Polling processing system applying big data analysis

Abstract

The invention relates to a polling processing system applying big data analysis. The polling processing system comprises a model building device, a data entry device, an information analysis mechanism, a big data analysis mechanism and a polling processing mechanism. A big data analysis mechanism can be introduced to improve the effect and efficiency of computer processing.

Description

Technical Field

[0001] The present invention relates to the field of big data analysis, and in particular to a polling processing system applying big data analysis. Background Art

[0002] Big data analysis refers to the analysis of huge amounts of data. Big data can be summarized as 5Vs: Volume, Velocity, Variety, Value, and Veracity. Big data is the hottest term in the IT industry today, and the use of the commercial value of big data, such as data warehouses, data security, data analysis, and data mining, has gradually become the focus of industry professionals. With the advent of the big data era, big data analysis has also emerged.

[0003] In the prior art, CN119322722A discloses a computer performance evaluation system and method based on big data and AI. It includes a request processing unit, a calculation time unit, a subset impact unit, and a performance evaluation unit. The subset impact unit calculates the response time long subset feature value based on the extracted feature value, and then calculates the comprehensive impact based on the calculated response time long subset feature value. The technical performance evaluation unit evaluates the probability of computer performance delay based on the calculated comprehensive impact and the calculated average response time. When the evaluated performance delay probability is greater than 1, it means that the extracted feature value has a significant impact on the calculated average response time in the response time long subset, and the computer performance has a delay effect. By evaluating the computer performance delay based on the comprehensive impact, it is possible to accurately identify that the feature has a significant impact on the response time, reduce the impact of subjective judgment, and provide a more accurate delay probability evaluation.

[0004] CN119324836A discloses a computer network intelligent analysis platform based on big data. The invention relates to the field of computer network technology and solves the problem that the network fluctuations generated by the relevant access items of the computer itself are not taken into account, resulting in errors in the abnormal situation assessed. The technology sets two processing stages to consider and analyze the data access waveform of the computer itself in advance to lock the regular waveform existing in the access waveform. For a single-wave waveform, feature confirmation is directly performed, and the regular waveform is locked based on the features of the corresponding waveform and the periodic interval. For a multi-wave waveform, feature confirmation is performed synchronously to identify whether it is a regular waveform. If it is not a regular waveform, waveform division is performed to decompose it into several single-wave waveforms, and relevant confirmation is performed on whether the single-wave waveform is a regular waveform. This related method of analysis and confirmation processing can achieve a more accurate regular waveform related confirmation effect.

[0005] CN119204744A discloses a computer data processing method and system based on big data. First, historical production and sales data and market fluctuation data are collected, and a first data set and a second data set are constructed respectively, and data processing is performed. Secondly, a raw material demand forecasting model is constructed using the data in the first data set. Then, a product score forecasting model is constructed using the second data set to obtain the product market score. Next, a cost objective function is constructed based on the raw material demand and the first data set to obtain the minimum cost value; a revenue objective function is constructed based on the first data set and the product market score to obtain the maximum revenue value; a replenishment objective function is constructed based on the first data set and the raw material demand to obtain the minimum replenishment value. Finally, the cost objective function, the revenue objective function and the replenishment objective function are optimized and calculated to obtain the optimal solution, thereby obtaining a supply chain strategy with minimum cost, maximum revenue and minimum replenishment. Summary of the invention

[0006] In order to solve the technical problems in the prior art, the present invention provides a polling processing system using big data analysis, which can introduce a big data analysis mechanism for intelligently analyzing whether a previous video frame is suitable as an identifier of an inter-frame coding reference frame for the red and green components of a current frame to be encoded, and also introduce a polling processing mechanism connected to the big data analysis mechanism, which is used to execute the intelligent analysis of the big data analysis mechanism on each previous video frame before the current frame to be encoded in a set order until a previous video frame suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded is obtained, wherein the set order is that the closer the previous video frame is to the current frame to be encoded, the earlier the order of executing the intelligent analysis is, so that before executing the inter-frame coding operation of the red and green components, the polling intelligent analysis mechanism is used to automatically search out the previous video frame suitable as the inter-frame coding reference frame for the red and green components of the current frame to be encoded in advance, thereby improving the speed, efficiency and quality of the entire inter-frame coding operation.

[0007] According to the present invention, a polling processing system for applying big data analysis is provided, the system comprising: A model building device, used for an AI analysis model corresponding to a radial basis neural network mapping, wherein the AI ​​analysis model corresponding to the radial basis neural network mapping includes: training the radial basis neural network for each time to obtain a radial basis neural network after each training and outputting it as an AI analysis model, wherein the number of times the radial basis neural network is trained is monotonically positively correlated with the number of noise types of a current frame to be encoded; A data entry device is used to obtain the red and green component values ​​corresponding to the respective pixel points of the current frame to be encoded output by the visual monitoring device, wherein each pixel point of the current frame to be encoded has a red and green component value, a black and white component value, and a yellow and blue component value in the LAB color space; The information parsing mechanism is used to obtain the number of repeated values ​​in each red and green component value corresponding to each pixel point of the current frame to be encoded as a first parsing number, and obtain the number of repeated values ​​in each red and green component value corresponding to each pixel point of a previous video frame before the current frame to be encoded as a second parsing number; A big data analysis mechanism, connected to the model building device, the data entry device and the information analysis mechanism respectively, for synchronously inputting the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a certain previous video frame before the current frame to be encoded, the first analysis quantity and the second analysis quantity into the AI ​​analysis model, so as to execute the AI ​​analysis model and obtain an identification output by the AI ​​analysis model indicating whether the certain previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded; A polling processing mechanism, connected to the big data analysis mechanism, for executing the intelligent analysis of the big data analysis mechanism on each preceding video frame before the current frame to be encoded in a set order until a preceding video frame suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded is obtained; The intelligent analysis of the big data analysis mechanism is performed on each preceding video frame before the current frame to be encoded in a set order until a preceding video frame suitable for serving as an inter-frame coding reference frame for red and green components of the current frame to be encoded is obtained, including: the set order is that the closer the preceding video frame is to the current frame to be encoded, the earlier the intelligent analysis is performed; The setting order is that the closer the preceding video frame is to the current frame to be encoded, the earlier the order of executing intelligent analysis is, including: the closer the visual acquisition time is to the visual acquisition time of the current frame to be encoded, the earlier the order of executing intelligent analysis is; Among them, the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first resolution quantity and the second resolution quantity are synchronously input into the AI ​​analysis model to execute the AI ​​analysis model, and the identification output by the AI ​​analysis model indicating whether the previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded includes: when the inter-frame coding operation of the red and green components of the current frame to be encoded is performed using the previous video frame as the inter-frame coding reference frame for the red and green components of the current frame to be encoded, and the deviation between the obtained reconstructed frame of the red and green components and the original frame of the red and green components of the current frame to be encoded is less than the set difference limit, it is judged that the previous video frame is suitable as an identification of the inter-frame coding reference frame for the red and green components of the current frame to be encoded.

[0008] The present invention has at least the following four important invention points: First: an AI analysis model corresponding to the radial basis neural network mapping is mapped, wherein the radial basis neural network is trained each time to obtain the radial basis neural network after each training and output as the AI ​​analysis model, and the number of times the radial basis neural network is trained is monotonically positively correlated with the number of noise types of the current frame to be encoded, thereby completing the customized structural design of the AI ​​analysis model; Second: obtain the red and green component values ​​corresponding to each pixel of the current frame to be encoded output by the visual monitoring device, each pixel of the current frame to be encoded has a red and green component value, a black and white component value and a yellow and blue component value in the LAB color space, obtain the number of repeated values ​​in the red and green component values ​​corresponding to each pixel of the current frame to be encoded as the first analysis number, obtain the number of repeated values ​​in the red and green component values ​​corresponding to each pixel of a previous video frame before the current frame to be encoded as the second analysis number, so as to screen out valuable basic information for subsequent intelligent analysis; Third: introducing a big data analysis mechanism to synchronously input the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first analysis quantity and the second analysis quantity into the AI ​​analysis model to execute the AI ​​analysis model and obtain an identification output by the AI ​​analysis model indicating whether the previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded; Fourth: introducing a polling processing mechanism connected to the big data analysis mechanism, which is used to execute the intelligent analysis of the big data analysis mechanism on each preceding video frame before the current frame to be encoded in a set order until a preceding video frame suitable for being an inter-frame coding reference frame of the red and green components of the current frame to be encoded is obtained, wherein the set order is that the closer the preceding video frame is to the current frame to be encoded, the earlier the order of executing the intelligent analysis is, so that before executing the inter-frame coding operation of the red and green components, the polling intelligent analysis mechanism is used to automatically search out the preceding video frame suitable for being an inter-frame coding reference frame of the red and green components of the current frame to be encoded in advance, thereby improving the speed, efficiency and quality of the entire inter-frame coding operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein: Figure 1 Schematic diagram of the internal structure of a polling processing system for applying big data analysis according to implementation A of the present invention. DETAILED DESCRIPTION

[0010] At present, there are relatively broad application fields of big data analysis that need to be enriched and expanded. For example, image processing technicians hope to design a method that can predict whether each preceding video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded. In this way, all preceding video frames of the current frame to be encoded can be polled, so as to automatically search in advance for preceding video frames that are suitable as inter-frame coding reference frames for the red and green components of the current frame to be encoded before inter-frame encoding of the current frame to be encoded, thereby improving the speed, efficiency and quality of the entire inter-frame coding operation. However, there is no corresponding technical solution in the prior art.

[0011] The implementation scheme of the polling processing system for applying big data analysis of the present invention will be described in detail below with reference to the accompanying drawings.

[0012] Figure 1 This is a schematic diagram of the internal structure of a polling processing system for applying big data analysis according to Embodiment A of the present invention, wherein the system comprises: A model building device, used for an AI analysis model corresponding to a radial basis neural network mapping, wherein the AI ​​analysis model corresponding to the radial basis neural network mapping includes: training the radial basis neural network for each time to obtain a radial basis neural network after each training and outputting it as an AI analysis model, wherein the number of times the radial basis neural network is trained is monotonically positively correlated with the number of noise types of a current frame to be encoded; Specifically, the model building device is used for an AI analysis model corresponding to a radial basis neural network mapping, and the AI ​​analysis model corresponding to the radial basis neural network mapping includes: training the radial basis neural network for each time to obtain the radial basis neural network after each training and outputting it as the AI ​​analysis model, and the number of times the radial basis neural network is trained is monotonically positively associated with the number of noise types of the current frame to be encoded, including: selecting a numerical mapping function to represent the numerical mapping relationship in which the number of times the radial basis neural network is trained is monotonically positively associated with the number of noise types of the current frame to be encoded; A data entry device is used to obtain the red and green component values ​​corresponding to the respective pixel points of the current frame to be encoded output by the visual monitoring device, wherein each pixel point of the current frame to be encoded has a red and green component value, a black and white component value, and a yellow and blue component value in the LAB color space; The information parsing mechanism is used to obtain the number of repeated values ​​in each red and green component value corresponding to each pixel point of the current frame to be encoded as a first parsing number, and obtain the number of repeated values ​​in each red and green component value corresponding to each pixel point of a previous video frame before the current frame to be encoded as a second parsing number; A big data analysis mechanism, connected to the model building device, the data entry device and the information analysis mechanism respectively, for synchronously inputting the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a certain previous video frame before the current frame to be encoded, the first analysis quantity and the second analysis quantity into the AI ​​analysis model, so as to execute the AI ​​analysis model and obtain an identification output by the AI ​​analysis model indicating whether the certain previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded; A polling processing mechanism, connected to the big data analysis mechanism, for executing the intelligent analysis of the big data analysis mechanism on each preceding video frame before the current frame to be encoded in a set order until a preceding video frame suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded is obtained; The intelligent analysis of the big data analysis mechanism is performed on each preceding video frame before the current frame to be encoded in a set order until a preceding video frame suitable for serving as an inter-frame coding reference frame for red and green components of the current frame to be encoded is obtained, including: the set order is that the closer the preceding video frame is to the current frame to be encoded, the earlier the intelligent analysis is performed; The setting order is that the closer the preceding video frame is to the current frame to be encoded, the earlier the order of executing intelligent analysis is, including: the closer the visual acquisition time is to the visual acquisition time of the current frame to be encoded, the earlier the order of executing intelligent analysis is; Wherein, the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a certain previous video frame before the current frame to be encoded, the first resolution quantity and the second resolution quantity are synchronously input into the AI ​​analysis model to execute the AI ​​analysis model, and the identification output by the AI ​​analysis model indicating whether the certain previous video frame is suitable as the inter-frame coding reference frame of the red and green components of the current frame to be encoded is obtained, including: when the inter-frame coding operation of the red and green components of the current frame to be encoded is performed on the current frame to be encoded using the certain previous video frame as the inter-frame coding reference frame of the red and green components of the current frame to be encoded, and the deviation between the obtained reconstructed frame of the red and green components and the original frame of the red and green components of the current frame to be encoded is less than the set difference limit, it is judged that the certain previous video frame is suitable as the identification of the inter-frame coding reference frame of the red and green components of the current frame to be encoded; Wherein, when the inter-frame coding operation of the red and green components of the current frame to be coded is performed on the current frame to be coded by using the certain preceding video frame as the inter-frame coding reference frame of the red and green components of the current frame to be coded, and the deviation between the obtained reconstructed frame of the red and green components and the original frame of the red and green components of the current frame to be coded is less than the set difference limit, judging that the certain preceding video frame is suitable as the identification of the inter-frame coding reference frame of the red and green components of the current frame to be coded includes: performing an operation of taking the absolute value of the difference between the red and green component values ​​of the pixel points at the same position of the reconstructed frame of the red and green components and the original frame of the red and green components of the current frame to be coded to obtain the absolute value of each difference corresponding to each position, and accumulating the absolute value of each difference to obtain the deviation between the reconstructed frame of the red and green components and the original frame of the red and green components of the current frame to be coded; The radial basis neural network is trained for each time to obtain a radial basis neural network after each training and output as an AI analysis model, and the number of times the radial basis neural network is trained is monotonically positively correlated with the number of noise types of the current frame to be encoded, including: using a content conversion formula to represent a content conversion relationship in which the number of times the radial basis neural network is trained is monotonically positively correlated with the number of noise types of the current frame to be encoded; Wherein, the data entry device is used to obtain the respective red and green component values ​​corresponding to the respective pixel points of the current frame to be encoded output by the visual monitoring device, and each pixel point of the current frame to be encoded has a red and green component value, a black and white component value, and a yellow and blue component value in the LAB color space, including: the value range of any color component value of the red and green component value, the black and white component value, and the yellow and blue component value of each pixel point of the current frame to be encoded in the LAB color space is between 0 and 255; The information parsing mechanism is used to obtain the number of repeated values ​​in each red and green component value corresponding to each pixel point of the current frame to be encoded as the first parsing number, and obtain the number of repeated values ​​in each red and green component value corresponding to each pixel point of a previous video frame before the current frame to be encoded as the second parsing number, including: a previous video frame before the current frame to be encoded is also output by the visual monitoring device and its visual acquisition time is earlier than the visual acquisition time of the current frame to be encoded; The step of synchronously inputting the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first analysis number and the second analysis number into the AI ​​analysis model to execute the AI ​​analysis model, and obtaining the identification of whether the previous video frame is suitable as the inter-frame coding reference frame of the red and green components of the current frame to be encoded output by the AI ​​analysis model also includes: using multiple different data processing channels to respectively implement the step of synchronously inputting the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first analysis number and the second analysis number into the AI ​​analysis model to execute the AI ​​analysis model, and obtaining the identification of whether the previous video frame is suitable as the inter-frame coding reference frame of the red and green components of the current frame to be encoded output by the AI ​​analysis model, and corresponding to multiple data processing processes; Among them, a plurality of different data processing channels are used to respectively input the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a certain previous video frame before the current frame to be encoded, the first resolution quantity and the second resolution quantity to the AI ​​analysis model synchronously, so as to execute the AI ​​analysis model, and obtain the multiple decomposition steps of the step of indicating whether the certain previous video frame is suitable as an inter-frame coding reference frame of the red and green components of the current frame to be encoded output by the AI ​​analysis model, and the multiple data processing processes respectively corresponding to the steps include: the multiple different data processing channels occupy the same signal control channel; Among them, a plurality of different data processing channels are used to respectively input the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a certain previous video frame before the current frame to be encoded, the first resolution quantity and the second resolution quantity to the AI ​​analysis model to execute the AI ​​analysis model, and the plurality of decomposition steps of the step of obtaining the identification of whether the certain previous video frame is suitable as the inter-frame coding reference frame of the red and green components of the current frame to be encoded output by the AI ​​analysis model respectively correspond to the plurality of data processing processes including: the plurality of different data processing channels use the same data buffer; Among them, a plurality of different data processing channels are used to respectively input the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a certain previous video frame before the current frame to be encoded, the first resolution quantity and the second resolution quantity to the AI ​​analysis model synchronously, so as to execute the AI ​​analysis model, and obtain the multiple decomposition steps of the step of indicating whether the certain previous video frame is suitable as an inter-frame coding reference frame of the red and green components of the current frame to be encoded output by the AI ​​analysis model, and the multiple data processing processes respectively corresponding to the steps include: the multiple different data processing channels occupy different data processing processes; Among them, a plurality of different data processing channels are used to respectively realize the synchronous input of the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a certain previous video frame before the current frame to be encoded, the first resolution quantity and the second resolution quantity into the AI ​​analysis model to execute the AI ​​analysis model, and the plurality of decomposition steps of the step of obtaining the identification of whether the certain previous video frame output by the AI ​​analysis model is suitable as the inter-frame coding reference frame of the red and green components of the current frame to be encoded respectively correspond to the plurality of data processing processes, including: the plurality of different data processing channels occupy the same data storage space; And wherein, the step of synchronously inputting the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first analysis number and the second analysis number into the AI ​​analysis model to execute the AI ​​analysis model, and obtaining the identification of whether the previous video frame is suitable as the inter-frame coding reference frame of the red and green components of the current frame to be encoded output by the AI ​​analysis model also includes: synchronously inputting the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first analysis number and the second analysis number into the AI ​​analysis model to execute the AI ​​analysis model, and obtaining the identification of whether the previous video frame is suitable as the inter-frame coding reference frame of the red and green components of the current frame to be encoded output by the AI ​​analysis model. The operation fault type of the step is monitored and analyzed by the fault detection process; And wherein, the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first analysis number and the second analysis number are synchronously input into the AI ​​analysis model to execute the AI ​​analysis model, and the step of obtaining the output of the AI ​​analysis model indicating whether the previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded also includes: the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first analysis number and the second analysis number are synchronously input into the AI ​​analysis model to execute the AI ​​analysis model, and the step of obtaining the output of the AI ​​analysis model indicating whether the previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded is captured and displayed in real time by the display process.

[0013] In addition, in the polling processing system that applies big data analysis, the information parsing mechanism is used to obtain the number of repeated values ​​in each red and green component value corresponding to each pixel point of the current frame to be encoded as a first parsing number, and obtain the number of repeated values ​​in each red and green component value corresponding to each pixel point of a previous video frame before the current frame to be encoded as a second parsing number, and also includes: a previous video frame before the current frame to be encoded and the current frame to be encoded have the same total number of pixels occupied.

[0014] The polling processing system using big data analysis of the present invention aims to solve the technical problem in the prior art that the most suitable inter-frame coding reference frame cannot be obtained in advance to ensure image processing effect and efficiency. A big data analysis mechanism is introduced to intelligently analyze whether a previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded, and then each previous video frame before the current frame to be encoded is polled until a previous video frame suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded is obtained, thereby solving the above technical problem.

[0015] Although the present invention has been described in considerable detail, it should be appreciated that the elements thereof may be modified by one skilled in the art without departing from the spirit and scope of the present invention. It is believed that the system of the present invention and the advantages attendant thereto will be understood by the foregoing description, and it is clear that various changes may be made to the form, structure and arrangement of its components therein without departing from the scope and spirit of the present invention or sacrificing all of the substantial advantages of the present invention, and since the form heretofore described is merely an illustrative embodiment of the present invention, no additional substantial changes will be provided. The claims are intended to cover and include such changes.

Claims

1. A polling processing system for applying big data analysis, characterized in that: The system comprises: A model building device, used for an AI analysis model corresponding to a radial basis neural network mapping, wherein the AI ​​analysis model corresponding to the radial basis neural network mapping includes: training the radial basis neural network for each time to obtain a radial basis neural network after each training and outputting it as an AI analysis model, wherein the number of times the radial basis neural network is trained is monotonically positively correlated with the number of noise types of a current frame to be encoded; A data entry device is used to obtain the red and green component values ​​corresponding to the respective pixel points of the current frame to be encoded output by the visual monitoring device, wherein each pixel point of the current frame to be encoded has a red and green component value, a black and white component value, and a yellow and blue component value in the LAB color space; The information parsing mechanism is used to obtain the number of repeated values ​​in each red and green component value corresponding to each pixel point of the current frame to be encoded as a first parsing number, and obtain the number of repeated values ​​in each red and green component value corresponding to each pixel point of a previous video frame before the current frame to be encoded as a second parsing number; A big data analysis mechanism, connected to the model building device, the data entry device and the information analysis mechanism respectively, for synchronously inputting the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a certain previous video frame before the current frame to be encoded, the first analysis quantity and the second analysis quantity into the AI ​​analysis model, so as to execute the AI ​​analysis model and obtain an identification output by the AI ​​analysis model indicating whether the certain previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded; A polling processing mechanism, connected to the big data analysis mechanism, for executing the intelligent analysis of the big data analysis mechanism on each preceding video frame before the current frame to be encoded in a set order until a preceding video frame suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded is obtained; The intelligent analysis of the big data analysis mechanism is performed on each preceding video frame before the current frame to be encoded in a set order until a preceding video frame suitable for serving as an inter-frame coding reference frame for red and green components of the current frame to be encoded is obtained, including: the set order is that the closer the preceding video frame is to the current frame to be encoded, the earlier the intelligent analysis is performed; The setting order is that the closer the preceding video frame is to the current frame to be encoded, the earlier the order of executing intelligent analysis is, including: the closer the visual acquisition time is to the visual acquisition time of the current frame to be encoded, the earlier the order of executing intelligent analysis is; Among them, the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first resolution quantity and the second resolution quantity are synchronously input into the AI ​​analysis model to execute the AI ​​analysis model, and the identification output by the AI ​​analysis model indicating whether the previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded includes: when the inter-frame coding operation of the red and green components of the current frame to be encoded is performed using the previous video frame as the inter-frame coding reference frame for the red and green components of the current frame to be encoded, and the deviation between the obtained reconstructed frame of the red and green components and the original frame of the red and green components of the current frame to be encoded is less than the set difference limit, it is judged that the previous video frame is suitable as an identification of the inter-frame coding reference frame for the red and green components of the current frame to be encoded.

2. The polling processing system for applying big data analysis as claimed in claim 1, characterized in that: When the inter-frame coding operation of the red and green components of the current frame to be coded is performed on the current frame to be coded by using the certain previous video frame as the inter-frame coding reference frame of the red and green components of the current frame to be coded, and the deviation between the obtained reconstructed frame of the red and green components and the original frame of the red and green components of the current frame to be coded is less than the set difference limit, judging that the certain previous video frame is suitable as an identifier of the inter-frame coding reference frame of the red and green components of the current frame to be coded includes: performing an absolute value operation of the difference between the red and green component values ​​of the pixel points at the same position of the reconstructed frame of the red and green components and the original frame of the red and green components of the current frame to be coded to obtain the absolute values ​​of the differences corresponding to each position, and accumulating the absolute values ​​of the differences to obtain the deviation between the reconstructed frame of the red and green components and the original frame of the red and green components of the current frame to be coded.

3. The polling processing system for applying big data analysis as claimed in claim 2, characterized in that: The radial basis neural network is trained for each time to obtain a radial basis neural network after each training and output as an AI analysis model, wherein the number of times the radial basis neural network is trained is monotonically positively correlated with the number of noise types of the current frame to be encoded, including: using a content conversion formula to represent a content conversion relationship in which the number of times the radial basis neural network is trained is monotonically positively correlated with the number of noise types of the current frame to be encoded; Wherein, the data entry device is used to obtain the respective red and green component values ​​corresponding to the respective pixel points of the current frame to be encoded output by the visual monitoring device, and each pixel point of the current frame to be encoded has a red and green component value, a black and white component value, and a yellow and blue component value in the LAB color space, including: the value range of any color component value of the red and green component value, the black and white component value, and the yellow and blue component value of each pixel point of the current frame to be encoded in the LAB color space is between 0 and 255; Among them, the information parsing mechanism is used to obtain the number of repeated values ​​in each red and green component value corresponding to each pixel point of the current frame to be encoded as a first parsing number, and obtain the number of repeated values ​​in each red and green component value corresponding to each pixel point of a previous video frame before the current frame to be encoded as a second parsing number, including: a previous video frame before the current frame to be encoded is also output by the visual monitoring device and its visual acquisition time is earlier than the visual acquisition time of the current frame to be encoded.

4. The polling processing system for applying big data analysis as claimed in claim 3, characterized in that: The step of synchronously inputting the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first analysis number and the second analysis number into the AI ​​analysis model to execute the AI ​​analysis model, and obtaining the identification of whether the previous video frame is suitable as the inter-frame coding reference frame of the red and green components of the current frame to be encoded output by the AI ​​analysis model also includes: using multiple different data processing channels to respectively realize the step of synchronously inputting the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first analysis number and the second analysis number into the AI ​​analysis model to execute the AI ​​analysis model, and obtaining the identification of whether the previous video frame is suitable as the inter-frame coding reference frame of the red and green components of the current frame to be encoded output by the AI ​​analysis model, and corresponding to multiple data processing processes.

5. The polling processing system for applying big data analysis as claimed in claim 4, characterized in that: A plurality of different data processing channels are used to respectively realize synchronously inputting the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first resolution quantity and the second resolution quantity into the AI ​​analysis model to execute the AI ​​analysis model, and the plurality of decomposition steps of the step of obtaining the output of the AI ​​analysis model indicating whether the previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded respectively correspond to the plurality of data processing processes, including: the plurality of different data processing channels occupy the same signal control channel.

6. The polling processing system for applying big data analysis as claimed in claim 4, characterized in that: A plurality of different data processing channels are used to respectively input the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first resolution quantity and the second resolution quantity into the AI ​​analysis model synchronously to execute the AI ​​analysis model, and obtain the output of the AI ​​analysis model indicating whether the previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded. The plurality of data processing processes respectively corresponding to the plurality of decomposition steps include: the plurality of different data processing channels use the same data buffer.

7. The polling processing system for applying big data analysis as claimed in claim 4, characterized in that: A plurality of different data processing channels are used to respectively realize synchronously inputting the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first resolution quantity and the second resolution quantity into the AI ​​analysis model to execute the AI ​​analysis model, and obtain the output of the AI ​​analysis model indicating whether the previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded. The plurality of data processing processes respectively corresponding to the plurality of decomposition steps include: the plurality of different data processing channels occupy different data processing processes.

8. The polling processing system for applying big data analysis as claimed in claim 4, characterized in that: A plurality of different data processing channels are used to respectively input the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first resolution quantity and the second resolution quantity into the AI ​​analysis model synchronously to execute the AI ​​analysis model, and obtain the output of the AI ​​analysis model indicating whether the previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded. The plurality of data processing processes respectively corresponding to the plurality of decomposition steps include: the plurality of different data processing channels occupy the same data storage space.

9. The polling processing system for applying big data analysis as claimed in claim 3, characterized in that: The step of synchronously inputting the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first analysis number and the second analysis number into the AI ​​analysis model to execute the AI ​​analysis model, and obtaining the identification of whether the previous video frame is suitable as the inter-frame coding reference frame of the red and green components of the current frame to be encoded output by the AI ​​analysis model also includes: synchronously inputting the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first analysis number and the second analysis number into the AI ​​analysis model to execute the AI ​​analysis model, and obtaining the identification of whether the previous video frame is suitable as the inter-frame coding reference frame of the red and green components of the current frame to be encoded output by the AI ​​analysis model. The operating fault type of the step is monitored and analyzed by the fault detection process.

10. The polling processing system for applying big data analysis as claimed in claim 3, characterized in that: The resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first analysis number and the second analysis number are synchronously input into the AI ​​analysis model to execute the AI ​​analysis model, and the step of obtaining the output of the AI ​​analysis model indicating whether the previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded also includes: the resolution of the visual monitoring device, the coordinate values ​​of each edge pixel point of the current frame to be encoded, the coordinate values ​​of each edge pixel point of a previous video frame before the current frame to be encoded, the first analysis number and the second analysis number are synchronously input into the AI ​​analysis model to execute the AI ​​analysis model, and the step of obtaining the output of the AI ​​analysis model indicating whether the previous video frame is suitable as an inter-frame coding reference frame for the red and green components of the current frame to be encoded is captured and displayed in real time by the display process.

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