A Visual Special Effect Processing Method, Device, Storage Medium and Terminal

Abstract

The present invention discloses a visual effect processing method, device, storage medium and terminal, which relates to the technical field of computer graphics and image processing, and is used to improve the problem that in a complex special effect scene, there are significant differences in the number and motion state of particles in different regions. By obtaining the range of the special effect processing area, counting the number and motion direction of particles in this area, and evaluating the running complex characteristics of the area; when the evaluation result is a high complex characteristic, perform area division, obtain the particle states of each divided area and calculate the static-dynamic particle ratio, detect the particle pushing speed, and calculate the total particle running loss; set a detection time point to extract the particle transparency and evaluate the degree of transparency change; based on the total loss and the degree of transparency change, determine whether to switch the particle pushing process to particle resurrection, so as to improve the special effect fluency and reduce particle loss.

Description

Technical Field

[0001] The present invention relates to the technical field of computer graphics and image processing. More specifically, the present invention relates to a visual effect processing method, device, storage medium and terminal. Background Art

[0002] With the development of computer graphics, real-time rendering and visual effect technologies, particle systems have been widely used in scenarios such as movie special effects, game engines, virtual reality, etc. to simulate complex dynamic phenomena such as smoke, fire, water flow, explosion, etc. The basic unit of a particle system is a particle, and each particle has attributes such as position, velocity, transparency, life cycle, etc. Through the collaborative evolution of a large number of particles, the expression of realistic special effects is achieved;

[0003] The existing technology has the following deficiencies:

[0004] Currently, in complex special effect scenarios, the number and motion states of particles in different regions show significant differences. Problems such as particle aggregation, state imbalance, and rapid resource dissipation may occur in some regions, resulting in flickering, distortion or delay in the overall visual performance. There is provided a visual effect processing method, device, storage medium and terminal. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the existing technology, embodiments of the present invention provide a visual effect processing method, device, storage medium and terminal. Through a partition optimization processing mechanism based on the complex characteristics of particle operation, refined analysis and dynamic regulation of the particle state in the special effect area are achieved, so as to solve the problems of unstable visual performance, waste of rendering resources and low processing efficiency caused by uneven particle distribution and unbalanced motion states in the existing technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A visual effect processing method includes the following steps:

[0008] Step S1: Obtain the range of the special effect processing area, count the number of particles within the range of the special effect processing area, summarize the anisotropic frequencies of the particles, and evaluate the complex characteristics of the operation of the special effect processing area based on the number of particles and the anisotropic frequencies;

[0009] Step S2: When the evaluation result of the special effect processing area is a high complex characteristic, divide the special effect processing area, obtain the particle states of each divided area and calculate the ratio of static and dynamic particles, and detect the particle migration speed in each divided area;

[0010] Step S3: Process the particle movement speed in the divided area, calculate the total particle operation loss in the divided area based on the dynamic and static particle ratio of the corresponding divided area, set the detection time point to screen the transparency of the particles in each divided area and evaluate the degree of transparency change of the divided area;

[0011] Step S4: Comprehensively consider the total loss of the divided area operation and the degree of transparency change to determine whether to switch the particle transfer processing in each divided area to particle resurrection.

[0012] In a preferred embodiment, in step S1, when counting the number of particles within the special effect processing area, the number of particles on the four boundaries of the special effect processing area is first counted, and then the number of particles within the special effect processing area is counted, and the particle numbers of the two parts are integrated to obtain the number of particles within the special effect processing area;

[0013] Particles with the same movement angle are set as isotropic particles; particles with different movement angles are set as anisotropic particles.

[0014] The frequency value is set when summarizing the anisotropic frequency of particles. Every time an anisotropic particle is detected within the special effect processing area, the frequency value is increased by one. After traversing all particles within the special effect processing area, the frequency value is used as the anisotropic frequency.

[0015] In a preferred embodiment, in step S1, the geometric mean method is used to evaluate the operation complexity characteristics of the special effect processing area based on the number of particles and the anisotropic frequency: , where S is the number of particles in the special effect processing area, P is the anisotropic frequency in the special effect processing area, and Y is the operation complexity characteristics of the special effect processing area.

[0016] In a preferred embodiment, in step S2, when the particle operation complexity feature of the special effect processing area exceeds a preset particle complexity threshold, the special effect processing area is judged to be a high complexity feature; otherwise, the special effect processing area is judged to be a low complexity feature.

[0017] In a preferred embodiment, in step S2, the particle state is divided into dynamic particles and static particles, the fine particles are particles that do not move in the divided area, and the dynamic particles are particles that move in the divided area. The ratio of the number of dynamic particles to the number of static particles is used as the dynamic-static particle ratio of the divided area.

[0018] In a preferred embodiment, in step S3, the particle movement speed in each divided area is detected according to the particle state of each divided area, the particle movement speed is processed, and the original particle movement speed sequence is smoothed by a sliding mean filter algorithm to obtain the average movement speed;

[0019] Standardize the average movement speed and the ratio of static and dynamic particles in the corresponding divided area, and substitute them into the logistic regression formula to calculate the total operating loss of the divided area.

[0020] In a preferred embodiment, in step S3, the transparency values of the particles in each divided area are extracted at two respectively set detection times, which are respectively recorded as the transparency set at the current detection time and the transparency set at the previous detection time. The difference calculation is performed one by one on the corresponding particle transparency values in each set to obtain the degree of transparency change of the divided area.

[0021] In a preferred embodiment, in step S4, standardize the total operating loss of the divided area and the degree of transparency change;

[0022] Substitute the standardized total operating loss of the divided area and the degree of transparency change into the hyperbolic tangent mapping model to obtain the particle movement coefficient.

[0023] In a preferred embodiment, in step S4, compare the particle movement coefficient with a preset movement threshold. If the particle movement coefficient is greater than or equal to the movement threshold, switch the particle movement process in each divided area to particle resurrection. If the particle movement coefficient is less than the movement threshold, maintain the original particle movement processing mechanism.

[0024] A visual effect processing device for implementing the above-mentioned visual effect processing method, including an effect evaluation device, a particle recognition device, an operation evaluation device, and a strategy control device;

[0025] The effect evaluation device is used to obtain the range of the special effect processing area, count the number of particles in the special effect processing area, summarize the anisotropic frequencies of the particles, and evaluate the operation complexity characteristics of the special effect processing area based on the number of particles and the anisotropic frequencies.

[0026] The particle recognition device is used to divide the special effect processing area when the evaluation result of the special effect processing area is a high complexity characteristic, obtain the particle states of each divided area and calculate the ratio of static and dynamic particles, and detect the particle movement speed in each divided area.

[0027] The operation evaluation device is used to process the particle movement speed in the divided area, calculate the total particle operation loss of the divided area in combination with the ratio of static and dynamic particles in the corresponding divided area, set a detection time point to screen the transparency of the particles in each divided area, and evaluate the degree of transparency change of the divided area.

[0028] The strategy control device is used to comprehensively judge whether to switch the particle movement process in each divided area to particle resurrection based on the total operating loss of the divided area and the degree of transparency change.

[0029] Technical effects and advantages of a visual special effect processing method, device, storage medium, and terminal according to the present invention:

[0030] The present invention obtains the range of the special effect processing area, counts the number of particles and the particle movement directions within the special effect processing area, evaluates the running complex characteristics of the special effect processing area. When the evaluation result of the special effect processing area is a high complex characteristic, the special effect processing area is divided, the particle states of each divided area are obtained and the static-dynamic particle ratio is calculated, the particle pushing speed in each divided area is detected, the total particle running loss of the divided area is calculated, a detection time point is set to perform transparency screening on the particles in each divided area and evaluate the transparency change degree of the divided area, and it is judged whether to switch the particle pushing process in each divided area to particle resurrection based on the total running loss and transparency change degree of the divided area, thereby improving the smoothness of the special effect and reducing the special effect loss. Brief Description of the Drawings

[0031] Figure 1 It is a schematic diagram of a visual special effect processing method according to the present invention.

[0032] Figure 2 It is a flowchart of a visual special effect processing device according to the present invention. Detailed Embodiments

[0033] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0034] The present invention obtains the range of the special effect processing area, counts the number of particles and the particle movement directions within the special effect processing area, evaluates the running complex characteristics of the special effect processing area. When the evaluation result of the special effect processing area is a high complex characteristic, the special effect processing area is divided, the particle states of each divided area are obtained and the static-dynamic particle ratio is calculated, the particle pushing speed in each divided area is detected, the total particle running loss of the divided area is calculated, a detection time point is set to perform transparency screening on the particles in each divided area and evaluate the transparency change degree of the divided area, and it is judged whether to switch the particle pushing process in each divided area to particle resurrection based on the total running loss and transparency change degree of the divided area, thereby improving the smoothness of the special effect and reducing the special effect loss.

[0035] Embodiment 1, a visual special effect processing method, as Figure 1 shown, includes the following steps:

[0036] Step S1: Obtain the range of the special effect processing area, count the number of particles within the special effect processing area, summarize the different-direction frequencies of the particles, and evaluate the operation complexity characteristics of the special effect processing area based on the particle quantity and different-direction frequencies;

[0037] Step S2: When the evaluation result of the special effect processing area is of high complexity characteristics, divide the special effect processing area, obtain the particle states of each divided area and calculate the static-dynamic particle ratio, and detect the particle migration speed in each divided area; Step S3: Process the particle migration speed in the divided area, calculate the total particle operation loss of the divided area in combination with the static-dynamic particle ratio of the corresponding divided area, set a detection time point to perform transparency screening on the particles in each divided area and evaluate the transparency change degree of the divided area;

[0038] Step S4: Comprehensively judge whether to switch the particle migration processing in each divided area to particle resurrection based on the total operation loss and transparency change degree of the divided area.

[0039] The specific implementation is as follows:

[0040] In step S1, when obtaining the range of the special effect processing area, use an axis-aligned bounding box to enclose the special effect processing area, and take the area of the rectangle or square enclosed by the axis-aligned bounding box as the range of the special effect processing area;

[0041] An axis-aligned bounding box is a simple geometric shape processing tool whose sides are parallel to the coordinate axes and is used to enclose the outer boundary of a three-dimensional object.

[0042] When counting the number of particles within the special effect processing area, it is necessary to count two parts of particles. First, count the number of particles on the four boundaries of the special effect processing area, and then count the number of particles within the special effect processing area, and integrate the two parts of particle quantities to obtain the number of particles within the special effect processing area;

[0043] In a two-dimensional coordinate, the movement directions of each different angle are different. Count the movement angles of each particle within the special effect processing area, and set the particles with the same movement angle as the same-direction particles; the particles with different movement angles as different-direction particles;

[0044] When summarizing the different-direction frequencies of the particles, set a frequency value. Every time a different-direction particle is detected within the special effect processing area, the frequency value is incremented by one. After traversing all the particles within the special effect processing area, take the frequency value as the different-direction frequency;

[0045] The more the number of particles in the special effect processing area or the higher the different-direction frequency, the higher the operation complexity characteristics within the special effect processing area range, and the more special effect optimization is required.

[0046] Use the geometric mean method to evaluate the running complexity characteristics of the special effect processing area by integrating the number of particles and the anisotropic frequency: where S is the number of particles in the special effect processing area, P is the anisotropic frequency of the special effect processing area, and Y is the running complexity characteristic of the special effect processing area.

[0047] It should be noted that the geometric mean method is a statistical method used to calculate the geometric mean of a set of numerical values. In this example, it is used to calculate the complex characteristics of particle motion in the special effect processing area.

[0048] By introducing an axis-aligned bounding box to accurately delineate the special effect processing area, combining the statistical count of the number of particles on the region boundary and inside the region, and introducing the anisotropic frequency index, comprehensively reflect the motion state and distribution characteristics of the particles; further use the geometric mean method to fuse and evaluate the number of particles and the anisotropic frequency, effectively quantify the running complexity of the special effect processing area, thus providing a scientific basis for subsequent region division and special effect optimization processing, and improving the processing efficiency and dynamic adaptability of the visual special effect system in high-complexity scenarios.

[0049] In step S2, compare the running complexity characteristic of the particles in the special effect processing area with a preset particle complexity threshold. When the running complexity characteristic of the particles in the special effect processing area exceeds the particle complexity threshold, it is determined that the special effect processing area has high complexity characteristics; when the running complexity characteristic of the particles in the special effect processing area is lower than the particle complexity threshold, it is determined that the special effect processing area has low complexity characteristics.

[0050] When the evaluation result of the special effect processing area is high complexity characteristics, perform region division on the special effect processing area to obtain the area of the special effect processing area, randomly set the division ratio to perform region division on the special effect processing area, and use the ratio of the area of the special effect processing area to the division ratio as the area of each divided region.

[0051] For example, if the area of the special effect processing area is 100 cm² and the randomly set division ratio is 1 / 4, then the area of each divided region is 25 cm².

[0052] The particle state is divided into dynamic particles and static particles. Static particles are particles that do not move in the divided region, and dynamic particles are particles that move in the divided region. Count the number of dynamic particles and static particles, and use the ratio of the number of dynamic particles to the number of static particles as the dynamic-to-static particle ratio of the divided region;

[0053] The larger the dynamic-to-static particle ratio of the divided region, the more dynamic particles in the divided region, and the higher the cost of maintaining particle displacement in the divided region, and the more special effect optimization is required.

[0054] The optical flow method is used to detect the particle movement speed in each divided area, and the movement speed of the object is estimated by the change of the same particle point between adjacent frames: , where c is the displacement change of the same particle between adjacent frames, t is the switching time between adjacent frames, and vd is the particle movement speed in the divided area.

[0055] A complex feature judgment mechanism is introduced into the special effects processing method. By comparing the complex characteristics of particle movement with the preset particle complexity threshold, accurate identification of highly complex areas can be achieved. Then, the special effects area is adaptively divided based on the division ratio to improve the targetedness and efficiency of the processing. The dynamic and static particle ratio is combined to evaluate the activity of particle movement, and the optical flow method is used to accurately measure the particle movement speed, comprehensively capturing the changes in particle state, effectively improving the refinement and dynamic perception ability of special effects processing, and ensuring the smoothness of visual performance and optimization of system performance.

[0056] It should be noted that the optical flow method is a technology for calculating the motion of objects in image sequences, which estimates the speed and direction of the object by analyzing the pixel changes between adjacent frames.

[0057] In step S3, the particle movement speed in each divided area is detected according to the particle state of each divided area, the particle movement speed is processed, and the original particle movement speed sequence is smoothed by a sliding mean filter algorithm to obtain the average movement speed;

[0058] The specific calculation formula of smooth moving speed is expressed as:

[0059] ;

[0060] Where, is the original moving velocity of the particle, is the sliding window width, , For the The smooth moving speed of each particle after processing, For particles Sum the speed values within the surrounding window range;

[0061] The specific calculation formula of the average moving speed is expressed as:

[0062] ;

[0063] Where, is the number of particles in the divided area;

[0064] The average velocity and the ratio of dynamic and static particles in the corresponding divided area are normalized and substituted into the logistic regression formula to calculate the total operating loss of the divided area.

[0065] It should be noted that the methods of standardization include, but are not limited to, standard linear transformation based on interval scaling, Z-Score standardization method based on statistics, or normalization method based on non-linear mapping function. The application methods of standardization will not be elaborated here;

[0066] The specific formula is expressed as follows:

[0067] ;

[0068] In the formula, L is the result of logistic regression calculation, that is, the total running loss of the divided area, e is the natural base, y is the linear combination term of the logistic regression model, and specifically y is set as:

[0069] ;

[0070] In the formula, is the bias term, is the moving average speed, is the ratio of static and dynamic particles in the corresponding divided area, and are the regression coefficients of the moving average speed and the ratio of static and dynamic particles in the corresponding divided area respectively;

[0071] As can be seen from the above, when the moving average speed and the ratio of static and dynamic particles in the corresponding divided area are higher, the total running loss of the divided area is higher, which indicates that the running activity of the particles in this area and the system load level increase synchronously, easily causing the accelerated attenuation of the particle life cycle, affecting the persistence and stability of the visual effects in the area, and causing violent fluctuations in the particle transparency, etc., which will not be elaborated here;

[0072] After calculating the running loss of the particles in each divided area, in order to further evaluate the dynamic characteristic evolution of the particle system at the visual performance level, it is necessary to introduce the particle transparency attribute as a supplementary criterion. Since the change of transparency directly affects the persistence and perceptibility of the visual effects in the time domain, it and the running loss together constitute a dual judgment standard for the activity and energy consumption state of the particles in the divided area;

[0073] Specifically, the degree of transparency change in the divided area refers to the mean difference of the transparency attribute values of all particles in the area at two set detection time points, which is used to quantify the visible and invisible fluctuations of the particles in the area at the visual performance level;

[0074] Furthermore, the acquisition logic of the degree of transparency change in the divided area is to extract the transparency values of the particles in each divided area through two respectively set detection times, which are respectively recorded as the transparency set at the current detection time and the transparency set at the previous detection time, and perform a one-by-one difference calculation on the corresponding particle transparency values in each set to obtain the degree of transparency change in the divided area;

[0075] It should be noted that the settings of the two detection times were obtained by our experimenters based on the periodic change characteristics of the particle motion state and the refresh rate and inter-frame stability requirements of the visual effects system, and will not be elaborated here;

[0076] Furthermore, the calculation formula for the degree of transparency change of the divided area is:

[0077] ;

[0078] Where, To divide the transparency of the area, is the total number of particles involved in transparency calculation in the divided area, To indicate the Particles at the detection time The transparency property value of For the Particles at the detection time The transparency property value of For the The absolute change in transparency of a particle between two detection time points is used to reflect the degree of visibility fluctuation of a single particle;

[0079] Specifically, the greater the degree of transparency change in the divided area, the more drastic the change in the particle state in the area, and there are problems such as life cycle consumption and visual flicker instability;

[0080] It should be noted that the transparency value extraction of particles in each divided area is based on the fusion technology of image attribute perception and particle state encoding. The experimenters can change the extraction method based on factors such as the actual configuration of the system rendering mechanism, the particle life cycle management strategy, and the GPU buffer transparency parameter reading capability. There is no limitation on the method of extracting the transparency value and it will not be elaborated here.

[0081] In step S4, the total operating loss and transparency change degree of the divided areas are standardized;

[0082] It should be noted that the standardization process has been described in the above content and will not be repeated here;

[0083] Substitute the normalized total operating loss and transparency change of the divided area into the hyperbolic tangent mapping model to obtain the particle displacement coefficient;

[0084] Among them, the hyperbolic tangent mapping model is based on the smooth S-shaped response characteristics of the hyperbolic tangent function. It can still maintain interval compression and has the ability of nonlinear enhancement when processing two high-amplitude inputs, which is suitable for the current scenario;

[0085] Specifically, the hyperbolic tangent mapping model is expressed as:

[0086] ;

[0087] In the formula, is the particle migration coefficient, is the total operating loss of the divided area after normalization processing, is the degree of transparency change after normalization processing, and are adjustable mapping coefficients for the total operating loss of the divided area and the degree of transparency change after normalization processing, used to balance the influence of both;

[0088] As can be seen from the above, when the total operating loss of the divided area and the degree of transparency change are greater, the particle migration coefficient is greater, which indicates that the instability of the particle state in the current divided area increases, the dissipation rate of particle resources accelerates, and the existing particle migration processing mechanism is difficult to maintain the continuity and stability of visual effects. Therefore, it is necessary to further determine whether to switch the current particle migration method to particle resurrection processing to improve the coordination of visual performance and the efficiency of system operation;

[0089] Compare the particle migration coefficient with the preset migration threshold. If the particle migration coefficient is greater than or equal to the migration threshold, switch the particle migration processing in each divided area to particle resurrection. If the particle migration coefficient is less than the migration threshold, maintain the original particle migration processing mechanism;

[0090] It should be noted that the preset migration threshold is comprehensively set by the experimenter based on the analysis results of historical rendering stability samples, output parameters of the particle life cycle evaluation model, etc., which will not be elaborated here;

[0091] Among them, particle resurrection refers to the particle reconstruction and regeneration process automatically triggered by the system after the end of the particle life cycle or when the particle state reaches the visual effect fault tolerance limit. This process includes re-initializing the core parameters such as particle position, velocity, transparency, and life cycle, and re-injecting them into the current special effect scene to restore the particle density and visual consistency in the area, and ensure the continuity and stability of special effect performance. The particle resurrection mechanism can not only improve the overall dynamic expressiveness of the picture, but also prevent the occurrence of visual hole effect after a large number of particles decay or die.

[0092] Embodiment 2

[0093] Please refer to Figure 2 , a visual effect processing device, including a special effect evaluation device, a particle recognition device, an operation evaluation device, and a strategy control device;

[0094] The special effect evaluation device is used to obtain the range of the special effect processing area, count the number of particles within the special effect processing area, summarize the anisotropic frequencies of the particles, and evaluate the running complexity characteristics of the special effect processing area based on the particle quantity and the anisotropic frequencies;

[0095] The particle recognition device is used to divide the special effect processing area when the evaluation result of the special effect processing area is of high complexity characteristics, obtain the particle states of each divided area, calculate the ratio of static and dynamic particles, and detect the particle migration speed in each divided area;

[0096] The running evaluation device is used to process the particle migration speed in the divided area, calculate the total particle running loss of the divided area in combination with the ratio of static and dynamic particles in the corresponding divided area, set a detection time point to screen the transparency of the particles in each divided area, and evaluate the degree of transparency change of the divided area;

[0097] The strategy regulation device is used to comprehensively judge whether to switch the particle migration process in each divided area to particle resurrection based on the total running loss of the divided area and the degree of transparency change.

[0098] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0099] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by the combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and the inventive constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0100] In addition, the functional modules in each embodiment of this application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0101] As described above, only the specific implementation manners of this application are provided, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all such changes or substitutions should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0102] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A visual effects processing method, characterized in that: The following steps are involved: Step S1: Obtain the range of the special effect processing area, count the number of particles within the special effect processing area, summarize the anisotropic frequency of the particles, and evaluate the operational complexity of the special effect processing area based on the number of particles and the anisotropic frequency; Step S2: When the evaluation result of the special effect processing area is a high complexity feature, the special effect processing area is divided into regions, the particle state of each divided region is obtained, the dynamic and static particle ratio is calculated, and the particle movement speed in each divided region is detected; Step S3: Process the particle movement speed in the divided area, calculate the total particle operation loss in the divided area based on the dynamic and static particle ratio of the corresponding divided area, set the detection time point to screen the transparency of the particles in each divided area and evaluate the degree of transparency change of the divided area; Step S4: Comprehensively consider the total loss of the divided area operation and the degree of transparency change to determine whether to switch the particle shifting process in each divided area to particle resurrection.

2. The method for processing visual effects according to claim 1, wherein: In step S1, when counting the number of particles within the special effect processing area, the number of particles on the four boundaries of the special effect processing area is first counted, and then the number of particles within the special effect processing area is counted. The particle numbers of the two parts are integrated to obtain the number of particles within the special effect processing area; Particles with the same movement angle are set as isotropic particles; particles with different movement angles are set as anisotropic particles. The frequency value is set when summarizing the anisotropic frequency of particles. Every time an anisotropic particle is detected within the special effect processing area, the frequency value is increased by one. After traversing all particles within the special effect processing area, the frequency value is used as the anisotropic frequency.

3. The method for processing visual effects according to claim 2, wherein: In step S1, the geometric mean method is used to evaluate the operational complexity of the special effects processing area based on the number of particles and the frequency of anomalies: , where S is the number of particles in the special effect processing area, P is the anisotropic frequency in the special effect processing area, and Y is the operation complexity characteristics of the special effect processing area.

4. The visual effects processing method according to claim 3, wherein: In step S2, when the particle operation complexity feature of the special effect processing area exceeds a preset particle complexity threshold, the special effect processing area is judged to have a high complexity feature; otherwise, the special effect processing area is judged to have a low complexity feature.

5. The method for processing visual effects according to claim 1, wherein: In step S2, the particle state is divided into dynamic particles and static particles. The fine particles are particles that do not move in the divided area, and the dynamic particles are particles that move in the divided area. The ratio of the number of dynamic particles to the number of static particles is used as the dynamic-static particle ratio of the divided area.

6. The method for processing visual effects according to claim 5, wherein: In step S3, the particle movement speed in each divided area is detected according to the particle state of each divided area, the particle movement speed is processed, and the original particle movement speed sequence is smoothed by a sliding mean filter algorithm to obtain the average movement speed; The average moving speed and the ratio of dynamic and static particles in the corresponding divided area are standardized and substituted into the logistic regression formula to calculate the total operating loss of the divided area.

7. The method for processing visual effects according to claim 6, wherein: In step S3, the transparency values of the particles in each divided area are extracted by setting two detection times respectively, which are recorded as the current detection time transparency set and the previous detection time transparency set respectively. The transparency values of the corresponding particles in each set are calculated one by one to obtain the degree of transparency change of the divided area.

8. The method for processing visual effects according to claim 7, wherein: In step S4, the total operating loss and transparency change degree of the divided areas are standardized; The normalized total operating loss of the divided area and the degree of transparency change are substituted into the hyperbolic tangent mapping model to obtain the particle displacement coefficient.

9. The visual effects processing method according to claim 8, characterized in that: In step S4, the particle shift coefficient is compared with the preset shift threshold. If the particle shift coefficient is greater than or equal to the shift threshold, the particle shift processing in each divided area is switched to particle resurrection. If the particle shift coefficient is less than the shift threshold, the original particle shift processing mechanism is maintained.

10. A visual effects processing device, based on a visual effects processing method according to any one of claims 1 to 9, characterized in that: It includes special effect evaluation device, particle identification device, operation evaluation device and strategy control device; The special effect evaluation device is used to obtain the range of the special effect processing area, count the number of particles within the special effect processing area, summarize the anisotropic frequency of the particles, and evaluate the operational complexity of the special effect processing area based on the number of particles and the anisotropic frequency; The particle recognition device is used to divide the special effect processing area into regions when the evaluation result of the special effect processing area is a high-complexity feature, obtain the particle state of each divided area, calculate the dynamic and static particle ratio, and detect the particle movement speed in each divided area; The operation evaluation device is used to process the particle movement speed in the divided area, calculate the total particle operation loss in the divided area based on the dynamic and static particle ratio of the corresponding divided area, set the detection time point to screen the transparency of the particles in each divided area and evaluate the degree of transparency change of the divided area; The strategy control device is used to comprehensively determine whether to switch the particle shifting process in each divided area to particle resurrection based on the total operating loss of the divided area and the degree of transparency change.

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