CN115941286B - Data processing method applied to Internet of things and live broadcast platform - Google Patents
Data processing method applied to Internet of things and live broadcast platform Download PDFInfo
- Publication number
- CN115941286B CN115941286B CN202211415419.4A CN202211415419A CN115941286B CN 115941286 B CN115941286 B CN 115941286B CN 202211415419 A CN202211415419 A CN 202211415419A CN 115941286 B CN115941286 B CN 115941286B
- Authority
- CN
- China
- Prior art keywords
- information
- node
- target
- generating
- dynamic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention relates to the technical field of Internet of things communication and big data, in particular to a data processing method applied to an Internet of things platform and a live broadcast platform. The method comprises the following steps: acquiring a path delay parameter corresponding to an execution parameter path corresponding to a current thread parameter and a corresponding path reliability index; generating a path delay curve according to the path delay parameter; acquiring node load information corresponding to terminal equipment for network communication according to curve node information in a path delay curve; adjusting the node load information according to the node interconnection information in the node load information to generate node target information; and calculating the target information in the node target information according to the node target information and the corresponding node interconnection information, and generating a preposed time delay value to perform data stream communication operation. The invention provides a safe and stable video communication service.
Description
Technical Field
The invention relates to the technical field of Internet of things communication and big data, in particular to a data processing method applied to an Internet of things platform and a live broadcast platform.
Background
Today, with the increasing development of internet technology, live video is coming into the public view as an information industry, for example, users can not only watch the wonderful performance of a live broadcast room on their own terminal device, but also interact with a host in real time. The highlight performance of the live broadcasting room is issued to the terminal equipment in the form of video stream data, so that the video stream data is easily intercepted by a third party and maliciously tampered, and potential safety hazards exist in the video stream data received by the terminal equipment.
Disclosure of Invention
The invention provides a data processing method applied to the Internet of things and a live broadcast platform to solve at least one technical problem.
The data processing method applied to the Internet of things and the live broadcast platform is applied to a cloud computing center in communication connection with both terminal equipment and the live broadcast platform, and comprises the following steps:
step S1: acquiring a path delay parameter and a path reliability index of an execution parameter path of a current thread parameter, wherein the path reliability index is the execution reliability of the execution parameter path of the current thread parameter, and the path reliability index comprises a first reliability index and a second reliability index;
Step S2: generating a path delay curve according to the path delay parameters, wherein the path delay curve comprises preset curve node information, and the curve node information is delay adjustment information of an execution parameter path which is positioned on the path delay curve and corresponds to the path delay information;
step S3: acquiring node load information corresponding to terminal equipment for network communication according to curve node information in a path delay curve, wherein the node load information comprises node interconnection information;
step S4: adjusting the node load information according to the node interconnection information in the node load information to generate node target information;
step S5: and calculating the target information in the node target information according to the node target information and the corresponding node interconnection information, and generating a preposed time delay value to perform data stream communication operation.
According to the embodiment, the path delay parameter corresponding to the execution parameter path corresponding to the current thread parameter and the path reliability index corresponding to the corresponding execution parameter path are obtained, and the pre-delay value is generated according to the provided method flow, so that reliable and safe data flow communication operation is performed, and therefore the situation that the data flow communication operation is prevented from being maliciously tampered, potential safety hazards are caused, and meanwhile the problem of network fluctuation under an emergency flow event is also reduced.
In one embodiment of the present specification, step S1 is preceded by the steps of:
step S01: generating a token generation rate according to a token generation rate calculation formula;
step S02: generating a token application set according to the token generation rate;
step S03: judging whether a token application mark exists in the token application set;
step S04: when determining that the token application mark exists in the token application set, continuously monitoring whether the communication link sends the session application mark;
step S05: when the session application mark is determined to exist, deleting one token application mark in the token application marks and judging whether the session application mark is a legal session application mark or not;
step S06: when the session application mark is determined to be the legal session application mark, step S1 is executed.
According to the method, the token generation rate is generated through the token generation rate calculation formula, and the token set is generated according to the token generation rate, so that the problem of fluctuation caused by large-scale traffic when an emergency traffic event occurs is avoided, wherein the token generation rate calculation formula fully considers the problem of applicable traffic to generate the applicable token generation rate, and data support is made for establishing reliable and stable session communication.
In one embodiment of the present specification, the token generation calculation formula is specifically:
t is the token generation rate, target history information curve delta, current token application set capacity information omega, target transmission rate mu, weighting information beta of the target transmission rate and correction term sigma.
The embodiment providesA token generation calculation formula fully considers a target historical record information curve delta, current token application set capacity information omega, target transmission rate mu and weighted information beta of the target transmission rate, wherein the target historical record information curve can be generated according to user historical record information stored locally through likelihood estimation operation, and a function relation is generated according to analysis of the target historical record information curve delta, the current token application set capacity information omega, the target transmission rate mu and the weighted information beta of the target transmission rate
And the generation token generation rate T is corrected according to the correction term sigma so as to provide more accurate and reasonable token generation rate, thereby providing data support for avoiding network fluctuation caused by traffic emergency.
In one embodiment of the present specification, step S1 includes the steps of:
step S11: continuously monitoring device log information generated by the terminal device in response to user operation to generate current thread information;
Step S12: generating current thread parameters according to the current thread information;
step S13: generating a path delay parameter according to the current thread parameter;
step S14: generating a path reliability index according to the current thread parameters and the communication log information pre-stored locally, wherein the path reliability index comprises a first reliability index and a second reliability index, the first reliability index is a packet loss rate, and the second reliability index is an error rate.
According to the embodiment, the path reliability index is generated through the preset method flow for the equipment log information, and data support is provided for the next step.
In one embodiment of the present specification, step S2 includes the steps of:
generating a path delay curve by the path delay parameter through a path delay calculation formula;
the path delay calculation formula specifically comprises:
l is a path delay curve, a receiving rate v of the terminal equipment and an uploading rate of the terminal equipment
And the weight information alpha corresponding to the terminal equipment, the time delay information mu corresponding to the previous period and the correction term tau.
The embodiment provides a path delay calculation formula, which fully considers the receiving rate v of the terminal equipment and the uploading rate of the terminal equipment
Weight information alpha corresponding to the terminal equipment and time delay information mu corresponding to the previous period, wherein the weight information alpha and the time delay information mu are respectively added according to the receiving rate v and the uploading rate +. >
Weight information alpha corresponding to terminal equipment and time delay information mu corresponding to previous period of time generate a functional relation +.>
And the correction is carried out through the correction term tau to generate an accurate and reliable path delay curve.
In one embodiment of the present specification, step S3 includes the steps of:
step S31: performing value calculation according to the path delay curve to generate curve node information;
step S32: acquiring a communication interaction log generated by network communication of the terminal equipment according to the curve node information;
step S33: and generating node load information according to the communication interaction log.
In the embodiment, the path delay curve is extracted to obtain the curve node information, the corresponding communication interaction log is obtained through the curve node information, and the node load information is generated by calculating the communication interaction log, so that the preparation work is prepared for the next premise.
In one embodiment of the present specification, step S4 includes the steps of:
step S41: acquiring user operation information;
step S42: generating user interconnection information according to the user operation information;
step S43: correcting node interconnection information in the node load information according to the user interconnection information to generate node correction interconnection information;
Step S44: and adjusting the node load information according to the node correction interconnection information to generate node target information.
According to the embodiment, the user operation information is obtained, the user interconnection information is generated according to the user operation information, the node load information is corrected according to the user interconnection information, so that node correction interconnection information is generated, the node load information is adjusted by the node correction interconnection information, the node target information is generated, and the preparation is made for the next step.
In one embodiment of the present specification, step S5 includes the steps of:
step S51: generating node dynamic information according to the node target information;
step S52: marking and visualizing the node dynamic information and the node interconnection information to generate a dynamic visualization graph;
step S53: graying the dynamic visual image to generate a gray dynamic visual image;
step S54: carrying out Gaussian distribution calculation on the gray level dynamic visualization degree to generate a Gaussian distribution dynamic association diagram;
step S55: generating a time delay associated feature vector by matching the Gaussian distribution dynamic associated graph with a preset time delay associated feature vector matching model;
step S56: calculating target information in node target information by using the time delay associated feature vector to generate a pre-delay value so as to perform data stream operation;
The construction step of the preset time delay associated feature vector matching model comprises the following steps of:
step S551: acquiring node target information and node interconnection information under each condition;
step S552: generating corresponding node dynamic information according to the target information of each node;
step S553: marking node interconnection information on node dynamic information to generate node marking dynamic information;
step S554: performing cluster analysis on the node mark dynamic information to generate node mark dynamic cluster information;
step S555: marking and visualizing the node dynamic information according to the node marking dynamic clustering information to generate node dynamic template visualization diagrams under various situations or various weight situations;
step S556: carrying out gray scale on the node dynamic template visual image to generate a gray scale dynamic template visual image;
step S557: carrying out Gaussian distribution calculation on the gray dynamic template visual map to generate a Gaussian distribution dynamic template distribution map;
step S558: and carrying out weighted calculation on the Gaussian distribution dynamic template distribution graph to generate a time delay associated feature vector matching model.
In this embodiment, an accurate time delay associated feature vector is generated by matching a time delay associated feature vector matching model, where the time delay associated feature vector matching model marks and visualizes according to node target information to generate a visualized image, and the visualized image is grayed and gaussian calculated to provide different front delay values according to changes of the node target information, so as to ensure stable communication operation of the communication flow.
In one embodiment of the present disclosure, generating the preamble delay value in step S5 for performing the data stream communication operation specifically includes the following steps:
step S501: acquiring target video stream data in current thread parameters;
step S502: determining a stage duration value for performing security check on target video stream data according to video image quality parameters of the target video stream data;
step S503: dividing the stage duration value according to the preposed time delay value to obtain a plurality of inspection time periods;
step S504: and carrying out security concurrent calculation on the target video stream data according to the checking period, generating a security checking period, and carrying out data stream communication operation by utilizing the corresponding target video stream data in the security checking period.
In one embodiment of the present description, the security concurrency computation includes the steps of:
step S5041: generating classification associated information in classification mark information and weighting influence sub-coefficients among different classification mark information according to a transmission protocol and an encryption protocol in a data interaction log generated when the target video stream data is received, wherein the classification mark information is generated by classification mark information generated by classification calculation according to video image quality parameters;
Step S5042: calculating the multiple classification associated information according to the classification associated information of the multiple generated classification mark information and the weighting influence sub-coefficients among different classification associated data so as to generate at least multiple target associated information, wherein the weighting influence sub-information corresponding to the target associated information is located in a set weighting factor interval and the weighting influence sub-coefficients among different target associated information are smaller than a preset threshold;
step S5043: classifying the video image quality parameters by the target related information in a preset classification mode to generate an image quality parameter classification set;
step S5044: generating a corresponding classified data detection list according to each image quality parameter classification in the image quality parameter classification set;
step S5045: generating a video image quality transaction resource allocation coefficient corresponding to the image quality parameter through a classified data detection list;
step S5046: and generating a security check period for performing security detection on the target video stream data according to the generated multiple video image quality transaction resource allocation coefficients and the utilization rate of the current event transaction resources.
The embodiment analyzes the video image quality parameters to generate the video image quality corresponding to the characterization target video stream data so as to ensure the safety and stability in video stream communication, so as to provide efficient and stable video communication operation, and simultaneously, performs safety inspection on the target video data according to the front delay value.
The invention ensures line load and thread parameters generated by the current communication task by adjusting and connecting the token generation rate, carries out safety detection and time delay calculation, generates an accurate and reliable front-end time delay value, ensures the safety of video stream data received by user terminal equipment, simultaneously reduces or even avoids the phenomenon of blocking generated by the terminal equipment when the video stream data is played, ensures the fluency and stability of playing live video by the user terminal equipment, and part of embodiments realize the improvement of the video stream data issuing rate so as to reduce the waiting time of users waiting for video playing, and improve the experience degree of the users for video playing in the practical process.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of the non-limiting implementation, made with reference to the accompanying drawings;
FIG. 1 is a flow chart illustrating steps of a data processing method applied to an Internet of things and live platform according to an embodiment;
FIG. 2 is a flow chart illustrating steps of a session information generation method of an embodiment;
FIG. 3 is a flow chart illustrating steps of a path reliability index generation method of an embodiment;
FIG. 4 is a flow chart illustrating steps of a method of generating node load information of an embodiment;
FIG. 5 is a flow diagram that illustrates steps of a method for generating node target information, according to one embodiment;
FIG. 6 is a flow chart illustrating steps of a method of generating a preamble delay value according to one embodiment;
FIGS. 7a-7b are flow diagrams illustrating steps of a method for generating a time delay associated feature vector matching model of an embodiment;
FIG. 8 is a flow chart illustrating the steps of a target video stream security detection method of an embodiment;
fig. 9a-9b illustrate a flow chart of steps of a target video stream security concurrency computation method of an embodiment.
Detailed Description
The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.
It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
The invention provides a data processing method applied to the Internet of things and a live broadcast platform, which is applied to a cloud computing center in communication connection with terminal equipment and the live broadcast platform, and referring to fig. 1 to 9b, the data processing method comprises the following steps:
step S1: acquiring a path delay parameter and a path reliability index of an execution parameter path of a current thread parameter, wherein the path reliability index is the execution reliability of the execution parameter path of the current thread parameter, and the path reliability index comprises a first reliability index and a second reliability index;
specifically, for example, the information of the communication log stored in the client and the local is scanned, and the keyword extraction is performed to generate the path delay parameter corresponding to the execution parameter path corresponding to the current thread parameter and the corresponding path reliability index, wherein the keyword extraction can be performed by generating a preset extraction formula through a regular expression.
Step S2: generating a path delay curve according to the path delay parameters, wherein the path delay curve comprises preset curve node information, and the curve node information is delay adjustment information of an execution parameter path which is positioned on the path delay curve and corresponds to the path delay information;
specifically, a path delay curve is established, for example, according to time information, node information and time information of the path delay parameter in a coordinate axis establishing manner.
Specifically, for example, a path delay parameter calculation formula is customized, and a path delay curve is generated according to the path delay parameter through the path delay parameter calculation formula.
Step S3: acquiring node load information corresponding to terminal equipment for network communication according to curve node information in a path delay curve, wherein the node load information comprises node interconnection information;
specifically, keyword matching is performed according to curve node information and communication log information, and corresponding node load information is generated;
specifically, for example, in a network communication link, monitoring communication thread information and generating corresponding node communication information, and matching according to curve node information in a path delay curve and the node communication information to generate node load information.
Step S4: adjusting the node load information according to the node interconnection information in the node load information to generate node target information;
specifically, for example, the node interconnection information adjusts video stream image quality parameters for the client, adjusts video stream image quality information in the node load information according to the client adjusting video stream image quality parameters, and generates node load information, wherein the node load information comprises the adjusted video stream image quality information.
Step S5: and calculating the target information in the node target information according to the node target information and the corresponding node interconnection information, and generating a preposed time delay value to perform data stream communication operation.
Specifically, for example, the pre-delay value is generated according to the transcoding time consumption and the resolution self-adaptive adjustment time consumption calculation according to the node target and the corresponding node interconnection information, so as to perform the data stream communication operation.
According to the embodiment, the path delay parameter corresponding to the execution parameter path corresponding to the current thread parameter and the path reliability index corresponding to the corresponding execution parameter path are obtained, and the pre-delay value is generated according to the provided method flow, so that reliable and safe data flow communication operation is performed, and therefore the situation that the data flow communication operation is prevented from being maliciously tampered, potential safety hazards are caused, and meanwhile the problem of network fluctuation under an emergency flow event is also reduced.
In one embodiment of the present disclosure, referring to fig. 2, step S1 is preceded by the following steps:
step S01: generating a token generation rate according to a token generation rate calculation formula;
specifically, the token generation rate may be generated, for example, by calculating with reference to a token generation rate calculation formula in other embodiments, such as one thousand per minute for each target generation rate.
Step S02: generating a token application set according to the token generation rate;
specifically, the token application set is generated from the token generation rate generated with reference to the token generation rate calculation formula in other embodiments, for example.
Step S03: judging whether a token application mark exists in the token application set;
specifically, the judgment job is performed by, for example, a judgment sentence.
Step S04: when determining that the token application mark exists in the token application set, continuously monitoring whether the communication link sends the session application mark;
specifically, monitoring whether the communication link has sent a session application flag is performed, for example, by a monitor, wherein the monitor is generated by a program technique.
Step S05: when the session application mark is determined to exist, deleting one token application mark in the token application marks and judging whether the session application mark is a legal session application mark or not;
Specifically, for example, when the sent session application mark is determined to exist, deleting one token application mark in the token application mark, matching the obtained user account information with the local legal account information, and if the matching is successful, determining the legal session application mark.
Step S06: when the session application mark is determined to be the legal session application mark, step S1 is executed.
Specifically, for example, the session application mark includes user basic information, the user basic information includes user account information and user password information, when the session application mark is determined to be legal session application mark, session information is generated, and corresponding thread information is generated according to the session information, wherein the thread information includes equipment log information.
According to the method, the token generation rate is generated through the token generation rate calculation formula, and the token set is generated according to the token generation rate, so that the problem of fluctuation caused by large-scale traffic when an emergency traffic event occurs is avoided, wherein the token generation rate calculation formula fully considers the problem of applicable traffic to generate the applicable token generation rate, and data support is made for establishing reliable and stable session communication.
In one embodiment of the present specification, the token generation calculation formula is specifically:
t is the token generation rate, target history information curve delta, current token application set capacity information omega, target transmission rate mu, weighting information beta of the target transmission rate and correction term sigma.
The present embodiment provides a token generation calculation formula, which fully considers a target history information curve δ, current token application set capacity information ω, a target transmission rate μ, and weighted information β of the target transmission rate, wherein the target history information curve may be generated by likelihood estimation operation according to locally stored user history information, and a function relationship is generated by analysis according to the target history information curve δ, the current token application set capacity information ω, the target transmission rate μ, and the weighted information β of the target transmission rate
And the generation token generation rate T is corrected according to the correction term sigma so as to provide more accurate and reasonable token generation rate, thereby providing data support for avoiding network fluctuation caused by traffic emergency.
In one embodiment of the present disclosure, referring to fig. 3, step S1 includes the following steps:
Step S11: continuously monitoring device log information generated by the terminal device in response to user operation to generate current thread information;
specifically, for example, a monitor is set to continuously monitor the generation of device log information by the corresponding user operation of the terminal device to generate current thread information, wherein the monitor comprises a current thread information selector generated by a regular expression.
Step S12: generating current thread parameters according to the current thread information;
specifically, the current thread parameters are generated by filtering, for example, by a regular expression generation current thread parameter filter, wherein the current thread parameters include a transmission time and a reception time.
Step S13: generating a path delay parameter according to the current thread parameter;
specifically, the path delay parameter is generated by calculating, for example, according to the transmission time and the reception time in the current thread parameter.
Step S14: generating a path reliability index according to the current thread parameters and the communication log information pre-stored locally, wherein the path reliability index comprises a first reliability index and a second reliability index, the first reliability index is a packet loss rate, and the second reliability index is an error rate.
Specifically, for example, the path reliability index is generated by calculating the number of transmitted data packets and the number of received data packets according to the path delay parameter and the pre-stored local communication log information, wherein the path reliability index comprises the packet loss rate and other communication path reliability values.
According to the embodiment, the path reliability index is generated through the preset method flow for the equipment log information, and data support is provided for the next step.
In one embodiment of the present specification, step S2 includes the steps of:
generating a path delay curve by the path delay parameter through a path delay calculation formula;
specifically, for example, the path delay curve is generated by calculating with reference to the path delay calculation formula in the present embodiment.
The path delay calculation formula specifically comprises:
l is a path delay curve, a receiving rate v of the terminal equipment and an uploading rate of the terminal equipment
And the weight information alpha corresponding to the terminal equipment, the time delay information mu corresponding to the previous period and the correction term tau.
The present embodiment provides a path delay deviceThe path delay calculation formula fully considers the receiving rate v of the terminal equipment and the uploading rate of the terminal equipment
Weight information alpha corresponding to the terminal equipment and time delay information mu corresponding to the previous period, wherein the weight information alpha and the time delay information mu are respectively added according to the receiving rate v and the uploading rate +.>
Weight information alpha corresponding to terminal equipment and time delay information mu corresponding to previous period of time generate a functional relation +.>
And the correction is carried out through the correction term tau to generate an accurate and reliable path delay curve.
In one embodiment of the present disclosure, referring to fig. 4, step S3 includes the following steps:
step S31: performing value calculation according to the path delay curve to generate curve node information;
specifically, for example, the value calculation is performed according to a path delay curve to generate curve node information, where the value calculation may be whole value calculation or value calculation may be performed according to a preset calculation formula generated according to a terminal device number.
Step S32: acquiring a communication interaction log generated by network communication of the terminal equipment according to the curve node information;
specifically, for example, a communication interaction log generated by the terminal device for network communication is obtained according to the curve node information, such as a communication interaction log generated by the terminal device for network communication stored locally according to the node number in the curve node information.
Step S33: and generating node load information according to the communication interaction log.
Specifically, for example, the node load information is generated by filtering through a regular expression generation node load information filter according to the communication interaction log.
In the embodiment, the path delay curve is extracted to obtain the curve node information, the corresponding communication interaction log is obtained through the curve node information, and the node load information is generated by calculating the communication interaction log, so that the preparation work is prepared for the next premise.
In one embodiment of the present disclosure, referring to fig. 5, step S4 includes the following steps:
step S41: acquiring user operation information;
specifically, for example, the user operation information is obtained through a user operation information obtaining control, wherein the user operation information obtaining control can be generated through control generation software or can be generated through programming technology, and the user operation information comprises selection operation on video stream image quality information types and selection on addresses.
Step S42: generating user interconnection information according to the user operation information;
specifically, for example, the user interconnection information includes a selection operation of a video stream image quality information category and a selection of an address.
Step S43: correcting node interconnection information in the node load information according to the user interconnection information to generate node correction interconnection information;
specifically, the node interconnection information in the node load information is modified according to, for example, a selection operation of a video stream image quality information category and a selection operation of an address in the user interconnection information, wherein the node interconnection information includes a selection operation of a currently selected video stream image quality information category and a currently selected selection operation of an address.
Step S44: and adjusting the node load information according to the node correction interconnection information to generate node target information.
Specifically, for example, according to the modified video stream image quality information type information and the modified information of the address, the load condition of the network communication is correspondingly adjusted.
According to the embodiment, the user operation information is obtained, the user interconnection information is generated according to the user operation information, the node load information is corrected according to the user interconnection information, so that node correction interconnection information is generated, the node load information is adjusted by the node correction interconnection information, the node target information is generated, and the preparation is made for the next step.
In one embodiment of the present disclosure, referring to fig. 6 to 7b, step S5 includes the steps of:
step S51: generating node dynamic information according to the node target information;
specifically, for example, the current modified video stream image quality information in the node target information is converted into image quality requirement information, wherein the node dynamic information includes the image quality requirement information, if the user selects high definition, the image quality requirement information is 768×1024 or is marked with a custom to be high definition, such as a digital mark 2, and the digital mark is correspondingly marked according to the current modified video stream image quality information, such as an ultra definition of 3, a high definition of 2 and a mark definition of 1.
Step S52: marking and visualizing the node dynamic information and the node interconnection information to generate a dynamic visualization graph;
specifically, for example, a two-dimensional vector group is established according to image quality requirement information in node dynamic information and time delay information in node interconnection information, and a dynamic visual map is generated by performing color labeling and arrangement in order from small to large according to the time axis and the value of the two-dimensional vector group, wherein the color labeling is from green to purple.
Step S53: graying the dynamic visual image to generate a gray dynamic visual image;
specifically, for example, the dynamic visual image is subjected to graying, and is converted into a gray dynamic visual image, wherein the graying operation can be converted by software, and also can be subjected to gray conversion by an image processing function, such as rbg gray in matlab, cvtcolor in opencv library and gray conversion functions in other function processing libraries.
Step S54: carrying out Gaussian distribution calculation on the gray dynamic visual image to generate a Gaussian distribution dynamic association image;
specifically, for example, the gray level dynamic visualization is calculated according to a gaussian distribution formula, and a gaussian distribution dynamic diagram is generated.
Step S55: generating a time delay associated feature vector by matching the Gaussian distribution dynamic associated graph with a preset time delay associated feature vector matching model;
Specifically, for example, the gaussian distribution dynamic association graph is matched with a preset time delay association feature vector matching model to generate a time delay association feature vector, wherein the preset time delay association feature vector matching model comprises the gaussian distribution dynamic association graph under different preset conditions and corresponding preset time delay association feature vectors.
Step S56: calculating target information in node target information by using the time delay associated feature vector to generate a pre-delay value so as to perform data stream operation;
specifically, for example, weighting calculation is performed on target information in the node target information according to the time delay associated feature vector, so as to generate a pre-experiment value.
The construction step of the preset time delay associated feature vector matching model comprises the following steps of:
step S551: acquiring node target information and node interconnection information under each condition;
specifically, for example, node target information and node interconnection information of high latency, medium latency, and low latency are acquired.
Step S552: generating corresponding node dynamic information according to the target information of each node;
specifically, for example, the current modified video stream image quality information in the node target information is converted into image quality requirement information, wherein the node dynamic information includes the image quality requirement information, if the user selects high definition, the image quality requirement information is 768×1024 or is marked with a custom to be high definition, such as a digital mark 2, and the digital mark is correspondingly marked according to the current modified video stream image quality information, such as an ultra definition of 3, a high definition of 2 and a mark definition of 1.
Step S553: marking node interconnection information on node dynamic information to generate node marking dynamic information;
specifically, for example, a two-dimensional vector group is established according to the image quality requirement information in the node dynamic information and the time delay information in the node interconnection information.
Step S554: performing cluster analysis on the node mark dynamic information to generate node mark dynamic cluster information;
specifically, for example, the method of cluster analysis includes a K-MEANS cluster analysis algorithm, a learning vector quantization algorithm, a Gaussian mixture cluster algorithm and a hierarchical clustering algorithm, and the generated node mark dynamic cluster information includes node mark dynamic information corresponding to high-delay information, medium-delay information and low-delay information.
Step S555: marking and visualizing the node dynamic information according to the node marking dynamic clustering information to generate node dynamic template visualization diagrams under various situations or various weight situations;
specifically, for example, the node marking dynamic clustering information comprises high-delay information, medium-delay information and low-delay information, the node dynamic information is marked and visualized, and a node dynamic template visualization map under various situations or various weight situations is generated.
Step S556: carrying out gray scale on the node dynamic template visual image to generate a gray scale dynamic template visual image;
specifically, for example, the node dynamic template visual image is subjected to gray-scale treatment to generate a gray-scale dynamic template visual image, wherein gray-scale treatment operation can be performed through software, and gray-scale conversion can also be performed through image processing functions, such as rbg gray in matlab, cvtcolor in opencv library and gray-scale conversion functions in other function processing libraries.
Step S557: carrying out Gaussian distribution calculation on the gray dynamic template visual map to generate a Gaussian distribution dynamic template distribution map;
specifically, for example, the gray scale dynamic template visualization map is calculated according to a gaussian distribution calculation formula, and a gaussian distribution dynamic template distribution map is generated.
Step S558: and carrying out weighted calculation on the Gaussian distribution dynamic template distribution graph to generate a time delay associated feature vector matching model.
Specifically, for example, the weighting calculation may be an association connection, such as generating and associating a specific delay association feature vector according to specific node target information and node interconnection information.
In this embodiment, an accurate time delay associated feature vector is generated by matching a time delay associated feature vector matching model, where the time delay associated feature vector matching model marks and visualizes according to node target information to generate a visualized image, and the visualized image is grayed and gaussian calculated to provide different front delay values according to changes of the node target information, so as to ensure stable communication operation of the communication flow.
In one embodiment of the present disclosure, referring to fig. 8, generating a preamble delay value in step S5 for performing a data stream communication operation specifically includes the following steps:
step S501: acquiring target video stream data in current thread parameters;
specifically, the target video information in the current thread parameters is obtained through a communication interaction log, and video stream data stored in a local database is queried according to the target video information to generate target video stream data.
Specifically, the target video stream data in the current thread parameters is continuously monitored, for example, at the network interface.
Step S502: determining a stage duration value for performing security check on target video stream data according to video image quality parameters of the target video stream data;
specifically, for example, a stage duration value for performing security inspection on target video stream data is determined according to a video image quality parameter of the target video stream data, for example, a stage duration value for performing security inspection on the target video stream data for super-definition image quality parameter generation is 50ms, a stage duration value for performing security inspection on the target video stream data for high-definition image quality parameter generation is 100ms, and a stage duration value for performing security inspection on the target video stream data for standard-definition image quality parameter generation is 150ms.
Step S503: dividing the stage duration value according to the preposed time delay value to obtain a plurality of inspection time periods;
specifically, for example, when the preamble delay value is 10ms, the phase duration value, for example, 100ms is divided into a plurality of inspection periods.
Step S504: and carrying out security concurrent calculation on the target video stream data according to the checking period, generating a security checking period, and carrying out data stream communication operation by utilizing the corresponding target video stream data in the security checking period.
Specifically, for example, according to the security concurrency calculation method provided by other real-time examples in the invention, security inspection is performed on the target video stream data, a security inspection period is generated, and data circulation operation is performed by using the corresponding target video stream data in the security inspection period.
In one embodiment of the present description, referring to fig. 9a to 9b, the security concurrency calculation comprises the steps of:
step S5041: generating classification associated information in classification mark information and weighting influence sub-coefficients among different classification mark information according to a transmission protocol and an encryption protocol in a data interaction log generated when the target video stream data is received, wherein the classification mark information is generated by classification mark information generated by classification calculation according to video image quality parameters;
Specifically, for example, different weighting factors are generated according to different transmission protocol types, for example, the weighting factor of UDP transmission protocol is 1.0, the weighting factor of tcp is 0.75, and the encryption protocol generates different weighting factors according to different lengths of encryption result, for example, the longer the length, the smaller the weighting factor.
Step S5042: calculating the multiple classification associated information according to the classification associated information of the multiple generated classification mark information and the weighting influence sub-coefficients among different classification associated data so as to generate at least multiple target associated information, wherein the weighting influence sub-information corresponding to the target associated information is located in a set weighting factor interval and the weighting influence sub-coefficients among different target associated information are smaller than a preset threshold;
specifically, the preset threshold is 2.5, for example.
Step S5043: classifying the video image quality parameters by the target related information in a preset classification mode to generate an image quality parameter classification set;
specifically, for example, the classification is performed according to the super definition, the high definition and the standard definition, and an image quality parameter classification set is generated.
Specifically, for example, the classification is performed according to 1440×1024, 1024×768, and 768×384, so as to generate the classification set of image quality parameters.
Step S5044: generating a corresponding classified data detection list according to each image quality parameter classification in the image quality parameter classification set;
specifically, for example, a super-definition classified data detection list, a high-definition classified data detection list, and a standard definition classified data detection list are generated.
Step S5045: generating a video image quality transaction resource allocation coefficient corresponding to the image quality parameter through a classified data detection list;
specifically, for example, corresponding video image quality transaction resource allocation coefficients are allocated according to the super-definition classified data detection list, the high-definition classified data detection list and the standard definition classified data detection list, for example, the corresponding video image quality transaction allocation coefficient in the super-definition classified data detection list is 7.5.
Step S5046: and generating a security check period for performing security detection on the target video stream data according to the generated multiple video image quality transaction resource allocation coefficients and the utilization rate of the current event transaction resources.
Specifically, for example, a security detection period for security detection of the target video stream data is generated according to the generated multiple video image quality transaction resource allocation coefficients and the utilization rate of the current transaction resource, for example, the utilization rate of the current transaction resource is 76%.
The embodiment analyzes the video image quality parameters to generate the video image quality corresponding to the characterization target video stream data so as to ensure the safety and stability in video stream communication, so as to provide efficient and stable video communication operation, and simultaneously, performs safety inspection on the target video data according to the front delay value.
The invention ensures line load and thread parameters generated by the current communication task by adjusting and connecting the token generation rate, carries out safety detection and time delay calculation, generates an accurate and reliable front-end time delay value, ensures the safety of video stream data received by user terminal equipment, simultaneously reduces or even avoids the phenomenon of blocking generated by the terminal equipment when the video stream data is played, ensures the fluency and stability of playing live video by the user terminal equipment, and part of embodiments realize the improvement of the video stream data issuing rate so as to reduce the waiting time of users waiting for video playing, and improve the experience degree of the users for video playing in the practical process.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (5)
1. The data processing method applied to the Internet of things and the live broadcast platform is characterized by being applied to a cloud computing center in communication connection with terminal equipment and the live broadcast platform, and comprises the following steps of:
generating a token generation rate;
generating a token application set according to the token generation rate;
judging whether a token application mark exists in the token application set;
when determining that the token application mark exists in the token application set, continuously monitoring whether the communication link sends the session application mark;
when the session application mark is determined to exist, deleting one token application mark in the token application marks and judging whether the session application mark is a legal session application mark or not;
When the session application mark is determined to be a legal session application mark, executing step S1;
step S1: acquiring a path delay parameter and a path reliability index of an execution parameter path of a current thread parameter, wherein the path reliability index is the execution reliability of the execution parameter path of the current thread parameter, and the path reliability index comprises a first reliability index and a second reliability index;
step S2: generating a path delay curve according to the path delay parameter, wherein the path delay curve comprises preset curve node information, and the curve node information is delay adjustment information of an execution parameter path which is positioned on the path delay curve and corresponds to the path delay parameter;
step S3: acquiring node load information corresponding to terminal equipment for network communication according to curve node information in a path delay curve, wherein the node load information comprises node interconnection information;
step S4: adjusting the node load information according to the node interconnection information in the node load information to generate node target information;
step S5: calculating target information in the node target information according to the node target information and the corresponding node interconnection information, and generating a preposed time delay value to perform data stream communication operation; in step S5, generating a preamble delay value for performing a data stream communication operation specifically includes the following steps:
Acquiring target video stream data in current thread parameters;
determining a stage duration value for performing security check on target video stream data according to video image quality parameters of the target video stream data;
dividing the stage duration value according to the preposed time delay value to obtain a plurality of inspection time periods;
carrying out security concurrent calculation on the target video stream data according to the checking period, generating a security checking period, and carrying out data stream communication operation by utilizing the corresponding target video stream data in the security checking period; the security concurrency calculation comprises the following steps:
generating classification associated information in classification mark information and weighting influence sub-coefficients among different classification mark information according to a transmission protocol and an encryption protocol in a data interaction log generated when the target video stream data is received, wherein the classification mark information is generated by classification mark information generated by classification calculation according to video image quality parameters;
calculating the multiple classification associated information according to the classification associated information of the multiple generated classification mark information and the weighting influence sub-coefficients among different classification associated data so as to generate at least multiple target associated information, wherein the weighting influence sub-information corresponding to the target associated information is located in a set weighting factor interval and the weighting influence sub-coefficients among different target associated information are smaller than a preset threshold;
Classifying the video image quality parameters by the target related information in a preset classification mode to generate an image quality parameter classification set;
generating a corresponding classified data detection list according to each image quality parameter classification in the image quality parameter classification set;
generating a video image quality transaction resource allocation coefficient corresponding to the image quality parameter through a classified data detection list;
and generating a security check period for performing security detection on the target video stream data according to the generated multiple video image quality transaction resource allocation coefficients and the utilization rate of the current event transaction resources.
2. The method according to claim 1, wherein step S1 comprises the steps of:
continuously monitoring device log information generated by the terminal device in response to user operation to generate current thread information;
generating current thread parameters according to the current thread information;
generating a path delay parameter according to the current thread parameter;
generating a path reliability index according to the current thread parameters and the communication log information pre-stored locally, wherein the path reliability index comprises a first reliability index and a second reliability index, the first reliability index is a packet loss rate, and the second reliability index is an error rate.
3. The method according to claim 1, wherein step S3 comprises the steps of:
performing value calculation according to the path delay curve to generate curve node information;
acquiring a communication interaction log generated by network communication of the terminal equipment according to the curve node information;
and generating node load information according to the communication interaction log.
4. The method according to claim 1, wherein step S4 comprises the steps of:
acquiring user operation information;
generating user interconnection information according to the user operation information;
correcting node interconnection information in the node load information according to the user interconnection information to generate node correction interconnection information;
and adjusting the node load information according to the node correction interconnection information to generate node target information.
5. The method according to claim 1, wherein step S5 comprises the steps of:
generating node dynamic information according to the node target information;
marking and visualizing the node dynamic information and the node interconnection information to generate a dynamic visualization graph;
graying the dynamic visual image to generate a gray dynamic visual image;
carrying out Gaussian distribution calculation on the gray level dynamic visualization degree to generate a Gaussian distribution dynamic association diagram;
Generating a time delay associated feature vector by matching the Gaussian distribution dynamic associated graph with a preset time delay associated feature vector matching model;
calculating target information in node target information by using the time delay associated feature vector to generate a pre-delay value so as to perform data stream operation;
the construction step of the preset time delay associated feature vector matching model comprises the following steps of:
acquiring node target information and node interconnection information under each condition;
generating corresponding node dynamic information according to the target information of each node;
marking node interconnection information on node dynamic information to generate node marking dynamic information;
performing cluster analysis on the node mark dynamic information to generate node mark dynamic cluster information;
marking and visualizing the node dynamic information according to the node marking dynamic clustering information to generate node dynamic template visualization diagrams under various situations or various weight situations;
carrying out gray scale on the node dynamic template visual image to generate a gray scale dynamic template visual image;
carrying out Gaussian distribution calculation on the gray dynamic template visual map to generate a Gaussian distribution dynamic template distribution map;
and carrying out weighted calculation on the Gaussian distribution dynamic template distribution graph to generate a time delay associated feature vector matching model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211415419.4A CN115941286B (en) | 2022-11-11 | 2022-11-11 | Data processing method applied to Internet of things and live broadcast platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211415419.4A CN115941286B (en) | 2022-11-11 | 2022-11-11 | Data processing method applied to Internet of things and live broadcast platform |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115941286A CN115941286A (en) | 2023-04-07 |
| CN115941286B true CN115941286B (en) | 2023-07-04 |
Family
ID=86655174
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211415419.4A Active CN115941286B (en) | 2022-11-11 | 2022-11-11 | Data processing method applied to Internet of things and live broadcast platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115941286B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014209494A1 (en) * | 2013-06-28 | 2014-12-31 | Wi-Lan Labs, Inc. | Video streaming quality of experience degradation control using a video quality metric |
| CN107135400A (en) * | 2017-06-12 | 2017-09-05 | 上海高顿教育培训有限公司 | A kind of method for reducing network direct broadcasting delay |
| CN112003795A (en) * | 2020-07-17 | 2020-11-27 | 苏州浪潮智能科技有限公司 | Method, system, equipment and storage medium for dynamically preventing traffic attack |
| CN112333189A (en) * | 2020-11-06 | 2021-02-05 | 蔡淦祺 | Data processing method based on Internet of things communication and live broadcast platform and cloud computing center |
| US10924562B1 (en) * | 2013-05-02 | 2021-02-16 | Amazon Technologies, Inc. | Real-time monitoring of IO load and latency |
| CN112399194A (en) * | 2021-01-20 | 2021-02-23 | 腾讯科技（深圳）有限公司 | Live data processing method and device, computer and readable storage medium |
| WO2022005346A1 (en) * | 2020-06-29 | 2022-01-06 | Telefonaktiebolaget Lm Ericsson (Publ) | Method for scheduling multiple replicated data flows over a number of wireless transmission paths |
| CN114448887A (en) * | 2022-01-26 | 2022-05-06 | 北京大学深圳研究生院 | Information center network cache management method and system based on token and popularity |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8055894B2 (en) * | 1999-11-09 | 2011-11-08 | Google Inc. | Process and streaming server for encrypting a data stream with bandwidth based variation |
| US10917353B2 (en) * | 2018-02-28 | 2021-02-09 | Microsoft Technology Licensing, Llc | Network traffic flow logging in distributed computing systems |
-
2022
- 2022-11-11 CN CN202211415419.4A patent/CN115941286B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10924562B1 (en) * | 2013-05-02 | 2021-02-16 | Amazon Technologies, Inc. | Real-time monitoring of IO load and latency |
| WO2014209494A1 (en) * | 2013-06-28 | 2014-12-31 | Wi-Lan Labs, Inc. | Video streaming quality of experience degradation control using a video quality metric |
| CN107135400A (en) * | 2017-06-12 | 2017-09-05 | 上海高顿教育培训有限公司 | A kind of method for reducing network direct broadcasting delay |
| WO2022005346A1 (en) * | 2020-06-29 | 2022-01-06 | Telefonaktiebolaget Lm Ericsson (Publ) | Method for scheduling multiple replicated data flows over a number of wireless transmission paths |
| CN112003795A (en) * | 2020-07-17 | 2020-11-27 | 苏州浪潮智能科技有限公司 | Method, system, equipment and storage medium for dynamically preventing traffic attack |
| CN112333189A (en) * | 2020-11-06 | 2021-02-05 | 蔡淦祺 | Data processing method based on Internet of things communication and live broadcast platform and cloud computing center |
| CN113395266A (en) * | 2020-11-06 | 2021-09-14 | 蔡淦祺 | Data processing method applied to Internet of things and live broadcast platform and cloud computing center |
| CN112399194A (en) * | 2021-01-20 | 2021-02-23 | 腾讯科技（深圳）有限公司 | Live data processing method and device, computer and readable storage medium |
| CN114448887A (en) * | 2022-01-26 | 2022-05-06 | 北京大学深圳研究生院 | Information center network cache management method and system based on token and popularity |
Non-Patent Citations (1)
| Title |
|---|
| 一个基于令牌桶的带宽动态分配研究;骆建歆;梁伟;刘超;;广东通信技术(03);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115941286A (en) | 2023-04-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9716644B2 (en) | Systems and methods for content type classification | |
| WO2018054342A1 (en) | Method and system for classifying network data stream | |
| US20170187738A1 (en) | Systems and methods for categorizing network traffic content | |
| US7952775B2 (en) | Gray scale image cleaning system and method | |
| CN112054967A (en) | Network traffic classification method and device, electronic equipment and storage medium | |
| CN108418727B (en) | Method and system for detecting network equipment | |
| WO2021169294A1 (en) | Application recognition model updating method and apparatus, and storage medium | |
| CN115237766A (en) | Fuzzy test case screening method and device, electronic equipment and storage medium | |
| CN115941286B (en) | Data processing method applied to Internet of things and live broadcast platform | |
| US11153193B2 (en) | Method of and system for testing a computer network | |
| Zhou et al. | Identifying QoS violations through statistical end‐to‐end analysis | |
| CN112465003A (en) | Identification method and system for encrypted discrete sequence message | |
| CN116095006A (en) | Dynamic flow control method and system for video live broadcast service | |
| CN113873001A (en) | Load balancing optimization method based on HTTP request classification | |
| CN112333189B (en) | Data processing method based on Internet of things communication and live broadcast platform and cloud computing center | |
| CN113792751A (en) | Cross-domain behavior identification method, device, equipment and readable storage medium | |
| CN113676341A (en) | Quality difference evaluation method and related equipment | |
| US20230419173A1 (en) | Discriminator generation device, discriminator generation method, and discriminator generation program | |
| US11606305B2 (en) | Quality of service adjusting method based on application categories and system thereof | |
| CN117671431B (en) | Industrial defect image generation method, device, equipment and storage medium | |
| KR20140088058A (en) | Apparatus and Method for Traffic Classification, Apparatus and Method for Building Traffic Classification Program, Computer Readable Media | |
| US20220058479A1 (en) | Method of judging reliability of deep learning machine | |
| KR20130101647A (en) | Apparatus and method for traffic classification, apparatus and method for building traffic classification program, computer readable media | |
| An et al. | Evaluating SIP-based VoIP communication quality and network security | |
| CN116582523A (en) | Collaborative configuration method, device, server and medium of terminal equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |