CN117873851A - Cloud computing-based data management dynamic analysis system and method - Google Patents
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Abstract
The invention discloses a cloud computing-based data management dynamic analysis system and a cloud computing-based data management dynamic analysis method, and belongs to the technical field of data management analysis. The invention comprises the following steps: s10: in the running process of the target software, images displayed by a software running terminal are collected, and the rendering complexity of each collected image is analyzed by combining the mapping conditions stored in a mapping folder of the target software; s20: analyzing the degree of continuity between the images; s30: determining the position to be optimized of the target software according to the running condition of the target software displayed by each software running terminal; s40: analyzing the optimizing degree of the position to be optimized, and determining the management sequence of the position code data to be optimized according to the analysis result. According to the method, the coincidence condition obtained by different methods is analyzed, the continuity degree between the images acquired by adjacent interval time points is analyzed, the influence of other influence factors on analysis results is eliminated, and the dynamic analysis precision of the target software code data is improved.
Description
Technical Field
The invention relates to the technical field of data management analysis, in particular to a cloud computing-based data management dynamic analysis system and a cloud computing-based data management dynamic analysis method.
Background
The data management refers to the process of collecting, sorting, analyzing and utilizing various data generated in the game, and through effectively managing and analyzing the data, a game operator can better know the behaviors of players and optimize game experience.
When the existing data management dynamic analysis system processes game data, the game data collected by each game terminal analyzes the running condition of the game, but the game data collected by each game terminal is influenced by the running environment of the game running terminal, namely, the accuracy of the analysis result obtained by the method is lower, and in the process of determining the game optimization position, the existing system only determines the game optimization position through the clamping condition of the game at the corresponding position, so that the management effect of the game data is reduced, the optimization degree of the position to be optimized of the game cannot be realized by the existing system, and the optimization sequence is effectively determined, thereby reducing the use experience of a user on the game.
Disclosure of Invention
The invention aims to provide a data management dynamic analysis system and method based on cloud computing, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a cloud computing-based data management dynamic analysis method, the method comprising:
s10: in the running process of the target software, images displayed by a software running terminal are collected, and the rendering complexity of each collected image is analyzed by combining the mapping conditions stored in a mapping folder of the target software;
s20: analyzing the degree of continuity between the images;
s30: determining the position to be optimized of the target software according to the running condition of the target software displayed by each software running terminal;
s40: analyzing the optimizing degree of the position to be optimized, and determining the management sequence of the position code data to be optimized according to the analysis result.
Further, the specific method for analyzing the rendering complexity of each acquired image in S10 is as follows:
the method comprises the steps that target software is placed in a standard operation environment to operate, images displayed by a software operation terminal are collected at time intervals T in the operation process of the target software, and the target software is game software;
determining the area of a mosaic area displayed on each collected image, determining the attached mapping situation on each collected image according to the mapping situation stored in a target software mapping folder, and predicting the rendering complexity of each collected image by combining the storage capacity corresponding to each mapping, wherein a specific prediction formula is as follows:
wherein i=1, 2, …, m represents the number corresponding to each map stored in the target software map folder, m represents the total number of maps stored in the target software map folder, j=1, 2, …, n represents the numbering process for each image according to the acquisition time sequence of the images, n represents the total number of the acquired images, U i Representing the storage capacity, k, corresponding to the map numbered i ij Representing the adhesion of a map numbered i to the jth image, k ij =1 or k ij When k is =0 ij When =1, the map with the number i is pasted on the j-th image, and when k ij When =0, the map with the number i is not attached to the j-th image, s j Representing the mosaic area displayed on the jth image,representing the standard value of the paste capacity of the image which is allowed to be pasted, S represents the total area of any one image, K j Representing the rendering complexity of the j-th image.
According to the attached mapping condition of each image and the mosaic area displayed on each image, the rendering complexity of each image is predicted, compared with the rendering complexity obtained by analyzing the factors such as texture and edge characteristics on the images, the calculation process is simpler, the rendering complexity is analyzed according to the storage capacity of the mapping, the analysis precision is higher, and the processing efficiency of the system on the target software code data is improved.
Further, the step S20 includes:
s201: map number jComparing the image with the number j+1, and according to the comparison result, obtaining the overlapping area g of the two images j→j+1 Determination is made according to g j→j+1 S calculates the picture change rate of the image with the number j+1 compared with the image with the number j;
s202: acquiring the length of an operation track of the software running terminal in each interval time and the average value of operation deflection angles in each interval time according to V j→j+1 ＝[(2LM j→j+1 tanθ j→j +1cosθ j→j+1 -M 2 j→j+1 tanθ j→j+1 cosθ 2 j→j+1 )+δ]S calculates the picture change rate of the image with the number j+1, L represents the length value of any one of the images, M j→j+1 Representing the length of the operation track of the software running terminal in the interval time from displaying the image with the number j to displaying the image with the number j+1, theta j→j+1 Representing the mean value of the operating deflection angle of the software running terminal in the interval time from displaying the image with the number j to displaying the image with the number j+1, delta represents the error compensation value, V j→j+1 Representing the picture change rate of the image numbered j+1 as compared to the image numbered j;
s203: if g j→j+1 /S＝[(2LM j→j+1 tanθ j→j+1 cosθ j→j+1 -M 2 j→j+1 tanθ j→j+1 cosθ 2 j→j+1 )+δ]S represents a degree of coherence between the image numbered j and the image numbered j+1 of 1;
if g j→j+1 /S≠[(2LM j→j+1 tanθ j→j+1 coSθ j→j+1 -M 2 j→j+1 tanθ j→j+1 coSθ 2 j→j+1 )+δ]According to Y j→j+1 ＝1-{|[(2LM j→j+1 tanθ j→j+1 cosθ j→j+1 -M 2 j→j+1 tanθ j→j+1 cosθ 2 j→j+1 )+δ]-g j→j+1 |/g j→j+1 Analyzing the degree of coherence between the j numbered image and the j+1 numbered image, wherein Y j→j+1 Representing the degree of coherence between the image numbered j and the image numbered j+1.
According to the method, the system and the device, the consistency degree of the images acquired at the adjacent interval time points is utilized to realize the dynamic analysis of the target software code data, the timeliness of the system in analyzing the target software code data is improved, in the process of analyzing the consistency degree, the superposition condition of the images acquired at the adjacent interval time points is analyzed according to the operation track condition of the software operation terminal in the interval time, the analysis result is compared with the superposition condition intuitively obtained by the images acquired at the adjacent interval time points, the dynamic analysis of the target software code data is realized, the influence of other factors on the analysis result is not needed to be considered in the analysis process, and the dynamic analysis precision of the target software code data is further improved.
Further, the specific method for determining the position to be optimized of the target software in S30 is as follows:
randomly selecting an image, acquiring the card pause time when a plurality of software running terminals display the selected image, calculating the ratio of the acquired card pause time to the interval time, and according to the ratioDetermining an optimization coefficient of the selected image, wherein p=1, 2, … and q represent numbers corresponding to each software running terminal, q represents the total number of the software running terminals, and h p The ratio between the blocking time and the interval time obtained when the software running terminal with the number p displays the selected image is represented, and H represents the optimization coefficient of the selected image;
if R is less than H and less than or equal to 1, the code corresponding to the selected image needs to be optimized, and if H is more than or equal to 0 and less than or equal to R, the code corresponding to the selected image does not need to be optimized, and R is more than or equal to 0.4 and less than or equal to 0.7.
And determining the position to be optimized of the target software according to the blocking condition of the image, thereby being beneficial to improving the determination rate.
Further, the specific method for determining the management sequence of the position code data to be optimized in S40 is as follows:
according to W j+1 ＝ d1 *K j+1 + d2 *(1-Y j→j+1 )+ d3 *H j+1 Analyzing the optimizing degree of the position to be optimized, wherein d 1 、d 2 、d 3 All represent the scale factor, and d 1 +d 2 +d 3 ＝1，H j+1 An optimization coefficient representing an image numbered j+1, W j+1 Representing the degree of optimization of the code corresponding to the image numbered j+1;
and determining the management sequence of the position code data to be optimized according to the sequence of the optimization degree from top to bottom.
According to the optimization degree of the analyzed position to be optimized, the optimization sequence of the position to be optimized can be determined, so that the use experience of a user on the target software is improved gradually, and according to the optimization degree of the analyzed position to be optimized, the target software manager can be helped to know the use experience of the user on the target software intuitively to a large extent.
The system comprises a rendering complexity analysis module, a coherence degree dynamic analysis module, a position determining module to be optimized and an optimization degree prediction module;
the rendering degree analysis module is used for analyzing the rendering complexity of each acquired image;
the coherence degree dynamic analysis module is used for analyzing the coherence degree between the images;
the to-be-optimized position determining module is used for determining the to-be-optimized position of the target software;
the optimization degree prediction module is used for determining the management sequence of the position code data to be optimized.
Further, the rendering degree analysis module comprises an image acquisition unit, a map pasting condition determining unit and a rendering degree analysis unit;
the image acquisition unit acquires images displayed by the software operation terminal at set time intervals in the operation process of the target software, and transmits the acquired images to the mapping and pasting condition determination unit;
the mapping pasting condition determining unit receives the images transmitted by the image acquisition unit, determines the mapping condition pasted on each acquired image according to the mapping condition stored in the target software mapping folder, and transmits the determined result to the rendering degree analyzing unit;
the rendering degree analysis unit receives the determination result transmitted by the map pasting condition determination unit, predicts the rendering complexity of each acquired image by combining the mosaic area displayed on each acquired image and the storage capacity corresponding to each map, and transmits the prediction result to the optimization degree prediction module.
Further, the coherence degree dynamic analysis module comprises a first calculation unit of a picture change rate, a second calculation unit of the picture change rate and a coherence degree dynamic analysis unit;
the first calculation unit of the picture change rate compares the collected images with adjacent numbers, determines the superposition area of the collected images with adjacent numbers according to the comparison result, combines the total area of the images, calculates the picture change rate of the image with the number of j+1 compared with the picture with the number of j, and transmits the calculation result to the dynamic analysis unit of the consistency degree;
the second calculation unit of the picture change rate obtains the length of the operation track of the software running terminal in each interval time and the average value of the operation deflection angles in each interval time, calculates the picture change rate of the image with the number of j+1 compared with the image with the number of j according to the obtained information, and transmits the calculation result to the dynamic analysis unit of the consistency degree;
the coherence degree dynamic analysis unit receives the calculation results respectively transmitted by the first calculation unit of the picture change rate and the second calculation unit of the picture change rate, analyzes the coherence degree between the collected images with adjacent numbers based on the received information, and transmits the analysis results to the optimization degree prediction module.
Further, the position to be optimized determining module obtains the click time when the selected images are displayed by the plurality of software running terminals, calculates the ratio between the obtained click time and the interval time, calculates the optimization coefficient of the selected images based on the calculation result, determines the position to be optimized of the target software based on the calculation result, and transmits the position to be optimized of the target software and the calculated optimization coefficient of the selected images to the optimization degree predicting module.
Further, the optimization degree prediction module receives the prediction result transmitted by the rendering degree analysis unit, the analysis result transmitted by the coherence degree dynamic analysis unit, the target software to-be-optimized position transmitted by the to-be-optimized position determination module and the calculated optimization coefficient of the selected image, predicts the optimization degree of the to-be-optimized position according to the received information, and determines the management sequence of the to-be-optimized position code data according to the prediction result.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the rendering complexity of each image is predicted according to the attached mapping condition of each image and the mosaic area displayed on each image, compared with the rendering complexity obtained by analyzing the factors such as texture, edge characteristics and the like on the image, the calculation process is simpler, the rendering complexity is analyzed according to the storage capacity of the mapping, the analysis precision is higher, and the processing efficiency of the system on code data is improved.
2. According to the method, the superposition conditions among the images acquired at the adjacent interval time points are analyzed according to the operation track length of the software running terminal in each interval time and the operation deviation angle mean value in each interval time, the analysis results are more fit with the actual conditions, the superposition conditions are intuitively obtained through the images acquired at the adjacent interval time points, the influence of other factors on the analysis results exists, the coherence degree among the images acquired at the adjacent interval time points is analyzed through the superposition conditions acquired by different methods, the influence of other influence factors on the analysis results is eliminated, and the dynamic analysis accuracy of the target software code data is further improved.
3. According to the method and the system, the position to be optimized of the target software is determined according to the cartoon condition of the images, the optimization degree of the images is predicted by combining the coherence degree among the images and the rendering complexity of each image, and the management sequence of the code data of the position to be optimized is determined according to the predicted optimization degree, so that the use experience of a user on the target software can be gradually improved, and the management effect of the system on the code data of the target software is further improved.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic workflow diagram of a cloud computing-based data management dynamic analysis system and method of the present invention;
fig. 2 is a schematic structural diagram of a working principle of a data management dynamic analysis system and a method based on cloud computing.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 and 2, the present invention provides the following technical solutions: a data management dynamic analysis method based on cloud computing comprises the following steps:
s10: in the running process of the target software, images displayed by a software running terminal are collected, and the rendering complexity of each collected image is analyzed by combining the mapping conditions stored in a mapping folder of the target software, wherein the software running terminal comprises a tablet, a mobile phone and a computer;
s10, analyzing the rendering complexity of each acquired image, wherein the specific method comprises the following steps of:
the method comprises the steps that target software is placed in a standard operation environment to operate, images displayed by a software operation terminal are collected at time intervals T in the operation process of the target software, and the standard operation environment refers to basic hardware configuration required by normal operation of the target software;
determining the area of a mosaic area displayed on each collected image, determining the attached mapping situation on each collected image according to the mapping situation stored in a target software mapping folder, and predicting the rendering complexity of each collected image by combining the storage capacity corresponding to each mapping, wherein a specific prediction formula is as follows:
wherein i=1, 2, …, m represents the number corresponding to each map stored in the target software map folder, m represents the total number of maps stored in the target software map folder, j=1, 2, …, n represents the numbering process for each image according to the acquisition time sequence of the images, n represents the total number of the acquired images, U i Representing the storage capacity, k, corresponding to the map numbered i ij Representing the adhesion of a map numbered i to the jth image, k ij =1 or k ij When k is =0 ij When =1, the map with the number i is pasted on the j-th image, and when k ij When =0, the map with the number i is not attached to the j-th image, s j Representing the mosaic area displayed on the jth image,representing the standard value of the paste capacity of the image which is allowed to be pasted, S represents the total area of any one image, K j Representing the rendering complexity of the j-th image; the total area of each collected image is the same;
s20: analyzing the degree of continuity between the images;
s20 includes:
s201: comparing the image with the number j with the image with the number j+1, and according to the comparison result, comparing the overlapping area g of the two images j→j+1 Determination is made according to g j→j+1 S calculates the picture change rate of the image with the number j+1 compared with the image with the number j;
s202: acquiring the length of an operation track of the software running terminal in each interval time and the average value of operation deflection angles in each interval time according to V j→j+1 ＝[(2LM j→j+1 tanθ j→j+1 cosθ j→j+1 -M 2 j→j+1 tanθ j→j+1 cosθ 2 j→j+1 )+δ]S calculates the picture change rate of the image with the number j+1, L represents the length value of any one of the images, M j→j+1 Representing the length of the operation track of the software running terminal in the interval time from displaying the image with the number j to displaying the image with the number j+1, theta j→j+1 Representing the mean value of the operating deflection angle of the software running terminal in the interval time from displaying the image with the number j to displaying the image with the number j+1, delta represents the error compensation value, V j→j+1 Representing the picture change rate of the image numbered j+1 as compared to the image numbered j;
length of operation track: determining two motion trail endpoints passed by a mobile key in a software running terminal in interval time, wherein a linear distance value between the two motion trail endpoints is the operation trail length of the software running terminal, and the mobile key can be an arrow displayed on a computer desktop;
operation deflection angle: the connecting line between the track points corresponding to the front and back time points of the mobile key in the software running terminal is compared with the included angle in the forward direction;
operating bias angle mean: mean value of operation deflection angle calculated in interval time;
s203: if g j→j+1 /S＝[(2LM j→j+1 tanθ j→j+1 cosθ j→j+1 -M 2 j→j+1 tanθ j→j+1 cosθ 2 j→j+1 )+δ]S is denoted by jThe degree of coherence between the image and the image numbered j+1 is 1;
if g j→j+1 /S≠[(2LM j→j+1 tanθ j→j+1 cosθ j→j+1 -M 2 j→j+1 tanθ j→j+1 cosθ 2 j→j+1 )+δ]According to Y j→j+1 ＝1-{|[(2LM j→j+1 tanθ j→j+1 cosθ j→j+1 -M 2 j→j+1 tanθ j→j+1 cosθ 2 j→j+1 )+δ]-g j→j+1 |/g j→j+1 Analyzing the degree of coherence between the j numbered image and the j+1 numbered image, wherein Y j→j+1 Representing the degree of coherence between the image numbered j and the image numbered j+1;
analyzing the superposition condition between the images acquired at the adjacent interval time points according to the length of the operation track of the software operation terminal in each interval time and the average value of the operation deflection angles in each interval time, wherein the analysis result is more fit with the actual condition, and the superposition condition intuitively obtained by the images acquired at the adjacent interval time has the influence of other factors on the analysis result, thus [ (2 LM) j→j+1 tanθ j→j+1 cosθ j→j+1 -M 2 j→j+1 tanθ j→j+1 cosθ 2 j→j+1 )+δ]-g j→j+1 ≥0；
S30: determining the position to be optimized of the target software according to the running condition of the target software displayed by each software running terminal;
the specific method for determining the position to be optimized of the target software in S30 comprises the following steps:
randomly selecting an image, acquiring the card pause time when a plurality of software running terminals display the selected image, calculating the ratio of the acquired card pause time to the interval time, and according to the ratioDetermining optimization coefficients of the selected images, wherein p=1, 2, … and q represent numbers corresponding to each software running terminal, and q represents softwareRunning total of terminals, h p The ratio between the blocking time and the interval time obtained when the software running terminal with the number p displays the selected image is represented, and H represents the optimization coefficient of the selected image;
if R is less than or equal to H and less than or equal to 1, the code corresponding to the selected image needs to be optimized, and if H is more than or equal to 0 and less than or equal to R, the code corresponding to the selected image does not need to be optimized, and R is more than or equal to 0.4 and less than or equal to 0.7;
s40: analyzing the optimizing degree of the position to be optimized, and determining the management sequence of the position code data to be optimized according to the analysis result;
s40, determining the management sequence of the position code data to be optimized, wherein the specific method comprises the following steps:
according to W j+1 ＝d 1 *K j+1 +d 2 *(1-Y j→j+1 )+d 3 *H j+1 Analyzing the optimizing degree of the position to be optimized, wherein d 1 、d 2 、d 3 All represent the scale factor, and d 1 +d 2 +d 3 ＝1，H j+1 An optimization coefficient representing an image numbered j+1, W j+1 Representing the degree of optimization of the code corresponding to the image numbered j+1;
and determining the management sequence of the position code data to be optimized according to the sequence of the optimization degree from top to bottom.
The system comprises a rendering complexity analysis module, a coherence degree dynamic analysis module, a position determining module to be optimized and an optimization degree prediction module;
the rendering degree analysis module is used for analyzing the rendering complexity of each acquired image;
the rendering degree analysis module comprises an image acquisition unit, a map pasting condition determination unit and a rendering degree analysis unit;
the image acquisition unit acquires images displayed by the software operation terminal at set time intervals in the operation process of the target software, and transmits the acquired images to the map pasting condition determination unit;
the mapping situation determining unit receives the images transmitted by the image acquisition unit, determines the mapping situation of pasting on each acquired image according to the mapping situation stored in the target software mapping folder, and transmits the determined result to the rendering degree analyzing unit;
the rendering degree analysis unit receives the determination result transmitted by the mapping pasting condition determination unit, predicts the rendering complexity of each acquired image by combining the mosaic area displayed on each acquired image and the storage capacity corresponding to each mapping, and transmits the prediction result to the optimization degree prediction module;
the consistency degree dynamic analysis module is used for analyzing the consistency degree between the images;
the coherence degree dynamic analysis module comprises a first calculation unit of a picture change rate, a second calculation unit of the picture change rate and a coherence degree dynamic analysis unit;
the first calculation unit of the picture change rate compares the collected images with adjacent numbers, determines the superposition area of the collected images with adjacent numbers according to the comparison result, combines the total area of the images, calculates the picture change rate of the image with the number of j+1 compared with the image with the number of j, and transmits the calculation result to the dynamic analysis unit of the coherence degree;
the second calculation unit of the picture change rate obtains the length of the operation track of the software running terminal in each interval time and the average value of the operation deflection angles in each interval time, calculates the picture change rate of the image with the number of j+1 compared with the image with the number of j according to the obtained information, and transmits the calculation result to the dynamic analysis unit of the consistency degree;
the method comprises the steps that a coherence degree dynamic analysis unit receives calculation results respectively transmitted by a first calculation unit of a picture change rate and a second calculation unit of the picture change rate, analyzes the coherence degree between collected images with adjacent numbers based on receiving information, and transmits the analysis results to an optimization degree prediction module;
the to-be-optimized position determining module is used for determining the to-be-optimized position of the target software;
the method comprises the steps that a to-be-optimized position determining module obtains the katon time when a plurality of software running terminals display a selected image, calculates the ratio between the obtained katon time and the interval time, calculates the optimization coefficient of the selected image based on a calculation result, determines the to-be-optimized position of target software based on the calculation result, and transmits the to-be-optimized position of the target software and the calculated optimization coefficient of the selected image to an optimization degree predicting module;
the optimization degree prediction module is used for determining the management sequence of the position code data to be optimized;
the optimization degree prediction module receives the prediction result transmitted by the rendering degree analysis unit, the analysis result transmitted by the coherence degree dynamic analysis unit, the target software position to be optimized and the calculated optimization coefficient of the selected image transmitted by the position determination module to be optimized, predicts the optimization degree of the position to be optimized according to the received information, and determines the management sequence of the position code data to be optimized according to the prediction result.
Example 1: the mosaic area displayed on the image with the number of 2 is set to be 6cm 2 The area of the image numbered 1 was 50cm 2 The images with the number of 2 are stuck with the stickers with the numbers of 3, 4, 5 and 7, U 3 ＝4MB，U 4 ＝6MB，U 5 ＝7MB，U 7 ＝8MB，The rendering complexity of the image numbered 2 is:
thus, the rendering complexity of the image numbered 2 is 1.4.
Example 2: is provided withDue to->The degree of coherence between the picture numbered 1 and the picture numbered 2 is:
thus, the degree of coherence between the image numbered 1 and the image numbered 2 is 0.75.
Example 3: let the optimization coefficient of the number 2 image be 0.5, d 1 ＝0.2，d 2 ＝0.4，d 3 =0.4, the degree of optimization of the code corresponding to the image numbered 2 is:
W 2 ＝d 1 *K 2 +d 2 *(1-Y 1→1+1 )+d 3 *H 2 ＝0.2*1.4+0.4*0.25+0.4*0.5＝0.58；
therefore, the code corresponding to the image numbered 2 has an optimization degree of 0.58.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A data management dynamic analysis method based on cloud computing is characterized by comprising the following steps: the method comprises the following steps:
s10: in the running process of the target software, images displayed by a software running terminal are collected, and the rendering complexity of each collected image is analyzed by combining the mapping conditions stored in a mapping folder of the target software;
s20: analyzing the degree of continuity between the images;
s30: determining the position to be optimized of the target software according to the running condition of the target software displayed by each software running terminal;
s40: analyzing the optimizing degree of the position to be optimized, and determining the management sequence of the position code data to be optimized according to the analysis result.
2. The cloud computing-based data management dynamic analysis method according to claim 1, wherein: the specific method for analyzing the rendering complexity of each acquired image in the S10 is as follows:
the method comprises the steps of placing target software in a standard operation environment to operate, and acquiring images displayed by a software operation terminal at time intervals T in the operation process of the target software;
determining the area of a mosaic area displayed on each collected image, determining the attached mapping situation on each collected image according to the mapping situation stored in a target software mapping folder, and predicting the rendering complexity of each collected image by combining the storage capacity corresponding to each mapping, wherein a specific prediction formula is as follows:
where i=1, 2, …, m, represents the storage in the target software map folderM represents the total number of the maps stored in the target software map folder, j=1, 2, …, n represents the numbering of the images according to the time sequence of the acquisition of the images, n represents the total number of the acquired images, U i Representing the storage capacity, k, corresponding to the map numbered i ij Representing the adhesion of a map numbered i to the jth image, k ij =1 or k ij ＝0，s j Representing the mosaic area displayed on the jth image,representing the standard value of the paste capacity of the image which is allowed to be pasted, S represents the total area of any one image, K j Representing the rendering complexity of the j-th image.
3. The cloud computing-based data management dynamic analysis method as claimed in claim 2, wherein: the S20 includes:
s201: comparing the image with the number j with the image with the number j+1, and according to the comparison result, comparing the overlapping area g of the two images j→j+1 Determination is made according to g j→j+1 S calculates the picture change rate of the image with the number j+1 compared with the image with the number j;
s202: acquiring the length of an operation track of the software running terminal in each interval time and the average value of operation deflection angles in each interval time according to V j→j+1 ＝[(2LM j→j+1 tanθ j→j+1 cosθ j→j+1 -M 2 j→j+1 tanθ j→j+1 cosθ 2 j→j+1 )+δ]S calculates the picture change rate of the image with the number j+1, L represents the length value of any one of the images, M j→j+1 Representing the length of the operation track of the software running terminal in the interval time from displaying the image with the number j to displaying the image with the number j+1, theta j→j+1 Representing the mean value of the operating deflection angle of the software running terminal in the interval time from displaying the image with the number j to displaying the image with the number j+1Delta represents the error compensation value, V j→j+1 Representing the picture change rate of the image numbered j+1 as compared to the image numbered j;
s203: if g j→j+1 /S＝[(2LM j→j+1 tanθ j→j+1 cosθ j→j+1 -M 2 j→j+1 tanθ j→j+1 cosθ 2 j→j+1 )+δ]S, then the degree of coherence between the image numbered j and the image numbered j+1 is 1;
if g j→j+1 /S≠[(2LM j→j+1 tanθ j→j+1 cosθ j→j+1 -M 2 j→j+1 tanθ j→j+1 cosθ 2 j→j+1 )+δ]According to Y j→j+1 ＝1-{|[(2LM j→j+1 tanθ j→j+1 cosθ j→j+1 -M 2 j→j+1 tanθ j→j+1 cosθ 2 j→j+1 )+δ]-g j→j+1 |/g j→j+1 Analyzing the degree of coherence between the j numbered image and the j+1 numbered image, wherein Y j→j+1 Representing the degree of coherence between the image numbered j and the image numbered j+1.
4. A cloud computing-based data management dynamic analysis method as claimed in claim 3, wherein: the specific method for determining the position to be optimized of the target software by S30 comprises the following steps:
randomly selecting an image, acquiring the card pause time when a plurality of software running terminals display the selected image, calculating the ratio of the acquired card pause time to the interval time, and according to the ratioDetermining an optimization coefficient of the selected image, wherein p=1, 2, … and q represent numbers corresponding to each software running terminal, q represents the total number of the software running terminals, and h p The ratio between the blocking time and the interval time obtained when the software running terminal with the number p displays the selected image is represented, and H represents the optimization coefficient of the selected image;
if R is less than H and less than or equal to 1, the code corresponding to the selected image needs to be optimized, and if H is more than or equal to 0 and less than or equal to R, the code corresponding to the selected image does not need to be optimized, and R is more than or equal to 0.4 and less than or equal to 0.7.
5. The cloud computing-based data management dynamic analysis method according to claim 4, wherein: the specific method for determining the management sequence of the position code data to be optimized in the S40 is as follows:
according to W j+1 ＝d 1 *K j+1 +d 2 *(1-Y j→j+1 )+d 3 *H j+1 Analyzing the optimizing degree of the position to be optimized, wherein d 1 、d 2 、d 3 All represent the scale factor, and d 1 +d 2 +d 3 ＝1，H j+1 An optimization coefficient representing an image numbered j+1, W j+1 Representing the degree of optimization of the code corresponding to the image numbered j+1;
and determining the management sequence of the position code data to be optimized according to the sequence of the optimization degree from top to bottom.
6. A cloud computing-based data management dynamic analysis system applied to the cloud computing-based data management dynamic analysis method of any one of claims 1 to 5, characterized in that: the system comprises a rendering complexity analysis module, a coherence degree dynamic analysis module, a position determination module to be optimized and an optimization degree prediction module;
the rendering degree analysis module is used for analyzing the rendering complexity of each acquired image;
the coherence degree dynamic analysis module is used for analyzing the coherence degree between the images;
the to-be-optimized position determining module is used for determining the to-be-optimized position of the target software;
the optimization degree prediction module is used for determining the management sequence of the position code data to be optimized.
7. The cloud computing-based data management dynamic analysis system of claim 6, wherein: the rendering degree analysis module comprises an image acquisition unit, a map pasting condition determining unit and a rendering degree analysis unit;
the image acquisition unit acquires images displayed by the software operation terminal at set time intervals in the operation process of the target software, and transmits the acquired images to the mapping and pasting condition determination unit;
the mapping pasting condition determining unit receives the images transmitted by the image acquisition unit, determines the mapping condition pasted on each acquired image according to the mapping condition stored in the target software mapping folder, and transmits the determined result to the rendering degree analyzing unit;
the rendering degree analysis unit receives the determination result transmitted by the map pasting condition determination unit, predicts the rendering complexity of each acquired image by combining the mosaic area displayed on each acquired image and the storage capacity corresponding to each map, and transmits the prediction result to the optimization degree prediction module.
8. The cloud computing based data management dynamic analysis system of claim 7, wherein: the coherence degree dynamic analysis module comprises a first calculation unit of a picture change rate, a second calculation unit of the picture change rate and a coherence degree dynamic analysis unit;
the first calculation unit of the picture change rate compares the collected images with adjacent numbers, determines the superposition area of the collected images with adjacent numbers according to the comparison result, combines the total area of the images, calculates the picture change rate of the image with the number of j+1 compared with the picture with the number of j, and transmits the calculation result to the dynamic analysis unit of the consistency degree;
the second calculation unit of the picture change rate obtains the length of the operation track of the software running terminal in each interval time and the average value of the operation deflection angles in each interval time, calculates the picture change rate of the image with the number of j+1 compared with the image with the number of j according to the obtained information, and transmits the calculation result to the dynamic analysis unit of the consistency degree;
the coherence degree dynamic analysis unit receives the calculation results respectively transmitted by the first calculation unit of the picture change rate and the second calculation unit of the picture change rate, analyzes the coherence degree between the collected images with adjacent numbers based on the received information, and transmits the analysis results to the optimization degree prediction module.
9. The cloud computing-based data management dynamic analysis system of claim 8, wherein: the position to be optimized determining module obtains the katon time when the plurality of software running terminals display the selected image, calculates the ratio between the obtained katon time and the interval time, calculates the optimization coefficient of the selected image based on the calculation result, determines the position to be optimized of the target software based on the calculation result, and transmits the position to be optimized of the target software and the calculated optimization coefficient of the selected image to the optimization degree predicting module.
10. The cloud computing based data management dynamic analysis system of claim 9, wherein: the optimization degree prediction module receives the prediction result transmitted by the rendering degree analysis unit, the analysis result transmitted by the coherence degree dynamic analysis unit, the target software to-be-optimized position transmitted by the to-be-optimized position determination module and the calculated optimization coefficient of the selected image, predicts the optimization degree of the to-be-optimized position according to the received information, and determines the management sequence of the to-be-optimized position code data according to the prediction result.
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