaluations do not completely reflect their ecological qualities. Defining the quality of an ecosystem in terms of human demand is therefore subjective, time-efficient and non-global.

Therefore, the existing ecological quality assessment method has the defects of angle limitation and parameter fixation, the potential value and quality of an ecological system are not considered, the global scale is not used as a background in model calculation, the difference among different systems and units is weakened, the result cannot reflect the real situation, and the change characteristics on a time sequence cannot be sensitively captured.

Disclosure of Invention

The invention aims to solve the technical problem of the prior art and provides a pixel scale-based ecological quality index construction method and system.

The technical scheme for solving the technical problems is as follows:

a pixel scale-based ecological quality index construction method comprises the following steps:

acquiring initial data of a preset time sequence of a target area, wherein the initial data comprises a plurality of index values for constructing an ecological quality index;

respectively preprocessing and standardizing the initial data to obtain standard data;

performing correlation analysis on all the index values according to the standard data to generate a correlation matrix, and screening all the index values according to a preset threshold;

calculating the weight of each index value pixel by pixel of the screened standard data according to a preset optimization searching algorithm;

and weighting each index value according to the weight to construct the ecological quality index of the target area.

Another technical solution of the present invention for solving the above technical problems is as follows:

a pixel scale-based ecological quality index construction system comprises:

the data acquisition unit is used for acquiring initial data of a preset time sequence of a target area, wherein the initial data comprises a plurality of index values for constructing an ecological quality index;

the standardization processing unit is used for respectively carrying out preprocessing and standardization processing on the initial data to obtain standard data;

the correlation analysis unit is used for performing correlation analysis on all the index values according to the standard data to generate a correlation matrix and screening all the index values according to a preset threshold;

the weight calculation unit is used for calculating the weight of each index value of the screened standard data pixel by pixel according to a preset optimization search algorithm;

and the index calculation unit is used for weighting each index value according to the weight and constructing the ecological quality index of the target area.

The invention has the beneficial effects that: the method is suitable for constructing the ecological quality index, a set of index weight based on historical data of pixels is constructed one by one, the difference between an ecological system and units is considered, different weight values can be generated for ecological units under different conditions, the problems of fixed score and insensitivity to annual change are avoided, the calculation precision completely depends on the resolution ratio of data, the method can be flexibly applied to analysis and research of large area, high precision and long time sequence, and the range and precision of a research area are not limited. Because the index system is mainly based on the historical data of each pixel, the change characteristics of the ecological quality can be more accurately captured and displayed along with the increase of time sequence. The ecological quality is defined from the self-perspective of the ecological environment by taking the global scale as the background, the potential value of an ecological system is considered, and the state of the ecological quality in a research area can be more accurately reflected. In addition, the method is mainly based on historical data for calculation, is low in cost and short in time consumption, saves a large amount of manpower and material resources, and provides possibility for long-term real-time monitoring of the quality of the ecosystem.

Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Drawings

FIG. 1 is a schematic flow chart of a method for constructing an ecological quality index according to an embodiment of the present invention;

FIG. 2 is a schematic view of an ecological quality evaluation system provided by an embodiment of the ecological quality index construction method of the present invention;

fig. 3 is a structural framework diagram provided by an embodiment of the ecological quality index construction system of the invention.

Detailed Description

The principles and features of this invention are described below in conjunction with the following drawings, which are set forth to illustrate, but are not to be construed to limit the scope of the invention.

As shown in fig. 1, a schematic flow chart provided by an embodiment of the method for constructing an ecological quality index of the present invention is implemented based on processing of a pixel scale and a historical background of data, and includes:

s1, acquiring initial data of a preset time sequence of the target area, wherein the initial data comprises a plurality of index values for constructing the ecological quality index;

it should be noted that the indexes are divided into a first-level index and a second-level index, as shown in fig. 2, an exemplary ecological quality evaluation system diagram is provided, the first-level conceptual index includes a regulation function, a support function and a maintenance function, the second-level index mainly includes data of three aspects of climate, vegetation and soil, and specifically includes a land surface temperature LST, a leaf area index LAI, a vegetation index NDVI, a vegetation coverage FVC, a total primary productivity GPP, a net primary productivity NPP, a net ecosystem productivity NEP, a wettability index IM, a Bowen, a water use efficiency WUE and a water storage index WSI.

And after selecting the corresponding secondary indexes according to the primary concept indexes, acquiring corresponding secondary index data as initial data.

It should be noted that the obtained initial data may be remote sensing data in the form of an image shot by a satellite, or may also be raster data, and if the obtained initial data is raster data, in the subsequent processing process involving pixels, each raster unit may be regarded as a pixel point.

S2, respectively preprocessing and standardizing the initial data to obtain standard data;

it should be understood that, for data of different time series, the format of the data may be different, and for example, remote sensing data, the spatial resolution, the spatial position, the projection, and the like may be different, so that the data format may be unified through preprocessing, thereby facilitating subsequent processing.

In addition, because dimensions of different indexes may be different, the initial data needs to be subjected to standard normalization processing to eliminate dimensional and dimensionless differences among the indexes.

For example, the standard normalization process may be performed using the maximum minimum method.

S3, performing correlation analysis on all the index values according to the standard data to generate a correlation matrix, and screening all the index values according to a preset threshold value;

specifically, since each sample is evaluated by a plurality of index values, the correlation analysis of each index value can be performed, so as to obtain the correlation between each index and other indexes, and the index values with high correlation can be removed, so as to reduce the data amount of processing and improve the representativeness of the index values.

It should be understood that the preset threshold may be set according to actual requirements, and may be 0.9 or 0.8, for example.

S4, calculating the weight of each index value pixel by pixel for the screened standard data according to a preset optimization searching algorithm;

it should be noted that the PSO-PPC (projection pursuit clustering method based on particle swarm optimization) can be used to calculate the weight of each index value pixel by pixel, and the cluster intelligence can be used to find the global optimal solution to optimize the speed, and simultaneously, the product of the projection standard deviation and the local density value is maximized, so as to achieve the purposes of overall dispersion and local density.

An exemplary calculation method is given below:

firstly, creating initial particles according to screened standard data, and configuring an iteration environment according to iteration control parameters. Each index corresponds to one dimension, each sample is regarded as one particle, and after an initial particle is created, initialization is required, including: construction of N M dimensions [0,1 ]]Projection vector a of_ij(ii) a Creating an initial velocity v_ijN M-dimensional particles of 0; creating a global optimal solution gbest, a global optimal projection vector gbesta, an individual optimal solution pbest and an individual optimal vector pbesta.

And judging whether the maximum iteration time T is reached or the iteration termination condition is met, and if the maximum iteration time T is reached or the iteration termination condition is met, calculating the evaluation value of each sample according to the M-dimensional global optimal projection vector gbesta.

And constructing a fitness function, calculating the fitness of each initial particle according to the fitness function, and starting circulation.

The fitness of each initial particle may be calculated according to the following formula:

Q＝Dz*Sz

wherein Dz is the local density of the particles, and Sz is the global inter-class dispersion of the particles.

And if the maximum iteration times T are not reached or the iteration termination condition is not met, circulating the I-th M-dimensional particle, wherein the value of I is from 1 to N, and N is the number of the particles. Updating the moving direction and speed v of the I-th particle_ijAnd updates the particle position.

For example, the velocity and position of the particles may be updated according to the following formula:

wherein the content of the first and second substances,representing the m-dimensional component of the velocity vector of the ith particle for the kth iteration,an m-dimensional component of a position vector representing the ith particle of the kth iteration,representing the historical optimum position experienced by the ith particle,representing the historical optimal position experienced by the particle swarm, c0 being the inertial weight, c₁And c₂For the learning factor, random (0, 1) denotes a random number from 0 to 1, i is 1,2, 3.., N is the number of samples, M is 1,2, 3.., M is the number of dimensions, k is 1,2, 3.., T is the total number of cycles.

It should be understood that,the two equations update the velocity and position of the particles, c₀For adjusting the search capability of the space, c₁And c₂To adjust the maximum step size of learning, random (0, 1) to increase the randomness of the search.

Then, the local density values D of the I-th particle cluster are respectively calculated_zAnd global inter-class dispersion S_zAccording to D_zAnd S_zCalculating the fitness Q of the I particle_aJudgment of Q_aWhether the value is greater than the individual optimal solution pbest or not, if so, firstly, the individual optimal vector pbesta of the I particle_iAssigned a value of a_IThen, Q is judged_aWhether the global optimal solution gbest is greater or not; if not, directly judging Q_aWhether it is greater than the global optimal solution gbest.

If Q is_aIf the global optimal solution is larger than the global optimal solution gbest, the global optimal projection vector gbesta of the particle swarm is assigned as a_IJudging whether all the particles are iterated; if Q is_aAnd if not, directly judging whether all the particles are iterated.

If all the particles are iterated, judging whether the maximum iteration time T is reached or an iteration termination condition is met; and if all the particles are not iterated, continuously updating the moving direction, speed and position of the particles, and continuously iterating and circulating.

Wherein, a_IThe current projection vector for the ith particle.

And after the iteration loop is stopped, outputting the weight value and the global optimal solution of each index.

And S5, weighting each index value according to the weight to construct the ecological quality index of the target area.

As shown in table 1, an exemplary corresponding relationship between the score of the ecological quality index and the grade is given, which can be used to evaluate the ecological quality of the target area, and a larger value indicates a higher ecological quality at this point, and the ecological quality can be evaluated according to the grade score lookup table.

TABLE 1

Ecological quality score	Ecological quality grade
		80–100	Superior food
60–80	Good wine
		40–60	Medium and high grade
20–40	Is poor
		0–20	Difference (D)

The ecological quality index construction method provided by the embodiment is suitable for construction of ecological quality indexes, a set of index weights based on historical data of pixels are constructed one by one, differences among ecological systems and units are considered, different weight values can be generated for ecological units under different conditions, the problems of fixed scores and insensitivity to annual change are avoided, the calculation accuracy completely depends on the resolution ratio of data, the method can be flexibly applied to analysis and research of large area, high accuracy and long time sequence, and the range and the accuracy of a research area are not limited. Because the index system is mainly based on the historical data of each pixel, the change characteristics of the ecological quality can be more accurately captured and displayed along with the increase of time sequence. The ecological quality is defined from the self-perspective of the ecological environment by taking the global scale as the background, the potential value of an ecological system is considered, and the state of the ecological quality in a research area can be more accurately reflected. In addition, the method is mainly based on historical data for calculation, is low in cost and short in time consumption, saves a large amount of manpower and material resources, and provides possibility for long-term real-time monitoring of the quality of the ecosystem.

Optionally, in some possible embodiments, the preprocessing the initial data specifically includes:

and clipping, resampling and re-projecting the initial data to unify the spatial resolution, the spatial position, the spatial coordinate reference and the projection of all the initial data.

For example, taking remote sensing data shot by a satellite as an example, the specification of the data is specified to be 1km resolution, 1 year time precision, the row number and the column number of a research area matrix are 4088 multiplied by 4998, and a coordinate system and projection adopt WGS1984 and Albers equal-area conical projection; cutting vegetation index data with the space precision of 1km and the time precision of 1 year by using an ArcGIS Extracted by Mask tool according to a research area; if the spatial precision of the data is not uniform, resampling the data is needed, and resampling vegetation index data with the spatial precision of 30m into data with the precision of 1km by an ArcGIS sample tool; if the coordinates or projections of the raw data are not consistent, the Project rater in ArcGIS can be used for unification; all the above operations can be carried out in model Builder or python of ArcGIS in batches.

Optionally, in some possible embodiments, the normalizing the initial data specifically includes:

judging the positive and negative directions of each index value in the initial data;

for forward data, a standard normalization process is performed using the following formula:

for negative going data, a standard normalization process was performed using the following formula:

wherein, F_iIs the normalized value of the ith pixel point, I_iIs the value of the ith pixel point, I_minIs the minimum value of the ith pixel point in time sequence, I_maxThe value range of i is the product of the row number and the column number of the data matrix, which is the maximum value of the ith pixel point in time sequence.

For example, a grid data of 11 parameters of the national land ecosystem in every year, namely LST, LAI, NDVI, FVC, GPP, NPP, NEP, IM, Bowen, WUE and WSI, is taken as an example, where Bowen is negative data and the rest are positive data.

By judging and distinguishing positive data and negative data in the data, the relevance between the data can be better mined.

Optionally, in some possible embodiments, screening all index values according to a preset threshold specifically includes:

respectively calculating the correlation between every two index values, selecting the index value pairs with the correlation larger than a preset threshold value, and respectively recording the index values in each index value pair as a first index value and a second index value;

calculating the average value of the correlation of the first index value and other index values except the second index value to obtain a first correlation value;

calculating the average value of the correlation between the second index value and other index values except the first index value to obtain a second correlation value;

and judging the sizes of the first correlation value and the second correlation value, and eliminating the index value corresponding to the larger one.

Assuming that there are 4 indexes, namely index a, index B, index C and index D, respectively, after correlation analysis, the correlation of index AB is 0.95, the correlation of index AC is 0.6, the correlation of index AD is 0.5, the correlation of index BC is 0.7, the correlation of index BD is 0.7, and the correlation of index CD is 0.3, the higher the correlation is, the more similar the two indexes are, so that one of the indexes with higher correlation can be deleted and retained, thereby reducing the data processing amount and retaining the characteristics of the data, and the conventional method usually deletes one of the indexes at random. In the scheme, assuming that the preset threshold is 0.9, only the correlation of the index AB is 0.95, and exceeds the preset threshold, so that the average value of the correlations of the index a and other indexes CD can be respectively judged, and then the average value of the correlations of the index B and other indexes CD can be judged, so as to determine which index is deleted. Through calculation, the average value of the correlation between the index A and other indexes CD is 0.55, the average value of the correlation between the broken index B and other indexes CD is 0.7, and the correlation between the broken index B and other indexes CD is more correlated, so that the index B can be deleted, the index A is reserved, and the source data characteristics are reserved to the maximum extent while the dimension is reduced efficiently.

For example, taking an annual grid data of 11 parameters of LST, LAI, NDVI, FVC, GPP, NPP, NEP, IM, Bowen, WUE, and WSI of a national terrestrial ecosystem of a certain year as an example, a strong correlation is determined by taking a correlation equal to 0.9 as a threshold value to remove a corresponding index, and 8 data with relatively weak correlation are left, which are LAI, NDVI, NPP, GPP, LST, Bowen, WUE, and WSI, respectively.

Illustratively, the final calculated weight results are shown in table 2.

TABLE 2

Index (I)	LAI	NDVI	NEP	NPP	Bowen	LST	WUE	WSI
									Weight of	0.0447	0.2385	0.1057	0.1399	0.0437	0.1324	0.1592	0.1354

Optionally, in some possible embodiments, the ecological quality index is calculated according to the following formula:

where i is 1,2,3, …, n, j is 1,2,3, …, m, n is the number of samples, m is the spatial dimension, x_ijIs a pixel value of_jIs a weight value, G_jIs an ecological quality index.

It is to be understood that some or all of the various embodiments described above may be included in some embodiments.

As shown in fig. 3, a structural frame diagram provided for an embodiment of the ecological quality index building system of the present invention is implemented based on processing of pixel dimensions and historical background of data, and includes:

the data acquisition unit 10 is configured to acquire initial data of a preset time sequence of a target area, where the initial data includes a plurality of index values for constructing an ecological quality index;

a standardization processing unit 20, configured to perform preprocessing and standardization processing on the initial data to obtain standard data;

the correlation analysis unit 30 is configured to perform correlation analysis on all the index values according to the standard data to generate a correlation matrix, and screen all the index values according to a preset threshold;

the weight calculation unit 40 is used for calculating the weight of each index value pixel by pixel on the screened standard data according to a preset optimization search algorithm;

and the index calculating unit 50 is used for weighting each index value according to the weight to construct the ecological quality index of the target area.

The ecological quality index construction system provided by the embodiment is suitable for construction of ecological quality indexes, a set of index weights based on historical data of pixels are constructed one by one, differences between an ecological system and units are considered, different weight values can be generated for ecological units under different conditions, the problems of fixed scores and insensitivity to annual change are avoided, the calculation accuracy completely depends on the resolution ratio of data, the ecological quality index construction system can be flexibly applied to analysis and research of large areas, high accuracy and long time sequence, and the research area and range are not limited. Because the index system is mainly based on the historical data of each pixel, the change characteristics of the ecological quality can be more accurately captured and displayed along with the increase of time sequence. The ecological quality is defined from the self-perspective of the ecological environment by taking the global scale as the background, the potential value of an ecological system is considered, and the state of the ecological quality in a research area can be more accurately reflected. In addition, the method is mainly based on historical data for calculation, is low in cost and short in time consumption, saves a large amount of manpower and material resources, and provides possibility for long-term real-time monitoring of the quality of the ecosystem.

Optionally, in some possible embodiments, the normalization processing unit 20 is specifically configured to crop, resample, and re-project the initial data, so that the spatial resolution, spatial position, spatial coordinate reference, and projection of all the initial data are uniform.

Alternatively, in some possible embodiments, the normalization processing unit 20 is configured to determine the positive and negative directions of each index value in the initial data;

for forward data, a standard normalization process is performed using the following formula:

for negative going data, a standard normalization process was performed using the following formula:

wherein, F_iIs the normalized value of the ith pixel point, I_iIs the value of the ith pixel point, I_minIs the minimum value of the ith pixel point in time sequence, I_maxThe value range of i is the product of the row number and the column number of the data matrix, which is the maximum value of the ith pixel point in time sequence.

Optionally, in some possible embodiments, the correlation analysis unit 30 is specifically configured to calculate correlations between each two index values, select an index value pair with a correlation greater than a preset threshold, and record the index value in each index value pair as a first index value and a second index value; calculating the average value of the correlation of the first index value and other index values except the second index value to obtain a first correlation value; calculating the average value of the correlation between the second index value and other index values except the first index value to obtain a second correlation value; and judging the sizes of the first correlation value and the second correlation value, and eliminating the index value corresponding to the larger one.

Optionally, in some possible embodiments, the index calculating unit 50 is specifically configured to calculate the ecological quality index according to the following formula:

wherein, i is 1,2,3, …, n, j is 1,2,3, …, m, n is the number of samplesQuantity, m is the spatial dimension, x_ijIs a pixel value of_jIs a weight value, G_jIs an ecological quality index.

It is to be understood that some or all of the various embodiments described above may be included in some embodiments.

It should be noted that the above embodiments are product embodiments corresponding to previous method embodiments, and for the description of the product embodiments, reference may be made to corresponding descriptions in the above method embodiments, and details are not repeated here.

The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described method embodiments are merely illustrative, and for example, the division of steps into only one logical functional division may be implemented in practice in another way, for example, multiple steps may be combined or integrated into another step, or some features may be omitted, or not implemented.

The above method, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.