CN114785791A - Cloud-side interactive data optimization method based on cloud computing and server - Google Patents

Cloud-side interactive data optimization method based on cloud computing and server Download PDF

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CN114785791A
CN114785791A CN202210572325.1A CN202210572325A CN114785791A CN 114785791 A CN114785791 A CN 114785791A CN 202210572325 A CN202210572325 A CN 202210572325A CN 114785791 A CN114785791 A CN 114785791A
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CN114785791B (en
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李继昂
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Beijing Global Guoguang Media Technology Co ltd
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Muling Guowei Network Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/14—Session management

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Abstract

The invention provides a cloud side interactive data optimization method and a server based on cloud computing, which can determine adaptive information security detection strategy instructions under various session message interaction conditions, so that statistical data of messages to be transmitted in various cloud side interactive scenes meet the interaction timeliness adjustment expectation, thereby reducing unnecessary resource overhead, flexibly meeting the message processing requirements of different cloud side interactive scenes, and avoiding service interaction delay caused by excessive messages and limited transmission resource waste caused by insufficient messages.

Description

Cloud-side interactive data optimization method based on cloud computing and server
Technical Field
The invention relates to the technical field of cloud computing, in particular to a cloud edge-side interactive data optimization method and a server based on cloud computing.
Background
At present, the heat of cloud computing is continuously reduced, and with the continuous development of services such as big data and 5G, the combination of edge computing and cloud computing is gradually mature nowadays, and a cloud edge system is formed to a certain extent. The cloud side end system can realize the advantage complementation of cloud computing and edge computing, and improve the efficiency of service interaction. With the continuous expansion of the scale of the cloud edge system, how to flexibly deal with the session message processing of the related service is a technical problem to be solved.
Disclosure of Invention
The invention provides a cloud side interactive data optimization method and a server based on cloud computing, and adopts the following technical scheme in order to achieve the technical purpose.
The first aspect is a cloud-side interactive data optimization method based on cloud computing, which is applied to a cloud computing server, and the method at least comprises the following steps:
identifying statistical data of the messages to be transmitted in at least one session interaction period by combining a session message interaction record, basic statistical data of the messages to be transmitted and an information security detection strategy indication of at least one session interaction period to be matched, which correspond to the cloud side end interaction scene, and a random approximation estimation strategy;
determining the information security detection strategy indication of not less than one session interaction time period according to the statistical data of the messages to be transmitted in not less than one session interaction time period and the interaction timeliness adjustment expectation configured by combining the statistical data of the messages to be transmitted;
and updating the session security detection thread of the cloud side end interaction scene according to the information security detection strategy instruction of not less than one session interaction period.
In some optional embodiments, the identifying, in combination with a random approximation estimation policy, statistical data of messages to be transmitted in at least one session interaction period in combination with a session message interaction record, basic statistical data of messages to be transmitted, and an information security detection policy indication of at least one session interaction period to be matched under each service interaction task corresponding to a cloud-side interaction scene, includes:
determining the activeness of a session message stream caused under each service interaction task by combining session message interaction records under each service interaction task corresponding to the cloud side interaction scene;
determining dynamic variables of statistical data of messages to be transmitted under each service interaction task by combining the activity of the session message stream;
and analyzing to obtain the to-be-transmitted message prediction data corresponding to each session interaction time interval in at least one session interaction time interval under each service interaction task by combining the dynamic variables and the basic to-be-transmitted message statistical data.
In some optional embodiments, the session message stream includes a waiting message stream, an acknowledgement message stream, and an aggregated message stream; the session message interaction record comprises received message statistics and message load statistics; the determining the activity of the session message stream caused by each service interaction task in combination with the session message interaction record under each service interaction task corresponding to the cloud side interaction scene comprises the following steps: the following contents are implemented for each service interaction task:
acquiring a received message statistic and a received message data volume under the service interaction task, wherein the message load statistic and the message load data volume are configured when a message load reaches an upper limit under the service interaction task, and a persistent message data volume is configured when a session message is overloaded under the service interaction task;
obtaining the activity of the waiting message flow according to the received message statistic, the received message data volume and the persistent message data volume;
obtaining the activity of the response message flow according to the message load statistic value, the message load data quantity and the persistent message data quantity;
and obtaining the activity of the converged message stream according to the received message statistic, the message load statistic, the received message data volume and the message load data volume.
In some optional embodiments, the obtaining of the received message data volume under the service interaction task includes:
and combining the received message data volume, the received message statistic value, the message load statistic value and the binding characteristic among the message load data volume indicated by the visual knowledge relationship network, and obtaining the received message data volume under the service interaction task according to the received message statistic value, the message load statistic value and the message load data volume.
In some optional embodiments, the information security detection policy includes a session message interception thread and a session message passing thread; the activity of the session message flow comprises the activity of a waiting message flow, the activity of a response message flow and the activity of an aggregation message flow; the determining the dynamic variable of the statistical data of the messages to be transmitted under each service interaction task by combining the activity of the session message stream includes:
determining a first dynamic variable of a corresponding time interval of a session message interception thread by combining the activity of the response message stream and the activity of the waiting message stream;
and determining a second dynamic variable of a corresponding time period of the session message release thread by combining the activity of the response message flow and the activity of the converged message flow.
In some optional embodiments, the information security detection policy indication includes a valid period of a session message interception thread and a valid period of a session message release thread; the analyzing, in combination with the dynamic variable and the statistical data of the basic to-be-transmitted message, to obtain the to-be-transmitted message prediction data corresponding to each session interaction time period in at least one session interaction time period under each service interaction task, includes: the following contents are implemented for each service interaction task:
analyzing to obtain first to-be-transmitted message prediction data corresponding to the conversation message intercepting thread when the conversation message intercepting thread is suspended and second to-be-transmitted message prediction data corresponding to the conversation message releasing thread when the conversation message releasing thread is suspended in each conversation interaction period by combining the first dynamic variable and the second dynamic variable, the effective period of the conversation message intercepting thread and the effective period of the conversation message releasing thread in each conversation interaction period of not less than one conversation interaction period and the basic to-be-transmitted message statistical data; in a corresponding time period of a session message interception thread, by combining statistical data of messages to be transmitted when a previous session message releasing thread is in a pause state, an effective time period of a current session message interception thread and the first dynamic variable, obtaining first prediction data of the messages to be transmitted after the session message interception thread is in a pause state;
and in the corresponding time period of the session message releasing thread, the second to-be-transmitted message prediction data after the session message releasing thread is in the suspended state is obtained by combining the statistical data of the to-be-transmitted message after the previous session message intercepting thread is in the suspended state, the effective time period of the current session message releasing thread and the second dynamic variable.
In some optional embodiments, the determining, according to the statistical data of the to-be-transmitted message in the at least one session interaction period and the interaction timeliness adjustment expectation configured by combining the statistical data of the to-be-transmitted message, the information security detection policy indication of the at least one session interaction period includes:
and obtaining the effective time interval of the session message interception thread and the effective time interval of the session message release thread in each session interaction time interval by combining the second to-be-transmitted message prediction data corresponding to the suspended session message release thread in each session interaction time interval and the interaction timeliness adjustment expectation configured by combining the statistical data of the to-be-transmitted messages.
In some optional embodiments, the interactive timeliness adjustment desirably includes one or more of: realizing the minimum processing of the statistical data of the messages to be transmitted of each service interaction task; the balance of the statistical data of the messages to be transmitted of each service interaction task is realized; and avoiding that the statistical data of the messages to be transmitted of each service interaction task is larger than the set statistical data.
In some optional embodiments, the minimizing process for implementing the statistical data of the messages to be transmitted of each service interaction task includes: the appointed calculation value of the statistical data of the messages to be transmitted of each service interaction task is close to a first appointed value; the balancing of the statistical data of the messages to be transmitted of each service interaction task comprises the following steps: the discrete index between the statistical data of the messages to be transmitted of each service interaction task is close to a second designated numerical value; the method for preventing the statistical data of the messages to be transmitted of each service interaction task from being larger than the set statistical data comprises the following steps: and the difference between the statistical data of the messages to be transmitted of each service interaction task and the set statistical data is not more than 0.
In some optional embodiments, the indicating, according to the information security detection policy of not less than one session interaction period, a session security detection thread for updating the cloud-side interaction scenario includes:
extracting information security detection strategy indications of a set number of session interaction periods from the determined information security detection strategy indications of not less than one session interaction period;
and updating the effective operation time period of the session safety detection threads of the subsequent session interaction time periods with the set number in combination with the extracted information safety detection strategy indication of the session interaction time periods with the set number.
In some optional embodiments, at every interval of time T, determining the information security detection policy indication of not less than one session interaction time period by combining session message interaction records under each service interaction task corresponding to the cloud-side interaction scene; the effective time intervals of the session message intercepting threads in each session interaction time interval in the information security detection strategy indication of not less than one session interaction time interval are consistent, and the effective time intervals of the session message releasing threads are also consistent;
the step of updating the session security detection thread of the cloud-side interaction scene according to the information security detection policy indication of not less than one session interaction period comprises the following steps: and updating the effective operation period of the session security detection thread in the period T by combining the information security detection strategy indication in any session interaction period in the at least one session interaction period.
In some optional embodiments, the session message interaction record comprises received message statistics; the received message statistic includes a received message statistic detected in real time by a message detection module configured under each service interaction task corresponding to the cloud edge interaction scene, or a previous received message statistic. .
A second aspect is a cloud computing server comprising a memory and a processor; the memory and the processor are coupled; the memory for storing computer program code, the computer program code comprising computer instructions; wherein the computer instructions, when executed by the processor, cause the cloud computing server to perform the method of the first aspect.
A third aspect is a computer-readable storage medium having stored thereon a computer program which, when executed, performs the method of the first aspect.
According to an embodiment of the invention, the statistical data of the messages to be transmitted in the cloud side interaction scene identified by the random approximation estimation strategy can be identified, and the statistical data of the messages to be transmitted in the cloud side interaction scene identified by the random approximation estimation strategy can be processed by combining the interaction timeliness adjustment expectation configured by the statistical data of the messages to be transmitted, so that when the statistical data of the messages to be transmitted in the cloud side interaction scene meets the interaction timeliness adjustment expectation, the information security detection strategy indication of the session interaction period of the subsequent session message interception/release threads is obtained, and the update of the session security detection thread is further completed. Compared with the traditional scheme, the adaptive information security detection strategy indication can be determined under various session message interaction conditions, so that the statistical data of the messages to be transmitted in the interaction scenes of the cloud edge terminals meet the interaction timeliness adjustment expectation, unnecessary resource overhead can be reduced, the message processing requirements of different interaction scenes of the cloud edge terminals can be flexibly met, service interaction delay caused by excessive messages is avoided, and limited transmission resource waste caused by insufficient messages is avoided.
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Fig. 1 is a schematic flowchart of a cloud-edge-based interactive data optimization method based on cloud computing according to an embodiment of the present invention.
Fig. 2 is a block diagram of modules of a cloud-edge-based interaction data optimization apparatus based on cloud computing according to an embodiment of the present invention.
Detailed Description
In the following, the terms "first", "second" and "third", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to imply that the number of indicated technical features is significant. Thus, a feature defined as "first," "second," or "third," etc., may explicitly or implicitly include one or more of that feature.
Fig. 1 is a schematic flowchart illustrating a cloud-computing-based cloud-edge-side interaction data optimization method according to an embodiment of the present invention, where the cloud-computing-based cloud-edge-side interaction data optimization method may be implemented by a cloud computing server, and the cloud computing server may include a memory and a processor; the memory and the processor are coupled; the memory for storing computer program code, the computer program code comprising computer instructions; wherein, when the processor executes the computer instructions, the cloud computing server is caused to execute the technical solution described in the following steps.
Step 102: and identifying the statistical data of the messages to be transmitted in at least one session interaction period by combining a session message interaction record, basic statistical data of the messages to be transmitted and an information security detection strategy indication of at least one session interaction period to be matched under each service interaction task corresponding to the cloud side end interaction scene and combining a random approximation estimation strategy.
In the embodiment of the present invention, the cloud edge interaction scene may be different types of cloud edge interaction scenes configured with a session security detection thread. For example, the cloud side interaction scenario may be an e-commerce interaction scenario, a cloud game interaction scenario, an intelligent government and enterprise interaction scenario, and the like.
Further, the cloud-edge interaction scenario may include at least one interaction metric. The interaction metric permits transmission of session messages based on only one service interaction task. The service interaction task refers to a task corresponding to the interaction requirement of the cloud edge interaction scene, for example, the service interaction task can match the time sequence interaction requirement, the resource sharing interaction requirement and the like of the cloud edge interaction scene.
In some possible examples, a stochastic approximation estimation strategy identification (recursive prediction) of the statistical data of the messages to be transmitted may be performed in conjunction with the conversation message stream concept. Therefore, the requirements for samples can be reduced, the session safety detection thread can be updated only by the received message statistical value data of the cloud side end interaction scene, and the resource requirements for the cloud side end interaction scene are reduced. In addition, random approximation estimation strategy identification of statistical data of messages to be transmitted can be carried out under various session message interaction conditions, so that the cloud edge-side interaction data optimization method based on cloud computing provided by the invention avoids the influence of session message interaction conditions as much as possible, for example, adaptive information security detection strategy indications can be obtained under different session message interaction conditions. Therefore, the self-adaptive processing of the session message is realized, and the message overload or the message underload is avoided.
In some possible embodiments, step 102 is further described below.
Step 202, determining the liveness of the session message stream caused by each service interaction task by combining the session message interaction records under each service interaction task corresponding to the cloud side interaction scene.
In the embodiment of the present invention, the session message stream may indicate a change condition of a session message queue caused by a change in the number of session messages in the interaction scenario at the cloud edge. The session message stream may include a waiting message stream, an acknowledgement message stream, and an aggregate message stream. The waiting message stream is obtained due to the termination of the transmission of the session message during the running of the session message interception thread, and the waiting message stream can cause the increase of statistical data of the messages to be transmitted. The reply message stream may be available during the session message passing thread as a result of the session message beginning to be transmitted, which may result in a reduction in the statistics of the messages to be transmitted. After the session message passing thread is switched, as the session message is continuously transmitted in the cloud side interactive scene and enters the to-be-transmitted message queue, the newly-added session message can obtain the aggregated message stream, the aggregated message stream can cause the increase of the statistical data of the to-be-transmitted message, and correspondingly, the reduction rate of the statistical data of the to-be-transmitted message is reduced.
It is understood that there is typically an interaction speed, i.e., liveness (which is understood by the present invention) of the conversational message stream. In the invention, the activity of the waiting message flow is regarded as active value1, the activity of the response message flow is regarded as active value2, and the activity of the aggregated message flow is regarded as active value 3.
In some possible embodiments, an exemplary determination of liveness of a conversation message stream is as follows.
Step 402, obtaining the received message statistics and the received message data volume under the service interaction task, where the message activation statistics and the message activation data volume are configured when the message load reaches the upper limit under the service interaction task, and the preset persistent message data volume is preset when the session message is overloaded under the service interaction task.
In some possible examples, the variables of these configurations may be stored in advance and then obtained according to requirements, where the statistical value corresponds to message traffic, and the data amount corresponds to the information amount size (for example, in KB, MB, and GB) of the message, and may also be understood as the density of the message.
In some possible examples, the received message statistics include received message statistics detected in real time by a message detection module configured under each service interaction task corresponding to the cloud-side interaction scenario, or previous received message statistics of statistics. Therefore, the activity can be determined, and the effective operation period optimization of the session safety detection thread can be flexibly carried out. The message detection module may include a module with a counting program, and the received message statistics of the cloud-edge interaction scene may be obtained in real time through the message detection modules.
In some possible examples, the received message data volume may be obtained under the condition of the a priori received message statistics, the message load statistics, and the message load data volume by combining the received message data volume, the received message statistics, the message load statistics, and the binding characteristics between the message load data volumes indicated by the visual knowledge relationship network, so as to improve the flexibility of the scheme.
Having obtained the a priori variables needed to calculate liveness of the conversational message stream, steps 404-408 may be performed.
Step 404, obtaining the activity of the waiting message flow according to the received message statistic, the received message data amount and the persistent message data amount.
And 406, obtaining the activity of the response message flow according to the message activation statistic, the message activation data volume and the persistent message data volume.
Step 408, obtaining the liveness of the converged message stream according to the received message statistic, the message activation statistic, the received message data volume and the message activation data volume.
In combination with steps 402 to 408, the activity of the three types of session message streams under each service interaction task corresponding to the cloud edge interaction scenario can be accurately determined according to the session message stream thought.
And 204, determining dynamic variables of the statistical data of the messages to be transmitted under each service interaction task by combining the activity of the session message stream.
The session security detection thread generally covers a session message interception thread and a session message passing thread.
When a certain service interaction task corresponds to a session message intercepting thread in a cloud edge side interaction scene, the statistical data of messages to be transmitted under the service interaction task is increased. In the invention, in the corresponding time period of the session message interception thread, the average dynamic variable of the statistical data of the messages to be transmitted caused by the session message flow is called a first dynamic variable which is regarded as change1, and the effective time period of the session message interception thread is regarded as thread _ Rt. When a certain service interaction task corresponds to a session message release thread in a cloud edge side interaction scene, the statistical data of messages to be transmitted under the service interaction task is reduced. In the invention, in the corresponding time period of the conversation message release thread, the average dynamic variable of the statistical data of the message to be transmitted caused by the conversation message flow is called as a second dynamic variable which is regarded as change2, and the effective time period of the conversation message release thread is regarded as thread _ Gt.
Combining the above contents and the liveness of the session message stream obtained in step 202, a first dynamic variable and a second dynamic variable corresponding to each other can be obtained when the session message interception thread and the session message release thread are executed. For example, determining a first dynamic variable of a corresponding time period of a session message interception thread by combining the activity of the response message stream and the activity of the waiting message stream; and determining a second dynamic variable of a corresponding time period of the session message release thread by combining the activity of the response message flow and the activity of the converged message flow. Furthermore, a dynamic variable is understood to mean the rate of change.
And step 206, analyzing and obtaining the to-be-transmitted message prediction data corresponding to each session interaction time interval in at least one session interaction time interval under each service interaction task by combining the dynamic variables and the basic to-be-transmitted message statistical data.
The statistical data of the messages to be transmitted after the session message interception thread is in the suspended state is referred to as first predicted data of the messages to be transmitted, and is regarded as calculation1_ Pi, i which identifies the session interaction period of the ith session message interception/release thread, and the statistical data of the messages to be transmitted after the session message release thread is in the suspended state is referred to as second predicted data of the messages to be transmitted, and is regarded as calculation2_ Pi, i which represents the session interaction period of the ith session message interception/release thread.
When the calculation1_ Pi is identified, the statistical data calculation2_ Pi-1 of the messages to be transmitted after the last round of the session message passing thread is in the suspended state, the valid period thread _ Rt of the session message intercepting thread, and the first dynamic variable change1 are obtained, in combination:
calculation1_ Pi = calculation2_ Pi-1+ thread _ Rt _ change1, the calculation1_ Pi can be obtained.
When identifying the calculation2_ Pi, it only needs to obtain the statistical data calculation1_ Pi of the messages to be transmitted after the previous round of the session message interception thread is in the suspended state, the valid period of the session message release thread _ Gt, and the second dynamic variable change2, in combination:
the calculation2_ Pi = calculation1_ Pi-thread _ Gt _ change2, so that the calculation2_ Pi can be obtained.
By combining the above contents, the message prediction data to be transmitted corresponding to each session interaction time period can be obtained by analyzing for each service interaction task.
In some possible examples, analyzing, by combining the first dynamic variable and the second dynamic variable, the effective time period of the session message interception thread and the effective time period of the session message release thread in each session interaction time period of the at least one session interaction time period, and the basic to-be-transmitted message statistical data, to obtain first to-be-transmitted message prediction data corresponding to the session message interception thread when the session message blocking thread is suspended and second to-be-transmitted message prediction data corresponding to the session message release thread when the session message release thread is suspended in each session interaction time period; in a corresponding time period of a session message interception thread, by combining statistical data of messages to be transmitted when a previous session message releasing thread is in a pause state, an effective time period of a current session message interception thread and the first dynamic variable, obtaining first prediction data of the messages to be transmitted after the session message interception thread is in a pause state; and in the corresponding time period of the session message releasing thread, the second to-be-transmitted message prediction data after the session message releasing thread is in the suspended state is obtained by combining the statistical data of the to-be-transmitted message after the previous session message intercepting thread is in the suspended state, the effective time period of the current session message releasing thread and the second dynamic variable.
Further, the basic to-be-transmitted message statistical data may be to-be-transmitted message statistical data after the session message interception thread or the session message release thread corresponding to the start of the session interaction period is optimized during the effective operation period of the session security detection thread is in a suspended state. The basic statistics of messages to be transmitted may be a predetermined value or actual basic statistics of messages to be transmitted (such as initial statistics of messages to be transmitted).
Taking the basic to-be-transmitted message statistical data as the to-be-transmitted message statistical data after the session message passing thread is in the suspended state as an example, the number _ Q0 can be regarded as the case may be.
The following contents are obtained through analysis in combination with the above contents:
calculation_P1=number_Q0+thread_Rt*change1。
the following contents are obtained by analysis in combination with the above contents:
number_Q1=calculation_P1-thread_Gt*change2。
based on the above content, performing loop processing until obtaining first to-be-transmitted message prediction data after the session message interception thread is in the suspended state and second to-be-transmitted message prediction data after the session message release thread is in the suspended state in each session interaction period.
For each service interaction task, by using the above-mentioned thought, the first to-be-transmitted message prediction data corresponding to the session message intercepting thread being suspended and the second to-be-transmitted message prediction data corresponding to the session message releasing thread being suspended in each session interaction period can be obtained by analysis.
And 104, determining the information security detection strategy indication of not less than one session interaction time period according to the statistical data of the message to be transmitted in not less than one session interaction time period and the interaction timeliness adjustment expectation configured by combining the statistical data of the message to be transmitted.
It is to be understood that the information security detection policy indicates a valid period of the session message interception/clearance thread including each session interaction period. By combining some optimization ideas (for example, AI algorithm minimization ideas), the information security detection policy indication of the session interaction period of the subsequent multiple session message interception/release threads can be obtained when the statistical data of the messages to be transmitted in the cloud edge-side interaction scene meets the interaction timeliness adjustment expectation. The configured interactive timeliness adjustment is expected to be understood as some limiting requirement for the statistical data of the messages to be transmitted.
For some possible examples, the interactive timeliness adjustment may desirably include one or more of: realizing the minimum processing of the statistical data of the messages to be transmitted of each service interaction task; the balance of the statistical data of the messages to be transmitted of each service interaction task is realized; and avoiding that the statistical data of the messages to be transmitted of each service interaction task is larger than the set statistical data.
And updating the session security detection thread according to the information security detection strategy instruction which is expected to be obtained by the interactive timeliness adjustment, so that the overload of the waiting message can be prevented, the statistical data of the message to be transmitted is reduced, and the throughput efficiency of the session message is improved on the premise of ensuring the accurate security detection of the session message.
The invention refers to the minimization processing of the statistical data of the messages to be transmitted for realizing each service interaction task as a first interaction timeliness adjustment expectation. The first AI algorithm corresponding to the first interactive timeliness adjustment expectation is considered expectection _ f 1.
In some possible examples, the minimizing process for implementing the statistical data of the messages to be transmitted of each service interaction task includes: and the appointed calculation value of the statistical data of the messages to be transmitted of each service interaction task is close to a first appointed value. Through the processing of expecteration _ f1, the effective time period of a session message intercepting thread and the effective time period of a session message releasing thread, which enable to-be-transmitted message statistical data of each service interaction task in the cloud edge side interaction scene to be as short as possible, can be obtained. The invention refers to the balance of the statistical data of the messages to be transmitted for realizing each service interaction task as a second interactive timeliness adjustment expectation. In some possible examples, the second AI algorithm corresponding to the second interactive timeliness adjustment expectation is considered expectection _ f 2. The balance of the statistical data of the messages to be transmitted of each service interaction task is realized, and the balance comprises the following steps: and the discrete index between the statistical data of the messages to be transmitted of each service interaction task is close to a second specified numerical value. Through the processing of expecteration _ f2, the effective time period of the session message intercepting thread and the effective time period of the session message releasing thread, which balance the traffic of each service interaction task in the cloud frontier interaction scene, can be obtained. The invention refers to the third interactive timeliness adjustment expectation that the statistical data of the messages to be transmitted of each service interaction task is prevented from being larger than the set statistical data. The third AI algorithm corresponding to the third interactive timeliness adjustment expectation is considered expectection _ f 3. The method for preventing the statistical data of the messages to be transmitted of each service interaction task from being larger than the set statistical data comprises the following steps: and the difference between the statistical data of the messages to be transmitted of each service interaction task and the set statistical data is not more than 0. Through the processing of expecteration _ f3, the effective time interval of the session message intercepting thread and the effective time interval of the session message releasing thread can be obtained, wherein the effective time interval of the session message intercepting thread and the effective time interval of the session message releasing thread can prevent the to-be-transmitted message statistical data of each service interaction task in the cloud side interaction scene from being overloaded (reaching the set statistical data).
And 106, updating the session security detection thread of the cloud side end interaction scene according to the information security detection strategy instruction of not less than one session interaction time period.
For the embodiment of the present invention, the obtained effective time period of the session message interception thread and the obtained effective time period of the session message release thread in each session interaction time period may be combined, and an update command may be sent to the update node that updates the session security detection thread, so that the session security detection thread may perform session message interception/release thread switching according to the obtained effective time period of the session message interception thread and the obtained effective time period of the session message release thread.
The method is applied to the technical scheme, the statistical data of the messages to be transmitted in the cloud side interaction scene can be identified by combining a random approximation estimation strategy, the statistical data of the messages to be transmitted in the cloud side interaction scene identified by combining the random approximation estimation strategy is processed by combining an interaction timeliness adjustment expectation configured by the statistical data of the messages to be transmitted, when the statistical data of the messages to be transmitted in the cloud side interaction scene meets the interaction timeliness adjustment expectation, the information security detection strategy indication of the session interaction period of the subsequent session message interception/release threads is obtained, and the session security detection thread updating is completed by combining the determined signal schemes of the subsequent session interaction periods. Compared with the traditional scheme, the adaptive information security detection strategy indication can be determined under various session message interaction conditions, so that statistical data of the messages to be transmitted in the interaction scenes of the cloud edge terminals meet the interaction timeliness adjustment expectation, unnecessary resource overhead can be reduced, the message processing requirements of different interaction scenes of the cloud edge terminals can be met flexibly, service interaction delay caused by too many messages is avoided, and limited transmission resource waste caused by too few messages is avoided.
For some possible examples, in step 104, the second to-be-transmitted message prediction data corresponding to the suspended session message release thread in each session interaction period and the interaction timeliness configured by combining the to-be-transmitted message statistical data under each service interaction task may be adjusted to obtain the valid period of the session message interception thread and the valid period of the session message release thread in each session interaction period. When the conversation message passing thread is in pause, the statistic data of the messages to be transmitted when the messages wait for the shortest time are corresponded. And reducing the statistical data of the messages to be transmitted after the session message releasing thread is suspended as much as possible.
In some embodiments, the period of validity of the session message interception/release thread in the next period T of the session security detection thread may be set every period T, and the period of validity of the session message interception thread and the session message interception thread in the period T is the same, and the period of validity of the session message release thread and the session message release thread is the same. The time period T may be set in conjunction with the event situation. The effective time periods of the session message interception/release threads in the session interaction time period of any session message interception/release thread of the session security detection thread can be set at any time, the effective time periods of the session message interception threads in the session interaction time period of each session message interception/release thread can be inconsistent, and the effective time periods of the session message release threads can also be inconsistent. For example, the validity period of the session message interception/release thread in the session interaction period of the subsequent session message interception/release thread or threads can be arbitrarily set.
For some possible examples, the invention proposes a flexible updating idea for the second way of the session security detection thread. In the flexible updating thought, the effective time interval of the session message interception thread and the effective time interval of the session message release thread in not less than one session interaction time interval in the following can be obtained by combining the session message interaction records under each service interaction task corresponding to the cloud side end interaction scene and combining the steps 102 to 104. And then extracting the information security detection policy indications of the set number of session interaction periods from the determined information security detection policy indications of not less than one session interaction period, and updating the effective operation period of the session security detection thread of the subsequent set number of session interaction periods by combining the extracted information security detection policy indications of the set number of session interaction periods. Therefore, the effective operation time period of the session safety detection thread can be flexibly updated, the timeliness of processing the session messages of the cloud side end interaction scene is better, the statistical data of the messages to be transmitted are more suitable for the actual session message flow of the cloud side end interaction scene, and the purpose of intelligently updating the effective operation time period of the session safety detection thread by combining the session message flow is achieved.
For example, the set number is 1. The session message stream can be reached by combining the current cloud-side interaction scene every other session message interception/release thread session interaction period, the effective period of the session message interception/release thread in each session interaction period in not less than one subsequent session interaction period (assuming 20 session interaction periods) is obtained by combining the optimization of the method illustrated in the steps 102 to 104, and then the effective period of the session message interception/release thread in the subsequent first session interaction period can be extracted as the effective period of the session message interception/release thread in the session interaction period of the next session message interception/release thread, so as to achieve the purpose of intelligently updating the effective operation period of the session message stream in the session interaction period of the next session message interception/release thread by combining the session interaction period of the previous session message interception/release thread .
For some possible examples, the invention proposes a directed update concept for the session security detection thread of the first concept. In the directional updating thought, the identified effective time periods of the session message interception threads in the session interaction time period of each session message interception/release thread in the session interaction time period of at least one session message interception/release thread are consistent, and the effective time periods of the session message release threads are also consistent. At intervals of time T, determining the information security detection strategy indication of not less than one session interaction time period by combining session message interaction records under each service interaction task corresponding to the cloud side interaction scene, and then updating the effective operation time period of the session security detection thread in the time period T by combining the information security detection strategy indication in any one session interaction time period of not less than one session interaction time period.
For example, the time period T is 30 min. The session message stream can be reached at intervals of 30min by combining the current cloud-side interaction scene, and the effective time period of the session message interception/release thread in each subsequent session interaction time period not less than one session interaction time period (assuming 20 session interaction time periods) is obtained by combining the optimization of the methods illustrated in the steps 102 to 104. The effective time interval of the session message interception thread is consistent with the effective time interval of the session message interception thread in each session interaction time interval, and the effective time interval of the session message release thread is consistent with the effective time interval of the session message release thread. Then, the effective time interval of the session message interception/release thread in any session interaction time interval can be extracted as the effective time interval of the session message interception/release thread in the following 30min of the session security detection thread, so as to achieve the purpose of optimizing the effective operation time interval of the session security detection thread of the first idea.
By applying the technical scheme, the following technical effects can be achieved.
(1) The statistical data of the messages to be transmitted in the interactive scene of the cloud side end can be predicted by combining with the thinking of the conversation message flow, and utilizing a thread parameter adjusting algorithm to perform min processing on the statistical data of the messages to be transmitted in all directions of the cloud side interactive scene, wherein the statistical data of the messages to be transmitted in all directions of the cloud side interactive scene do not exceed a threshold value, the statistical data of the messages to be transmitted in all directions of the cloud side interactive scene are balanced, and the like, so that a session safety detection thread updating scheme of not less than one session interactive period is obtained, compared with the traditional scheme, can prevent the message waiting overload, reduce the statistical data of the messages to be transmitted, keep the message waiting longer, thus reducing the unnecessary resource expenditure, and the method and the device flexibly meet the message processing requirements of different cloud edge interaction scenes, avoid service interaction delay caused by excessive messages and avoid limited transmission resource waste caused by insufficient messages.
(2) The message prediction data to be transmitted by using the conversation message stream thought is utilized, the requirement on a sample is reduced, the conversation safety detection thread can be updated only by the received message statistical value data of the cloud edge side interaction scene, and the updating efficiency of the conversation safety detection thread is improved.
(3) The effective operation time interval of the session interaction time interval session safety detection thread of the next session message interception/release thread can be intelligently updated by combining the session interaction time interval session message flow of the previous session message interception/release thread, and the method is more suitable for the actual session message flow of the cloud side end interaction scene.
Under some design ideas which can be independently implemented, after a session security detection thread of the cloud-side interaction scene is updated, the method can further include the following steps: responding to the session security detection request, and extracting the to-be-processed session message carrying the risk label from the session message interception thread; mining a risk intention vector of the conversation message to be processed; and performing information protection processing through the risk intention vector.
In the embodiment of the invention, on the basis of receiving the session security detection request, the to-be-processed session message carrying the risk label can be extracted from the session message intercepting thread, the to-be-processed session message carrying the risk label can be the to-be-processed session message which is preliminarily analyzed and intercepted by the session message intercepting thread and possibly has the information security risk, and the intrusion preference of the to-be-processed session message can be mined by determining the risk intention vector, so that the targeted information protection processing is performed based on the risk intention vector.
Under some design ideas which can be independently implemented, in the process of mining the risk intention vector of the conversation message to be processed, in order to improve the accuracy of the risk intention vector, disturbance vector removal is required, and based on the disturbance vector removal, the mining of the risk intention vector of the conversation message to be processed can be realized through the following technical scheme: extracting risk intention vectors of the conversation messages to be processed by utilizing a preset GCN model to obtain multiple groups of associated risk intention vectors, wherein the multiple groups of associated risk intention vectors comprise a Kth group of risk intention vectors, a Jth group of risk intention vectors and a Jth group of risk intention vectors for completing disturbance vector removal, K is a positive integer, and J = K-1; identifying vector elements of risk intention vectors to be processed to obtain a disturbance removal indicating unit, wherein the risk intention vectors to be processed are obtained by weighting the K-th group of risk intention vectors, the J-th group of risk intention vectors and the J-th group of risk intention vectors subjected to disturbance vector removal on an attention level; changing the size of the disturbance elimination indication unit to make the number of attention indexes of the disturbance elimination indication unit consistent with the number of attention indexes of target risk intention vectors of the K-th group of risk intention vectors; identifying vector elements of target risk intention vectors of the K-th group of risk intention vectors by the disturbance removal indicating unit with changed size to obtain the first target risk intention vector; and mapping the first target risk intention vector to obtain the K-th group of risk intention vectors for completing disturbance vector removal.
By the design, the disturbance vector can be removed based on the continuous risk intention vector, so that the disturbance elimination precision is improved.
Under some design ideas which can be independently implemented, the identifying operation is performed on the vector elements of the to-be-processed risk intention vector to obtain a disturbance removal indicating unit, which includes: identifying the risk intention vectors to be processed to extract the variation characteristics of the vector elements of the J-th group of risk intention vectors relative to the vector elements of the K-th group of risk intention vectors to obtain a matching unit; and performing feature conversion processing on the matching unit to obtain the disturbance removal indicating unit.
Under some independently implementable design considerations, the identifying operation performed on the to-be-processed risk intention vectors to extract variation features of vector elements of the J-th group of risk intention vectors relative to vector elements of the K-th group of risk intention vectors, and after obtaining the matching unit, the method further includes: and identifying vector elements of target risk intention vectors of the J-th group of risk intention vectors by the matching unit to obtain a second target risk intention vector.
Under some independently implementable design considerations, said identifying, by said matching unit, vector elements of target risk intention vectors of said jth group of risk intention vectors to obtain a second target risk intention vector, includes: changing the size of the matching means such that the number of attention indices of the matching means matches the number of attention indices of the target risk intention vectors of the jth group of risk intention vectors; and identifying vector elements of target risk intention vectors of the risk intention vectors of which the disturbance vector removal is completed in the J-th group by the matching unit with changed sizes to obtain the second target risk intention vector.
Under some design ideas which can be independently implemented, the mapping the first target risk intention vector to obtain the kth group of risk intention vectors for completing disturbance vector removal includes: performing fusion processing on the first target risk intention vector and the second target risk intention vector to obtain a third target risk intention vector; and mapping the third target risk intention vector to obtain the K-th group of risk intention vectors for completing the removal of the disturbance vectors.
Under some independently implementable design considerations, the identifying operation on the to-be-processed risk intention vectors to extract variation features of vector elements of the J-th group of risk intention vectors relative to vector elements of the K-th group of risk intention vectors to obtain a matching unit includes: weighting the K-th group of risk intention vectors, the J-th group of risk intention vectors and the J-th group of risk intention vectors subjected to disturbance vector removal on an attention level to obtain the to-be-processed risk intention vectors; performing feature conversion processing on the to-be-processed risk intention vector to obtain a fourth target risk intention vector; identifying the fourth target risk intention vector to obtain a fifth target risk intention vector; and adjusting the attention index number of the fifth target risk intention vector to a first preset value through identification operation to obtain the matching unit.
Under some design ideas which can be independently implemented, the matching unit performs feature conversion processing to obtain the disturbance removal indicating unit, and the method includes: adjusting the attention index number of the matching unit to a second preset value through identification operation to obtain a sixth target risk intention vector; performing fusion processing on the fourth target risk intention vector and the sixth target risk intention vector to obtain a seventh target risk intention vector; and performing identification operation on the seventh target risk intention vector to extract the disturbance removing information of the vector elements of the J-th group of risk intention vectors which finish disturbance vector removal relative to the vector elements of the J-th group of risk intention vectors, so as to obtain the disturbance removing indication unit.
Under some design ideas which can be independently implemented, the identifying operation on the seventh target risk intention vector is performed to extract the disturbance removing information of the J-th group of risk intention vectors which complete disturbance vector removal relative to the vector elements of the J-th group of risk intention vectors, so as to obtain the disturbance removing indication unit, and the disturbance removing indication unit includes: identifying the seventh target risk intention vector to obtain an eighth target risk intention vector; and adjusting the attention index number of the eighth target risk intention vector to the first preset value through identification operation to obtain the disturbance removal indicating unit.
Under some design ideas which can be independently implemented, the mapping is performed on the third target risk intention vector to obtain the kth group of risk intention vectors for completing disturbance vector removal, including: performing reverse recognition operation on the third target risk intention vector to obtain a ninth target risk intention vector; identifying the ninth target risk intention vector to obtain a Kth group of mapped risk intention vectors; and summing the vector value of the first vector element of the K-th group of risk intention vectors and the vector value of the second vector element of the K-th group of mapped risk intention vectors to obtain the K-th group of risk intention vectors with disturbance vector removal completed, wherein the distribution of the first vector element in the K-th group of risk intention vectors is consistent with the distribution of the second vector element in the K-th group of mapped risk intention vectors.
Based on the same inventive concept, fig. 2 shows a block diagram of a cloud-side interaction data optimization device based on cloud computing according to an embodiment of the present invention, where the cloud-side interaction data optimization device based on cloud computing may include a data identification module 21 for implementing the relevant method steps shown in fig. 1, and is configured to identify statistical data of messages to be transmitted in at least one session interaction period by combining session message interaction records, basic statistical data of messages to be transmitted, and information security detection policy indication of at least one session interaction period to be matched under each service interaction task corresponding to a cloud-side interaction scene; the indication determining module 22 is configured to determine the information security detection policy indication of the at least one session interaction period according to the statistical data of the to-be-transmitted message in the at least one session interaction period and an interaction timeliness adjustment expectation configured by combining the statistical data of the to-be-transmitted message; and the thread updating module 23 is configured to update the session security detection thread of the cloud edge interaction scene according to the information security detection policy indication of not less than one session interaction period.
The related embodiment applied to the invention can achieve the following technical effects: the statistical data of the messages to be transmitted in the cloud edge side interaction scene identified by the random approximation estimation strategy can be identified by combining the random approximation estimation strategy, the statistical data of the messages to be transmitted in the cloud edge side interaction scene identified by the random approximation estimation strategy is processed by combining the interaction timeliness adjustment expectation configured by the statistical data of the messages to be transmitted, and when the statistical data of the messages to be transmitted in the cloud edge side interaction scene meets the interaction timeliness adjustment expectation, the information security detection strategy indication of the session interaction period of the follow-up session message interception/release threads is obtained, and then the update of the session security detection thread is completed. Compared with the traditional scheme, the adaptive information security detection strategy indication can be determined under various session message interaction conditions, so that the statistical data of the messages to be transmitted in the interaction scenes of the cloud edge terminals meet the interaction timeliness adjustment expectation, unnecessary resource overhead can be reduced, the message processing requirements of different interaction scenes of the cloud edge terminals can be flexibly met, service interaction delay caused by excessive messages is avoided, and limited transmission resource waste caused by insufficient messages is avoided.
The above description is only a specific embodiment of the present invention. Those skilled in the art can conceive of changes or substitutions based on the specific embodiments provided by the present invention, and all such changes or substitutions are intended to be included within the scope of the present invention.

Claims (10)

1. A cloud-side interactive data optimization method based on cloud computing is applied to a cloud computing server, and the method at least comprises the following steps:
identifying statistical data of the messages to be transmitted in not less than one session interaction period by combining session message interaction records, basic statistical data of the messages to be transmitted and information security detection strategy indication of not less than one session interaction period to be matched, which correspond to the cloud side end interaction scene, and a random approximation estimation strategy;
determining the information security detection strategy indication of not less than one session interaction time period according to the statistical data of the messages to be transmitted in not less than one session interaction time period and the interaction timeliness adjustment expectation configured by combining the statistical data of the messages to be transmitted;
and updating the session security detection thread of the cloud side end interaction scene according to the information security detection strategy indication of not less than one session interaction time period.
2. The method of claim 1, wherein the identifying the statistical data of the messages to be transmitted in the at least one session interaction period by combining a random approximation estimation strategy with the session message interaction record, the basic statistical data of the messages to be transmitted, and the information security detection strategy indication of the at least one session interaction period to be matched under each service interaction task corresponding to the cloud-side interaction scene comprises:
determining the liveness of a session message stream caused under each service interaction task by combining session message interaction records under each service interaction task corresponding to the cloud edge interaction scene;
determining dynamic variables of statistical data of messages to be transmitted under each service interaction task by combining the activity of the session message stream;
and analyzing to obtain the to-be-transmitted message prediction data corresponding to each session interaction time interval in at least one session interaction time interval under each service interaction task by combining the dynamic variables and the basic to-be-transmitted message statistical data.
3. The method of claim 2, wherein the conversational message flow comprises a waiting message flow, an acknowledgement message flow, an aggregate message flow; the session message interaction record comprises received message statistics and message load statistics; the determining the activity of the session message stream caused by each service interaction task in combination with the session message interaction record under each service interaction task corresponding to the cloud side interaction scene comprises the following steps: aiming at each service interaction task, the following contents are implemented: acquiring a received message statistic and a received message data volume under the service interaction task, wherein the message load statistic and the message load data volume are configured when a message load reaches an upper limit under the service interaction task, and a persistent message data volume is configured when a session message is overloaded under the service interaction task; obtaining the activity of the waiting message flow according to the received message statistic, the received message data volume and the persistent message data volume; obtaining the activity of the response message flow according to the message load statistic, the message load data quantity and the temporary message data quantity; obtaining the liveness of the converged message stream according to the received message statistic, the message load statistic, the received message data volume and the message load data volume;
the method for acquiring the received message data volume under the service interaction task comprises the following steps: and combining the received message data volume, the received message statistic value, the message load statistic value and the binding characteristic among the message load data volume indicated by the visual knowledge relationship network, and obtaining the received message data volume under the service interaction task according to the received message statistic value, the message load statistic value and the message load data volume.
4. The method of claim 2, wherein the information security detection policy comprises a session message interception thread and a session message passing thread; the activity of the session message stream comprises the activity of a waiting message stream, the activity of a response message stream and the activity of an aggregation message stream; the determining dynamic variables of statistical data of the messages to be transmitted under each service interaction task by combining the activity of the session message stream comprises the following steps:
determining a first dynamic variable of a corresponding time interval of a session message interception thread by combining the activity of the response message stream and the activity of the waiting message stream;
and determining a second dynamic variable of a corresponding period of the session message release thread by combining the activity of the response message stream and the activity of the aggregated message stream.
5. The method of claim 4, wherein the information security detection policy indication comprises a validity period of a session message interception thread and a validity period of a session message passing thread; the analyzing, in combination with the dynamic variable and the basic statistical data of the messages to be transmitted, to obtain prediction data of the messages to be transmitted corresponding to each session interaction period in at least one session interaction period under each service interaction task, includes: aiming at each service interaction task, the following contents are implemented:
analyzing to obtain first to-be-transmitted message prediction data corresponding to the conversation message intercepting thread in the pause state and second to-be-transmitted message prediction data corresponding to the conversation message releasing thread in the pause state in each conversation interaction period by combining the first dynamic variable and the second dynamic variable, the effective period of the conversation message intercepting thread and the effective period of the conversation message releasing thread in each conversation interaction period in at least one conversation interaction period, and the basic to-be-transmitted message statistical data; in a corresponding time period of a session message interception thread, by combining statistical data of messages to be transmitted when a previous session message releasing thread is in a pause state, an effective time period of a current session message interception thread and the first dynamic variable, obtaining first prediction data of the messages to be transmitted after the session message interception thread is in a pause state;
in the corresponding time period of the session message releasing thread, the statistical data of the messages to be transmitted after the previous session message intercepting thread is in the suspended state, the effective time period of the current session message releasing thread and the second dynamic variable are combined to obtain the prediction data of the second messages to be transmitted after the session message releasing thread is in the suspended state;
the determining the information security detection policy indication of the at least one session interaction period according to the statistical data of the to-be-transmitted message in the at least one session interaction period and the interaction timeliness adjustment expectation configured by combining the statistical data of the to-be-transmitted message includes: and obtaining the effective time interval of the session message interception thread and the effective time interval of the session message release thread in each session interaction time interval by combining the second to-be-transmitted message prediction data corresponding to the suspended session message release thread in each session interaction time interval and the interaction timeliness adjustment expectation configured by combining the statistical data of the to-be-transmitted messages.
6. The method of claim 1, wherein the interactive timeliness adjustment expectations include one or more of: realizing the minimum processing of the statistical data of the messages to be transmitted of each service interaction task; the balance of the statistical data of the messages to be transmitted of each service interaction task is realized; the statistical data of the messages to be transmitted of each service interaction task is prevented from being larger than the set statistical data;
the minimizing process of the statistical data of the messages to be transmitted of the service interaction tasks includes: the appointed calculation value of the statistical data of the messages to be transmitted of each service interaction task is close to a first appointed value; the balancing of the statistical data of the messages to be transmitted of each service interaction task comprises the following steps: the discrete index between the statistical data of the messages to be transmitted of each service interaction task is close to a second designated numerical value; avoiding that the statistical data of the messages to be transmitted of each service interaction task is greater than the set statistical data, comprises: and the difference between the statistical data of the messages to be transmitted of each service interaction task and the set statistical data is not more than 0.
7. The method of claim 1, wherein the indicating of the information security detection policy for not less than one session interaction period to update the session security detection thread of the cloud-frontend interaction scenario comprises:
extracting information security detection strategy instructions of a set number of session interaction time periods from the determined information security detection strategy instructions of not less than one session interaction time period;
and updating the effective operation time period of the session safety detection threads of the subsequent session interaction time periods with the set number in combination with the extracted information safety detection strategy indication of the session interaction time periods with the set number.
8. The method according to claim 1, wherein at intervals of time T, session message interaction records under each service interaction task corresponding to the cloud side interaction scene are combined to determine the information security detection policy indication of not less than one session interaction time period; the effective time intervals of the session message intercepting threads of all session interaction time intervals in the information security detection strategy indication of not less than one session interaction time interval are consistent, and the effective time intervals of the session message releasing threads are also consistent;
the step of updating the session security detection thread of the cloud side end interaction scene according to the information security detection policy indication of not less than one session interaction time period comprises the following steps: and updating the effective operation period of the session security detection thread in the period T by combining the information security detection strategy indication in any session interaction period in the at least one session interaction period.
9. The method of claim 1, wherein the session message interaction record comprises received message statistics; the received message statistic comprises a received message statistic detected in real time by a message detection module under each service interaction task corresponding to the cloud side interaction scene, or a previous received message statistic.
10. A cloud computing server, comprising: a memory and a processor; the memory and the processor are coupled; the memory for storing computer program code, the computer program code comprising computer instructions; wherein the computer instructions, when executed by the processor, cause the cloud computing server to perform the method of any of claims 1-9.
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