Information security management system based on cloud network

Abstract

The invention discloses an information security management system based on a cloud network, and relates to the technical field of information security, and the system comprises a static electricity pre-judgment module which collects environment dryness and real-time static electricity data, calculates a dryness trend coefficient through a time sequence, obtains a static electricity deviation coefficient through the difference between the dryness trend coefficient and historical static electricity data, and comprehensively builds a static electricity generation model; if static electricity is pre-judged, the humidifying module is adjusted, otherwise, the information safety analysis module is adjusted; the humidifying module is used for humidifying the environment; and the information security analysis module calculates a token information threat coefficient, builds a risk model in combination with the electrostatic deviation coefficient, analyzes the token risk, optimizes if the token is required to be improved, and ends if the token is secure. According to the invention, the static electricity pre-judgment module is used for collecting data, calculating coefficients and building a model, static electricity is pre-judged, the humidification module is started to prevent accumulation, the static electricity is prevented from disturbing gateway signals to cause authentication misjudgment, the communication safety of the cloud network and the VPN is ensured, the information safety analysis module is used for counting token abnormity and building a risk model, the threshold value is exceeded, the level is adjusted and dislocation is corrected, the token failure is prevented, and the confidentiality and integrity of data are ensured. The leakage risk is reduced.

Description

Technical Field

[0001] This invention relates to the field of information security technology, specifically to an information security management system based on cloud networks. Background Technology

[0002] VPN is a collection of devices and technologies that establish end-to-end private communication links in open public networks such as the Internet and public communication networks through encrypted tunneling technologies such as IPsec, SSL, or TLS. It plays a key role as a secure connection hub in cloud network architecture and is a core supporting component for cloud network information security management systems to achieve secure cross-network communication. It can provide end-to-end encryption protection for various transmission scenarios: such as access data between enterprise employee terminals and cloud databases and storage resources, business data interaction between cross-regional nodes within the cloud, and transmission traffic between branch network and public or private cloud platforms. All of these are protected by high-strength encryption algorithms to prevent data from being eavesdropped on or tampered with. At the same time, VPN can divide multi-tenant dedicated communication domains through virtual network isolation technology to avoid mutual interference between network data of different enterprise users. It can also support trusted communication in remote office and hybrid cloud architectures, providing a solid underlying link protection for cloud network systems to ensure the confidentiality of data transmission and network isolation, and filling security vulnerabilities in public networks.

[0003] The cloud network information security management system addresses the core characteristics of cloud environments: blurred security boundaries due to distributed deployment, isolation risks associated with multi-tenant shared resources, and the need for dynamic adaptation of security policies to elastic business expansion. Its core objective is to ensure the confidentiality, integrity, and availability of core cloud data, IT resources, and critical business operations. This system is not merely a collection of single technologies; rather, it deeply integrates secure connection technologies such as VPNs, layering multiple security mechanisms: at the identity authentication level, it employs multi-factor authentication and biometric verification to ensure the legitimacy of the access subject; at the access control level, it restricts users to operating only resources within their authorized scope through role-based and attribute-based fine-grained access control; and at the risk monitoring level, it relies on real-time traffic analysis and daily... The system uses tools such as auditing to capture abnormal behavior. In addition, it covers the entire lifecycle of data protection from collection, storage, transmission, use to destruction, and provides customized protection solutions for special scenarios such as high-concurrency transactions and sensitive data anonymization. Among them, the hardware VPN gateway, as an important hardware component of the system, is an independent VPN module integrated by a dedicated router or next-generation firewall. It has a built-in SM4 and national cryptographic algorithm dedicated encryption chip and a high-performance multi-core processor, with an encryption rate of up to Gbps. It can efficiently handle high-concurrency encryption tasks and has the characteristics of strong anti-interference and high stability. It is suitable for deployment at the boundary between traditional physical networks and cloud networks, and can especially meet the needs of cloud network scenarios with high data security requirements and large business traffic, such as finance, government, and energy.

[0004] For example, Chinese Patent Publication No. CN214316057U discloses a cloud network-based information security management system, including an installation frame. Three guide posts are fixedly installed on one side of the installation frame, and three adjusting screws are installed on the other side. A third server mounting plate is installed on the outer side of the bottom end of one adjusting screw, a second server mounting plate is installed on the outer side of the middle part of another adjusting screw, and a first server mounting plate is installed on the outer side of the upper end of the remaining adjusting screw. Four transparent sealing plates are evenly installed on the front face of the installation frame, and four sealing frames are fixedly installed on the inner side of the installation frame. Two of the sealing frames have first elastic sleeves installed on their inner sides, and the other two have second elastic sleeves fixedly installed on their inner sides. This invention, through adjustment and protection of the server, effectively ensures stable data transmission and facilitates overall heat dissipation.

[0005] However, when a hardware VPN gateway is deployed in a high-static-area environment, the dry environment causes static electricity to be generated near the hardware VPN gateway. The electric field formed by the static electricity will induce opposite charges in the conductors inside the gateway. The additional electric field formed by these charges will disrupt the directional movement of charges in signal transmission, causing signal distortion and resulting in misjudgment during the authentication stage, i.e. blocking legitimate access or allowing illegal credentials, ultimately threatening the information security of the cloud network. Summary of the Invention

[0006] Technical problems to be solved

[0007] To address the shortcomings of existing technologies, this invention provides an information security management system based on cloud networks, which solves the problem that static electricity generated in a dry environment causes induced charges inside the hardware VPN gateway through the action of an electric field, disrupting signals and leading to authentication misjudgments, thus threatening cloud network security.

[0008] To achieve the above objectives, this invention provides the following technical solution: A cloud-based information security management system, comprising the following specific modules: A static electricity prediction module: Calculates the slope of collected environmental dryness data in a time series to obtain a dryness trend coefficient; calculates the difference between collected real-time static electricity data and historical normal static electricity data to obtain a static electricity deviation coefficient; comprehensively calculates the dryness trend coefficient and the static electricity deviation coefficient to obtain a static electricity generation model; predicts and analyzes static electricity generation based on the static electricity generation model; if static electricity is about to be generated, the humidification module is invoked until static electricity is not generated; if static electricity is not generated, the information security analysis module is invoked; a humidification module: humidifies the environment; an information security analysis module: comprehensively calculates the degree and frequency of change of the generated dynamic token verification code in a time series to obtain an information threat coefficient; comprehensively calculates the static electricity deviation coefficient and the information threat coefficient to obtain an information risk model; analyzes the information risk based on the information leakage model; if the dynamic token verification code risk is increased, the dynamic token verification code security is enhanced through an information security control algorithm; if the dynamic token verification code is secure, the process ends.

[0009] Furthermore, the specific method for obtaining the electrostatic generation model is as follows: JM = ln(k + GX) + ln(k + PX); where JM represents the electrostatic generation model, GX represents the drying trend coefficient, PX represents the electrostatic deviation coefficient, and k is a positive real number.

[0010] Furthermore, the specific method for obtaining the drying trend coefficient is as follows: Under a time series, environmental drying data for each moment is recorded to obtain the humidity at each moment. A coordinate system is established based on the time series and the humidity at each moment, where the vertical axis of the coordinate system represents the time series and the horizontal axis represents the humidity at each moment. Coordinate points related to the time series and the humidity at each moment are obtained and denoted as drying coordinates. The slope of the previous drying coordinate and the next drying coordinate is calculated to obtain the drying slope value. This process continues until both the previous and next drying coordinates have been calculated. The number of drying slope values ​​is statistically analyzed to obtain the number of slope values. Based on the number of slope values, the difference between the next drying coordinate and the previous drying coordinate is calculated to obtain several trend values. The trend values ​​are then summed to obtain the drying trend coefficient.

[0011] Furthermore, the specific method for obtaining the drying slope value is as follows: calculate the difference between the humidity at each moment of the next drying coordinate and the humidity at each moment of the previous drying coordinate to obtain the humidity deviation value; calculate the difference between the time of the next drying coordinate and the time of the previous drying coordinate to obtain the time deviation value; and calculate the quotient between the humidity deviation value and the time deviation value to obtain the drying slope value.

[0012] Furthermore, the specific method for obtaining the electrostatic deviation coefficient is as follows: calculate the difference between the real-time electrostatic data and the historical normal electrostatic data to obtain the electrostatic deviation value, set a deviation threshold, compare the electrostatic deviation value with the deviation threshold, if the electrostatic deviation value is greater than the deviation threshold, then the electrostatic deviation value is recorded as the electrostatic deviation coefficient, if the electrostatic deviation value is less than or equal to the deviation threshold, then no operation is performed.

[0013] Furthermore, the specific method for obtaining the information risk model is as follows: the number of times any binary number of the dynamic token verification code changes outside of a specific rule is recorded as the severity of the abnormal binary number; the frequency of changes of any binary number of the dynamic token verification code outside of a specific rule is recorded as the frequency of the abnormal binary number severity; and the information risk model is obtained by multiplying the severity of the abnormal binary number and the frequency of the abnormal binary number severity.

[0014] Furthermore, the specific method for obtaining the severity of the abnormal binary number is as follows: sum the number of changes of any binary number that is not within the specific rules of the dynamic token verification code to obtain the number of changes of a certain binary number, then sum the number of changes of each changed binary number to obtain the number of abnormal binary numbers, and sum the number of changes of a certain binary number based on the number of abnormal binary numbers to obtain the severity of the abnormal binary number.

[0015] Furthermore, the specific method for obtaining the severity frequency of the abnormal binary number is as follows: the time when the binary number is switched outside of a specific rule is recorded as the abnormal time. The abnormal time is used to calculate the quotient of the number of changes of a certain binary number within the abnormal time to obtain the abnormal change frequency of this binary number. The abnormal change frequency of each of these binary numbers is summed according to the number of abnormal binary numbers to obtain the total abnormal change frequency. The total abnormal change frequency is then used to calculate the quotient of the number of abnormal binary numbers to obtain the severity frequency of the abnormal binary number.

[0016] Furthermore, the specific method for analyzing information risks based on the information leakage model is as follows: set an information leakage threshold, compare the information leakage model with the information leakage threshold, if the information leakage model is greater than the information leakage threshold, it indicates that the risk of dynamic token verification code has increased, if the information leakage model is less than or equal to the information leakage threshold, it indicates that dynamic token verification code is secure.

[0017] Furthermore, the specific method for enhancing the security of dynamic token verification codes through information security control algorithms is as follows: set high-level thresholds and low-level thresholds. When the information leakage model is greater than the information leakage threshold, if a certain voltage is greater than or equal to the high-level threshold and disorder occurs, then the high-level threshold is lowered. Similarly, if a certain voltage is less than or equal to the low-level threshold and disorder occurs, then the low-level threshold is raised.

[0018] Beneficial effects

[0019] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0020] 1. The static electricity prediction module first collects environmental dryness data and real-time static electricity data around the hardware VPN gateway. It calculates the dryness trend coefficient through time series analysis, obtains the static electricity deviation coefficient by comparing with historical data, and then obtains the static electricity generation model through logarithmic model synthesis to accurately predict the risk of static electricity generation. If static electricity is predicted to be about to occur, the humidification module is immediately called to adjust the ambient humidity to avoid static electricity accumulation. This process can effectively prevent the additional electric field formed by static electricity from disturbing the directional movement of charges in the internal signal transmission of the gateway, avoiding authentication misjudgments such as blocking legitimate access or allowing illegal credentials due to signal distortion. It provides underlying link protection for secure communication between the cloud network and the hardware VPN gateway, ensuring the confidentiality of data transmission and network isolation.

[0021] 2. By constructing a full-process risk control mechanism through the information security analysis module, the security of dynamic tokens is enhanced. The module first counts the number of times and frequency of binary number changes that are not within specific rules in the dynamic token verification code. The product of these two counts forms an information risk model, which is quickly identified by comparing it with a preset threshold. If the risk increases, the level threshold is further adjusted through the information security control algorithm. When voltage disturbances occur near the high level threshold, the threshold is lowered; when voltage disturbances occur near the low level threshold, the threshold is raised. This corrects voltage anomalies and binary data misalignment caused by static electricity. This mechanism can respond in real time to the interference of static electricity on the token, avoiding encryption failure of data interaction between the hardware VPN gateway and the cloud platform due to token failure. This ensures the confidentiality and integrity of cloud network data throughout its entire lifecycle and reduces the risk of information leakage.

[0022] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0023] Figure 1 This invention provides a flowchart of an information security management method based on cloud networks.

[0024] Figure 2 This invention relates to a structural diagram of an information security management system based on cloud networks. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0027] Example 1:

[0028] like Figures 1-2 As shown, this embodiment of the invention provides an information security management system based on a cloud network, comprising the following specific modules:

[0029] Static Electricity Prediction Module: In dry environments, the low water content in the air and on object surfaces makes it difficult for generated charges to dissipate, leading to continuous accumulation and the formation of static electricity. Therefore, a humidity sensor is placed near the hardware VPN gateway to collect real-time environmental dryness data. A non-contact electrostatic field sensor is placed near the power supply, circuit board, and seams of the hardware VPN gateway, areas prone to static electricity generation. This static electricity near the hardware VPN gateway can affect the electrical levels within the gateway, interfering with internal signal transmission lines and causing changes in the binary sequence of the dynamic token verification code. This can invalidate the encryption of data exchanged between the hardware VPN gateway and the cloud network platform, thus compromising information security. The module uses a non-contact electrostatic field sensor to collect real-time static electricity data, combining environmental dryness data with real-time static electricity data... The system performs data cleaning to remove redundant values ​​and improve the data quality of environmental dryness data and real-time static electricity data. The cleaned environmental dryness data is then analyzed over time to calculate the slope and obtain the dryness trend coefficient. The real-time static electricity data is then compared with historical normal static electricity data to obtain the static electricity deviation coefficient. The dryness trend coefficient and static electricity deviation coefficient are then combined and standardized to eliminate dimensional differences between them and convert their values ​​from different orders of magnitude to a unified range, thus obtaining a static electricity generation model. Based on this model, static electricity generation is predicted and analyzed. If static electricity is predicted to be generated, the humidification module is invoked until static electricity is determined not to be generated. If static electricity is determined not to be generated, the information security analysis module is invoked.

[0030] Humidification module: For example, a mist-free humidifier can increase the ambient humidity while avoiding water mist from causing a short circuit in the hardware VPN gateway. After the humidification module is activated, it is used to humidify the environment, reduce the dryness of the environment of the hardware VPN gateway, avoid the generation of static electricity, and thus prevent information security vulnerabilities caused by static electricity.

[0031] Information Security Analysis Module: This module generates dynamic token verification codes using a pseudo-random function based on specific rules of the hardware VPN gateway and cloud network platform. It comprehensively calculates the degree and frequency of change of the dynamic token verification codes over time to obtain an information threat coefficient. Simultaneously, it standardizes the calculation of the electrostatic deviation coefficient and the information threat coefficient to eliminate dimensional differences between them and transform values ​​of different orders of magnitude into a unified numerical range, resulting in an information risk model. Based on the information leakage model, it analyzes the information risk. If the analysis indicates an increased risk to the dynamic token verification code, it enhances the security of the dynamic token verification code through information security prevention and control algorithms. If the analysis indicates that the dynamic token verification code is secure, the process terminates.

[0032] Example 2 differs from Example 1 in that:

[0033] The specific method for obtaining the static electricity generation model is as follows:

[0034] JM = ln(k+GX) + ln(k+PX);

[0035] Wherein, JM represents the static electricity generation model, reflecting whether static electricity is generated; GX represents the drying trend coefficient, reflecting the drying trend of the environment; PX represents the static electricity deviation coefficient, reflecting the static electricity deviation; and k is a positive real number to avoid the static electricity generation model being invalid when the drying trend coefficient or the static electricity deviation coefficient is zero.

[0036] The specific method for obtaining the drying trend coefficient is as follows:

[0037] In the time series, environmental dryness data at each moment is recorded to obtain the humidity at each moment. A coordinate system is established based on the time series and the humidity at each moment, where the vertical axis of the coordinate system is the time series and the horizontal axis is the humidity at each moment. The coordinate points with respect to the time series and the humidity at each moment are obtained and are denoted as dryness coordinates. The slope of the previous dryness coordinate and the next dryness coordinate are calculated to obtain the dryness slope value. This process is repeated until both the previous and next dryness coordinates have been calculated. The number of dryness slope values ​​is counted to obtain the number of slope values. Based on the number of slope values, the difference between the next dryness coordinate and the previous dryness coordinate is calculated to obtain several trend values. The trend values ​​are summed to obtain the dryness trend coefficient.

[0038] The specific method for obtaining the drying slope value is as follows:

[0039] The difference between the humidity at each moment of the next drying coordinate and the humidity at each moment of the previous drying coordinate is calculated to obtain the humidity deviation value. The difference between the time of the next drying coordinate and the time of the previous drying coordinate is calculated to obtain the time deviation value. The quotient between the humidity deviation value and the time deviation value is calculated to obtain the drying slope value. Since the time of the next drying coordinate is different from the time of the previous drying coordinate, the time deviation value is not zero, so the drying slope value is valid.

[0040] The specific method for obtaining the electrostatic deviation coefficient is as follows:

[0041] The difference between real-time static electricity data and historical normal static electricity data is calculated to obtain the static electricity deviation value. A deviation threshold is set, and the static electricity deviation value is compared with the deviation threshold. If the static electricity deviation value is greater than the deviation threshold, the static electricity deviation value is recorded as the static electricity deviation coefficient, that is, this static electricity has an impact on information security. If the static electricity deviation value is less than or equal to the deviation threshold, no operation is performed, that is, this static electricity has no impact on information security.

[0042] The specific method for predicting and analyzing static electricity generation based on the static electricity generation model is as follows:

[0043] By setting a threshold for static electricity generation through historical experiments, the static electricity generation model is compared with the static electricity generation threshold. If the static electricity generation model is greater than the static electricity generation threshold, it means that static electricity is about to be generated. If the static electricity generation model is less than or equal to the static electricity generation threshold, it means that static electricity will not be generated.

[0044] The specific methods for obtaining the information risk model are as follows:

[0045] Since dynamic token verification codes are in binary form and are switched between zero and one at certain times within specific rules to improve security, if a binary number in the dynamic token verification code is not switched within the specific rules, it indicates a security threat to the dynamic token verification code, which may fail to protect the data exchanged between the hardware VPN gateway and the cloud network platform. Therefore, the number of times any binary number in the dynamic token verification code changes outside the specific rules is recorded as the severity of the abnormal binary number, and the frequency of any binary number change outside the specific rules is recorded as the frequency of the abnormal binary number severity. The information risk model is obtained by multiplying the severity of the abnormal binary number and the frequency of the abnormal binary number severity.

[0046] The specific method for obtaining the severity of abnormal binary numbers is as follows:

[0047] The number of changes of any binary number that is not within a specific rule in the dynamic token verification code is summed to obtain the number of changes of a certain binary number. Then, the number of changes of each changed binary number is summed to obtain the number of abnormal binary numbers. Based on the number of abnormal binary numbers, the number of changes of a certain binary number is summed to obtain the severity of the abnormal binary numbers.

[0048] The specific method for obtaining the severity frequency of abnormal binary numbers is as follows:

[0049] The time when a binary number is switched outside of a specific rule is recorded as an abnormal time. The abnormal time is used to calculate the quotient of the number of changes of a certain binary number within the abnormal time to obtain the abnormal change frequency of this binary number. The abnormal change frequency of each of these binary numbers is summed according to the number of abnormal binary numbers to obtain the total abnormal change frequency. The total abnormal change frequency is then divided by the number of abnormal binary numbers to obtain the severe frequency of abnormal binary numbers.

[0050] The specific methods for analyzing information risk based on the information leakage model are as follows:

[0051] By setting an information leakage threshold through historical experiments, the information leakage model is compared with the information leakage threshold. If the information leakage model is greater than the information leakage threshold, it indicates that the dynamic token verification code is at increased risk. If the information leakage model is less than or equal to the information leakage threshold, it indicates that the dynamic token verification code is secure.

[0052] The specific methods for enhancing the security of dynamic token verification codes through information security control algorithms are as follows:

[0053] Because static electricity affects the voltage levels within the hardware VPN gateway, voltage levels in digital circuits represent the physical state of binary data, where high or low voltage corresponds to a digit 1 or digit 0. High and low voltage thresholds are set. Under normal circumstances, a voltage greater than or equal to the high voltage threshold represents a digit 1, and a voltage less than or equal to the low voltage threshold represents a digit 0. However, due to static electricity, a voltage that was originally greater than or equal to the high voltage threshold may now be less than the high voltage threshold, and vice versa, leading to binary data corruption. Therefore, when the information leakage model exceeds the information leakage threshold, if corruption occurs when a voltage is greater than or equal to the high voltage threshold, the high voltage threshold is lowered to expand the range of voltages greater than or equal to the high voltage threshold, thus preventing binary data corruption. Similarly, if corruption occurs when a voltage is less than or equal to the low voltage threshold, the low voltage threshold is raised.

[0054] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An information security management system based on cloud networks, characterized in that: Includes the following specific modules: Static electricity prediction module: Calculates the slope of the collected environmental dryness data in time series to obtain the dryness trend coefficient. Calculates the difference between the collected real-time static electricity data and historical normal static electricity data to obtain the static electricity deviation coefficient. Combines the dryness trend coefficient and the static electricity deviation coefficient to obtain the static electricity generation model. Based on the static electricity generation model, it performs predictive analysis on static electricity generation. If the analysis indicates that static electricity is about to be generated, it calls the humidification module until the analysis indicates that static electricity will not be generated. If the analysis indicates that static electricity will not be generated, it calls the information security analysis module. Humidification module: Used to humidify the environment; Information security analysis module: Under time series conditions, the degree and frequency of change of the generated dynamic token verification code are comprehensively calculated to obtain the information threat coefficient. The electrostatic deviation coefficient and the information threat coefficient are comprehensively calculated to obtain the information risk model. The information risk is analyzed according to the information leakage model. If the analysis shows that the risk of dynamic token verification code has increased, the security of dynamic token verification code is improved through information security prevention and control algorithms. If the analysis shows that dynamic token verification code is secure, the process ends.

2. The information security management system based on cloud networks according to claim 1, characterized in that: The specific method for obtaining the electrostatic generation model is as follows: JM = ln(k+GX) + ln(k+PX); Where JM represents the static electricity generation model, GX represents the drying trend coefficient, PX represents the static electricity deviation coefficient, and k is a positive real number.

3. The information security management system based on a cloud network according to claim 2, characterized in that: The specific method for obtaining the drying trend coefficient is as follows: In the time series, environmental dryness data at each moment is recorded to obtain the humidity at each moment. A coordinate system is established based on the time series and the humidity at each moment, where the vertical axis of the coordinate system is the time series and the horizontal axis is the humidity at each moment. The coordinate points with respect to the time series and the humidity at each moment are obtained and are denoted as dryness coordinates. The slope of the previous dryness coordinate and the next dryness coordinate are calculated to obtain the dryness slope value. This process is repeated until both the previous and next dryness coordinates have been calculated. The number of dryness slope values ​​is counted to obtain the number of slope values. Based on the number of slope values, the difference between the next dryness coordinate and the previous dryness coordinate is calculated to obtain several trend values. The trend values ​​are summed to obtain the dryness trend coefficient.

4. The information security management system based on a cloud network according to claim 3, characterized in that: The specific method for obtaining the drying slope value is as follows: The humidity deviation value is obtained by calculating the difference between the humidity at each moment of the next drying coordinate and the humidity at each moment of the previous drying coordinate. The time deviation value is obtained by calculating the difference between the time of the next drying coordinate and the time deviation value. The drying slope value is obtained by quotienting the humidity deviation value and the time deviation value.

5. The information security management system based on a cloud network according to claim 2, characterized in that: The specific method for obtaining the electrostatic deviation coefficient is as follows: The difference between real-time electrostatic data and historical normal electrostatic data is calculated to obtain the electrostatic deviation value. A deviation threshold is set, and the electrostatic deviation value is compared with the deviation threshold. If the electrostatic deviation value is greater than the deviation threshold, the electrostatic deviation value is recorded as the electrostatic deviation coefficient. If the electrostatic deviation value is less than or equal to the deviation threshold, no operation is performed.

6. The information security management system based on a cloud network according to claim 1, characterized in that: The specific method for obtaining the information risk model is as follows: The number of times any binary number in the dynamic token verification code changes outside of a specific rule is recorded as the severity of the abnormal binary number. The frequency of changes of any binary number in the dynamic token verification code outside of a specific rule is recorded as the frequency of the abnormal binary number severity. The information risk model is obtained by multiplying the severity of the abnormal binary number and the frequency of the abnormal binary number severity.

7. The information security management system based on a cloud network according to claim 6, characterized in that: The specific method for obtaining the severity of the abnormal binary number is as follows: The number of changes of any binary number that is not within a specific rule in the dynamic token verification code is summed to obtain the number of changes of a certain binary number. Then, the number of changes of each changed binary number is summed to obtain the number of abnormal binary numbers. Based on the number of abnormal binary numbers, the number of changes of a certain binary number is summed to obtain the severity of the abnormal binary numbers.

8. The information security management system based on a cloud network according to claim 6, characterized in that: The specific method for obtaining the severity frequency of the abnormal binary number is as follows: The time when a binary number is switched outside of a specific rule is recorded as an abnormal time. The abnormal time is used to calculate the quotient of the number of changes of a certain binary number within the abnormal time to obtain the abnormal change frequency of this binary number. The abnormal change frequency of each of these binary numbers is summed according to the number of abnormal binary numbers to obtain the total abnormal change frequency. The total abnormal change frequency is then divided by the number of abnormal binary numbers to obtain the severe frequency of abnormal binary numbers.

9. The information security management system based on a cloud network according to claim 1, characterized in that: The specific method for analyzing information risk based on the information leakage model is as follows: Set an information leakage threshold and compare the information leakage model with the information leakage threshold. If the information leakage model is greater than the information leakage threshold, it means that the dynamic token verification code is at increased risk. If the information leakage model is less than or equal to the information leakage threshold, it means that the dynamic token verification code is secure.

10. The information security management system based on a cloud network according to claim 1, characterized in that: The specific method for enhancing the security of dynamic token verification codes through information security control algorithms is as follows: Set high-level and low-level thresholds. When the information leakage model is greater than the information leakage threshold, if a certain voltage is greater than or equal to the high-level threshold and disorder occurs, then the high-level threshold is lowered. Similarly, if a certain voltage is less than or equal to the low-level threshold and disorder occurs, then the low-level threshold is raised.

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