CN112565417A

CN112565417A - Communication data processing method applied to edge computing and Internet of things and cloud server

Info

Publication number: CN112565417A
Application number: CN202011411636.7A
Authority: CN
Inventors: 李彩云
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-06-06
Filing date: 2020-06-06
Publication date: 2021-03-26
Also published as: CN111741083B; CN112565408A; CN111741083A

Abstract

The method comprises the steps of firstly generating a flow change trajectory graph according to collected real-time data flow of a user terminal and calculating the channel occupancy rate of a target network, secondly acquiring a flow processing log of each user terminal when the channel occupancy rate is detected to reach a set occupancy rate, thirdly analyzing the flow processing log to obtain data to be transmitted and a terminal identifier corresponding to each user terminal, and fourthly generating a tag implantation instruction according to the terminal identifier and issuing the tag implantation instruction to the corresponding user terminal so as to instruct the user terminal to generate a data tag according to the tag implantation instruction and implanting the data to be transmitted to obtain target transmission data. Therefore, the target user terminal can analyze the data label when receiving the data to be transmitted so as to determine whether to receive the data to be transmitted or forward the data to be transmitted, and the phenomenon that the data is in a string state when being transmitted between the user terminals is avoided.

Description

Communication data processing method applied to edge computing and Internet of things and cloud server

Technical Field

The application relates to the technical field of edge computing, in particular to a communication data processing method and a cloud server applied to edge computing and the Internet of things.

Background

With the popularization of internet of things devices and 5G technologies, a series of problems (such as time delay and large-area outage) are generated by providing services and analyzing data by means of a centralized cloud platform, and the rebuilding of network infrastructures becomes more and more important. And the edge computing can deploy important data processing functions at a position closer to the terminal side of the network edge, which provides a feasible solution for solving the problems brought by the centralized cloud platform. By making the data closer to the user terminal, the communication delay can be effectively improved, and the interruption of the data processing process caused by large-area shutdown can be avoided. However, in the process of interaction based on the edge computing technology, data "string" often occurs in the terminal device.

Disclosure of Invention

The application provides a communication data processing method and a cloud server applied to edge computing and the Internet of things, so as to solve the technical problems in the prior art.

In a first aspect, a communication data processing method applied to edge computing and the internet of things is provided, and the method includes:

acquiring real-time data traffic of each user terminal from each user terminal through a traffic monitoring interface established with each user terminal, converting each group of real-time data traffic into a traffic change trajectory graph according to a set time step length, and calculating the channel occupancy rate of a target network where the user terminal is located according to each traffic change trajectory graph;

when the occupancy rate of the channel is detected to reach the set occupancy rate, acquiring a flow processing log of each user terminal through a log monitoring program configured for each user terminal in advance;

analyzing the flow processing log of each user terminal to obtain data to be transmitted of each user terminal in the current time period determined based on the real-time data flow corresponding to the user terminal and a terminal identifier of a target terminal corresponding to the data to be transmitted; the target terminal is a user terminal for receiving the data to be transmitted;

generating a label implanting instruction according to a terminal identifier corresponding to data to be transmitted of each user terminal in the current time period, and sending the label implanting instruction to the corresponding user terminal, so that the user terminal generates a data label according to the label implanting instruction and implants the data label into the data to be transmitted to obtain target transmission data; when the user terminal transmits the target transmission data through the target network, if a first target terminal receives the target transmission data, the first target terminal analyzes a data label in the target transmission data to obtain a target transmission path of the target transmission data, the first target terminal caches the data to be transmitted when judging that a target terminal identifier corresponding to a path node of the target transmission path is consistent with a current identifier of the first target terminal, and the first target terminal forwards the target transmission data when judging that the target terminal identifier corresponding to the path node of the target transmission path is inconsistent with the current identifier of the first target terminal.

In a second aspect, a cloud server is provided, where the cloud server is configured to:

In a third aspect, a cloud server is provided, including: the system comprises a processor, a memory and a network interface, wherein the memory and the network interface are connected with the processor; the network interface is connected with a nonvolatile memory in the cloud server; when the processor is operated, the computer program is called from the nonvolatile memory through the network interface, and the computer program is operated through the memory so as to execute the method.

In a fourth aspect, a readable storage medium applied to a computer is provided, and a computer program is burned in the readable storage medium, and when the computer program runs in a memory of a cloud server, the method is implemented.

The communication data processing method and the cloud server applied to the edge computing and the internet of things provided by the embodiment of the application have the advantages that firstly, a flow change trajectory graph is generated according to collected real-time data flow of user terminals, the channel occupancy rate of a target network is calculated, secondly, flow processing logs of each user terminal are obtained when the channel occupancy rate is detected to reach the set occupancy rate, thirdly, the flow processing logs are analyzed, to-be-transmitted data and terminal identifications corresponding to each user terminal are obtained, and fourthly, a label implanting instruction is generated according to the terminal identifications and is issued to the corresponding user terminals, so that the user terminals are instructed to generate data labels according to the label implanting instruction and implant the data labels into the to-be-transmitted data to obtain target transmission data. Therefore, the target user terminal can analyze the data label when receiving the data to be transmitted so as to decide whether to receive the data to be transmitted or forward the data to be transmitted. Thus, the phenomenon of 'line cross' when data is transmitted between user terminals can be avoided.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

Fig. 1 is a communication architecture diagram of a communication data processing system applied to edge computing and the internet of things according to an exemplary embodiment of the present application.

Fig. 2 is a flowchart illustrating a communication data processing method applied to edge computing and internet of things according to an exemplary embodiment of the present application.

Fig. 3 is a block diagram illustrating an embodiment of a communication data processing apparatus applied to edge computing and internet of things according to an exemplary embodiment of the present application.

Fig. 4 is a hardware structure diagram of a cloud server where a communication data processing apparatus applied to edge computing and internet of things is located according to the present application.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

The inventor analyzes and discovers when data transmission of the terminal equipment is carried out, and the data interaction between the terminal equipment can be freely carried out because the data processing function of the terminal equipment breaks the original centralized processing mode. However, the data amount at the terminal device side is increased rapidly by the data interaction method, and different data occupy the communication channel successively under the limited communication channel, so that an error occurs in data transmission, for example, target data which is originally transmitted to the terminal device b by the terminal device a is finally transmitted to the terminal device c due to the fact that the communication channel is occupied and a "serial line" occurs. This can result in large areas of confusion in data transmission.

In order to solve the problems, the present disclosure discloses a communication data processing method and a cloud server applied to edge computing and the internet of things, which can monitor real-time data traffic of a user terminal through a traffic detection interface established between the user terminal and the user terminal, and instruct the user terminal to implant a data tag into data to be transmitted when a communication channel which may cause data transmission is crowded due to excessive real-time data traffic, so that a target user terminal can analyze the data tag when receiving the data to be transmitted, thereby determining whether to receive the data to be transmitted or forward the data to be transmitted. Thus, the phenomenon of 'line cross' when data is transmitted between user terminals can be avoided.

Referring to fig. 1, a communication architecture diagram of a communication data processing system 100 applied to edge computing and the internet of things is shown, where the communication data processing system 100 may include a cloud server 110 and a plurality of user terminals 120 communicating with each other. The user terminal 120 may be a mobile phone, a tablet computer, a smart home, or a data server. The cloud server 110 may establish a traffic monitoring interface with each user terminal 120, so that data traffic of each user terminal 120 may be monitored through the traffic monitoring interface, thereby avoiding "crosstalk" when the user terminals 120 transmit data.

On the basis, referring to fig. 2, a flow diagram of a communication data processing method applied to edge computing and the internet of things is given, the method may be applied to the cloud server 110 in fig. 1, and the cloud server 110 specifically performs the following steps S21 to S24 when implementing the method.

Step S21, collecting the real-time data traffic of each user terminal from each user terminal through the traffic monitoring interface established with each user terminal, converting each group of real-time data traffic into a traffic change trajectory graph according to a set time step, and calculating the channel occupancy rate of the target network where the user terminal is located according to each traffic change trajectory graph.

In the disclosure, when the cloud server collects the real-time data traffic, normal receiving, sending and processing of the data traffic by the user terminal are not affected. The flow change trajectory diagram is a continuous two-dimensional coordinate graph, the abscissa of which represents time (moment) and the ordinate represents flow size. The change of the data flow can be visualized through the flow change trajectory graph. The occupancy rate is used to characterize the degree of congestion of the communication channel of the target network, and the greater the occupancy rate, the more severe the degree of congestion of the communication channel.

And step S22, when detecting that the channel occupancy rate reaches the set occupancy rate, acquiring the flow processing log of each user terminal through a log monitoring program configured for each user terminal in advance.

In the present disclosure, the set occupancy is determined according to network parameters of the target network, where the network parameters include channel resource allocation parameters, network protocol configuration parameters, and the like. The log monitoring program can be implemented by a code program, for example, the code can be written by java. The flow processing log records the processing duration, the processing mode and the processing result of the user terminal to the real-time data flow. It can be understood that the data to be transmitted and the transmission object of the user terminal can be determined by analyzing the flow processing log, so that the implantation of the data tag of the data to be transmitted is realized.

Step S23, analyzing the flow processing log of each user terminal to obtain the data to be transmitted of the current time period and the terminal identification of the target terminal corresponding to the data to be transmitted, which are determined by each user terminal based on the real-time data flow corresponding to the user terminal; and the target terminal is a user terminal for receiving the data to be transmitted.

Step S24, generating a label implanting instruction according to a terminal identifier corresponding to the data to be transmitted of each user terminal in the current time period, and sending the label implanting instruction to the corresponding user terminal, so that the user terminal generates a data label according to the label implanting instruction and implants the data label into the data to be transmitted to obtain target transmission data; when the user terminal transmits the target transmission data through the target network, if a first target terminal receives the target transmission data, the first target terminal analyzes a data label in the target transmission data to obtain a target transmission path of the target transmission data, the first target terminal caches the data to be transmitted when judging that a target terminal identifier corresponding to a path node of the target transmission path is consistent with a current identifier of the first target terminal, and the first target terminal forwards the target transmission data when judging that the target terminal identifier corresponding to the path node of the target transmission path is inconsistent with the current identifier of the first target terminal.

When the content described in the above step S21-step S24 is executed, the first step generates a traffic change trajectory graph according to the collected real-time data traffic of the user terminal and calculates the channel occupancy rate of the target network, the second step acquires a traffic processing log of each user terminal when the channel occupancy rate is detected to reach the set occupancy rate, the third step analyzes the traffic processing log to obtain the data to be transmitted and the terminal identifier corresponding to each user terminal, and the fourth step generates a tag implantation instruction according to the terminal identifier and issues the instruction to the corresponding user terminal to instruct the user terminal to generate a data tag according to the tag implantation instruction and implant the data to be transmitted to obtain the target transmission data. Therefore, the target user terminal can analyze the data label when receiving the data to be transmitted so as to decide whether to receive the data to be transmitted or forward the data to be transmitted. Thus, the phenomenon of 'line cross' when data is transmitted between user terminals can be avoided.

The inventor finds out through research and analysis that when data flow is converted into a flow change trajectory graph, the defect value of the data flow when the time sequence characteristics are displayed in an image form needs to be considered, so that the integrity and the accuracy of the obtained flow change trajectory graph can be ensured. To achieve the above purpose, the step S21 of converting each group of real-time data traffic into a traffic change trajectory graph according to a set time step may specifically include the following steps S2111 to S2115.

Step S2111, flow segment marks are periodically arranged in each group of real-time data flow according to the set time step, the time sequence information of each flow segment mark and a first time sequence parameter conversion list of the set time step are determined, and a first defect value of the time sequence information corresponding to each flow segment mark is determined according to the first time sequence parameter conversion list.

In this embodiment, the set time step can be adjusted according to the actual service requirement, and will not be further described herein. The flow segmentation mark can be a segmentation character, and the first defect value is used for representing the flow defect condition of the real-time data flow during flow segmentation.

Step S2112, in the process of extracting the traffic feature of the target data traffic between every two adjacent traffic segment markers, a second deficiency value between the traffic feature information of each segment of the target data traffic and the time sequence information of the two traffic segment markers corresponding to the target data traffic is calculated.

In this embodiment, the second defect value is used to characterize a defect condition of the feature value caused by thread delay when feature extraction is performed on the real-time data traffic.

Step S2113, determining a defect ratio between the first defect value of each of the two traffic segment markers corresponding to the target data traffic and the second defect value corresponding to the target data traffic.

Step S2114, aiming at each group of real-time data flow, if the real-time data flow has a defect proportion exceeding a set proportion, adopting a preset characteristic compensation value to compensate the flow characteristic of each section of target data flow corresponding to the real-time data flow, so as to obtain a compensation flow characteristic.

In this embodiment, the characteristic compensation value is used to numerically compensate for the defective flow characteristic.

Step S2115, calculating a plurality of track coordinates corresponding to each group of real-time data flow according to the compensation flow characteristics, and fitting the track coordinates according to the time sequence to obtain a flow change track chart.

When the contents described in the above steps S2111 to S2115 are applied, the first defect value and the second defect value of each group of real-time data flow rates can be calculated, the flow characteristics corresponding to each group of real-time data flow rates are compensated according to the defect proportion of the first defect value and the second defect value, and then the trajectory coordinates calculated according to the compensated flow characteristics are fitted to obtain the flow change trajectory diagram. Therefore, the defect value of the data flow when the time sequence characteristics are displayed in an image form can be considered, so that the track coordinate is accurately calculated, and the integrity and the accuracy of the obtained flow change track graph are further ensured.

When the channel occupancy rate is calculated, the traffic change error caused by the equipment loss of the user terminal corresponding to each traffic change trajectory diagram needs to be considered, so that the calculated channel occupancy rate is avoided to be low. For this purpose, in step S21, the occupancy rate of the target network where the ue is located is calculated according to each traffic variation trace map, which may specifically include the contents described in the following steps S2121 to S2123.

Step S2121, extracting the track description information of each flow change track map, and determining the equipment loss record of the user terminal corresponding to each flow change track map according to the track description information.

In step S2121, the trajectory description information is used to characterize a flow rate of the flow change trajectory graph, and the device loss record is used to characterize loss parameters of the user terminal during operation, where the loss parameters include device heating loss and device oxidation loss.

And S2122, extracting loss factors of the determined loss records of each device to obtain loss factors corresponding to the loss records of each device.

In step S2122, the loss factor has a value range of 0 to 1.

Step S2123, determining a target time interval with the maximum number of track curves with slopes larger than a set slope in all the traffic change track maps, calculating an initial channel occupancy proportion of each traffic change track map in the target time interval, and weighting the initial channel occupancy proportion through a loss factor corresponding to each traffic change track map to obtain the channel occupancy of the target network.

It is understood that, through the descriptions of the above steps S2121-S2123, the loss factor can be determined by taking the device loss of the user terminal into account. Therefore, the loss factors are adopted for weighting when the channel occupancy rate is calculated, so that the calculated channel occupancy rate is low, and accurate judgment basis is provided for the execution of the whole method.

In specific implementation, in order to quickly and accurately obtain a traffic processing log to improve timeliness of implementation of the whole scheme, the problem of interface heterogeneity between a log monitoring program and a cloud server needs to be solved. In order to solve the above problem, in step S22, the traffic processing log of each user terminal is acquired by the log monitoring program configured for each user terminal in advance, and further, the following contents described in step S221 to step S224 may be included.

Step S221, collecting a plurality of interface requirement identifications of the log monitoring program, listing first interface parameters corresponding to each interface requirement identification, and constructing an interface matching list of the log monitoring program according to the listed first interface parameters; the interface matching list comprises a plurality of list blocks, each list block corresponds to one interface protocol, each interface protocol corresponds to at least one first interface parameter, and each list block of the interface matching list has different protocol compatibility coefficients.

Step S222, reading an interface expansion variable of the cloud server, and extracting at least one second interface parameter, which is included in the interface expansion variable of the cloud server and corresponds to the first interface parameter in the interface matching list, from the interface expansion variable.

Step S223 of establishing a parameter transformation list between at least one second interface parameter included in the interface extension variable and corresponding to the first interface parameter in the interface matching list and the interface matching list, and determining interface difference information between the log monitoring program and the cloud server based on the parameter transformation list; determining interface difference information between the log monitoring program and the cloud server based on the parameter transformation list specifically comprises: converting the parameter format of each second interface parameter into a target format corresponding to the script file format of the log monitoring program, and respectively generating at least one group of parameter sequences of each second interface parameter in the corresponding parameter format; acquiring a parameter sequence which is not repeated and corresponds to each second interface parameter to form a parameter heterogeneous sequence of each second interface parameter; mapping each parameter sequence in the parameter heterogeneous sequences to the interface matching list to obtain interface difference information between the log monitoring program and the cloud server.

Step S224, determining a log extraction path corresponding to the log monitoring program configured by the cloud server and each user terminal according to the interface difference information, and extracting a flow processing log of each user terminal from a storage area corresponding to the log monitoring program configured by each user terminal according to the log extraction path.

When the contents described in steps S221 to S224 are executed, the interface difference information between the log monitoring program and the cloud server can be determined by taking the interface heterogeneity of the log monitoring program and the cloud server into consideration, and then the log extraction path for extracting the traffic processing log is determined according to the interface difference information, so that the problem of the interface heterogeneity of the log monitoring program and the cloud server can be solved, and the traffic processing log can be quickly and accurately acquired to improve the timeliness of the whole implementation scheme.

In the above embodiment, similar log information is more in the traffic processing log, and it is necessary to check the log information obtained by analysis in order to avoid interference by the similar log information when analyzing the log. To achieve the above object, in step S23, the traffic processing log of each user terminal is analyzed to obtain data to be transmitted in the current time period determined by each user terminal based on the real-time data traffic corresponding to the user terminal and the terminal identifier of the target terminal corresponding to the data to be transmitted, which may specifically include the contents described in the following steps S231 to S234.

Step S231, analyzing the flow processing log to obtain a log information list corresponding to the flow processing log, and generating a behavior data set and a state data set of the log information list; the behavior data set is user behavior data generated by a user terminal corresponding to the log information list according to an operation instruction, the state data set is running state data of the user terminal corresponding to the log information list when the user behavior data set is generated, the behavior data set comprises multiple groups of behavior data, the state data set comprises multiple groups of state data, and each group of behavior data and each group of state data comprise data identification coefficients.

Step S232, determining a current data signature of the behavior data with the minimum data identification coefficient in the behavior data set, taking one group of state data in the state data set as reference data, and acquiring a mapping label of the current data label in the reference data; wherein the current data tag and the mapping tag have the same number of encoding characters.

Step S233, determining the similarity interference degree of the behavior data set and the state data set according to the consistency comparison result of the characters of the current data label and the mapping label on the same coding bit; the consistency comparison result is a first comparison result representing that the characters of the current data label and the mapping label on the same coding bit are the same or a second comparison result representing that the characters of the current data label and the mapping label on the same coding bit are different, the number of the consistency comparison results is multiple, and the similarity interference degree is used for representing the occupation ratio of similar log information in the traffic processing log.

Step S234, extracting first log information with a first information identifier and second log information with a second information identifier from the traffic processing log, verifying the first log information based on the similarity interference degree to obtain first target log information, and verifying the second log information to obtain second target log information; the first information identifier is used for representing that the first log information is data to be transmitted in the current time period, and the second information identifier is used for representing that the second log information is a terminal identifier of a target terminal corresponding to the data to be transmitted.

Step S235, when the similarity between the first target log information and the second target log information is smaller than a preset threshold, extracting the data to be transmitted from the first log information and extracting a terminal identifier from the second log information.

Through the steps S231 to S235, the first log information and the second log information obtained by analysis can be verified according to the determined similarity interference degree, so that interference caused by similar log information when the log is analyzed is avoided, and thus, the data to be transmitted and the terminal identifier can be accurately analyzed from the traffic processing log.

In a possible implementation manner, when generating the tag implantation instruction, not only the transmission timeliness of the instruction but also the feature recognition degree of the instruction need to be considered, so that it can be ensured that the user terminal can accurately generate the corresponding data tag according to the tag implantation instruction on the premise of rapidly issuing the tag implantation instruction. For this purpose, in step S24, a tag embedding instruction is generated according to a terminal identifier corresponding to data to be transmitted in the current time period of each user terminal, which may specifically include the contents described in steps S241 to S244 below.

Step S241, determining the feature comparison result of the terminal identifier and the current terminal identifiers of other user terminals in the target network; and the characteristic comparison result is obtained by calculating the text distance between the terminal identifier and the current terminal identifier.

Step S242, when the contrast value corresponding to the feature comparison result is greater than the set contrast value, extracting a first feature array having a first feature dimension of the terminal identifier, and generating a tag implantation instruction according to the first feature array.

Step S243, when the contrast value corresponding to the feature comparison result is less than or equal to a set contrast value, extracting a second feature array with a second feature dimension of the terminal identifier, and generating a tag implantation instruction according to the second feature array; wherein the second characteristic dimension is greater than the first characteristic dimension.

In specific implementation, based on the steps S241 to S243, the feature recognition degree of the terminal identifier can be determined based on the feature comparison result of the terminal identifier, so that the tag implantation instruction is generated according to the feature arrays of different feature dimensions of the terminal identifier. Therefore, the transmission timeliness of the instruction can be ensured, and the characteristic identification degree of the instruction can be ensured, so that the user terminal can be ensured to accurately generate the corresponding data tag according to the tag implantation instruction on the premise of rapidly issuing the tag implantation instruction.

In one possible example, in order to ensure that the tag implantation instruction is accurately issued, the issuing of the tag implantation instruction to the corresponding user terminal, which is described in step S24, specifically includes: extracting interface configuration information of a flow monitoring interface corresponding to the label implantation instruction, building an instruction transmission interface corresponding to the user terminal according to the interface configuration information, and issuing the label implantation instruction to the corresponding user terminal by adopting the instruction transmission interface. Therefore, the label implantation instruction can be accurately issued based on different equal-time instruction transmission interfaces.

In an alternative embodiment, in order to implement dynamic adjustment of the data transmission efficiency of the target network to reduce the extra workload generated by the user terminal to embed the data tag into the data to be transmitted, on the basis of the above steps S21-S24, the method may further include the following steps: and when the calculated current channel occupancy rate is lower than the set occupancy rate, sending a control instruction for suspending data signature implantation to each user terminal so that each user terminal stops implanting the data tags into the data to be transmitted. By the design, the data transmission efficiency of the target network can be dynamically adjusted, so that the extra workload generated by implanting the data label into the data to be transmitted by the user terminal is reduced.

In another alternative embodiment, in order to ensure that the first target terminal does not miss a path node when parsing the data label, in step S24, the first target terminal parses the data label in the target transmission data to obtain the target transmission path of the target transmission data, which is specifically implemented by the following steps (11) - (15).

(11) And after the label coding value of the data label is determined, extracting the characteristic character of the label coding value.

(12) Determining path information reflected by the data tag based on the characteristic characters.

(13) And segmenting the path information according to the segmentation characters of the label code values to obtain a plurality of groups of information segments.

(14) And generating a target transmission path of the target transmission data through the address identification in each group of information segments.

Based on the steps (11) - (15), it can be ensured that the first target terminal does not miss path nodes when analyzing the data label, so that the target transmission path can be accurately and completely generated.

In yet another alternative embodiment, in order to ensure that the user terminal does not interfere with the data to be transmitted when performing data tag implantation, in step S24, the user terminal generates a data tag according to the tag implantation instruction and implants the data tag into the data to be transmitted to obtain target transmission data, specifically, through the following steps (21) and (22).

(21) And determining label file information corresponding to the message information segment of the label implantation instruction and the character type of the message information segment.

(22) Determining an idle data segment of the data to be transmitted according to the character type, generating the data label according to the label file information and implanting the data label into the idle data segment; and the data implanted in the idle data segment is not associated with the service data segment of the data to be transmitted.

It can be understood that, by executing the step (21) and the step (22), the idle data segment of the data to be transmitted is determined according to the character type of the message information segment of the tag embedding instruction, so that the generated data tag is embedded into the idle data segment to avoid the influence on the service data segment of the data to be transmitted, and thus, the user terminal can be ensured not to interfere with the data to be transmitted when the data tag is embedded.

On the basis of the above, please refer to fig. 3 collectively, a block diagram of functional modules of a communication data processing apparatus 200 applied to edge computing and internet of things is provided, where the communication data processing apparatus 200 includes, but is not limited to, the following functional modules.

The channel calculation module 210 is configured to collect real-time data traffic of each user terminal from each user terminal through a traffic monitoring interface established with each user terminal, convert each group of real-time data traffic into a traffic change trajectory diagram according to a set time step, and calculate a channel occupancy rate of a target network where the user terminal is located according to each traffic change trajectory diagram;

a log obtaining module 220, configured to, when detecting that the channel occupancy reaches a set occupancy, obtain a traffic processing log of each user terminal through a log monitoring program configured for each user terminal in advance;

the log analysis module 230 is configured to analyze a traffic processing log of each user terminal to obtain data to be transmitted at a current time period, which is determined by each user terminal based on real-time data traffic corresponding to the user terminal, and a terminal identifier of a target terminal corresponding to the data to be transmitted; the target terminal is a user terminal for receiving the data to be transmitted;

the tag implanting module 240 is configured to generate a tag implanting instruction according to a terminal identifier corresponding to data to be transmitted of each user terminal in a current time period, and issue the tag implanting instruction to the corresponding user terminal, so that the user terminal generates a data tag according to the tag implanting instruction and implants the data tag into the data to be transmitted to obtain target transmission data; when the user terminal transmits the target transmission data through the target network, if a first target terminal receives the target transmission data, the first target terminal analyzes a data tag in the target transmission data to obtain a target transmission path of the target transmission data, the first target terminal caches the data to be transmitted when judging that a target terminal identifier corresponding to a path node of the target transmission path is consistent with a current identifier of the first target terminal, and the first target terminal forwards the target transmission data when judging that a target terminal identifier corresponding to a path node of the target transmission path is inconsistent with the current identifier of the first target terminal;

wherein, the tag implanting module 240 is further configured to: and when the calculated current channel occupancy rate is lower than the set occupancy rate, sending a control instruction for suspending data signature implantation to each user terminal so that each user terminal stops implanting the data tags into the data to be transmitted.

Preferably, the channel calculating module 210 is configured to:

extracting the track description information of each flow change track map, and determining the equipment loss record of the user terminal corresponding to each flow change track map according to the track description information;

performing loss factor extraction on each determined equipment loss record to obtain a loss factor corresponding to each equipment loss record;

determining a target time period with the maximum number of track curves with slopes larger than a set slope in all the traffic change track maps, calculating an initial channel occupancy proportion of each traffic change track map in the target time period, and weighting the initial channel occupancy proportion through a loss factor corresponding to each traffic change track map to obtain the channel occupancy of the target network.

Preferably, the channel calculating module 210 is configured to:

periodically setting flow segmentation marks in each group of real-time data flow according to the set time step, determining time sequence information of each flow segmentation mark and a first time sequence parameter conversion list of the set time step, and determining a first defect value of the time sequence information corresponding to each flow segmentation mark according to the first time sequence parameter conversion list;

in the process of carrying out flow characteristic extraction on the target data flow between every two adjacent flow segmentation markers, calculating a second defect value between the flow characteristic information of each segment of target data flow and the time sequence information of the two flow segmentation markers corresponding to the target data flow;

determining a defect proportion between a first defect value of each of two flow segmentation marks corresponding to the target data flow and a second defect value corresponding to the target data flow;

aiming at each group of real-time data flow, if the real-time data flow has a defect proportion exceeding a set proportion, adopting a preset characteristic compensation value to compensate the flow characteristic of each section of target data flow corresponding to the real-time data flow to obtain a compensation flow characteristic;

and calculating a plurality of track coordinates corresponding to each group of real-time data flow according to the compensation flow characteristics, and fitting the track coordinates according to the time sequence to obtain a flow change track graph.

Preferably, the log obtaining module 220 is configured to:

collecting a plurality of interface requirement identifications of the log monitoring program, listing first interface parameters corresponding to the interface requirement identifications and constructing an interface matching list of the log monitoring program according to the listed first interface parameters; the interface matching list comprises a plurality of list blocks, each list block corresponds to one interface protocol, each interface protocol corresponds to at least one first interface parameter, and each list block of the interface matching list has different protocol compatibility coefficients;

reading interface expansion variables of a cloud server and extracting at least one second interface parameter corresponding to the first interface parameter in the interface matching list from the interface expansion variables;

establishing a parameter transformation list between at least one second interface parameter corresponding to the first interface parameter in the interface matching list and the interface matching list, wherein the second interface parameter is contained in the interface extension variable, and the interface difference information between the log monitoring program and the cloud server is determined based on the parameter transformation list; determining interface difference information between the log monitoring program and the cloud server based on the parameter transformation list specifically comprises: converting the parameter format of each second interface parameter into a target format corresponding to the script file format of the log monitoring program, and respectively generating at least one group of parameter sequences of each second interface parameter in the corresponding parameter format; acquiring a parameter sequence which is not repeated and corresponds to each second interface parameter to form a parameter heterogeneous sequence of each second interface parameter; mapping each parameter sequence in the parameter heterogeneous sequences to the interface matching list to obtain interface difference information between the log monitoring program and the cloud server;

and determining a log extraction path corresponding to the log monitoring program configured by the cloud server and each user terminal according to the interface difference information, and extracting a flow processing log of each user terminal from a storage area corresponding to the log monitoring program configured by each user terminal according to the log extraction path.

Preferably, the log parsing module 230 is configured to:

analyzing the flow processing log to obtain a log information list corresponding to the flow processing log, and generating a behavior data set and a state data set of the log information list; the behavior data set is user behavior data generated by a user terminal corresponding to the log information list according to an operation instruction, the state data set is running state data of the user terminal corresponding to the log information list when the user behavior data set is generated, the behavior data set comprises a plurality of groups of behavior data, the state data set comprises a plurality of groups of state data, and each group of behavior data and each group of state data comprise data identification coefficients;

determining a current data signature of the behavior data with the minimum data identification coefficient in the behavior data set, taking one group of state data in the state data set as reference data, and acquiring a mapping label of the current data label in the reference data; wherein the current data tag and the mapping tag have the same number of encoding characters;

determining the similarity interference degree of the behavior data set and the state data set according to the consistency comparison result of the characters of the current data label and the mapping label on the same coding bit; the consistency comparison result is a first comparison result representing that the characters of the current data label and the mapping label on the same coding bit are the same or a second comparison result representing that the characters of the current data label and the mapping label on the same coding bit are different, the number of the consistency comparison results is multiple, and the similarity interference degree is used for representing the proportion of similar log information in a traffic processing log;

extracting first log information with a first information identifier and second log information with a second information identifier from the traffic processing log, verifying the first log information based on the similarity interference degree to obtain first target log information, and verifying the second log information to obtain second target log information; the first information identifier is used for representing that the first log information is data to be transmitted in the current time period, and the second information identifier is used for representing that the second log information is a terminal identifier of a target terminal corresponding to the data to be transmitted;

and when the similarity between the first target log information and the second target log information is smaller than a preset threshold value, extracting the data to be transmitted from the first log information and extracting a terminal identifier from the second log information.

Preferably, a tag implantation module 240 for:

determining a feature comparison result of the terminal identification and current terminal identifications of other user terminals in the target network; the feature comparison result is obtained by calculating the text distance between the terminal identifier and the current terminal identifier;

when the contrast value corresponding to the feature comparison result is greater than the set contrast value, extracting a first feature array with a first feature dimension of the terminal identifier, and generating a tag implantation instruction according to the first feature array;

when the contrast value corresponding to the feature comparison result is smaller than or equal to a set contrast value, extracting a second feature array with a second feature dimension of the terminal identifier, and generating a tag implantation instruction according to the second feature array; wherein the second characteristic dimension is greater than the first characteristic dimension.

Preferably, a tag implantation module 240 for:

extracting interface configuration information of a flow monitoring interface corresponding to the label implantation instruction;

setting up an instruction transmission interface corresponding to the user terminal according to the interface configuration information;

and issuing the label implantation instruction to a corresponding user terminal by adopting the instruction transmission interface.

On the basis, the communication data processing system applied to the edge computing and the Internet of things is further provided, and comprises a cloud server and a user terminal which are communicated with each other;

the cloud server is configured to:

generating a label implantation instruction according to a terminal identifier corresponding to data to be transmitted of each user terminal in the current time period, and issuing the label implantation instruction to the corresponding user terminal;

the user terminal is configured to:

generating a data tag according to the tag implantation instruction and implanting the data tag into the data to be transmitted to obtain target transmission data; when the user terminal transmits the target transmission data through the target network, if a first target terminal receives the target transmission data, the first target terminal analyzes a data label in the target transmission data to obtain a target transmission path of the target transmission data, the first target terminal caches the data to be transmitted when judging that a target terminal identifier corresponding to a path node of the target transmission path is consistent with a current identifier of the first target terminal, and the first target terminal forwards the target transmission data when judging that the target terminal identifier corresponding to the path node of the target transmission path is inconsistent with the current identifier of the first target terminal.

Preferably, the analyzing, by the first target terminal, the data tag in the target transmission data to obtain the target transmission path of the target transmission data specifically includes:

after the label coding value of the data label is determined, extracting the characteristic characters of the label coding value;

determining path information reflected by the data tag based on the characteristic characters;

segmenting the path information according to the segmentation characters of the label coding values to obtain a plurality of groups of information segments;

and generating a target transmission path of the target transmission data through the address identification in each group of information segments.

Preferably, the step of generating a data tag according to the tag embedding instruction and embedding the data tag into the data to be transmitted by the user terminal to obtain target transmission data specifically includes:

determining label file information corresponding to a message information segment of the label implantation instruction and a character type of the message information segment;

determining an idle data segment of the data to be transmitted according to the character type, generating the data label according to the label file information and implanting the data label into the idle data segment; and the data implanted in the idle data segment is not associated with the service data segment of the data to be transmitted.

Preferably, the calculating, by the cloud server according to each traffic change trajectory graph, the channel occupancy rate of the target network where the user terminal is located specifically includes:

Preferably, the step of converting each group of real-time data traffic into the traffic change trajectory graph by the cloud server according to the set time step length specifically includes:

Preferably, the acquiring, by the cloud server, the traffic processing log of each user terminal through a log monitoring program configured for each user terminal in advance includes:

Preferably, the analyzing, by the cloud server, the traffic processing log of each user terminal to obtain the data to be transmitted of each user terminal at the current time period determined based on the real-time data traffic corresponding to the user terminal and the terminal identifier of the target terminal corresponding to the data to be transmitted includes:

Preferably, the generating, by the cloud server, the tag implanting instruction according to the terminal identifier corresponding to the data to be transmitted of each user terminal in the current time period includes:

Preferably, the issuing, by the cloud server, the tag implantation instruction to the corresponding user terminal includes:

Referring to fig. 4 in combination, there is also provided a cloud server 110, including: a processor 111, and a memory 112 and a network interface 113 connected to the processor 111. The network interface 113 is connected to a nonvolatile memory 114 in the cloud server 110. The processor 111, when running, retrieves a computer program from the non-volatile memory 114 via the network interface 113 and runs the computer program via the memory 112 to perform the above-described method.

On the basis of fig. 4, a readable storage medium applied to a computer is further provided, and the readable storage medium is burned with a computer program, and the computer program implements the method when running in the memory 112 of the cloud server 110.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A communication data processing method applied to edge computing and the Internet of things is characterized by comprising the following steps:

generating a label implanting instruction according to a terminal identifier corresponding to data to be transmitted of each user terminal in the current time period, and sending the label implanting instruction to the corresponding user terminal, so that the user terminal generates a data label according to the label implanting instruction and implants the data label into the data to be transmitted to obtain target transmission data; when the user terminal transmits the target transmission data through the target network, if a first target terminal receives the target transmission data, the first target terminal analyzes a data tag in the target transmission data to obtain a target transmission path of the target transmission data, the first target terminal caches the data to be transmitted when judging that a target terminal identifier corresponding to a path node of the target transmission path is consistent with a current identifier of the first target terminal, and the first target terminal forwards the target transmission data when judging that a target terminal identifier corresponding to a path node of the target transmission path is inconsistent with the current identifier of the first target terminal;

the analyzing, by the first target terminal, the data tag in the target transmission data to obtain a target transmission path of the target transmission data includes: after the label coding value of the data label is determined, extracting the characteristic characters of the label coding value; determining path information reflected by the data tag based on the characteristic characters; segmenting the path information according to the segmentation characters of the label coding values to obtain a plurality of groups of information segments; and generating a target transmission path of the target transmission data through the address identification in each group of information segments.

2. The method of claim 1, wherein calculating the channel occupancy of the target network in which the ue is located according to each traffic variation trace map comprises:

3. The communication data processing method according to claim 1, wherein converting each group of real-time data traffic into a traffic change trajectory graph according to a set time step includes:

4. The communication data processing method according to claim 1, wherein obtaining the traffic processing log of each user terminal by a log monitoring program configured for each user terminal in advance comprises:

5. The communication data processing method according to claim 1, wherein analyzing the traffic processing log of each user terminal to obtain the data to be transmitted of each user terminal at the current time period determined based on the real-time data traffic corresponding to the user terminal and the terminal identifier of the target terminal corresponding to the data to be transmitted, comprises:

6. The communication data processing method according to claim 1, wherein generating a tag embedding instruction according to a terminal identifier corresponding to data to be transmitted in a current time period of each user terminal comprises:

7. The communication data processing method according to any one of claims 1 to 6, wherein issuing the tag embedding instruction to the corresponding user terminal includes:

8. A cloud server, comprising:

a processor, and

a memory and a network interface connected with the processor;

the network interface is connected with a nonvolatile memory in the cloud server;

the processor, when running, retrieves a computer program from the non-volatile memory via the network interface and runs the computer program via the memory to perform the method of any of claims 1-7.

9. A readable storage medium applied to a computer, wherein the readable storage medium is burned with a computer program, and the computer program realizes the method of any one of the above claims 1 to 7 when running in the memory of the cloud server.