CN116055484A - Interactive data processing method and server based on big data - Google Patents

Interactive data processing method and server based on big data Download PDF

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CN116055484A
CN116055484A CN202211436149.5A CN202211436149A CN116055484A CN 116055484 A CN116055484 A CN 116055484A CN 202211436149 A CN202211436149 A CN 202211436149A CN 116055484 A CN116055484 A CN 116055484A
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李继昂
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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 an interactive data processing method and a server based on big data, wherein the method comprises the following steps: responding to the session security detection request, extracting a to-be-processed session message carrying a risk tag from a session message interception thread; mining a risk intention vector of the session message to be processed; and carrying out information protection processing through the risk intention vector.

Description

Interactive data processing method and server based on big data
The application is a divisional application with application number 2022105723251, application date 20220525 and application name of 'cloud side end interaction data optimization method and server based on cloud computing'.
Technical Field
The invention relates to the technical field of cloud computing, in particular to an interactive data processing method and a server based on big data.
Background
At present, the heat of cloud computing is continuously not reduced, with the continuous development of big data, 5G and other services, the combination of edge computing and cloud computing is gradually mature, 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 improves the efficiency of service interaction. Along with the continuous expansion of the system scale of the cloud side, how to flexibly deal with the session message processing of related services is a technical problem to be solved at present.
Disclosure of Invention
The invention provides an interactive data processing method and a server based on big data, and the following technical scheme is adopted to achieve the technical purposes.
The first aspect is an interactive data processing method based on big data, applied to a cloud computing server, the method at least comprising:
combining session message interaction records, basic message statistics data to be transmitted and information security detection strategy indication of at least one session interaction period to be matched under each service interaction task corresponding to a cloud side interaction scene, and combining a random approximation estimation strategy to identify the message statistics data to be transmitted in the at least one session interaction period;
determining information security detection policy indication of the session interaction period according to the message statistics data to be transmitted in the session interaction period and the interaction timeliness adjustment expectations configured by combining the message statistics data to be transmitted;
and updating the session security detection thread of the cloud side interaction scene according to the information security detection policy indication of at least one session interaction period.
In some optional embodiments, the identifying the statistical data of the message to be transmitted in the at least one session interaction period by combining with a random approximation estimation policy includes:
Combining session message interaction records under each service interaction task corresponding to the cloud side interaction scene, and determining the activity of a session message stream caused under each service interaction task;
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;
and analyzing and obtaining the predicted data of the message to be transmitted corresponding to each session interaction period in the at least one session interaction period under each service interaction task by combining the dynamic variable and the basic statistical data of the message to be transmitted.
In some alternative embodiments, the session message flows include a wait message flow, a reply message flow, and 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 flow caused by the service interaction tasks according to the session message interaction records under the service interaction tasks corresponding to the cloud side interaction scene comprises the following steps: the following is implemented for each business interaction task:
acquiring a received message statistical value and a received message data volume under the service interaction task, wherein the message load statistical value and the message load data volume are configured when the message load reaches the upper limit under the service interaction task, and the reserved message data volume is configured when the conversation message is overloaded under the service interaction task;
Obtaining the activity of waiting message flow according to the received message statistic value, the received message data quantity and the temporary message data quantity;
obtaining the activity of the response message flow according to the message load statistic value, the message load data quantity and the reserved message data quantity;
and obtaining the activity of the converged message stream according to the received message statistic value and the message load statistic value, wherein the received message data quantity and the message load data quantity.
In some optional embodiments, the acquiring the received message data volume under the service interaction task includes:
and combining the received message data quantity, the received message statistical value, the message load statistical value and the binding characteristics among the message load data quantity indicated by the visual knowledge relation network, and obtaining the received message data quantity under the service interaction task according to the received message statistical value, the message load statistical value and the message load data quantity.
In some alternative embodiments, the information security detection policy includes a session message interception thread and a session message release thread; the activity level of the session message flow comprises the activity level of the waiting message flow, the activity level of the response message flow and the activity level of the convergence message flow; the determining the dynamic variable of the statistical data of the message to be transmitted under each business interaction task by combining the activity of the conversation message stream comprises the following steps:
Determining a first dynamic variable of a corresponding period of a session message interception thread by combining the liveness of the response message stream and the liveness of the waiting message stream;
and combining the activity level of the response message stream and the activity level of the converged message stream to determine a second dynamic variable of the corresponding period of the session message release thread.
In some optional embodiments, the information security detection policy indication includes a validity period of the session message interception thread and a validity period of the session message release thread; the step of analyzing the dynamic variable and the basic message statistical data to be transmitted to obtain the predicted data of the message to be transmitted corresponding to each session interaction period in the at least one session interaction period under each service interaction task, including: the following is implemented for each business interaction task:
analyzing to obtain first predicted data to be transmitted corresponding to the session message interception thread in the session interaction period and second predicted data to be transmitted corresponding to the session message passing thread in the suspension period by combining the first dynamic variable and the second dynamic variable, the effective period of the session message interception thread in each session interaction period and the effective period of the session message passing thread in each session interaction period, and the basic statistical data of the message to be transmitted; combining statistical data of messages to be transmitted when a previous round of session message release thread is in a pause state in a corresponding period of a session message interception thread, and obtaining first predicted data of messages to be transmitted after the session message interception thread is in the pause state by using an effective period of the current session message interception thread and the first dynamic variable;
And combining the statistical data of the message to be transmitted after the session message interception thread is in a pause state in the corresponding period of the session message release thread, the effective period of the current session message release thread and the second dynamic variable to obtain the predicted data of the second message to be transmitted after the session message release thread is in the pause state.
In some optional embodiments, the determining the information security detection policy indication of the not less than one session interaction period according to the statistics of the messages to be transmitted in the not less than one session interaction period and the interaction timeliness adjustment expectations configured in combination with the statistics of the messages to be transmitted includes:
and combining with the interaction timeliness adjustment expectations of the second message prediction data to be transmitted and the interaction timeliness configuration of the message statistics data to be transmitted, which correspond to the time-out threads of the session message in the session interaction time periods when the session message in the session interaction time periods are in suspension, so as to obtain the effective time periods of the session message interception threads and the effective time periods of the session message in the session interaction time periods.
In some alternative embodiments, the interactive timeliness adjustment desirably includes one or more of: the method comprises the steps of realizing the minimum processing of the statistical data of the information to be transmitted of each business interaction task; the balance of the statistical data of the messages to be transmitted of each business interaction task is realized; and the statistical data of the messages to be transmitted of each business interaction task is prevented from being larger than the set statistical data.
In some optional embodiments, the minimizing the statistics of the message to be transmitted for implementing each service interaction task includes: the appointed calculation value of the statistical data of the information to be transmitted of each business interaction task is close to a first appointed value; the balancing of the statistical data of the message to be transmitted for each service interaction task comprises the following steps: the discrete index between the statistical data of the messages to be transmitted of each business interaction task is close to a second appointed value; the method for avoiding that the statistical data of the message to be transmitted of each business interaction task is 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 business interaction task and the set statistical data is not more than 0.
In some optional embodiments, the updating the session security detection thread of the cloud-edge interaction scenario according to the information security detection policy indication of the not less than one session interaction period includes:
extracting information security detection policy indications of a 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 time periods of the session security detection threads of the set number of session interaction time periods in the follow-up process according to the extracted information security detection policy indication of the set number of session interaction time periods.
In some optional embodiments, each interval period T is combined with a session message interaction record under each service interaction task corresponding to a cloud side interaction scene, and an information security detection policy indication of at least one session interaction period is determined; the effective time periods of the session message interception threads of each session interaction period in the information security detection policy indication of at least one session interaction period are consistent, and the effective time periods of the session message release threads are also consistent;
the session security detection thread for updating the cloud side interaction scene according to the information security detection policy indication of at least one session interaction period comprises: and updating the effective running period of the session security detection thread in the period T according to the information security detection policy indication in any session interaction period of the session interaction period.
In some alternative embodiments, the session message interaction record includes received message statistics; the received message statistics value comprises received message statistics values detected in real time by a message detection module under each business interaction task corresponding to the cloud side interaction scene, or previous received message statistics values. .
A second aspect is a cloud computing server comprising a memory and a processor; the memory is coupled to the processor; the memory is used for storing computer program codes, and the computer program codes comprise 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 run, performs the method of the first aspect.
According to the embodiment of the invention, the statistical data of the messages to be transmitted in the cloud side interaction scene can be identified by combining the random approximation estimation strategy, the interactive timeliness adjustment expectations configured by combining the statistical data of the messages to be transmitted are combined, the statistical data of the messages to be transmitted in the cloud side interaction scene identified by combining the random approximation estimation strategy are processed, and the information security detection strategy indication of the session interaction time periods of the subsequent multiple session message interception/release threads is obtained when the statistical data of the messages to be transmitted in the cloud side interaction scene meets the interactive timeliness adjustment expectations, so that the updating of the session security detection threads 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 statistics data of the messages to be transmitted in each cloud side interaction scene meets the interaction timeliness adjustment expectations, unnecessary resource expenditure can be reduced, message processing requirements of different cloud side interaction scenes can be flexibly met, service interaction delay caused by too many messages is avoided, and limited transmission resource waste caused by too few messages is avoided.
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Fig. 1 is a flow chart of an interactive data processing method based on big data according to an embodiment of the present invention.
Fig. 2 is a block diagram of an interactive data processing device based on big data according to an embodiment of the present invention.
Detailed Description
Hereinafter, 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 implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", or "a third", etc., may explicitly or implicitly include one or more such feature.
Fig. 1 shows a flow chart of a big data based interactive data processing method according to an embodiment of the present invention, where the big data based interactive data processing method may be implemented by a cloud computing server, and the cloud computing server may include a memory and a processor; the memory is coupled to the processor; the memory is used for storing computer program codes, and the computer program codes comprise computer instructions; and when the processor executes the computer instructions, the cloud computing server is caused to execute the technical scheme described in the following steps.
Step 102: combining session message interaction records, basic message statistics data to be transmitted and information security detection strategy indication of at least one session interaction period to be matched under each service interaction task corresponding to cloud side interaction scene, and combining a random approximation estimation strategy to identify the message statistics data to be transmitted in the at least one session interaction period.
In the embodiment of the invention, the cloud side interaction scene can be different types of cloud side interaction scenes configured with the session security detection thread. For example, the cloud side interaction scene can be an electronic commerce interaction scene, a cloud game interaction scene, an intelligent government enterprise interaction scene and the like.
Further, the cloud-edge interaction scenario may include at least one interaction indicator. The interaction index permits only session messages to be transmitted based on one business interaction task. The business interaction task in the invention refers to a task corresponding to the interaction requirement of the cloud side interaction scene, for example, the business interaction task can match the time sequence interaction requirement, the resource sharing interaction requirement and the like of the cloud side interaction scene.
In some possible examples, random approximation estimation policy identification (recursive prediction) of the message statistics to be transmitted may be performed in conjunction with conversational message flow ideas. Therefore, the requirements on samples can be reduced, and the session security detection thread can be updated only by the received message statistical value data of the cloud side interaction scene, so that the resource requirements on the cloud side interaction scene are reduced. In addition, random approximation estimation policy identification of statistical data of the message to be transmitted can be performed under various session message interaction conditions, so that the interaction data processing method based on big data provided by the invention avoids the influence of the session message interaction conditions as much as possible, for example, the adaptive information security detection policy indication 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, further description of step 102 follows.
Step 202, determining the activity of a session message stream caused by each service interaction task by combining session message interaction records under each service interaction task corresponding to the cloud side interaction scene.
In the embodiment of the invention, the session message flow can indicate the change condition of the session message queue caused by the change of the session message quantity in the cloud side interaction scene. The session message flows may include a wait message flow, a reply message flow, and an aggregate message flow. The waiting message stream may be available during the running of the session message interception thread as a result of the session message terminating transmission, which may cause an increase in the statistics 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 transmission, which may result in a reduction in the statistics of the messages to be transmitted. After switching to the session message release thread, as the session message is continuously transmitted in the cloud side interactive scene and enters the message queue to be transmitted, the newly added session message can obtain the converged message stream, and the converged message stream can cause the increase of the statistical data of the message to be transmitted, so that the reduction rate of the statistical data of the message to be transmitted is correspondingly reduced.
It will be appreciated that the session message flow typically has an interaction speed, i.e. the liveness of the session message flow (which the present invention may understand). In the invention, the activity of waiting message stream is regarded as active value1, the activity of response message stream is regarded as active value2, and the activity of aggregate message stream is regarded as active value3.
In some possible embodiments, an exemplary determination of liveness of a conversation message stream is as follows.
Step 402, obtaining a received message statistic value and a received message data volume under the service interaction task, wherein the message activation statistic value and the message activation data volume configured for the message load reaching the upper limit under the service interaction task, and the preset temporary message data volume when the session message is overloaded under the service interaction task.
In some possible examples, these configured variables may be pre-stored and then obtained as needed, with statistics corresponding to message traffic, and data corresponding to message size (e.g., in KB, MB, GB), and also understood as message density.
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 counted previous received message statistics. Therefore, the activity degree can be determined, and the effective operation period optimization of the session security detection thread can be flexibly performed. The message detection modules can comprise modules with counting programs, and received message statistics values of cloud side interaction scenes can be obtained in real time through the message detection modules.
In some possible examples, the received message data amount may be obtained under a priori conditions of the received message statistics, the message load statistics, and the message load data amount in combination with binding characteristics between the received message data amount, the received message statistics, the message load statistics, and the message load data amount indicated by the visual knowledge relationship network, thereby improving flexibility of the scheme.
After obtaining the a priori variables required to calculate the liveness of the conversation message stream, steps 404-408 may be performed.
Step 404, obtaining the activity of the waiting message stream according to the received message statistics, the received message data amount and the reserved message data amount.
And step 406, obtaining the liveness of the response message flow according to the message activation statistic value, the message activation data quantity and the reserved message data quantity.
Step 408, obtaining the activity of the aggregated message flow according to the received message statistics and the message activation statistics, the received message data volume and the message activation data volume.
In combination with steps 402-408, the liveness of three session message flows under each service interaction task corresponding to the cloud side interaction scene can be accurately determined according to the session message flow thought.
Step 204, determining the dynamic variable of the statistical data of the message to be transmitted under each business interaction task by combining the activity of the session message stream.
The session security detection thread typically encompasses a session message interception thread, a session message release thread.
When a certain service interaction task of the cloud side interaction scene corresponds to a session message interception thread, the statistical data of the message to be transmitted under the service interaction task can be increased. In the invention, the average dynamic variable of the statistical data of the message to be transmitted, which is caused by the conversation message flow, is called a first dynamic variable and is regarded as change1, and the effective time period of the conversation message interception thread is regarded as thread_Rt. When a certain service interaction task of the cloud side interaction scene corresponds to a session message release thread, the statistic data of the message to be transmitted under the service interaction task can be reduced. In the invention, the average dynamic variable of the statistical data of the message to be transmitted, which is caused by the session message flow, is called a second dynamic variable and is regarded as change2, and the effective period of the session message release thread is regarded as thread_Gt.
Combining the above and the activity of the session message flow obtained in step 202, a first dynamic variable and a second dynamic variable corresponding to each session message interception thread and session message release thread can be obtained. For example, determining a first dynamic variable of a corresponding period of a session message interception thread by combining the liveness of the response message stream and the liveness of the waiting message stream; and combining the activity level of the response message stream and the activity level of the converged message stream to determine a second dynamic variable of the corresponding period of the session message release thread. Further, a dynamic variable can be understood as a rate of change.
And 206, analyzing and obtaining the predicted data of the message to be transmitted corresponding to each session interaction period in the at least one session interaction period under each service interaction task by combining the dynamic variable and the basic statistical data of the message to be transmitted.
The invention refers to the statistical data of the message to be transmitted after the session message interception thread is in a pause state as the first predicted data of the message to be transmitted, and is regarded as the session interaction period of the ith session message interception/release thread, and refers to the statistical data of the message to be transmitted after the session message release thread is in a pause state as the second predicted data of the message to be transmitted, and is regarded as the calculation2_Pi, wherein i represents the session interaction period of the ith session message interception/release thread.
When the computing 1_pi is identified, only the statistical data computing 2_pi-1 of the message to be transmitted after the last round of session message releasing thread is in the pause state, the effective period thread_rt of the session message intercepting thread, and the first dynamic variable change1 are needed to be obtained, and in combination:
calcilytic 1_pi=calcilytic 2_pi-1+thread_rt.
When the computing 2_pi is identified, only the statistical data computing 1_pi of the message to be transmitted after the last round of session message interception thread is in the pause state, the effective period thread_gt of the session message release thread, and the second dynamic variable change2 are needed to be obtained, and in combination:
calcilytic 2_pi=calcilytic 1_pi-thread_gt—change2.
By combining the above, the predicted data of the message to be transmitted corresponding to each session interaction period can be obtained by analyzing each service interaction task.
In some possible examples, analyzing to obtain first predicted data to be transmitted corresponding to a session message interception thread in a pause time and second predicted data to be transmitted corresponding to a session message passing thread in a pause time in each session interaction period by combining the first dynamic variable and the second dynamic variable, the effective time period of the session message interception thread in each session interaction period and the effective time period of the session message passing thread in each session interaction period, and the basic statistical data to be transmitted; combining statistical data of messages to be transmitted when a previous round of session message release thread is in a pause state in a corresponding period of a session message interception thread, and obtaining first predicted data of messages to be transmitted after the session message interception thread is in the pause state by using an effective period of the current session message interception thread and the first dynamic variable; and combining the statistical data of the message to be transmitted after the session message interception thread is in a pause state in the corresponding period of the session message release thread, the effective period of the current session message release thread and the second dynamic variable to obtain the predicted data of the second message to be transmitted after the session message release thread is in the pause state.
Further, the basic message statistical data to be transmitted may be the message statistical data to be transmitted after the session message interception thread or the session message release thread is in the suspension state, where the session interaction period is optimized according to the effective operation period of the session security detection thread. The base message statistics to be transmitted may be predetermined values or actual base message statistics to be transmitted (e.g., initial message statistics to be transmitted).
Taking the basic message statistic data to be transmitted as the example of the message statistic data to be transmitted after the session message release thread is in the pause state, the message statistic data to be transmitted can be regarded as number_q0.
Combining the analysis of the content to obtain:
calculation_P1=number_Q0+thread_Rt*change1。
combining the analysis of the content to obtain:
number_Q1=calculation_P1-thread_Gt*change2。
and based on the above, performing loop processing until the first predicted data of the message to be transmitted after the session message interception thread is in the pause state and the second predicted data of the message to be transmitted after the session message release thread is in the pause state are obtained in each session interaction period.
Aiming at each service interaction task, the thought can be utilized to analyze and obtain the first predicted data of the message to be transmitted corresponding to the time when the session message interception thread is in pause and the second predicted data of the message to be transmitted corresponding to the time when the session message release thread is in pause in each session interaction period.
Step 104, determining information security detection policy indication of the session interaction period according to the message statistics to be transmitted in the session interaction period and the interaction timeliness adjustment expectations configured by combining the message statistics to be transmitted.
It will be appreciated that the information security detection policy indicates the active periods of the session message interception/release threads that include each session interaction period. Combining with some optimization ideas (for example, an AI algorithm minimization ideas), information security detection strategy indication of session interaction time periods of a plurality of subsequent session message interception/release threads can be obtained when the statistical data of the messages to be transmitted in the cloud side interaction scene meets the interaction time efficiency adjustment expectations. The interactive timeliness adjustment expectations of the configuration may be understood as some limiting requirements for the statistics of the messages to be transmitted.
For some possible examples, the interactive timeliness adjustment desirably includes one or more of: the method comprises the steps of realizing the minimum processing of the statistical data of the information to be transmitted of each business interaction task; the balance of the statistical data of the messages to be transmitted of each business interaction task is realized; and the statistical data of the messages to be transmitted of each business interaction task is prevented from being larger than the set statistical data.
And updating the session security detection thread according to the information security detection policy indication expected by the interactive timeliness adjustment, so that overload of waiting messages can be prevented, statistical data of the messages to be transmitted are reduced, and the session message throughput efficiency is improved on the premise of ensuring accurate security detection of the session messages.
The invention refers to the minimization process of the statistical data of the message 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 desire is considered as an expectation_f1.
In some possible examples, the minimizing the statistics of the message to be transmitted for implementing each service interaction task includes: the assigned calculated value of the statistical data of the message to be transmitted of each business interaction task is close to the first assigned value. Through processing the expect_f1, the effective time period of the session message interception thread and the effective time period of the session message release thread, which are as short as possible, of the statistical data of the message to be transmitted of each service interaction task of the cloud side interaction scene, can be obtained. The invention refers to the balance of the statistical data of the messages to be transmitted for realizing the business interaction tasks as second interaction timeliness adjustment expectation. In some possible examples, a second AI algorithm corresponding to the second interactive timeliness adjustment desire is considered to be an expectation_f2. The balancing of the statistical data of the message to be transmitted for each service interaction task comprises the following steps: the discrete index between the statistical data of the messages to be transmitted of each business interaction task is close to a second appointed value. Through processing the expect_f2, the effective time period of the session message interception thread and the effective time period of the session message release thread which balance the traffic of each business interaction task of the cloud side interaction scene can be obtained. The invention refers to the situation that the statistical data of the information to be transmitted of each service interaction task is prevented from being larger than the set statistical data, and the third interaction timeliness adjustment expectation is avoided. A third AI algorithm corresponding to the third interactive timeliness adjustment desire is deemed to be an expectation_f3. The avoiding that the statistics data of the message to be transmitted of each service interaction task is larger than the set statistics data includes: and the difference between the statistical data of the messages to be transmitted of each business interaction task and the set statistical data is not more than 0. Through the processing of the expect_f3, the effective time period of the session message interception thread and the effective time period of the session message release thread, which enable the statistical data of the messages to be transmitted of each service interaction task of the cloud side interaction scene not to be overloaded (reaching the set statistical data), can be obtained.
And step 106, updating the session security detection thread of the cloud side interaction scene according to the information security detection policy indication of at least one session interaction period.
For the embodiment of the invention, the obtained effective time periods of the session message interception threads and the obtained effective time periods of the session message passing threads in the session interaction time periods can be combined to send the update command to the update node for updating the session security detection threads, so that the session security detection threads can perform session message interception/passing thread switching according to the obtained effective time periods of the session message interception threads and the session message passing threads.
By the technical scheme, the information security detection strategy indication of the session interaction time periods of the subsequent multiple session message interception/release threads can be obtained when the information security detection strategy indication of the session interaction time periods of the subsequent multiple session message interception/release threads meets the interaction time period adjustment expectations, and the session security detection thread update is completed by combining the determined signal scheme of the subsequent multiple session interaction time periods. Compared with the traditional scheme, the adaptive information security detection strategy indication can be determined under various session message interaction conditions, so that the statistics data of the messages to be transmitted in each cloud side interaction scene meets the interaction timeliness adjustment expectations, unnecessary resource expenditure can be reduced, message processing requirements of different cloud side interaction scenes can be flexibly met, 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 predicted data of the message to be transmitted corresponding to the session message passing thread in each session interaction period when the session message passing thread is in a pause under each service interaction task and the interaction timeliness adjustment expectations configured by combining the statistical data of the message to be transmitted may be obtained, so as to obtain an effective period of the session message interception thread and an effective period of the session message passing thread in each session interaction period. When the session message release thread is in suspension, the corresponding message waits for the shortest message statistics to be transmitted. And reducing the statistical data of the message to be transmitted as much as possible after the session message release thread is in a pause state.
In some embodiments, each interval period T may set an active period of the session message interception/release thread in a period T next to the session security detection thread, where the active period of the session message interception thread and the active period of the session message interception thread are the same, and the active period of the session message release thread are the same. The period T may be set in connection with event situations. The effective time period of the session message interception/release thread of the session security detection thread in the session interaction time period of any session message interception/release thread can be set at any time, and the effective time period of the session message interception thread of the session interaction time period of each session message interception/release thread can be inconsistent, and the effective time period of the session message release thread 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 may be arbitrarily set by the session security detection thread.
For some possible examples, the present invention proposes a flexible update idea for the session security detection thread of the second way. In the flexible updating thinking, the effective time period of the session message interception thread and the effective time period of the session message release thread in at least one subsequent session interaction time period can be obtained by combining the session message interaction records under each service interaction task corresponding to the cloud side interaction scene and combining the steps 102-104. And then extracting information security detection policy indications of a 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 period of the session safety detection thread can be flexibly updated, the time efficiency of session message processing of the cloud side interaction scene is better, and the statistical data of the message to be transmitted is more suitable for the actual session message flow of the cloud side interaction scene, so that the purpose of intelligently updating the effective operation period of the session safety detection thread in combination with the session message flow is achieved.
For example, the set number is 1. The session message interception/release thread effective time periods of each session interaction time period in the subsequent session interaction time periods (assuming 20 session interaction time periods) can be obtained by optimizing the session message interception/release thread effective time periods of each session interaction time period in combination with the current cloud end interaction scene to achieve the session message flow and combining the method illustrated in the steps 102-104, and then the session message interception/release thread effective time periods of the subsequent first session interaction time period can be extracted from the session message interception/release thread effective time periods as the session message interception/release thread effective time periods of the session security detection thread in the session interaction time periods of the next session message interception/release thread, so that the purpose of intelligently updating the session security detection thread effective operation time periods of the session interaction time periods of the next session message interception/release thread in combination with the session interaction time periods of the previous session message interception/release thread is achieved.
For some possible examples, the present invention proposes a directional update approach to the session security detection thread of the first approach. In the directional updating thinking, the effective time periods of the session message interception threads in the session interaction time periods of all the session message interception/release threads in the session interaction time periods of at least one session message interception/release thread are identified to be consistent, and the effective time periods of the session message release threads are also consistent. And determining information security detection policy indication of at least one session interaction period according to session message interaction records under each service interaction task corresponding to cloud side interaction scenes at each interval period T, and updating the effective operation period of the session security detection thread in the period T according to the information security detection policy indication in any session interaction period of the at least one session interaction period.
For example, the period T is 30 min. The session message flow can be achieved by combining the current cloud side interaction scene every 30min, and the effective time periods of session message interception/release threads of each session interaction time period in the subsequent session interaction time periods (assuming 20 session interaction time periods) are obtained by combining the methods illustrated in the steps 102-104. The effective time period of the session message interception thread is consistent with the effective time period of the session message interception thread, and the effective time period of the session message release thread is consistent with the effective time period of the session message release thread. And then the effective time period of the session message interception/release thread of any session interaction time period can be extracted from the session message interception/release thread as the effective time period of the session message interception/release thread of the session security detection thread within the subsequent 30min, so as to achieve the purpose of optimizing the effective operation time period of the session security detection thread of the first thought.
By applying the technical scheme, the following technical effects can be achieved.
(1) According to the method, a conversation message flow thought can be combined, message statistical data to be transmitted in a cloud side interaction scene can be predicted, a thread parameter adjustment algorithm is utilized to conduct min processing of the message statistical data to be transmitted in all directions of the cloud side interaction scene, the message statistical data to be transmitted in all directions of the cloud side interaction scene cannot exceed a threshold value, multiple threads such as balanced message statistical data to be transmitted in all directions of the cloud side interaction scene are processed, a conversation safety detection thread update scheme of not less than one conversation interaction period is obtained, compared with a traditional scheme, the conversation safety detection thread update scheme can prevent overload of messages, reduce the message statistical data to be transmitted, keep the message waiting longer, therefore unnecessary resource expenditure can be reduced, message processing requirements of different cloud side interaction scenes can be flexibly met, service interaction delay caused by too many messages is avoided, and limited transmission resource waste caused by too few messages is avoided.
(2) The method reduces the requirement on samples by using the session message flow thought to-be-transmitted message prediction data, and can update the session security detection thread only by using the received message statistical value data of the cloud side interaction scene, thereby improving the updating efficiency of the session security detection thread.
(3) The effective operation period of the session safety detection thread of the session interaction period of the next session message interception/release thread can be intelligently updated by combining the session interaction period session message flow of the previous session message interception/release thread, so that the method is more suitable for the actual session message flow of the cloud side interaction scene.
Under some design ideas which can be implemented independently, after updating the session security detection thread of the cloud side interaction scene, the method can further comprise the following contents: responding to the session security detection request, extracting a to-be-processed session message carrying a risk tag from a session message interception thread; mining a risk intention vector of the session message to be processed; and carrying out 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 tag can be extracted from the session message interception thread, the to-be-processed session message carrying the risk tag can be the to-be-processed session message which is preliminarily analyzed and intercepted by the session message interception thread and possibly has information security risks, and the intrusion preference of the to-be-processed session message can be mined by determining the risk intention vector, so that targeted information protection processing is performed based on the risk intention vector.
Under some design ideas which can be implemented independently, in the process of mining the risk intention vector of the session 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 session message to be processed can be realized by the following technical scheme: extracting risk intention vectors of session messages to be processed by using a preset GCN model to obtain a plurality of groups of associated risk intention vectors, wherein the plurality of groups of associated risk intention vectors comprise a K-th group risk intention vector, a J-th group risk intention vector and a J-th group risk intention vector for completing disturbance vector removal, K is a positive integer, and J=K-1; the method comprises the steps of carrying out identification operation on vector elements of a risk intention vector to be processed to obtain a disturbance removal indicating unit, wherein the risk intention vector to be processed is obtained by weighting a risk intention vector of a K-th group, a risk intention vector of a J-th group and a risk intention vector of the J-th group, from which disturbance vectors are removed, on an attention level; changing the size of the disturbance removal indicating unit to enable the attention index number of the disturbance removal indicating unit to be consistent with the attention index number of the target risk intention vector of the K-th group of risk intention vectors; the vector elements of the target risk intention vectors of the K-th group of risk intention vectors are identified through the disturbance removal indication unit with the changed size, so that the first target risk intention vector is obtained; mapping the first target risk intention vector to obtain a risk intention vector of the K group for completing disturbance vector removal.
By the design, disturbance vector removal can be performed based on continuous risk intention vectors, so that the accuracy of disturbance elimination is improved.
Under some design ideas which can be implemented independently, the identifying operation is performed on the vector elements of the risk intention vector to be processed to obtain a disturbance removal indicating unit, which comprises: identifying the risk intention vector to be processed to extract the change characteristics of the vector elements of the J-th set of risk intention vectors relative to the vector elements of the K-th set of risk intention vectors, so as to obtain a matching unit; and performing feature conversion processing on the matching unit to obtain the disturbance removal indicating unit.
Under some design ideas which can be implemented independently, the identifying operation is performed on the risk intention vector to be processed, so as to extract the change characteristics of the vector elements of the risk intention vector of the J-th group relative to the vector elements of the risk intention vector of the K-th group, and after obtaining a matching unit, the method further includes: and carrying out recognition operation on vector elements of the target risk intention vectors of the J-th group of risk intention vectors through the matching unit to obtain a second target risk intention vector.
Under some design ideas which can be implemented independently, the identifying, by the matching unit, the vector elements of the target risk intention vector of the J-th group of risk intention vectors to obtain a second target risk intention vector includes: changing the size of the matching unit to enable the attention index number of the matching unit to be consistent with the attention index number of the target risk intention vector of the J-th group risk intention vector; and carrying out recognition operation on vector elements of a target risk intention vector of the risk intention vector of which the disturbance vector is removed by the matching unit after the size is changed, so as to obtain the second target risk intention vector.
Under some design ideas which can be implemented independently, mapping the first target risk intention vector to obtain a risk intention vector of the kth group for completing disturbance vector removal, including: fusion processing is carried out 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 a risk intention vector of the K group for completing disturbance vector removal.
Under some design ideas which can be implemented independently, the identifying operation is performed on the risk intention vector to be processed, so as to extract the change characteristics of the vector elements of the risk intention vector of the J-th group relative to the vector elements of the risk intention vector of the K-th group, and obtain a matching unit, which includes: weighting the K-th group risk intention vector, the J-th group risk intention vector and the risk intention vector from which the J-th group disturbance vector is removed on the attention level to obtain the risk intention vector to be processed; performing feature conversion processing on the risk intention vector to be processed to obtain a fourth target risk intention vector; performing identification operation on 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 implemented independently, the matching unit performs feature conversion processing to obtain the disturbance removal indicating unit, which comprises: 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; fusion processing is carried out on the fourth target risk intention vector and the sixth target risk intention vector to obtain a seventh target risk intention vector; and carrying out identification operation on the seventh target risk intention vector so as to extract disturbance removal information of vector elements of the risk intention vector removed by the disturbance vector of the J-th group relative to vector elements of the risk intention vector of the J-th group, and obtaining the disturbance removal indication unit.
Under some design ideas which can be implemented independently, the identifying operation is performed on the seventh target risk intention vector to extract disturbance removal information of the risk intention vector removed by the disturbance vector of the J-th group relative to vector elements of the risk intention vector of the J-th group, so as to obtain the disturbance removal indicating unit, which includes: performing identification operation on 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 implemented independently, mapping the third target risk intention vector to obtain a risk intention vector of the kth group 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; performing identification operation on the ninth target risk intention vector to obtain a K-th group of mapped risk intention vectors; summing the vector value of the first vector element of the K-th set of risk intention vectors with the vector value of the second vector element of the K-th set of mapped risk intention vectors to obtain a risk intention vector of which the K-th set is subjected to disturbance vector removal, wherein the distribution of the first vector element in the K-th set of risk intention vectors is consistent with the distribution of the second vector element in the K-th set of mapped risk intention vectors.
Based on the same inventive concept, fig. 2 shows a block diagram of a big data based interaction data processing device provided by an embodiment of the present invention, where the big data based interaction data processing device may include a data identification module 21 for implementing the relevant method steps shown in fig. 1, and is configured to identify, in combination with a random approximation estimation policy, the to-be-transmitted message statistics data in the to-be-transmitted message interaction period in combination with a session message interaction record, basic to-be-transmitted message statistics data, and an information security detection policy indication of at least one to-be-matched session interaction period under each service interaction task corresponding to a cloud side interaction scene; an indication determining module 22, configured to determine an information security detection policy indication of the not less than one session interaction period according to the statistics of the messages to be transmitted in the not less than one session interaction period and the interaction timeliness adjustment expectations configured in combination with the statistics of the messages to be transmitted; the thread updating module 23 is configured to update the session security detection thread of the cloud side interaction scenario according to the information security detection policy indication of the at least one session interaction period.
The related embodiments applied to the present invention can achieve the following technical effects: the method comprises the steps of identifying information statistical data to be transmitted in a cloud side interaction scene by combining a random approximation estimation strategy, processing the information statistical data to be transmitted in the cloud side interaction scene by combining interaction timeliness adjustment expectations configured by combining the information statistical data to be transmitted, and obtaining information security detection strategy indication of session interaction time periods of a plurality of subsequent session message interception/release threads when the information statistical data to be transmitted in the cloud side interaction scene meets the interaction timeliness adjustment expectations, so that updating of the session security detection threads 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 statistics data of the messages to be transmitted in each cloud side interaction scene meets the interaction timeliness adjustment expectations, unnecessary resource expenditure can be reduced, message processing requirements of different cloud side interaction scenes can be flexibly met, service interaction delay caused by too many messages is avoided, and limited transmission resource waste caused by too few messages is avoided.
The foregoing is only a specific embodiment of the present invention. Variations and alternatives will occur to those skilled in the art based on the detailed description provided herein and are intended to be included within the scope of the invention.

Claims (10)

1. An interactive data processing method based on big data is characterized by being applied to a cloud computing server, and the method at least comprises the following steps:
responding to the session security detection request, extracting a to-be-processed session message carrying a risk tag from a session message interception thread;
mining a risk intention vector of the session message to be processed;
and carrying out information protection processing through the risk intention vector.
2. The method of claim 1, wherein the mining the risk intent vector for the pending conversation message comprises:
extracting risk intention vectors of session messages to be processed by using a preset GCN model to obtain a plurality of groups of associated risk intention vectors, wherein the plurality of groups of associated risk intention vectors comprise a K-th group risk intention vector, a J-th group risk intention vector and a J-th group risk intention vector for completing disturbance vector removal, K is a positive integer, and J=K-1;
the method comprises the steps of carrying out identification operation on vector elements of a risk intention vector to be processed to obtain a disturbance removal indicating unit, wherein the risk intention vector to be processed is obtained by weighting a risk intention vector of a K-th group, a risk intention vector of a J-th group and a risk intention vector of the J-th group, from which disturbance vectors are removed, on an attention level;
Changing the size of the disturbance removal indicating unit to enable the attention index number of the disturbance removal indicating unit to be consistent with the attention index number of the target risk intention vector of the K-th group of risk intention vectors;
the vector elements of the target risk intention vectors of the K-th group of risk intention vectors are identified through the disturbance removal indication unit with the changed size, so that a first target risk intention vector is obtained;
mapping the first target risk intention vector to obtain a risk intention vector of the K group for completing disturbance vector removal.
3. The method of claim 1, wherein before extracting the pending session message carrying the risk tag from the session message interception thread in response to the session security detection request, the method further comprises:
combining session message interaction records, basic message statistics data to be transmitted and information security detection strategy indication of at least one session interaction period to be matched under each service interaction task corresponding to a cloud side interaction scene, and combining a random approximation estimation strategy to identify the message statistics data to be transmitted in the at least one session interaction period;
Determining information security detection policy indication of the session interaction period according to the message statistics data to be transmitted in the session interaction period and the interaction timeliness adjustment expectations configured by combining the message statistics data to be transmitted;
and updating the session security detection thread of the cloud side interaction scene according to the information security detection policy indication of at least one session interaction period.
4. The method of claim 2 as in claim 3, wherein the identifying the message statistics to be transmitted in the at least one session interaction period in combination with the stochastic approximation estimation policy by combining the session message interaction record, the basic message statistics to be transmitted, and the information security detection policy indication of the at least one session interaction period to be matched under each service interaction task corresponding to the cloud end interaction scenario comprises:
combining session message interaction records under each service interaction task corresponding to the cloud side interaction scene, and determining the activity of a session message stream caused under each service interaction task;
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;
And analyzing and obtaining the predicted data of the message to be transmitted corresponding to each session interaction period in the at least one session interaction period under each service interaction task by combining the dynamic variable and the basic statistical data of the message to be transmitted.
5. The method of claim 2, wherein the session message flows include a wait message flow, a reply 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 flow caused by the service interaction tasks according to the session message interaction records under the service interaction tasks corresponding to the cloud side interaction scene comprises the following steps: the following is implemented for each business interaction task: acquiring a received message statistical value and a received message data volume under the service interaction task, wherein the message load statistical value and the message load data volume are configured when the message load reaches the upper limit under the service interaction task, and the reserved message data volume is configured when the conversation message is overloaded under the service interaction task; obtaining the activity of waiting message flow according to the received message statistic value, the received message data quantity and the temporary message data quantity; obtaining the activity of the response message flow according to the message load statistic value, the message load data quantity and the reserved message data quantity; obtaining the activity of the converged message stream according to the received message statistic value and the message load statistic value, wherein the received message data quantity and the message load data quantity;
The method for acquiring the received message data volume under the service interaction task comprises the following steps: and combining the received message data quantity, the received message statistical value, the message load statistical value and the binding characteristics among the message load data quantity indicated by the visual knowledge relation network, and obtaining the received message data quantity under the service interaction task according to the received message statistical value, the message load statistical value and the message load data quantity.
6. The method of claim 4, wherein the information security detection policy comprises a session message interception thread and a session message release thread; the activity level of the session message flow comprises the activity level of the waiting message flow, the activity level of the response message flow and the activity level of the convergence message flow; the determining the dynamic variable of the statistical data of the message to be transmitted under each business interaction task by combining the activity of the conversation message stream comprises the following steps:
determining a first dynamic variable of a corresponding period of a session message interception thread by combining the liveness of the response message stream and the liveness of the waiting message stream;
and combining the activity level of the response message stream and the activity level of the converged message stream to determine a second dynamic variable of the corresponding period of the session message release thread.
7. The method of claim 6, wherein the information security detection policy indication comprises a period of validity of a session message interception thread and a period of validity of a session message release thread; the step of analyzing the dynamic variable and the basic message statistical data to be transmitted to obtain the predicted data of the message to be transmitted corresponding to each session interaction period in the at least one session interaction period under each service interaction task, including: the following is implemented for each business interaction task:
analyzing to obtain first predicted data to be transmitted corresponding to the session message interception thread in the session interaction period and second predicted data to be transmitted corresponding to the session message passing thread in the suspension period by combining the first dynamic variable and the second dynamic variable, the effective period of the session message interception thread in each session interaction period and the effective period of the session message passing thread in each session interaction period, and the basic statistical data of the message to be transmitted; combining statistical data of messages to be transmitted when a previous round of session message release thread is in a pause state in a corresponding period of a session message interception thread, and obtaining first predicted data of messages to be transmitted after the session message interception thread is in the pause state by using an effective period of the current session message interception thread and the first dynamic variable;
Combining statistical data of the message to be transmitted after the conversation message interception thread is in a pause state in the corresponding period of the conversation message release thread, and obtaining second predicted data of the message to be transmitted after the conversation message release thread is in the pause state by the effective period of the current conversation message release thread and the second dynamic variable;
wherein determining the information security detection policy indication of the session interaction period according to the statistics of the message to be transmitted in the session interaction period and the interaction timeliness adjustment expectations configured by combining the statistics of the message to be transmitted includes: and combining with the interaction timeliness adjustment expectations of the second message prediction data to be transmitted and the interaction timeliness configuration of the message statistics data to be transmitted, which correspond to the time-out threads of the session message in the session interaction time periods when the session message in the session interaction time periods are in suspension, so as to obtain the effective time periods of the session message interception threads and the effective time periods of the session message in the session interaction time periods.
8. The method of claim 3, wherein the interactive timeliness adjustment expectations include one or more of: the method comprises the steps of realizing the minimum processing of the statistical data of the information to be transmitted of each business interaction task; the balance of the statistical data of the messages to be transmitted of each business interaction task is realized; the statistical data of the messages to be transmitted of each business interaction task are prevented from being larger than the set statistical data;
The minimizing processing of the statistical data of the message to be transmitted for implementing each service interaction task includes: the appointed calculation value of the statistical data of the information to be transmitted of each business interaction task is close to a first appointed value; the balancing of the statistical data of the message to be transmitted for each service interaction task comprises the following steps: the discrete index between the statistical data of the messages to be transmitted of each business interaction task is close to a second appointed value; the method for avoiding that the statistical data of the message to be transmitted of each business interaction task is 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 business interaction task and the set statistical data is not more than 0.
9. The method of claim 3, wherein the updating the session security detection thread of the cloud-side interaction scenario according to the information security detection policy indication of the not less than one session interaction period comprises:
extracting information security detection policy indications of a 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 time periods of the session security detection threads of the set number of session interaction time periods in the follow-up process according to the extracted information security detection policy indication of the set number of session interaction time periods.
10. A cloud computing server, comprising: a memory and a processor; the memory is coupled to the processor; the memory is used for storing computer program codes, and the computer program codes comprise 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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