Communication data encryption intelligent processing system

Abstract

The invention discloses a communication data encryption intelligent processing system, which belongs to the technical field of communication data encryption, and comprises a demand analysis module, an encryption configuration module and an encryption module, the demand analysis module is used for carrying out encryption demand analysis and determining an encryption demand of a user and an encryption requirement corresponding to the encryption demand; the encryption configuration module is used for a platform side to set an encryption library and an encryption strategy model according to the encryption demand and the encryption requirement of a user; the encryption module is used for encrypting the communication data and identifying a communication data type and an application background corresponding to the target data; respectively marking a communication data type and an application background corresponding to the target data as a target type and a target background; analyzing the target type and the target background through an encryption strategy model to obtain an encryption strategy; obtaining correlation analysis data according to the target type; and screening the encryption strategies according to the association analysis data to obtain a target encryption strategy, and performing encryption processing on the target data according to the target encryption strategy.

Description

Technical Field

[0001] The invention belongs to the technical field of communication data encryption, and in particular is a communication data encryption intelligent processing system. Background Art

[0002] In the current digital age, the encryption requirements for communication data are becoming increasingly diverse and complex. With the rapid development of technologies such as cloud computing, big data, and the Internet of Things, the transmission and storage of communication data are facing unprecedented security challenges. Traditional static encryption methods and single encryption strategies can no longer meet the diverse encryption needs, especially in the face of evolving decryption technologies and potential attack methods, the existing encryption systems often seem to be unable to cope with them.

[0003] Most encryption systems in the existing technology rely on fixed encryption algorithms and strategies, and lack the ability to flexibly respond to different demand scenarios. For example, when using equipment in public places, if the encryption level can be improved, the security of data transmission will be better guaranteed. In addition, these systems often lack analysis of real-time data such as encrypted data attack records and existing decryption technology progress, making it difficult to adjust and optimize encryption strategies in a timely manner, thereby increasing the risk of data leakage.

[0004] Based on this, the present invention provides a communication data encryption intelligent processing system. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned solutions, the present invention provides a communication data encryption intelligent processing system.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A communication data encryption intelligent processing system, comprising a demand analysis module, an encryption configuration module and an encryption module;

[0008] The demand analysis module is used to perform encryption demand analysis to determine the encryption demand of the user and the encryption requirements of the corresponding encryption demand.

[0009] Furthermore, encryption requirements analysis is performed, including:

[0010] The platform sets a data background statistics table, which is used to count various data backgrounds;

[0011] Determine a communication data range according to the user, wherein the communication data range is composed of various communication data types;

[0012] According to the data background statistics table, the data background corresponding to each communication data type within the communication data range is obtained and marked as application background;

[0013] A user encryption analysis table is formed according to the communication data scope and application background; encryption needs and encryption requirements are set according to the user encryption analysis table.

[0014] Furthermore, the setting of data background includes:

[0015] Step SA1: Acquire various types of communication data within the service scope of the platform, and count the attack record data corresponding to the communication data types;

[0016] Identify each initial background corresponding to the attack record data, and count the attack representative values ​​corresponding to the initial background;

[0017] Sorting the initial backgrounds in descending order of attack representative numbers to obtain a first sequence;

[0018] Step SA2: marking the initial background ranked first in the first sequence as the basic background, and marking the initial background ranked adjacent to the basic background as the candidate background;

[0019] When there is no basic background, end the analysis;

[0020] When there is no background to be selected, the basic background is marked as the data background, and the analysis ends;

[0021] Step SA3: Calculate the absolute value of the difference between the attack representative value of the basic background and the background to be selected, and mark it as the basic difference;

[0022] Step SA4: judging whether the basic background and the selected background meet the merging requirement according to the basic difference;

[0023] When it is determined that the merging requirement is met, the basic background is merged with the background to be selected to form a new basic background, and the initial background that is adjacent to the new reference background is marked as the background to be selected, and the process returns to step SA3; when there is no background to be selected, the new basic background is marked as the data background for output, and the initial background corresponding to the data background in the first sequence is deleted, and the process returns to step SA2;

[0024] When it is determined that the merging requirement is not met, the basic background is marked as a data background for output, and the initial background corresponding to the data background in the first sequence is deleted, and the process returns to step SA2.

[0025] Furthermore, judging whether the basic background and the candidate background meet the merging requirements includes:

[0026] A merging judgment formula is established, and the expression of the merging judgment formula is:

[0027]

[0028] Where: q is the benchmark difference; p is the merge requirement; q→p means that the benchmark difference meets the merge requirement; HP(q, p) is the merge judgment value;

[0029] Calculate the merge judgment value between the basic background and the candidate background through the merge judgment formula;

[0030] When the merge judgment value is 1, it is judged that the merge requirement is met;

[0031] When the merge judgment value is 0, it is determined that the non-merge requirement is met.

[0032] Furthermore, the calculation of the attack representative value includes:

[0033] The platform establishes a sensitive statistical table, which is used to count the sensitive values ​​of the corresponding communication data types;

[0034] Matching a corresponding sensitivity value from the sensitive statistical table according to the initial background; identifying a number representative value of the initial background;

[0035] The attack representative value of the corresponding initial background is calculated according to the attack representative value formula; the attack representative value formula is:

[0036] GB=MZ×log 10 (C+1);

[0037] Where: GB is the representative value of attack; MZ is the sensitivity value of the corresponding initial background; C is the representative value of the number of times, and C≠0.

[0038] The encryption configuration module is used by the platform to set up the encryption library and encryption policy model according to the user's encryption needs and encryption requirements; the encryption library is used to store encryption algorithms and encryption methods; the encryption policy model is used to determine the encryption policy that meets the encryption requirements according to the type of communication data and application background.

[0039] The encryption module is used to encrypt the communication data, identify the communication data to be encrypted, mark it as target data, identify the communication data type and application background corresponding to the target data; mark the communication data type and application background corresponding to the target data as target type and target background respectively;

[0040] Analyze the target type and target background through the encryption strategy model to obtain the corresponding encryption strategy;

[0041] The associated analysis data is obtained according to the target type; the encryption strategy is screened according to the associated analysis data to obtain a target encryption strategy, and the target data is encrypted according to the target encryption strategy.

[0042] Furthermore, the encryption strategy is screened based on the correlation analysis data, including:

[0043] Generate a frequency representative value curve based on the correlation analysis data. The horizontal axis of the frequency representative value curve is time, and the vertical axis is the frequency representative value. Fit the frequency representative value curve to obtain a frequency representative function. Determine the frequency representative value at the current time according to the frequency representative function, mark it as the monitoring representative value, and mark the monitoring representative value as QB.

[0044] Identify the frequency representative value corresponding to the communication data type, mark it as the reference representative value, and mark the reference representative value as QD.

[0045] Identify the cracking probability of the encryption policy according to the correlation analysis data, and mark the cracking probability of the encryption policy as μ i , where i represents the corresponding encryption policy, i = 1, 2,..., n, and n is the number of encryption policies;

[0046] Calculate the screening value of the corresponding encryption policy according to the evaluation formula. The evaluation formula is:

[0047] YR i = [b1 × QA(QB, QD)] × [b2 × (exp(μ i ) - 1)];

[0048] In the formula: YR i is the screening value of the corresponding encryption policy; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; exp() is the exponential function with the natural constant e as the base; QA(QB, QD) is a piecewise function;

[0049] Mark the encryption policies with screening values less than the threshold X1 as candidate policies, and determine the target encryption policy according to the candidate policies.

[0050] Furthermore,

[0051] Furthermore, when the screening values of all encryption policies are less than the threshold X1, an encryption library optimization warning is issued.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] Through the cooperation between the demand analysis module, encryption configuration module and encryption module, intelligent encryption processing of communication data is realized, and the encryption method is dynamically adjusted according to actual needs, avoiding the shortcomings of single encryption method and insufficient flexibility in traditional encryption methods. This can not only meet the encryption requirements in different application scenarios, but also improve the security and reliability of the encryption process. By adopting the strategy of combining encryption with multiple encryption methods, the advantages of different encryption algorithms can be fully utilized to form a complementary effect, thereby improving the overall encryption effect. This combined encryption method not only increases the difficulty of cracking, but also improves the confidentiality and integrity of encrypted data. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] like Figure 1 As shown, a communication data encryption intelligent processing system includes a demand analysis module, an encryption configuration module and an encryption module;

[0058] The demand analysis module is used to perform encryption demand analysis and determine each encryption demand and the encryption requirements corresponding to each encryption demand.

[0059] In one embodiment, encryption needs and encryption requirements can be determined directly based on existing technologies. For example, when a user has relevant experience and knowledge, he or she can directly inform the platform of his or her encryption needs and encryption requirements. The encryption needs and encryption requirements can also be determined directly by the platform staff through communication with the user.

[0060] In one embodiment, determining encryption requirements and encryption requirements in the above manner requires the platform to spend a certain amount of time and manpower for communication, which is inefficient. Therefore, in this embodiment, the process of performing encryption requirement analysis is as follows:

[0061] The user uploads various communication data that need to be encrypted, and then summarizes and determines the range of communication data that needs to be encrypted. The communication data range consists of various types of communication data;

[0062] The platform party presets each data background based on encryption experience, common sense and service scope. The data background is set according to the risk differences that the communication data may encounter, that is, the data background is set according to the corresponding risks of the transmission method, storage environment, potential security threats, etc., and a class of backgrounds that can be regarded as having the same risk difference will form a data background, such as using wireless networks for communication in public places such as cafes, libraries, and airports, using public Wi-Fi hotspots for communication in public places, and internal communication within the enterprise. It can be directly set by the platform party’s professionals to form a data background statistical table;

[0063] According to the data background statistics table, the data background corresponding to each communication data type within the communication data range is obtained and marked as application background;

[0064] A user encryption analysis table is formed according to the communication data scope and application background; encryption needs and encryption requirements are set according to the user encryption analysis table; the user encryption analysis table is displayed to platform staff and users to understand the application background of various communication data types, and then different encryption needs and encryption requirements are set according to the risks in different application backgrounds. The encryption need refers to the conditions under which encryption needs to be completed, and the encryption requirement refers to the degree of security requirements that need to be achieved, such as a security rate of 99%; setting encryption needs and encryption requirements according to the user encryption analysis table will greatly assist the platform in determining encryption needs and encryption requirements for users.

[0065] In one embodiment, the method for setting the data background further includes:

[0066] Step SA1: Acquire various types of communication data within the service scope of the platform, and count the attack record data corresponding to various types of communication data, where the attack record data is the record data of malicious behaviors such as attacks and cracking of the communication data of this type;

[0067] Identify various backgrounds in the attack record data and mark the initial background, such as sending communication data with a mobile phone in a subway station; count the attack representative values ​​corresponding to each initial background;

[0068] Sort all initial backgrounds in descending order according to the attack representative number, referring to all initial backgrounds of each communication data type; obtain a first sequence;

[0069] Step SA2: marking the initial background ranked first in the first sequence as the basic background, and marking the initial background ranked adjacent to the basic background as the candidate background;

[0070] When there is no basic background, end the analysis;

[0071] When there is no background to be selected, the basic background is marked as the data background and the analysis ends;

[0072] Step SA3: Calculate the absolute value of the difference between the attack representative value of the basic background and the background to be selected, and mark it as the basic difference;

[0073] Step SA4: judging whether the merging requirement is met according to the basic difference. The merging requirement is set by the platform and is a preset maximum difference of the attack representative value that cannot be exceeded. It is specifically set by technicians in this field according to actual conditions or obtained by a large amount of data simulation. If it is greater than the value corresponding to the merging requirement, it means that the merging requirement is not met, otherwise it means that the merging requirement is met;

[0074] You can also create a merge judgment formula, the expression of the merge judgment formula is:

[0075]

[0076] Where: q is the benchmark difference; p is the merge requirement; q→p means that the benchmark difference meets the merge requirement, that is, the benchmark difference is not greater than the value corresponding to the merge requirement; HP(q, p) is the merge judgment value;

[0077] Calculate the merge judgment value between the basic background and the candidate background through the merge judgment formula; determine whether the merge requirement is met according to the merge judgment value;

[0078] When it is determined that the merging requirements are met, the basic background is merged with the candidate background to form a new basic background, and the initial background that is adjacent to the new basic background is marked as the candidate background, and the process returns to step SA3; wherein the attack representative value corresponding to the new baseline background remains unchanged, that is, the maximum attack representative value; when there is no candidate background, the basic background is marked as the data background for output, and the initial background corresponding to the data background in the first sequence is deleted, and the process returns to step SA2.

[0079] When it is determined that the merging requirement is not met, the basic background is marked as the data background for output, and the initial background corresponding to the data background in the first sequence is deleted, and the process returns to step SA2.

[0080] In one embodiment, the attack representative value is a representative value such as an average or mode of the number of attacks corresponding to the initial background in a corresponding time period.

[0081] In one embodiment, the attack representative value is calculated as follows:

[0082] Set the sensitivity value of each communication data type. The sensitivity value is set according to the sensitivity of the communication data type, and is generally determined based on the existing data sensitivity analysis method; organize the communication data types and sensitivity values, and establish a sensitivity statistics table;

[0083] Identify the type of communication data corresponding to the initial background, match the corresponding sensitivity value from the sensitive statistical table according to the type of communication data; use the sensitivity value as the sensitivity value of each initial background corresponding to the type of communication data;

[0084] Identify the attack times data of each initial background in the corresponding time period, set its representative value, marked as the times representative value, that is, the attack representative value in the previous embodiment, and take the average, mode and other representative values ​​of the attack times;

[0085] The attack representative value of the corresponding initial background is calculated according to the attack representative value formula; the attack representative value formula is:

[0086] GB=MZ×log 10 (C+1);

[0087] Where: GB is the representative value of attack; MZ is the sensitivity value of the corresponding initial background; C is the representative value of the number of times, and C≠0.

[0088] For the setting of sensitive values, data can be divided into different categories according to the sensitivity of the data, such as public data, internal data, confidential data, etc. Each category can be further subdivided into different subcategories to more accurately represent the sensitivity of the data. Define a sensitive value for each data category or subcategory, which can be an integer or floating point number to quantify the sensitivity of the data. The range of sensitive values ​​can be set according to actual needs, for example, from 0 (completely insensitive) to 100 (extremely sensitive). According to the characteristics and application scenarios of the data, a corresponding sensitive value is assigned to each data item. Map the sensitive value to a specific sensitivity level or category to more intuitively represent the sensitivity of the data. For example, the sensitivity value 0-20 can be mapped to "low sensitivity", 21-50 to "medium sensitivity", 51-80 to "high sensitivity", and 81-100 to "extremely high sensitivity".

[0089] For example, a financial institution needs to process a large amount of customer data, including name, ID number, bank card number, transaction records, etc. These data are critical to the business, but they also face the risk of leakage and abuse. Therefore, it is necessary to classify the sensitivity of these data and set corresponding sensitivity values. They are divided into: Very low sensitivity: data that will not cause serious impact on individuals or institutions after data leakage, such as the customer's ordinary name (excluding sensitive information such as ID number and address), with a sensitivity value range of 1-10. Low sensitivity: data that may have a certain impact on individuals or institutions after data leakage, but the impact is limited, such as the customer's ordinary contact information (telephone number, email address, etc.), with a sensitivity value range of 11-30. Medium sensitivity: data that will have a greater impact on individuals or institutions after data leakage, such as the customer's ID number and some transaction records (not involving large amounts of funds), with a sensitivity value range of 31-70. High sensitivity: data that will have a serious impact on individuals or institutions after data leakage, such as the customer's bank card number, password, large transaction records, etc., with a sensitivity value range of 71-100. Matching is performed later based on actual data.

[0090] In other embodiments, an intelligent model may be developed based on existing intelligent algorithms such as deep neural networks, and the corresponding sensitive values ​​may be intelligently determined through the intelligent model; the platform staff may also directly perform manual settings.

[0091] The encryption configuration module is used by the platform to set up an encryption library according to the user's encryption needs and encryption requirements. The encryption library is used to store various encryption algorithms and encryption methods; the platform sets up an encryption policy model according to the encryption library, encryption needs and encryption requirements. The encryption policy model is used to determine the encryption policy that meets the corresponding encryption requirements according to the type of communication data to be encrypted and the application background. The encryption policy is what method to use for encryption under the current conditions, such as combining symmetric encryption and asymmetric encryption for hybrid encryption; the encryption policy model is specifically established by the platform based on existing intelligent technology.

[0092] Exemplarily, the establishment of an encryption policy model includes:

[0093] Collect a large number of communication data samples, which should cover different types of communication data and application backgrounds. Preprocess the collected data, including data cleaning, format conversion, feature extraction, etc., so that it can be used for subsequent neural network training.

[0094] Design a deep neural network structure that can process input data features and output corresponding encryption strategies. The neural network can include an input layer, multiple hidden layers, and an output layer. The input layer is used to receive preprocessed data features; the hidden layer performs nonlinear transformations through multiple neurons and activation functions; and the output layer outputs the classification results or specific parameters of the encryption strategy.

[0095] According to the encryption library and encryption requirements, each data sample is labeled with a corresponding encryption strategy label. The labeled data samples are divided into training sets, validation sets, and test sets for training, validation, and testing of neural networks.

[0096] The neural network is trained using the training set data, and the weights and biases of the neural network are adjusted through the back propagation algorithm to minimize the loss function. During the training process, appropriate optimization algorithms (such as Adam, SGD, etc.) can be used to accelerate the training process and improve the performance of the model. At the same time, the neural network can be verified using the validation set data to evaluate the generalization ability and performance of the model. According to the verification results, the structure, parameters or training strategy of the neural network are adjusted and optimized.

[0097] When it is necessary to generate an encryption strategy for new communication data, the input data is preprocessed and then input into the trained neural network. The neural network will output the corresponding encryption strategy candidate solutions, which may include a combination of multiple encryption methods.

[0098] The encryption module is used to encrypt the communication data, identify the communication data to be encrypted, mark it as target data, identify the type of communication data and the application background corresponding to the target data, the application background is determined based on existing methods, such as based on the sending and receiving location, content, user behavior, etc.; the communication data type and application background corresponding to the target data are marked as target type and target background respectively;

[0099] Analyze the target type and target background through the encryption strategy model to obtain the corresponding encryption strategy;

[0100] Real-time statistics of the correlation analysis data of each type of communication data. The correlation analysis data is various encryption information related to the type of communication data, such as the representative value record data of the number of times in a recent period of time and the statistical probability of each encryption strategy being cracked in the current encryption field, marked as the cracking probability, that is, the correlation analysis data includes the representative value record data of the number of times and the cracking probability of the communication data type under different encryption strategies. The cracking probability can be statistically analyzed based on the decryption technology and decryption historical records of the current encryption field.

[0101] The obtained encryption policies are screened according to the correlation analysis data, the encryption policy to be applied is determined, marked as the target encryption policy, and the target data is encrypted according to the target encryption policy.

[0102] In one embodiment, the encryption strategies obtained are screened according to the association analysis data. The screening can be performed based on existing screening methods, such as directly screening according to the cracking probability, and selecting the encryption strategy with the highest cracking probability for output.

[0103] In one embodiment, the method for screening the obtained encryption policies according to the association analysis data includes:

[0104] Generate a frequency representative value curve based on the association analysis data, that is, the horizontal axis is time and the vertical axis is the frequency representative value; fit the frequency representative value curve to obtain a frequency representative function; determine the frequency representative value at the current time according to the frequency representative function, and mark it as the monitoring representative value. Due to various reasons such as data statistics, it may not be possible to obtain the frequency representative value at the current time in a timely manner, but based on the frequency representative function, the frequency representative value at the current time can be inferred, such as determined based on various existing common prediction algorithms and speculation techniques; mark the obtained monitoring representative value as QB;

[0105] Identify the frequency representative value corresponding to the type of communication data when setting the encryption policy model, and mark it as the reference representative value. The frequency representative value in the requirements analysis module can be applied; mark the reference representative value as QD;

[0106] Identify the cracking probability of each encryption policy according to the association analysis data, and mark the cracking probability of the corresponding encryption policy as μ i , where i represents the corresponding encryption policy, i = 1, 2,..., n, and n is the number of encryption policies;

[0107] Calculate the screening value of the corresponding encryption policy according to the evaluation formula. The evaluation formula is:

[0108] YR i = [b1 × QA(QB, QD)] × [b2 × (exp(μ i ) - 1)];

[0109] In the formula: YR i is the screening value of the corresponding encryption policy; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; exp() is the exponential function with the natural constant e as the base; QA(QB, QD) is a piecewise function,

[0110] The platform party sets the threshold X1 according to the user's needs. Specifically, it is set by those skilled in the art according to the actual situation or obtained through a large amount of data simulation. For example, set several sets of simulation data corresponding to the encryption policies in the critical state, calculate the corresponding screening values, and select the smallest evaluation value as the threshold X1;

[0111] Mark the encryption policies with screening values less than the threshold X1 as candidate policies, and select the target encryption policy from the candidate policies. Generally, select the one with the smallest cracking probability as the target encryption policy, but in order to improve security, the target encryption policy can also be determined by means of accidental random selection.

[0112] In one embodiment, when the screening values ​​of all encryption strategies are less than the threshold value X1, an encryption library optimization warning is issued, that is, it is prompted that the encryption algorithms, encryption methods, etc. in the encryption library need to be optimized, and encryption algorithms and encryption methods with better encryption effects need to be supplemented, and corresponding warning processing is performed, such as stopping the corresponding data from occurring, applying the encryption strategy with the lowest probability of cracking or randomly selected as the target encryption strategy after authorization, etc.

[0113] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0114] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A communication data encryption intelligent processing system, characterized in that: Including demand analysis module, encryption configuration module and encryption module; The demand analysis module is used to perform encryption demand analysis to determine the encryption demand of the user and the encryption requirements of the corresponding encryption demand; The encryption configuration module is used by the platform to set up an encryption library and an encryption policy model according to the user's encryption needs and encryption requirements; the encryption library is used to store encryption algorithms and encryption methods; the encryption policy model is used to determine an encryption policy that meets the encryption requirements according to the type of communication data and application background; The encryption module is used to encrypt the communication data, identify the communication data to be encrypted, mark it as target data, identify the communication data type and application background corresponding to the target data; mark the communication data type and application background corresponding to the target data as target type and target background respectively; Analyze the target type and target background through the encryption strategy model to obtain the corresponding encryption strategy; Acquire association analysis data according to the target category; The encryption strategy is screened according to the association analysis data to obtain a target encryption strategy, and the target data is encrypted according to the target encryption strategy.

2. A communication data encryption intelligent processing system according to claim 1, characterized in that: Conduct an encryption needs analysis, including: The platform sets a data background statistics table, which is used to count various data backgrounds; Determine a communication data range according to the user, wherein the communication data range is composed of various communication data types; According to the data background statistics table, the data background corresponding to each communication data type within the communication data range is obtained and marked as application background; A user encryption analysis table is formed according to the communication data scope and application background; encryption needs and encryption requirements are set according to the user encryption analysis table.

3. A communication data encryption intelligent processing system according to claim 2, characterized in that: Data background settings, including: Step SA1: Acquire various types of communication data within the service scope of the platform, and count the attack record data corresponding to the communication data types; Identify each initial background corresponding to the attack record data, and count the attack representative values ​​corresponding to the initial background; Sorting the initial backgrounds in descending order of attack representative numbers to obtain a first sequence; Step SA2: marking the initial background ranked first in the first sequence as the basic background, and marking the initial background ranked adjacent to the basic background as the candidate background; When there is no basic background, end the analysis; When there is no background to be selected, the basic background is marked as the data background, and the analysis ends; Step SA3: Calculate the absolute value of the difference between the attack representative value of the basic background and the background to be selected, and mark it as the basic difference; Step SA4: judging whether the basic background and the selected background meet the merging requirement according to the basic difference; When it is determined that the merging requirement is met, the basic background is merged with the background to be selected to form a new basic background, and the initial background that is adjacent to the new reference background is marked as the background to be selected, and the process returns to step SA3; when there is no background to be selected, the new basic background is marked as the data background for output, and the initial background corresponding to the data background in the first sequence is deleted, and the process returns to step SA2; When it is determined that the merging requirement is not met, the basic background is marked as a data background for output, and the initial background corresponding to the data background in the first sequence is deleted, and the process returns to step SA2.

4. A communication data encryption intelligent processing system according to claim 3, characterized in that: Determine whether the basic background and the candidate background meet the merging requirements, including: A merging judgment formula is established, and the expression of the merging judgment formula is: Where: q is the benchmark difference; p is the merge requirement; q→p means that the benchmark difference meets the merge requirement; HP(q, p) is the merge judgment value; Calculate the merge judgment value between the basic background and the candidate background through the merge judgment formula; When the merge judgment value is 1, it is judged that the merge requirement is met; When the merge judgment value is 0, it is determined that the non-merge requirement is met.

5. A communication data encryption intelligent processing system according to claim 3, characterized in that: The calculation of the attack representative value includes: The platform establishes a sensitive statistical table, which is used to count the sensitive values ​​of the corresponding communication data types; Matching a corresponding sensitivity value from the sensitive statistical table according to the initial background; identifying a number representative value of the initial background; The attack representative value of the corresponding initial background is calculated according to the attack representative value formula; the attack representative value formula is: GB=MZ×log 10 (C+1); Where: GB is the representative value of attack; MZ is the sensitivity value of the corresponding initial background; C is the representative value of the number of times, and C≠0.

6. A communication data encryption intelligent processing system according to claim 1, characterized in that: Filter encryption policies based on correlation analysis data, including: Generate a frequency representative value curve according to the association analysis data, wherein the horizontal axis of the frequency representative value curve is time and the vertical axis is the frequency representative value; fit the frequency representative value curve to obtain a frequency representative function; determine the frequency representative value of the current time according to the frequency representative function, mark it as a monitoring representative value, and mark the monitoring representative value as QB; Identify a number representative value corresponding to the communication data type, mark it as a reference representative value, and mark the reference representative value as QD; Identify the cracking probability of the encryption strategy based on the association analysis data, and mark the cracking probability of the encryption strategy as μ i , i represents the corresponding encryption strategy, i = 1, 2, ..., n, n is the number of encryption strategies; The screening value of the corresponding encryption policy is calculated according to the evaluation formula. The evaluation formula is: YR i =[b1×QA(QB,QD)]×[b2×(exp(μ i )-1)]; where: YR i is the screening value of the corresponding encryption policy; b1 and b2 are both proportionality coefficients, and the value ranges are 0 < b1 ≤ 1, 0 < b2 ≤ 1; exp() is the exponential function with the natural constant e as the base; QA(QB, QD) is a piecewise function; The encryption policies whose screening values ​​are less than the threshold value X1 are marked as candidate policies, and the target encryption policy is determined according to the candidate policies.

7. A communication data encryption intelligent processing system according to claim 6, characterized in that: The piecewise function is:

8. A communication data encryption intelligent processing system according to claim 6, characterized in that: When the screening values ​​of all encryption policies are less than the threshold X1, an encryption library optimization warning is issued.

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