Real-time animation generation method and system

Abstract

The invention discloses a real-time animation generation method and system, relates to the technical field of real-time animation generation, and solves the technical problems that in a data transmission link, data priority and network fluctuation are not fully considered, data delay or loss is easily caused, and the real-time performance and fluency of animations are seriously influenced. According to the method, the original data is preprocessed, noise interference is effectively removed, a high-quality data basis is provided for subsequent animation generation, statistical analysis is performed on the distribution condition of the preprocessed data, change data and conventional data are distinguished, key data are determined from the change data, the key data are analyzed from two aspects of importance and real-time performance, and the accuracy of animation generation is improved. And then the priority values of the key data are obtained and sorted, and data transmission is performed according to the priorities, so that the key data are ensured to be transmitted preferentially and timely, and the animation generation quality and fluency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of real-time animation generation, and specifically provides a real-time animation generation method and a generation system. Background Art

[0002] In the field of real-time animation generation, with the continuous expansion of animation application scenarios, such as real-time games, virtual live broadcasts, immersive interactive experiences, etc., higher requirements are put forward for the quality, fluency, and real-time performance of animation generation.

[0003] A patent application with the publication number CN107481303A discloses a real-time animation generation method and system, including the following steps: defining a variable set related to a moving object; collecting the object action information and generating first action data; collecting at least part of the data in the first action data; generating second action data according to the variable set and the at least part of the data in the collected first action data; and synthesizing an animation based on the second action data.

[0004] However, some existing generation methods have many limitations in data collection, processing, and transmission. The data processing process lacks effective classification and cannot accurately distinguish key data from conventional data, resulting in insufficient prominence in the presentation of important details of the animation. In the data transmission link, the data priority and network fluctuations are not fully considered, which easily causes data delay or loss, seriously affecting the real-time performance and fluency of the animation. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a real-time animation generation method and a generation system, which solve the problem that in the data transmission link, the data priority and network fluctuations are not fully considered, which easily causes data delay or loss, seriously affecting the real-time performance and fluency of the animation.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A real-time animation generation method, which specifically includes the following steps:

[0007] Step S1: Collect animation data and record it as original action data, and at the same time preprocess the original action data to obtain preprocessed data;

[0008] Step S2: Classify the preprocessed data according to its distribution to obtain variable data and conventional data, and at the same time analyze the change frequency of the variable data to determine the key data and conventional data after secondary classification;

[0009] S3: Analyze the importance and real-time performance of the key data, calculate the priority of the key data to determine the corresponding transmission order, and at the same time sort and mark according to the priority.

[0010] S4. Perform transmission processing on the sorted and marked key data. Determine parallel data by analyzing the parallel relationship between the key data and the regular data, and perform asynchronous transmission on the parallel data. At the same time, perform transmission analysis processing based on the transmission protocol and the transmission network situation to generate corresponding transmission analysis information.

[0011] As a further solution of the present invention, the specific manner of obtaining the preprocessed data in step S1 is as follows:

[0012] Obtain the original action data, and at the same time obtain the preprocessed data by performing denoising and filtering processing on the original action data. Among them, the denoising processing can be performed by mean filtering, median filtering, and wavelet denoising, and the filtering processing can be performed by low-pass filtering, high-pass filtering, and band-pass filtering.

[0013] As a further solution of the present invention, the specific manner of classifying the preprocessed data in step S2 to obtain the changed data and the regular data is as follows:

[0014] Obtain all the preprocessed data, then perform statistical analysis on the collected preprocessed data to obtain the distribution of the data. At the same time, determine the preprocessed data that changes within the normal range according to the data distribution and record it as the regular data, and record the preprocessed data that does not change within the normal range as the changed data.

[0015] As a further solution of the present invention, the specific manner of analyzing the change frequency of the changed data in step S2 to determine the key data and the regular data after secondary classification is as follows:

[0016] Obtain all the changed data, then calculate the corresponding change frequency of the changed data. According to the sampling frequency f s Collect the changed data and record it as the data sequence x(n), where n = 1, 2,..., N - 1. Here, n represents the number of sampling points. Then perform frequency domain conversion on the time domain data sequence x(n) using the FFT algorithm to obtain the spectrum X(k), where k = 1, 2,..., N - 1. At the same time, according to the sampling theorem, determine the range of the spectrum as [0, f s / 2], and calculate the actual frequency corresponding to the frequency point

[0017] Compare the actual frequency f k of the changed data with the preset value, and at the same time screen the changed data corresponding to the actual frequency f k greater than the preset value and record it as the key data, and record the changed data corresponding to the actual frequency f k less than the preset value as the regular data.

[0018] As a further solution of the present invention, the specific manner of analyzing the importance of the key data in step S3 is as follows:

[0019] Establish an animation feature vector Meanwhile, establish a key data feature vector Then, according to the cosine similarity formula Calculate to obtain the animation feature vector And the key data feature vector The cosine similarity value, and take the calculated cosine similarity value as the key data importance value Qi, and i = 1, 2,..., j, where j represents the number of key data.

[0020] As a further solution of the present invention, the specific manner of analyzing the real-time performance of the key data in step S3 is as follows:

[0021] Analyze the real-time performance of the key data to obtain the maximum delay corresponding to the key data i. Meanwhile, take the maximum delay as the delay tolerance value Gi of the key data i. Then, calculate the matching degree value between the key data i and the animation frame rate, obtain the time stamp ti corresponding to the key data i, and obtain the frame rate of the animation denoted as y. Meanwhile, calculate the frame time interval according to the frame rate y Then, for each key data point i, find the animation frame time t j = j×Δt, where j is an integer, and then calculate the time difference Δt i = |t i -t j |. Then, according to the formula Calculate the average time difference corresponding to the key data i, and denote it as the animation frame rate matching value Hi. And sum the calculated delay tolerance value Gi and the animation frame rate matching value Hi to obtain the key data real-time performance value Si.

[0022] As a further solution of the present invention, the specific manner of calculating the priority of the key data to determine the corresponding transmission order in step S3 is as follows:

[0023] Sum the key data importance value Qi and the key data real-time performance value Si to calculate the priority value Wi of the key data, and Wi = Qi + Si. Then, sort in descending order according to the key data priority value Wi, and mark the time stamps of the key data at the same time.

[0024] As a further solution of the present invention, the specific manner of performing transmission processing on the sorted and marked key data in step S4 is as follows:

[0025] Obtain all the key data and regular data, and determine the parallel data through data feature-based recognition. Meanwhile, perform asynchronous transmission on the obtained parallel data;

[0026] Next, obtain the transmission protocol, obtain the transmission format in the transmission protocol, and at the same time match the transmission format with the format of the key data. If the two are the same, transmit using the current transmission protocol. Conversely, if the two are different, select the transmission protocol based on the format of the key data to generate a standard transmission protocol. At the same time, obtain the transmission network situation, and obtain the maximum transmission speed peak and the minimum transmission speed peak corresponding to the transmission network situation within time T. Then calculate the difference between the two and record it as the peak difference. At the same time, compare the obtained peak difference with the preset difference;

[0027] If the peak difference is greater than the preset difference, it indicates that the transmission network situation is unstable, and at the same time generate an unstable transmission signal. Conversely, if the peak difference is less than the preset difference, it indicates that the transmission network situation is stable, and at the same time generate a stable transmission signal, and perform transmission processing according to the generated signals respectively.

[0028] As a further solution of the present invention, the specific manner of performing transmission processing according to the generated signal in step S4 is:

[0029] Perform transmission processing on the generated unstable transmission signal, obtain the minimum fluctuation range value corresponding to the transmission network situation within time t1, and use the minimum value corresponding to the minimum fluctuation range value as the standard transmission speed. Then, divide and transmit the key data according to the standard transmission speed;

[0030] Perform transmission processing on the generated stable transmission signal, obtain the average transmission speed corresponding to the transmission network situation within time T, and divide and transmit the key data based on the average transmission speed to generate transmission analysis information.

[0031] A real-time animation generation system includes a data acquisition module, a data preprocessing and classification module, a priority determination and analysis module, a transmission analysis module, and a transmission information output module;

[0032] The data acquisition module is used to collect animation data and transmit it to the data preprocessing and classification module;

[0033] The data preprocessing and classification module is used to preprocess the obtained animation data to obtain preprocessed data, and at the same time classify it according to the distribution of the preprocessed data to obtain variable data and regular data. At the same time, analyze the change frequency of the variable data to determine the key data and regular data after secondary classification, and transmit the key data and regular data to the priority determination and analysis module;

[0034] The priority determination and analysis module analyzes the importance and real-time nature of the key data, calculates the priority of the key data to determine the corresponding transmission order, and at the same time sorts and marks it according to the priority to generate sorted key data, and transmits it to the transmission analysis module;

[0035] A transmission analysis module, which is used to analyze the obtained sorting key data, determine parallel data by analyzing the parallel relationship between the key data and the conventional data, perform asynchronous transmission on the parallel data, and at the same time perform transmission analysis and processing based on the transmission protocol and the transmission network situation to generate corresponding transmission analysis information, and transmit the transmission analysis information to the transmission information output module;

[0036] A transmission information output module, which is used to transmit the animation data according to the obtained transmission analysis information.

[0037] The present invention provides a real-time animation generation method and a generation system. Compared with the prior art, it has the following beneficial effects:

[0038] By preprocessing the original data, the present invention effectively removes noise interference, provides a high-quality data basis for subsequent animation generation. Through statistical analysis of the distribution of the preprocessed data and calculation of the data change frequency, it can accurately distinguish the changing data from the conventional data, and further determine the key data from the changing data. Analyze the key data from the aspects of importance and timeliness, calculate the importance value by calculating the cosine similarity between the key data and the animation theme style, calculate the timeliness value in combination with the delay tolerance value and the animation frame rate matching value, and then obtain the priority value of the key data and sort it. Transmit the data according to the priority to ensure the priority and timely transmission of the key data, and improve the quality and smoothness of the animation generation. Description of the Drawings

[0039] Figure 1 It is a flowchart of the method steps of the present invention;

[0040] Figure 2 It is a block diagram of the system principle of the present invention. Detailed Embodiments

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0042] Example 1, please refer to Figure 1 , the present application provides a real-time animation generation method, which specifically includes the following steps:

[0043] Step S1: Collect the animation data. Select a high-performance motion capture device, such as a professional optical motion capture system like OptiTrack, which has high-precision sensors and fast data acquisition capabilities. It can obtain accurate motion data in a short time and record it as the original motion data. At the same time, preprocess the original motion data to obtain preprocessed data.

[0044] Obtain the original motion data. At the same time, perform denoising and filtering on the original motion data to obtain preprocessed data. Specific denoising can be carried out by methods such as mean filtering, median filtering, and wavelet denoising, while filtering can be carried out by methods such as low-pass filtering, high-pass filtering, and band-pass filtering. The specific processing method is selected by the operator.

[0045] Step S2: Classify the preprocessed data according to its distribution to obtain variable data and regular data. At the same time, analyze the change frequency of the variable data to determine the key data and regular data after secondary classification.

[0046] S21: Obtain all the preprocessed data. Then, perform statistical analysis on the collected preprocessed data to understand the data distribution, such as the average value and standard deviation of joint angles. Suppose in a set of motion data recording a person walking, the values of the right knee joint angle at multiple sampling points are 120°, 125°, 118°, 122°, etc. After calculation, the average value is 121°, which represents the average angle state of the right knee joint in this walking motion. At the same time, determine the preprocessed data that changes within the normal range according to the data distribution and record it as regular data, and record the preprocessed data that does not change within the normal range as variable data. For example, for the angle change of the left hip joint in the walking motion of normal adults, based on the statistics of a large number of existing normal walking motion data, its angle usually fluctuates between 90° and 130°. Then, the left hip joint angle data within this range is recognized as regular data. For example, in the above left hip joint angle data, if the angle value at a certain sampling point is 150°, which significantly exceeds the normal range of 90° - 130°, then this data point and a series of related data can be marked as variable data;

[0047] S22: Obtain all the variable data. Then, calculate the change frequency corresponding to the variable data. According to the sampling frequency f s Collect the variable data and record it as the data sequence x(n), where n = 1, 2,..., N - 1, and n represents the number of sampling points. Then, perform frequency domain conversion on the time domain data sequence x(n) using the FFT algorithm to obtain the spectrum X(k), where k = 1, 2,..., N - 1. At the same time, according to the sampling theorem, determine the range of the spectrum as [0, f s / 2], and calculate the actual frequency corresponding to the frequency point

[0048] S23. Compare the actual frequency f corresponding to the changed data with a preset value. The specific value of the preset value is set by the operator. Meanwhile, filter the actual frequency f k and record the changed data where the actual frequency f is greater than the preset value as key data, and record the changed data where the actual frequency f k is less than the preset value as regular data. k j

[0049] Step S3. Analyze the importance and timeliness of the key data, calculate the priority of the key data to determine the corresponding transmission order, and sort and mark them according to the priority.

[0050] Obtain the key data, and then analyze the key data from the aspects of importance and timeliness respectively;

[0051] S31. Analyze the importance of the key data and establish an animation feature vector and the feature vector here represents the vector information corresponding to the theme and style information of the animation. Meanwhile, establish a key data feature vector For example, for an animation with a martial arts style, the feature vector of the animation theme and style may include dimensions such as the stiffness of the movements, the speed, and the smoothness of the moves; and the key data may be the movement trajectories of the character's limbs, the changes in joint angles, etc. Convert these key data into corresponding feature vectors, such as the curvature of the movement trajectory, the amplitude of the joint angle change, etc. Then, according to the cosine similarity formula calculate the cosine similarity value between the animation feature vector and the key data feature vector and take the calculated cosine similarity value as the importance value Qi of the key data, where i = 1, 2,..., j, and j represents the number of key data;

[0052] S32. Analyze the timeliness of the key data, obtain the maximum delay corresponding to the key data i, and take the maximum delay as the delay tolerance value Gi of the key data i. Then, calculate the matching degree value between the key data i and the animation frame rate, obtain the time stamp ti corresponding to the key data i, and obtain the frame rate of the animation denoted as y. Meanwhile, calculate the frame time interval according to the frame rate y Then, for each key data point i, find the animation frame time t j = j×Δt, where j is an integer, and then calculate the time difference Δt i = |t i - t j |. Then, according to the formula Calculate the average time difference corresponding to the key data i, denote it as the animation frame rate matching value Hi, and sum the calculated latency tolerance value Gi and the animation frame rate matching value Hi to obtain the key data real-time value Si. The specific calculation formula is Si = Gi × a 1 + Hi × a 2 , where a 1 and a 2 are the corresponding weight coefficients, and the specific values are set by the operator.

[0053] S33. Sum the key data importance value Qi and the key data real-time value Si to calculate the priority value Wi of the key data, and Wi = Qi + Si. Sort the key data in descending order according to the key data priority value Wi, and mark the timestamps of the key data at the same time.

[0054] Step S4. Perform transmission processing on the sorted and marked key data. Determine the parallel data by analyzing the parallel relationship between the key data and the regular data, and perform asynchronous transmission on the parallel data. At the same time, perform transmission analysis processing based on the transmission protocol and the transmission network situation to generate corresponding transmission analysis information.

[0055] Obtain all the key data and regular data, and determine the parallel data through identification based on data characteristics. Specifically, parallel data usually has similar data structures and formats. For example, in text data, parallel sentences or paragraphs may have the same grammatical structure, part-of-speech distribution, or follow similar patterns. Taking English-Chinese bilingual parallel corpora as an example, the corresponding sentences may have certain similarities in word order and the arrangement of sentence components. Parallel data is closely related in content and theme, often describing the same or similar things, concepts, events, etc. At the same time, perform asynchronous transmission on the obtained parallel data;

[0056] Then obtain the transmission protocol, obtain the transmission format in the transmission protocol, and match the transmission format with the format of the key data. If the two are the same, transmit using the current transmission protocol. Conversely, if the two are different, select the transmission protocol based on the format of the key data to generate a standard transmission protocol. At the same time, obtain the transmission network situation, and obtain the maximum transmission speed peak and the minimum transmission speed peak corresponding to the transmission network situation within time T. Then calculate the difference between the two and denote it as the peak difference. At the same time, compare the obtained peak difference with the preset difference, and the specific value of the preset difference is set by the operator;

[0057] If the peak difference is greater than the preset difference, it means that the transmission network situation is unstable, and at the same time, generate an unstable transmission signal. Conversely, if the peak difference is less than the preset difference, it means that the transmission network situation is stable, and at the same time, generate a stable transmission signal, and perform transmission processing according to the generated signals respectively;

[0058] Perform transmission processing on the generated unstable transmission signal, obtain the minimum fluctuation range value corresponding to the transmission network situation within time t1, and use the minimum value corresponding to the minimum fluctuation range value as the standard transmission speed. Then, segment and transmit the key data according to the standard transmission speed;

[0059] Perform transmission processing on the generated stable transmission signal, obtain the average transmission speed corresponding to the transmission network situation within time T, and segment and transmit the key data based on the average transmission speed to generate transmission analysis information; specifically, here the key data is segmented and transmitted according to its priority, and the processing method for normal data is the same as that for key data.

[0060] Embodiment 2, please refer to Figure 2 , this application provides a real-time animation generation system, including: an action data acquisition module, a data preprocessing and classification module, a priority determination and analysis module, a transmission analysis module, and a transmission information output module. At the same time, combined with Figure 2 It can be known that the above functional modules are unidirectionally electrically connected.

[0061] The data acquisition module is used to collect animation data and transmit it to the data preprocessing and classification module;

[0062] The data preprocessing and classification module is used to preprocess the obtained animation data to obtain preprocessed data, and at the same time classify it according to the distribution of the preprocessed data to obtain variable data and regular data. At the same time, analyze the change frequency of the variable data to determine the key data and regular data after secondary classification, and transmit the key data and regular data to the priority determination and analysis module. The specific generation method is the same as the processing process of step S2 in Embodiment 1;

[0063] The priority determination and analysis module analyzes the importance and real-time nature of the key data, calculates the priority of the key data to determine the corresponding transmission order, sorts and marks it according to the priority to generate sorted key data, and transmits it to the transmission analysis module. The specific generation method is the same as the processing process of step S3 in Embodiment 1;

[0064] The transmission analysis module is used to analyze the obtained sorted key data, determine parallel data by analyzing the parallel relationship between the key data and the regular data, asynchronously transmit the parallel data, and perform transmission analysis and processing based on the transmission protocol and the transmission network situation to generate corresponding transmission analysis information. At the same time, transmit the transmission analysis information to the transmission information output module. The specific processing method is the same as the processing process of step S4 in Embodiment 1;

[0065] A transmission information output module, which is used to transmit animation data according to the obtained transmission analysis information.

[0066] Some of the data in the above formula are used for numerical calculation by taking their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0067] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A real-time animation generation method, characterized in that: The method specifically comprises the following steps: Step S1, collecting animation data and recording it as original motion data, and preprocessing the original motion data to obtain preprocessed data; Step S2: classify the preprocessed data according to its distribution to obtain the changed data and the regular data, and analyze the change frequency of the changed data to determine the key data and the regular data after secondary classification; S3. Analyze the importance and real-time nature of key data, calculate the priority of key data, determine the corresponding transmission order, and sort and mark them according to the priority; S4. Perform transmission processing on the sorted and marked key data, determine the parallel data by analyzing the parallel relationship between the key data and the regular data, and asynchronously transmit the parallel data. At the same time, perform transmission analysis processing based on the transmission protocol and the transmission network conditions to generate corresponding transmission analysis information.

2. A real-time animation generation method according to claim 1, characterized in that: The specific method of obtaining the pre-processed data in step S1 is: The original motion data is obtained, and pre-processed data is obtained by denoising and filtering the original motion data. The denoising process can be processed by mean filtering, median filtering and wavelet denoising, and the filtering process can be processed by low-pass filtering, high-pass filtering and band-pass filtering.

3. A real-time animation generation method according to claim 1, characterized in that: The specific method of classifying the pre-processed data to obtain the changed data and the normal data in step S2 is: All preprocessed data are obtained, and then statistical analysis is performed on the collected preprocessed data to obtain the distribution of the data. At the same time, the preprocessed data that is within the normal range of variation is determined based on the data distribution and recorded as regular data, and the preprocessed data that is not within the normal range of variation is recorded as changed data.

4. A real-time animation generation method according to claim 1, characterized in that: The specific method of step S2 for analyzing the change frequency of the change data to determine the key data and regular data after secondary classification is: Get all the change data, then calculate the change frequency corresponding to the change data, according to the sampling frequency f s The changing data is collected and recorded as a data sequence x(n), where n = 1, 2, ..., N-1, where n represents the number of sampling points. Then, the time domain data sequence x(n) is converted into the frequency domain using the FFT algorithm to obtain the spectrum X(k), where k = 1, 2, ..., N-1. At the same time, according to the sampling theorem, the range of the spectrum is determined to be [0, f s / 2] and calculate the actual frequency corresponding to the frequency point The actual frequency f corresponding to the change data k Compare with the preset value and filter the actual frequency f k The change data corresponding to the preset value is recorded as the key data, and the actual frequency f k The change data corresponding to the value less than the preset value is recorded as normal data.

5. A real-time animation generation method according to claim 1, characterized in that: The specific method of analyzing the importance of key data in step S3 is: Building the animation feature vector At the same time, establish key data feature vector Then according to the cosine similarity formula Calculate the animation feature vector With key data feature vector The cosine similarity value is calculated, and the calculated cosine similarity value is used as the key data importance value Qi, and i = 1, 2, ..., j, where j represents the number of key data.

6. A real-time animation generation method according to claim 1, characterized in that: The specific method of analyzing the real-time performance of key data in step S3 is: Analyze the real-time performance of key data, obtain the maximum delay corresponding to key data i, and use the maximum delay as the delay tolerance value Gi of key data i. Then calculate the matching value between key data i and animation frame rate, obtain the timestamp ti corresponding to key data i, and obtain the frame rate of animation as y, and calculate the frame time interval according to the frame rate y. Then for each key data point i, find the animation frame time t closest to it j =j×Δt, where j is an integer, and then calculate the time difference Δt i =|t i -t j |, then according to the formula The average time difference corresponding to the key data i is calculated and recorded as the animation frame rate matching value Hi, and the calculated delay tolerance value Gi and the animation frame rate matching value Hi are summed to obtain the key data real-time value Si.

7. A real-time animation generation method according to claim 1, characterized in that: The specific method of calculating the priority of key data and determining the corresponding transmission order in step S3 is: The key data importance value Qi and the key data real-time value Si are summed to obtain the key data priority value Wi, and Wi=Qi+Si. The key data are sorted from large to small according to the key data priority value Wi, and the timestamp of the key data is marked.

8. A real-time animation generation method according to claim 1, characterized in that: The specific method of the step S4 for transmitting and processing the sorted and marked key data is as follows: Obtain all key data and regular data, determine parallel data by identifying data features, and asynchronously transmit the obtained parallel data; Then, the transmission protocol is obtained, and the transmission format in the transmission protocol is obtained, and the transmission format is matched with the format of the key data. If the two are the same, the current transmission protocol is used for transmission. Otherwise, if the two are different, the transmission protocol is selected based on the format of the key data to generate a standard transmission protocol. At the same time, the transmission network situation is obtained, and the maximum transmission speed peak value and the minimum transmission speed peak value corresponding to the transmission network situation within the time T are obtained. Then the difference between the two is calculated and recorded as the peak difference, and the obtained peak difference is compared with the preset difference; If the peak difference is greater than the preset difference, it means that the transmission network is unstable and an unstable transmission signal is generated. Conversely, if the peak difference is less than the preset difference, it means that the transmission network is stable and a stable transmission signal is generated, and transmission processing is performed according to the generated signals.

9. A real-time animation generation method according to claim 8, characterized in that: The specific method of performing transmission processing according to the generated signal in step S4 is: The generated unstable transmission signal is processed for transmission, the minimum fluctuation range value corresponding to the transmission network condition within time t1 is obtained, and the minimum value corresponding to the minimum fluctuation range value is used as the standard transmission speed, and then the key data is segmented and transmitted according to the standard transmission speed; The generated stable transmission signal is processed for transmission, the average transmission speed corresponding to the transmission network situation within the time T is obtained, and the key data is segmented and transmitted based on the average transmission speed to generate transmission analysis information.

10. A real-time animation generation system, used to execute a real-time animation generation method according to any one of claims 1 to 9, characterized in that: It includes a data acquisition module, a data preprocessing classification module, a priority determination analysis module, a transmission analysis module and a transmission information output module; A data acquisition module is used to collect animation data and transmit it to a data preprocessing and classification module; A data preprocessing classification module is used to preprocess the acquired animation data to obtain preprocessed data, and classify the preprocessed data according to its distribution to obtain change data and regular data, and analyze the change frequency of the change data to determine the key data and regular data after secondary classification, and transmit the key data and regular data to the priority determination analysis module; The priority determination and analysis module is used to analyze the importance and real-time nature of key data, calculate the priority of key data, determine the corresponding transmission order, sort and mark according to the priority, generate sorted key data, and transmit it to the transmission analysis module; The transmission analysis module is used to analyze the obtained sorting key data, determine the parallel data by analyzing the parallel relationship between the key data and the regular data, and asynchronously transmit the parallel data. At the same time, it performs transmission analysis processing based on the transmission protocol and the transmission network situation, generates corresponding transmission analysis information, and transmits the transmission analysis information to the transmission information output module; The transmission information output module is used to transmit the animation data according to the acquired transmission analysis information.

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