CN118071890A

CN118071890A - Animation production process optimization method and system

Info

Publication number: CN118071890A
Application number: CN202410241952.6A
Authority: CN
Inventors: 董刚
Original assignee: Chensui Guangzhou Network Technology Co ltd
Current assignee: Chensui Guangzhou Network Technology Co ltd
Priority date: 2024-03-04
Filing date: 2024-03-04
Publication date: 2024-05-24

Abstract

The invention discloses an animation production process optimization method and system, in particular relates to the field of image rendering optimization, and is used for solving the problem of the requirement of the existing dynamic rendering optimization; the method comprises the steps of comparing and analyzing historical rendering data with current rendering data, judging a current rendering environment meeting requirements, utilizing rendering animation loading and parameter monitoring to obtain an energy dynamic regulation toughness index, and evaluating energy regulation capability in the animation rendering process. And then, extracting a time sequence correlation index and a frequency domain energy distribution index by using an energy dynamic regulation toughness index data set obtained from the recording time to generate a foreseeable signal, so as to help predict the energy consumption management capability in the unit time in the future and guide the deployment and the scheduling of rendering tasks. And then through the energy condition in the real-time monitoring rendering process, the potential energy fluctuation problem is found in time, the management efficiency and the energy utilization rate of the rendering task are improved, and the smooth proceeding of the rendering task and the improvement of the rendering quality are ensured.

Description

Animation production process optimization method and system

Technical Field

The invention relates to the technical field of image rendering optimization, in particular to an animation production process optimization method and system.

Background

There are some problems in the current animation rendering optimization process. Firstly, the traditional rendering optimization method generally only considers static rendering environment factors, and lacks of real-time monitoring and adjustment on the rendering environment which dynamically changes, so that environment changes cannot be timely dealt with in the rendering process, and rendering efficiency and quality are affected. Secondly, the existing rendering optimization method often lacks comprehensive consideration on energy consumption management and regulation, so that the energy consumption is unstable in the animation rendering process, and effective control and management of the energy consumption are difficult to realize. In addition, the traditional rendering optimization method only focuses on static rendering task scheduling, and ignores the influence of dynamic scheduling on rendering efficiency and energy utilization efficiency, so that the defects and waste exist in the task scheduling process. Thus, there is a need for an innovative approach that integrates dynamic rendering environments, energy regulation and task scheduling to address these issues.

In order to solve the above problems, a technical solution is now provided.

Disclosure of Invention

In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present invention provides an animation process optimization method and system, which first determines whether a current rendering environment meets a condition of a historical normal rendering environment by comparing and analyzing historical rendering data and current rendering data, so as to generate a signal for developing rendering with low risk or high risk. After the low-risk development rendering signal is obtained, the rendering animation loading and parameter monitoring are utilized to obtain the energy dynamic regulation toughness index, and the energy regulation capacity in the animation rendering process is evaluated. And then, extracting a time sequence correlation index and a frequency domain energy distribution index by using an energy dynamic regulation toughness index data set obtained from the recording time to generate a foreseeable signal, so as to help predict the energy consumption management capability in the unit time in the future and guide the deployment and the scheduling of rendering tasks. And then through the energy condition in the real-time monitoring rendering process, the potential energy fluctuation problem is found in time, the management efficiency and the energy utilization rate of the rendering task are improved, so that the smooth proceeding of the rendering task and the improvement of the rendering quality are ensured, and the problems in the background technology are solved.

In order to achieve the above purpose, the present invention provides the following technical solutions:

A method of animation process optimization comprising the steps of:

s1: calling historical rendering data and current rendering data, judging the degree of the current rendering environment belonging to the historical normal rendering environment, and obtaining a rendering notification signal;

s2: based on the rendering notification signal, loading the rendering animation into a rendering tool, and performing parameter monitoring on the rendering process to obtain an energy dynamic regulation toughness index, so as to clearly determine the energy consumption regulation condition of unit time when rendering the animation;

s3: and constructing a data set to be analyzed based on the energy dynamic regulation toughness index, and respectively obtaining a time sequence correlation index and a frequency domain energy distribution index according to the time correlation characteristic and the frequency distribution characteristic.

S4: and analyzing the regularity and the frequency characteristic of the rendering energy consumption management by using the time sequence correlation index and the frequency domain energy distribution index, predicting the schedulable condition of the rendering task in the future, and providing a foreseeable signal.

In a preferred embodiment, S101, statistics is performed on each rendering quality standard reaching historical rendering task, for each judgment item, statistics is performed on the values of the corresponding judgment items of the history, union processing is performed to obtain a data set corresponding to each judgment item, abnormal data in the data set is identified and removed, then the remaining data points are recombined to obtain the data set, the maximum value and the minimum value in each data set are identified, the data interval of each judgment item is obtained, and then the data intervals of all the judgment items are summarized to obtain a historical data interval set;

S102, carrying out statistics recording on the data of each judgment item in the current time to obtain a clash data set.

In a preferred embodiment, S103, calculating a degree of attribution of the clash data set to the historical data interval set based on the historical data interval set and the clash data set;

assuming that the clash data set includes n data points, the historical data interval set includes n data intervals, for each data point in the clash data set, the attribution degree of the clash data set and the historical data interval set is calculated, and the calculation formula is as follows:

Where x _o represents the o data point in the clash dataset, a _o represents the upper bound of the o data interval in the historical dataset, b _o represents the lower bound of the o data interval in the historical dataset, and S represents the degree of attribution;

S104, comparing the attribution degree with a degree threshold to obtain a rendering notification signal, wherein the rendering notification signal is used for judging the coincidence degree of the current rendering environment and the historical environment capable of being normally rendered, and comprises a low-risk development rendering signal and a high-risk development rendering signal;

If the attribution degree is greater than or equal to the degree threshold, generating a low-risk development rendering signal;

if the attribution degree is smaller than the degree threshold, generating a high risk developing rendering signal, and sending out an early warning prompt.

In a preferred embodiment, step S2 comprises the following:

after the low-risk development rendering signal is obtained, loading the rendering animation into a rendering tool, inputting rendering parameters to start rendering operation, and performing parameter monitoring on the rendering process, wherein the parameters comprise an energy dynamic regulation toughness index.

In a preferred embodiment, the energy dynamic regulation toughness index acquisition logic is as follows:

S201, collecting energy consumption data in the rendering process, and preprocessing, including removing abnormal values to ensure accuracy of the data;

S202, dividing energy consumption data based on unit time, and calculating to obtain an energy dynamic regulation toughness index, wherein the calculation formula is as follows:

Wherein E _i is the energy consumption data of each data point in a unit time; mu _E is the average of the energy content data per unit time; σ _E is the standard deviation of the unit energy consumption data; λ and β are weight coefficients; fast fourier transform of energy consumption data in a unit time represented by FFT (E _j), M represents the number of energy consumption data subjected to FFT operation, i.e., the length of the sequence; Representing the maximum value of the energy consumption data in a unit time, specifically representing the highest energy consumption level of the unit energy consumption data in a given time period, k representing each time point in the time series, and N representing the length of the time series;

s203, comparing the energy dynamic regulation toughness index with a rendering state threshold;

If the energy dynamic regulation toughness index is smaller than the rendering state threshold, generating a low-reliability signal;

and if the energy dynamic regulation toughness index is greater than or equal to the rendering state threshold, generating a high-reliability signal.

In a preferred embodiment, step S3 comprises the following:

In the process of rendering the animation, a group of data sets of energy dynamic regulation and control toughness indexes are obtained according to the recording time, the data sets are marked as data sets to be analyzed, time sequence correlation indexes are obtained according to the time correlation characteristics extracted from the data sets to be analyzed, and frequency domain energy distribution indexes are obtained according to the frequency distribution characteristics extracted from the data sets to be analyzed;

The calculation formula of the time sequence correlation index is as follows:

Where y _t is the value of the t-th time point in the data set to be analyzed, Is the average value of the data set to be analyzed, and T is the length of the data set to be analyzed;

The calculation formula of the frequency domain energy distribution index is as follows:

Where i is the ordinal unit and T is the length of the data set to be analyzed.

In a preferred embodiment, step S4 comprises the following:

And comparing the time sequence correlation index and the frequency domain energy distribution index with the corresponding thresholds respectively, and generating a foreseeable signal if the time sequence correlation index and the frequency domain energy distribution index are respectively larger than or equal to the corresponding thresholds.

An animation production process optimization system comprises an environment attribution judging module, a rendering monitoring module, a characteristic extraction module and a foreseeable analysis module;

The environment attribution judging module calls the historical rendering data and the current rendering data, judges the degree of attribution of the current rendering environment to the historical normal rendering environment, obtains a rendering notification signal, and sends the rendering notification signal to the rendering monitoring module;

the rendering monitoring module loads the rendering animation into the rendering tool based on the rendering notification signal, monitors parameters of the rendering process to obtain an energy dynamic regulation toughness index, and sends the energy dynamic regulation toughness index to the feature extraction module when the energy dynamic regulation toughness index is definitely regulated and controlled in unit time during rendering the animation;

The characteristic extraction module constructs a data set to be analyzed based on the energy dynamic regulation toughness index, respectively obtains a time sequence correlation index and a frequency domain energy distribution index according to the time correlation characteristic and the frequency distribution characteristic, and sends the time sequence correlation index and the frequency domain energy distribution index to the foreseeable analysis module;

The foreseeable analysis module analyzes the regularity and the frequency characteristic of the rendering energy consumption management by utilizing the time sequence correlation index and the frequency domain energy distribution index, foresees the scheduling condition of the rendering task in the future and provides foreseeable signals.

The invention discloses an animation production process optimization method and a system thereof, which have the technical effects and advantages that:

1. Based on the comparison analysis of the historical rendering data and the current rendering data, whether the current rendering environment accords with the situation of the historical normal rendering environment or not is judged. Firstly, a historical data interval set is constructed through statistics and cleaning of historical rendering data, abnormal data are identified and removed, and a clean historical data set is obtained. And then, counting and recording various parameters of the current rendering environment, and constructing a clash data set. Then, the degree of similarity between the current rendering environment and the historical environment is evaluated by calculating the degree of attribution between the clash data set and the historical data interval set. And finally, generating a signal for developing rendering with low risk or high risk according to the comparison of the attribution degree and the degree threshold value. The method is further beneficial to timely finding out the difference between the current rendering environment and the historical environment and early warning the rendering risk possibly existing in advance, so that further inspection and adjustment are guided, and normal development of animation rendering work is ensured;

2. According to the invention, after a low-risk development rendering signal is obtained, the energy dynamic regulation toughness index is obtained through loading and parameter monitoring of the rendering animation so as to evaluate the energy regulation capability in the animation rendering process. Firstly, energy consumption data in a rendering process are collected and preprocessed, so that an accurate data set is obtained. Then, dividing the energy consumption data based on unit time, calculating the energy dynamic regulation toughness index, and comprehensively considering the average value, variance, spectral characteristics and other multi-aspect characteristics of the energy consumption data. And then, comparing the obtained index with a rendering state threshold value so as to generate a low or high reliable signal to guide the scheduling and management of the subsequent rendering task. And the method is beneficial to monitoring the energy condition in the rendering process in real time and finding out the potential energy fluctuation problem in time, so that the smooth proceeding of the rendering task and the improvement of the rendering quality are ensured.

3. The invention utilizes the energy dynamic regulation toughness index data set obtained by the recording time to extract the time sequence correlation index and the frequency domain energy distribution index. The time sequence correlation index reflects the time correlation of the energy consumption management by analyzing the correlation degree of the data at different time points, and the frequency domain energy distribution index analyzes the energy distribution condition of the signal in the frequency domain and reveals the characteristics of the energy consumption data at different frequencies. By comparing the indexes with the corresponding thresholds, a foreseeable signal is generated, so that the energy consumption management capability in the unit time in the future is predicted, and the deployment and the scheduling of rendering tasks are guided. And the management efficiency and the energy utilization rate of the rendering task are improved, so that the system can more flexibly cope with the fluctuation of energy consumption, and the reliability and the efficiency of the rendering task are improved.

Drawings

FIG. 1 is a schematic flow chart of an animation production process optimization method and system according to the present invention;

FIG. 2 is a schematic diagram of an animation process optimization method and system according to the present invention.

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.

Example 1

FIG. 1 shows an animation process optimization method of the present invention, comprising the steps of:

Step S1 includes the following:

S101, counting the historical rendering tasks with the up-to-standard rendering quality, counting the numerical values of corresponding judgment items of a history for each judgment item, performing union processing to obtain a data set corresponding to each judgment item, identifying abnormal data in the data set, removing the abnormal data, and performing common methods including a method based on statistics (such as a Z-score method and a box line diagram method) and a method based on machine learning (such as a clustering and outlier detection algorithm), selecting and removing the identified abnormal data points, recombining residual data points after the abnormal data is removed to form a clean data set, identifying the maximum value and the minimum value in each data set, acquiring the data interval of each judgment item, and summarizing the data intervals of all the judgment items to obtain a historical data interval set.

When the difference of the rendering environment is judged by calling the historical rendering data and the current rendering data, the judgment items refer to various parameters or indexes for evaluating the rendering environment. These decision items may include information on hardware configuration (e.g., CPU model, GPU model, memory size, etc.), software version (e.g., operating system version, rendering software version, etc.), network environment (e.g., bandwidth, latency, etc.), etc. For example, the judgment items in the historical rendering data may include an old version of CPU model, GPU with graphics card model a, wi ndows with operating system, etc.; the judging items in the current rendering data may include a new version of CPU model, GPU with graphics card model B, wi ndows operating system, etc. For each judgment item, the corresponding value and the current value of the history can be counted, then the value of the judgment item of the history is used for union processing, abnormal data in the union data set are identified and removed, and a history basis data set is obtained. Such operations are performed separately for the history of each judgment item from the data set, forming a history data set. Meanwhile, a clash data set is constructed according to the numerical value of the current judgment item, namely, each item of current data forms a data set. Finally, the degree of correspondence between the analysis dataset and the clash dataset may be calculated using an appropriate similarity or distance metric (e.g., euclidean distance, cosine similarity, etc.) to evaluate the variability of the historical and current rendering environments.

S102, carrying out statistics recording on the data of each judgment item in the current time to obtain a clash data set;

The clash data set refers to a data set of each judgment item in the current time. These data sets reflect the rendering environment conditions at the current time, including information on hardware configuration, software version, network conditions, etc. For example, the clash dataset may include a CPU model, GPU model, memory size, operating system version, rendering software version, etc. at the current time, where the judging items in the clash dataset and the historical data interval set are in one-to-one correspondence. By statistically recording the data, the rendering environment state at the current moment can be obtained, and a basis is provided for subsequent analysis and comparison.

S103, calculating the degree of attribution of the clash data set to the historical data interval set based on the historical data interval set and the clash data set;

Where x _o represents the o data point in the clash dataset, a _o represents the upper bound of the o data interval in the historical dataset, b _o represents the lower bound of the o data interval in the historical dataset, and S represents the degree of attribution.

The attribution degree of the clash data set and the historical data interval set is used for judging whether the current rendering environment belongs to the historical normal rendering environment. The similarity of the current rendering environment and the historical environment can be evaluated by calculating the attribution degree of each data point in the clash data set and the corresponding data set in the historical data interval set. If the attribution degree of the data points in the clash data set in the historical data interval set is higher, the current rendering environment is similar to the historical environment, and the current rendering environment can be considered to belong to the historical normal rendering environment. Conversely, if the degree of attribution is low, it may indicate that there is a large difference between the current rendering environment and the environment that the history can render correctly, so having a departure from correctness may require further attention and adjustment. Therefore, the attribution degree of the clash data set and the historical data interval set can be used as a reference index for judging whether the current rendering environment is normal or not.

And S104, comparing the attribution degree with a degree threshold value to obtain a rendering notification signal, wherein the rendering notification signal is used for judging the coincidence degree of the current rendering environment and the historical environment which can be normally rendered, and comprises a low-risk development rendering signal and a high-risk development rendering signal.

If the attribution degree is larger than or equal to the degree threshold, the attribution degree is identical with the historical environment on most judgment items, and the current rendering environment can be considered to be similar or matched with the historical normal rendering environment, so that the method further has the precondition of normally developing animation rendering work and generates a low-risk developing rendering signal;

If the attribution degree is smaller than the degree threshold, the coincidence degree of the current rendering environment and the historical environment capable of normally rendering is lower, namely, the current environment has larger difference from the historical environment on most judgment projects, a high-risk rendering signal is generated, an early warning prompt is sent, and further examination and adjustment may be needed.

Based on the comparison analysis of the historical rendering data and the current rendering data, whether the current rendering environment accords with the situation of the historical normal rendering environment or not is judged. Firstly, a historical data interval set is constructed through statistics and cleaning of historical rendering data, abnormal data are identified and removed, and a clean historical data set is obtained. And then, counting and recording various parameters of the current rendering environment, and constructing a clash data set. Then, the degree of similarity between the current rendering environment and the historical environment is evaluated by calculating the degree of attribution between the clash data set and the historical data interval set. And finally, generating a signal for developing rendering with low risk or high risk according to the comparison of the attribution degree and the degree threshold value. And further, the difference between the current rendering environment and the historical environment can be found in time, and rendering risks possibly existing are early-warned in advance, so that further inspection and adjustment are guided, and normal development of animation rendering work is ensured.

Step S2 includes the following:

The acquisition logic of the energy dynamic regulation toughness index is as follows:

Wherein E _i is the energy consumption data of each data point in unit time, and is used for reflecting the energy consumption change condition in the rendering process; mu _E is the average value of the energy consumption data in unit time and is used for measuring the overall level of the energy consumption data; sigma _E is standard deviation of unit energy consumption data and is used for measuring fluctuation degree of the energy consumption data; λ and β are weight coefficients for balancing the effects of different indicators; the fast fourier transform of the energy consumption data in unit time, represented by the FFT (E _j), is used for analyzing the spectral characteristics of the energy consumption data, and by analyzing the spectral characteristics of the energy consumption data, the distribution situation of the energy consumption data under different frequencies can be known, the energy consumption change rule in the rendering process is further known, and M represents the number of the energy consumption data performing the FFT operation, namely the length of the sequence; represents the maximum value in the energy consumption data per unit time, specifically the highest energy consumption level of the energy consumption data per unit time within a given period, k represents each point in time in the time series, and N represents the length of the time series.

Lambda is used for balancing the influence of the statistical characteristics of the energy consumption data on the dynamic regulation and control toughness index of the energy. Statistical features include mean, variance, etc. of energy consumption reflecting the overall level and fluctuation degree of energy consumption data. The contribution degree of the statistical characteristics to the dynamic regulation and control of the toughness index of the energy can be controlled by regulating the numerical value of lambda. A larger lambda value will increase the importance of the statistical features, while a smaller lambda value will reduce the impact on the statistical features, thereby making the energy dynamic regulation toughness index more concerned about the variation of the statistical features.

Beta is used for balancing the influence of the frequency spectrum characteristic of the energy consumption data on the dynamic regulation and control toughness index of the energy. The spectral characteristics reflect the distribution of the energy consumption data at different frequencies and can provide information about the volatility and periodicity of the energy consumption. By adjusting the value of beta, the contribution degree of the frequency spectrum characteristic to the dynamic regulation and control of the toughness index of the energy can be controlled. A larger beta value will increase the importance of the spectral characteristics, while a smaller beta value will reduce the impact on the spectral characteristics, thereby making the energy dynamic regulation toughness index more concerned about the variation of the spectral characteristics.

The energy dynamic regulation toughness index is calculated to comprehensively integrate various characteristics of energy consumption data in the animation rendering process, including mean value, variance, spectrum characteristics and the like, so as to comprehensively evaluate the stability of energy consumption utilization. By combining these features, the stability of the energy consumption data can be more fully understood, providing a reference for further optimization.

When the energy dynamic regulation toughness index is used for evaluating the energy regulation capability in the animation rendering process.

When the dynamic energy regulation and control toughness index is smaller, the regulation and control capability of energy consumption utilization is weaker in the animation rendering process, and the energy stability is lower in the rendering process, which means that the system has problems in the aspects of energy management and regulation, and in the task scheduling process, the fluctuation of energy consumption needs to be considered, so that a large number of tasks are prevented from being intensively scheduled in a period with larger fluctuation of energy consumption, the waste of energy and the fluctuation of equipment performance are prevented, and the rendering quality is prevented from being damaged;

When the dynamic regulation toughness index of the energy source is larger, the regulation capability of energy consumption utilization is stronger in the process of animation rendering, and the stability of the energy source in the rendering process is higher. This means that the system is relatively sound in terms of energy management and regulation, and can effectively manage and regulate energy, and maintain the stability of energy consumption. In the task scheduling process, the tasks can be arranged more flexibly, and the fluctuation of energy consumption is not needed to be worried excessively. Therefore, when the dynamic regulation toughness index of the energy source is smaller.

S203, comparing the energy dynamic regulation toughness index with a rendering state threshold.

If the energy dynamic regulation toughness index is smaller than the rendering state threshold, the energy consumption regulation capability of the system in the animation rendering process is weak, and the energy stability in the rendering process is insufficient to meet the requirement of the rendering state. The problems of overlarge energy fluctuation, unstable performance and the like in the rendering process can be caused, so that the rendering quality is affected or rendering tasks cannot be completed, and a low-reliability signal is generated.

If the energy dynamic regulation toughness index is larger than or equal to the rendering state threshold, the energy consumption regulation capability of the system in the animation rendering process is relatively strong, and the stability of the energy can be maintained to meet the requirement of the rendering state. The system can effectively manage and regulate the energy, and ensure the stability of the energy in the rendering process, so that the smooth proceeding of rendering tasks and the improvement of rendering quality are ensured, the tasks can be arranged more flexibly, and the fluctuation of energy consumption is not needed to be worried excessively. Therefore, when the energy dynamic regulation toughness index is smaller, a high-reliability signal is generated.

According to the invention, after a low-risk development rendering signal is obtained, the energy dynamic regulation toughness index is obtained through loading and parameter monitoring of the rendering animation so as to evaluate the energy regulation capability in the animation rendering process. Firstly, energy consumption data in a rendering process are collected and preprocessed, so that an accurate data set is obtained. Then, dividing the energy consumption data based on unit time, calculating the energy dynamic regulation toughness index, and comprehensively considering the average value, variance, spectral characteristics and other multi-aspect characteristics of the energy consumption data. And then, comparing the obtained index with a rendering state threshold value so as to generate a low or high reliable signal to guide the scheduling and management of the subsequent rendering task. And the method is beneficial to monitoring the energy condition in the rendering process in real time and finding out the potential energy fluctuation problem in time, so that the smooth proceeding of the rendering task and the improvement of the rendering quality are ensured.

Step S3 includes the following:

The calculation formula of the time sequence correlation index is as follows:

Where y _t is the value of the t-th time point in the data set to be analyzed, Is the mean value of the data set to be analyzed, and T is the length of the data set to be analyzed.

The time sequence correlation index is used for analyzing the correlation between each time point of the time sequence data. By calculating the correlation between time series data and its lagging versions at different time delays, the correlation structure of the data in time can be understood. The autocorrelation function can reveal the regularity characteristics of the data, such as periodicity, trending, and randomness, thereby helping to understand the time evolution law of the data.

The magnitude of the time sequence dependency index reflects the degree of dependency of energy consumption performance management in animation rendering on time. When the time sequence correlation index is larger, the energy consumption management index has stronger correlation between different time points, namely, more obvious time correlation exists between data. This may mean that the trend of the data over time is more consistent or there is a periodic change, i.e. it is easier to foresee future situations in terms of energy consumption management, and it is easy to explicitly obtain high and low reliable signals over different unit time, so that it is convenient to schedule rendering tasks in advance. Conversely, when the time-series correlation index is smaller, it means that the correlation between data at different points in time is weaker, i.e., the time correlation between data is weaker or does not exist. This may mean that the data varies more randomly or irregularly in time. In summary, a larger time-series correlation index indicates a stronger correlation of data in time, and a smaller time-series correlation index indicates a weaker correlation of data in time.

Where i is the ordinal unit and T is the length of the data set to be analyzed.

The frequency domain energy distribution index is used for analyzing the energy distribution condition of the signal in the frequency domain. By carrying out Fourier transform on the signals, the signals can be converted from the time domain to the frequency domain, and the energy distribution condition of the signals under different frequencies is obtained. The spectral density function can reveal the frequency components, the periodicity characteristic, the noise level and other regularity characteristics of the signal, and has important significance for understanding the frequency characteristic and the oscillation property of the signal.

Where a larger spectral density function value may indicate that there are significant frequency components in the rendering process, and a smaller spectral density function value may indicate that the energy distribution in the rendering process is more uniform or that there are fewer periodic features.

Step S4 includes the following:

and comparing the time sequence correlation index and the frequency domain energy distribution index with the corresponding thresholds respectively, and if the time sequence correlation index and the frequency domain energy distribution index are respectively larger than or equal to the corresponding thresholds, generating predictable signals, wherein the predictable signals represent that the energy consumption management and control capacity in unit time in the future is easy to be foreseen when the animation rendering is carried out, namely the low-reliability signals or the high-reliability signals in the animation rendering process are easy to be foreseen and analyzed, the rendering tasks are deployed and scheduled in advance with more confidence, and the task is helped to be more efficiently completed.

Otherwise, if the time sequence correlation index or the frequency domain energy distribution index is smaller than the corresponding threshold value, the correlation or the energy distribution in the animation rendering process is lower, namely the correlation of the data in time is weaker or the energy distribution is more uniform. This may mean that the energy consumption control capacity in the future unit time is relatively unstable or difficult to predict, and the future energy consumption change situation cannot be accurately predicted. Therefore, for the case that the time-series correlation index or the frequency domain energy distribution index is smaller than the threshold, a predictable signal cannot be generated, that is, it is difficult to predict the energy consumption control and regulation capability in the animation rendering process. In this case, the task deployment and scheduling may face a large uncertainty, so that the rendering task deployment cannot be dynamically adjusted, static task processing can be performed only according to advanced deployment, flexibility and initiative are lost, flexible rendering task deployment cannot be performed in combination with volatility of energy consumption management, and unpredictable signals are generated.

The invention utilizes the energy dynamic regulation toughness index data set obtained by the recording time to extract the time sequence correlation index and the frequency domain energy distribution index. The time sequence correlation index reflects the time correlation of the energy consumption management by analyzing the correlation degree of the data at different time points, and the frequency domain energy distribution index analyzes the energy distribution condition of the signal in the frequency domain and reveals the characteristics of the energy consumption data at different frequencies. By comparing the indexes with the corresponding thresholds, a foreseeable signal is generated, so that the energy consumption management capability in the unit time in the future is predicted, and the deployment and the scheduling of rendering tasks are guided. And the management efficiency and the energy utilization rate of the rendering task are improved, so that the system can more flexibly cope with the fluctuation of energy consumption, and the reliability and the efficiency of the rendering task are improved.

Example 2

FIG. 2 is a schematic diagram of an animation production process optimization system according to the present invention, which includes an environment attribution judging module, a rendering monitoring module, a feature extracting module, and a foreseeable analyzing module;

The foreseeable analysis module analyzes the regularity and the frequency characteristic of the rendering energy consumption management by utilizing the time sequence correlation index and the frequency domain energy distribution index, foresees the scheduling condition of the rendering task in the future and provides foreseeable signals. The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.

Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system, apparatus and module may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, may be located in one place, or may be distributed over multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.

The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or other various media capable of storing program codes.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims

1. A method for optimizing an animation process, comprising the steps of:

S3: constructing a data set to be analyzed based on the energy dynamic regulation toughness index, and respectively obtaining a time sequence correlation index and a frequency domain energy distribution index according to the time correlation characteristic and the frequency distribution characteristic;

2. A method of optimizing an animation process as claimed in claim 1, wherein:

S101, counting historical rendering tasks with up-to-standard rendering quality, counting the numerical values of corresponding judgment items of a history for each judgment item, performing union processing to obtain a data set corresponding to each judgment item, identifying abnormal data in the data set, removing the abnormal data, then recombining remaining data points to obtain the data set, identifying the maximum value and the minimum value in each data set, obtaining the data interval of each judgment item, and summarizing the data intervals of all the judgment items to obtain a historical data interval set;

3. A method of optimizing an animation process as claimed in claim 2, wherein:

4. A method of optimizing an animation process as claimed in claim 3, wherein:

Step S2 includes the following:

5. The method of claim 4, wherein:

6. The method of claim 5, wherein the animation process is optimized by:

Step S3 includes the following:

The calculation formula of the time sequence correlation index is as follows:

wherein y _t is the value of the T time point in the data set to be analyzed, y is the average value of the data set to be analyzed, and T is the length of the data set to be analyzed;

Where i is the ordinal unit and T is the length of the data set to be analyzed.

7. The method of claim 6, wherein:

Step S4 includes the following:

8. An animation process optimization system for implementing an animation process optimization method according to any one of claims 1-7, comprising an environment attribution judging module, a rendering monitoring module, a feature extraction module and a foreseeable analysis module;