Network information consultation platform based on zero trust architecture and communication method

Abstract

The application discloses a network information consultation platform and communication method based on a zero-trust architecture, and relates to the technical field of digital platform communication.The method comprises the following steps: authenticating the identity of a user; when the identity authentication is passed, obtaining user data; inputting the user data into a trust evaluation model to output a comprehensive trust degree; obtaining access rights according to the comprehensive trust degree; monitoring the consultation behavior of the user in real time to calculate a real-time trust degree; and managing the access rights according to the real-time trust degree.The application obtains the user data of the user whose identity authentication is successful, calculates the comprehensive trust degree of the user according to the user data to quantify the credit level of the user, monitors the user throughout the consultation process, manages the access rights of the user in combination with the credit level of the user and the real-time monitoring, and improves the security of the platform.

Description

Technical Field

[0001] This invention relates to the field of digital platform communication technology, specifically to a network information consultation platform and communication method based on a zero-trust architecture. Background Technology

[0002] An online information consulting platform is a service platform based on the internet that provides efficient and convenient information consulting and resource access. It encompasses resources from multiple fields and helps users quickly obtain the information they need by integrating data resources and professional knowledge. However, with the increasing complexity of the network environment, traditional online information consulting platforms have certain security deficiencies.

[0003] Existing online information consulting platforms employ static access control mechanisms, granting users fixed access permissions upon authentication. However, when faced with access to multiple resources, this can lead to issues of over- or under-assignment of permissions, negatively impacting the user experience and compromising platform security. Therefore, there is an urgent need for a communication method in online information consulting platforms that enables fine-grained access control. Summary of the Invention

[0004] The purpose of this invention is to provide a network information consultation platform and communication method based on a zero-trust architecture. This invention obtains user data of users who have successfully authenticated their identities, calculates the user's comprehensive trust level based on the user data to quantify the user's credit rating, monitors the user throughout the consultation process, and manages user access permissions by combining user credit rating and real-time monitoring, thereby improving the platform's security.

[0005] The objective of this invention is achieved through the following technical means:

[0006] In a first aspect, the present invention provides a communication method for a network information consultation platform based on a zero-trust architecture, comprising the following steps:

[0007] User authentication;

[0008] When identity authentication is successful, user data is retrieved;

[0009] The user data is input into the trust assessment model, which outputs a comprehensive trust level.

[0010] Access permissions are granted based on the overall trust level.

[0011] Real-time monitoring of user inquiry behavior and calculation of real-time trust levels;

[0012] Access permissions are managed based on the real-time trust level.

[0013] Preferably, the step of inputting the user data into the trust assessment model and outputting a comprehensive trust score includes the following steps:

[0014] The user data is normalized, and the weight coefficient of each user data is calculated.

[0015] The first level of trust is calculated based on the normalized user data and the weighting coefficients.

[0016] Retrieve historical access data;

[0017] Obtain the consultation score for each of the historical access data and calculate the time difference of the data;

[0018] Calculate the second level of trust based on the consultation score and the time difference;

[0019] The first trust level and the second trust level are weighted and summed to obtain the comprehensive trust level.

[0020] Preferably, the formula for calculating the first level of trust is as follows:

[0021] ,

[0022] in, As the highest level of trust, For the amount of user data, For the first Weighting coefficients for individual user data For the first Normalized values ​​of individual user data;

[0023] The formula for calculating the second level of trust is as follows:

[0024] ,

[0025] in, As the second level of trust, The number of historical access data. The decay rate over time. For the first The time difference between the historical access data and the current access. For the first Consultation score of each historical access data;

[0026] The formula for calculating the overall trust level is as follows:

[0027] , ,

[0028] in, To assess overall trust levels, and These are the weighting coefficients. As the highest level of trust, This is the second level of trust.

[0029] Preferably, obtaining access rights based on the comprehensive trust level includes the following steps:

[0030] Analyze the consultation content to identify the consultation topics;

[0031] Select a decision tree based on the consultation topic;

[0032] The overall trust level is compared with the trust threshold, and the initial permissions are obtained from the decision tree based on the comparison result;

[0033] Calculate the relevance between the consultation topic and the initial permissions;

[0034] The initial permissions are removed based on the correlation to obtain the access permissions.

[0035] Preferably, the real-time monitoring of users' consultation behavior and the calculation of real-time trust levels include the following steps:

[0036] Monitor users' inquiry behavior in real time and record the number of times it is completed;

[0037] When abnormal behavior is detected, the number of abnormalities is recorded and an abnormality score is obtained.

[0038] The real-time trust level is calculated based on the number of completions, the number of anomalies, and the anomaly score.

[0039] Preferably, the formula for calculating the real-time trust level is as follows:

[0040] ,

[0041] in, For real-time trust levels, To assess overall trust levels, The number of types of abnormal behavior. For the first Abnormal scores for various abnormal behaviors For the first The number of abnormal behaviors. For normal score, This represents the number of consultations completed.

[0042] Preferably, managing the access permissions based on the real-time trust level includes the following steps:

[0043] Compare the real-time trust level with the trust threshold;

[0044] When the real-time trust level is less than the trust threshold, the user's consultation session is terminated;

[0045] When the real-time trust level is greater than or equal to the trust threshold, the real-time difference is calculated;

[0046] When the real-time difference is greater than or equal to zero, the access permission is expanded;

[0047] When the real-time difference is less than zero, the access permission is reduced;

[0048] When a consultation session ends, the consultation score is recorded and added to the historical access data.

[0049] Secondly, the present invention provides a network information consultation platform based on a zero-trust architecture, and the communication method of the network information consultation platform based on a zero-trust architecture described above includes: an identity management module, a data acquisition module, a trust assessment module, an access management module, a real-time monitoring module, and an access management module.

[0050] The identity management module is used to authenticate the user's identity;

[0051] The data acquisition module is used to acquire user data when identity authentication is successful;

[0052] The trust assessment module is used to input the user data into the trust assessment model and output a comprehensive trust level.

[0053] The access management module is used to obtain access permissions based on the comprehensive trust level;

[0054] The real-time monitoring module is used to monitor users' consultation behavior in real time and calculate the real-time trust level.

[0055] The access management module is used to manage the access permissions based on the real-time trust level.

[0056] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, and when the processor executes the computer instructions, the electronic device executes the above-described communication method for a network information consultation platform based on a zero-trust architecture.

[0057] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor of an electronic device, cause the processor to execute the aforementioned communication method of a network information consultation platform based on a zero-trust architecture.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0059] This invention obtains user data of users who have successfully authenticated their identities, calculates the user's comprehensive trust level based on the user data to quantify the user's credit rating, monitors the user throughout the consultation process, and manages user access permissions by combining user credit rating and real-time monitoring, thereby improving the platform's security.

[0060] This invention combines users' static data and dynamic behavior. It obtains static data, performs normalization processing, and evaluates and calculates the first level of trust from three aspects: payment level, authentication security, and address security. It obtains consultation scores and time differences for each visit through historical access data, evaluates and calculates the second level of trust from historical behavior, and calculates the user's comprehensive trust level based on the first and second levels of trust to measure the user's trust level. This provides a data foundation for the subsequent initial allocation of permissions and improves the platform's security.

[0061] This invention analyzes users' consultation topics using NLP technology, selects a suitable decision tree from multiple pre-built resource decision trees and obtains initial permissions, thereby improving the efficiency of resource query. At the same time, it calculates the correlation between consultation topics and initial permissions using NLP technology, eliminates redundant permissions, which is conducive to the accurate allocation of permissions and improves the security of the platform.

[0062] This invention dynamically responds to changes in user behavior by acquiring both normal and abnormal behavior. When a user's real-time trust level is low, the session is terminated promptly, and the user's access permissions are dynamically adjusted based on the real-time trust level. The user's consultation score is stored in historical access data, providing a data foundation for trust assessment of future user visits and improving the platform's security. Attached Figure Description

[0063] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This embodiment provides a flowchart illustrating a communication method for a network information consultation platform based on a zero-trust architecture.

[0066] Figure 2The flowchart for step S3 in this embodiment, which involves inputting user data into the trust assessment model and outputting the comprehensive trust level, is shown below.

[0067] Figure 3 This embodiment provides a flowchart illustrating step S4, which involves obtaining access permissions based on the overall trust level.

[0068] Figure 4 This embodiment provides a flowchart for step S5, which involves real-time monitoring of user consultation behavior and calculation of real-time trust level.

[0069] Figure 5 This is a flowchart illustrating step S6 of this embodiment, which manages access permissions based on real-time trust levels.

[0070] Figure 6 This is a schematic diagram of the structure of a network information consultation platform based on a zero-trust architecture provided in this embodiment;

[0071] Figure 7 This is a schematic diagram of the structure of an electronic device provided in this embodiment. Detailed Implementation

[0072] 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 a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0073] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0074] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" and "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0075] Example 1

[0076] This embodiment provides a communication method for a network information consultation platform based on a zero-trust architecture, such as... Figure 1 As shown, it includes the following steps:

[0077] S1, to authenticate the user's identity;

[0078] S2, when identity authentication is successful, retrieves user data;

[0079] S3 inputs user data into the trust assessment model and outputs a comprehensive trust score;

[0080] S4: Grant access based on overall trust level;

[0081] S5 monitors users' inquiry behavior in real time and calculates real-time trust levels.

[0082] S6 manages access permissions based on real-time trust levels.

[0083] It's important to note that user authentication is the foundation of a zero-trust architecture. Its essence lies in ensuring that only verified users can access platform resources through pre-defined security policies. The security policies used for authentication are existing technologies and will not be elaborated upon here. User data includes: user account type, authentication level, and device security status. Specifically, user account types include free users, paid users, and subscribers; authentication levels are rated based on the authentication method used; and device security status is assessed based on the user's device IP address. Using the above user data, a comprehensive trust level is calculated. The trust assessment model is a mathematical calculation model designed based on platform data, used to calculate the comprehensive trust level based on user data. The comprehensive trust level measures the user's trust level, and access permissions are determined based on the trust level. During user consultations, user consultation behavior is monitored in real time, the user's trust level is updated, and access permissions are adjusted accordingly.

[0084] In this embodiment, by acquiring user data of users who have successfully authenticated their identities, calculating the user's overall trust level based on the user data to quantify the user's credit rating, and monitoring the user throughout the consultation process, the platform improves its security by combining the user's credit rating with real-time monitoring to manage user access permissions.

[0085] In some embodiments, step S3 involves inputting user data into a trust assessment model and outputting a comprehensive trust score, such as... Figure 2 As shown, it includes the following steps:

[0086] S31, Normalize the user data and calculate the weight coefficient of each user data;

[0087] S32, calculate the first level of trust based on the normalized user data and weighting coefficients;

[0088] S33, retrieve historical access data;

[0089] S34, obtain the consultation score for each historical access data and calculate the time difference of the data;

[0090] S35, calculate the second level of trust based on the consultation score and time difference;

[0091] S36, the first trust level and the second trust level are weighted and summed to obtain the comprehensive trust level.

[0092] It should be noted that the overall user trust level is measured from two aspects: user static data and user dynamic behavior. The user static data portion is calculated from user data. Since the range of values ​​for user data varies, user data with a larger value range can significantly impact the calculation results. Therefore, the acquired user data undergoes linear normalization. This normalization is achieved by obtaining parameters from statistical analysis of other user data stored historically on the platform, and then using the entropy weight method to process the stored user data to obtain the weight coefficients of each indicator in the user data. Specifically, historical user data stored on the platform is used as samples. Each user data set includes three indicators: user account type, authentication level, and device security status. A mapping table is established for each indicator. For example, user account types include free users, paid users, and subscription users, with scores of 30, 50, and 90 respectively based on payment level. Authentication level and device status are scored according to a pre-defined scoring table, based on the type of user data they match. Specifically, authentication level includes common authentication methods such as password, SMS verification code, fingerprint, and facial recognition, as well as combinations of two or more methods. The authentication security strength score is summed with the authentication method score. Device security status is evaluated based on device IP location and device signature. In other words, user trust is measured from three aspects: payment level, authentication security, and address security. The sample data uses the entropy weight method to calculate the weight coefficient of each indicator. The specific calculation process of the entropy weight method for calculating the weight coefficient of each indicator is existing technology and will not be elaborated here. The first trust level is obtained by weighting and summing the normalized user data and the weight coefficients and mapping it to [0,1]. The first trust level is the score of the user's static data portion.

[0093] For the user dynamic behavior component, the results are calculated using historical access data. Since historical access data varies in time, earlier access data has a smaller impact on the calculation results. Therefore, the time difference between each historical access and the current access is calculated, and combined with the consultation score for each historical access, a second trust level is obtained. The second trust level is the user dynamic behavior score. The consultation score is based on the user's behavior during access; a user receives points for completing a consultation, and conversely, points are deducted for abnormal behavior. The consultation score is obtained by summing the scores after the user completes consultations within a given time period. After obtaining the first and second trust levels, a weighted sum of the first and second trust levels is obtained to obtain the comprehensive trust score.

[0094] Specifically, the formula for calculating the first level of trust is as follows:

[0095] ,

[0096] in, As the highest level of trust, For the amount of user data, For the first Weighting coefficients for individual user data For the first Normalized values ​​of individual user data;

[0097] The formula for calculating the second level of trust is as follows:

[0098] ,

[0099] in, As the second level of trust, The number of historical access data. The decay rate over time. For the first The time difference between the historical access data and the current access. For the first Consultation scores based on historical access data; the time decay rate controls the weight of historical access data at different times on the current trust assessment, and is set according to actual needs. For example, if it is desired that the influence of historical access data is halved after one week, then set... The value of the time decay rate is not limited here.

[0100] The formula for calculating overall trust level is as follows:

[0101] , ,

[0102] in, To assess overall trust levels, and These are the weighting coefficients. As the highest level of trust, This is the second level of trust. and The settings are configured according to actual needs. In some embodiments, the zero-trust architecture consulting platform places greater emphasis on the static data of initial identity authentication, and in order to improve the interception rate of abnormal access and reduce the false blocking rate of normal access, the weight coefficient is set to... , .

[0103] In this embodiment, by combining the user's static data and dynamic behavior, the static data is obtained, normalized, and evaluated from three aspects: payment level, authentication security, and address security to calculate the first level of trust. The consultation score and time difference of each visit are obtained through historical access data, and the second level of trust is evaluated and calculated from historical behavior. The user's comprehensive trust level is calculated based on the first and second levels of trust to measure the user's trust level, providing a data foundation for the subsequent initial allocation of permissions and improving the platform's security.

[0104] In some embodiments, step S4 involves obtaining access permissions based on the overall trust level, such as... Figure 3 As shown, it includes the following steps:

[0105] S41, Analyze the consultation content to obtain the consultation topic;

[0106] S42, Select a decision tree based on the consultation topic;

[0107] S43, compare the overall trust level with the trust threshold, and obtain the initial permissions from the decision tree based on the comparison result;

[0108] S44, Calculate the relevance between the consultation topic and the initial permissions;

[0109] S45, based on relevance, remove the initial permissions to obtain access permissions.

[0110] It's important to note that the online information consultation platform stores information from multiple fields and aspects. It needs to quickly locate the relevant information based on the user's consultation content and determine the user's access permissions. The platform categorizes all internal resources into multiple decision trees, each corresponding to a topic. NLP technology is used to extract the consultation topic from the user's consultation content. This includes preprocessing the consultation text, such as removing stop words and word segmentation, identifying entities in the consultation text, generating topic vectors, and calculating the relevance between the topic vectors and the corresponding topics in the decision trees to quickly select the appropriate decision tree. For example, when a user inquires about medical insurance, the platform analyzes the user's consultation topic and categorizes it into legal and medical categories. Therefore, it can quickly select the legal or medical decision tree for subsequent processing. Multiple decision trees from different fields form a decision forest, with each decision tree containing multiple nodes, each node representing the access permissions for a type of resource. The online information consultation platform supports consultations in fields such as law, medicine, and education. For example, the intermediate nodes of a legal decision tree include various legal resources, such as criminal law, civil law, and labor law. The leaf nodes under each node represent the user's operational permissions, such as viewing, downloading, or editing, and the trust level for granting those permissions. Similarly, the intermediate nodes of a medical decision tree include resources such as medical equipment, medicines, and medical records. The leaf nodes under each node also represent the user's operational permissions, such as viewing, downloading, editing, or analyzing, and the trust level for granting those permissions. The depth and conditions for dividing the decision tree nodes are existing technology and can be manually adjusted according to actual needs, and will not be elaborated upon here.

[0111] Following this, the platform compares the user's overall trust level with the trust threshold range within the corresponding decision tree to obtain initial access permissions. For example, in the legal decision tree, the trust threshold for granting permissions is 0.2. When the user's overall trust level is less than 0.2, the user will not be able to access the platform, and the initial permissions will be empty. When the user's overall trust level is greater than or equal to 0.2, the user will be granted viewing permissions, and further access to more functions and data will be granted based on the user's overall trust level, thus forming the initial permissions.

[0112] Initial permissions include the user's required permissions and redundant permissions. Further filtering of initial permissions is needed to remove redundant permissions, achieving fine-grained resource isolation and preventing unauthorized access. When acquiring the consultation topic, NLP technology has already been used to analyze the consultation content, converting the text features of the consultation content into vector features. When calculating relevance, NLP technology is used to analyze the node descriptions of the initial permissions, obtaining the vector features of each node description. The relevance is obtained by calculating the similarity between the consultation topic and the initial permission vectors. When the relevance is greater than the matching threshold, the permission is retained; otherwise, the permission is removed, thus determining the user's access permissions for this consultation.

[0113] In this embodiment, NLP technology is used to analyze the user's consultation topic, select the appropriate decision tree from multiple pre-built resource decision trees and obtain initial permissions, which improves the efficiency of resource query. At the same time, NLP technology is used to calculate the correlation between consultation topic and initial permissions, eliminate redundant permissions, which is conducive to the accurate allocation of permissions and improves the security of the platform.

[0114] In some embodiments, step S5 involves monitoring the user's consultation behavior in real time and calculating the real-time trust level, such as... Figure 4 As shown, it includes the following steps:

[0115] S51 monitors users' inquiry behavior in real time and records the number of times it is completed;

[0116] S52, when abnormal behavior is detected, record the number of abnormalities and obtain an abnormality score;

[0117] S53 calculates real-time trust level based on the number of completions, the number of anomalies, and the anomaly score.

[0118] It's important to note that a continuous monitoring system covering the entire consultation cycle is necessary during user consultations. This system dynamically adjusts user access permissions based on real-time monitoring of user consultation behavior to ensure platform security. Consultation behavior includes normal and abnormal behavior. Each time a user completes a consultation, the number of completions is counted, representing normal behavior. Abnormal behavior is categorized into several types, such as multiple URL redirects, batch repetitive operations, access to sensitive data, and abnormal exits. Each abnormal behavior has a corresponding score. These scores are set according to actual needs; for example, 0.1 points are awarded for each URL redirect, 0.1 points for each repetitive operation, 0.15 points for each access to sensitive data, and 0.05 points for each abnormal exit. When abnormal behavior is detected, it is counted to obtain the number of times each type of abnormal behavior occurs. Normal behavior is added to the score, while abnormal behavior is subtracted from the score, calculating the real-time trust level. Therefore, whenever a user exhibits abnormal behavior, the real-time trust level is updated based on the normal and abnormal behaviors prior to the occurrence of the abnormal behavior.

[0119] Specifically, the formula for calculating real-time trust level is as follows:

[0120] ,

[0121] in, For real-time trust levels, To assess overall trust levels, The number of types of abnormal behavior. For the first Abnormal scores for various abnormal behaviors For the first The number of abnormal behaviors. For normal score, This represents the number of consultations completed. In some embodiments, the normal score is 0.05.

[0122] In some embodiments, step S6 involves managing access permissions based on real-time trust levels, such as... Figure 5 As shown, it includes the following steps:

[0123] S61 compares the real-time trust level with the trust threshold;

[0124] S62, when the real-time trust level is less than the trust threshold, terminate the user's consultation session;

[0125] S63, when the real-time trust level is greater than or equal to the trust threshold, calculate the real-time difference;

[0126] S64, when the real-time difference is greater than or equal to zero, expand access permissions;

[0127] S65, when the real-time difference is less than zero, reduce access permissions;

[0128] S66. When the consultation session ends, record the consultation score and add the consultation score to the historical access data.

[0129] It's important to note that a continuous monitoring system covering the entire consultation cycle is necessary during user consultations. This system dynamically adjusts user access permissions by calculating the user's real-time trust level to ensure platform security. Real-time trust level is calculated using the user's overall trust level and consultation behavior score. The higher the overall trust level, the higher the platform's tolerance for abnormal behavior. However, when the real-time trust level drops to a trust threshold, the platform automatically terminates the user's consultation session and adds the consultation score to the user's historical access data for future access permissions. In this case, users with high overall trust levels will have lower recorded consultation scores, severely impacting their access permissions for future consultations. The real-time difference is the difference between the real-time trust level and the real-time trust level at the previous update. Access permissions are controlled and managed in real-time based on this difference. Dynamic adjustment of access permissions can be achieved by calculating initial permissions in the decision tree based on the real-time trust level, and then further filtering these initial permissions to obtain the actual access permissions. The consultation score is the difference between the real-time trust level and the overall trust level at the end of the consultation session. Access permissions for future consultations are controlled and managed based on this consultation score.

[0130] In this embodiment, by acquiring users' normal and abnormal behaviors, the system dynamically responds to changes in user behavior. When a user's real-time trust level is low, the session is terminated in a timely manner, and the user's access permissions are dynamically adjusted based on the real-time trust level. The user's consultation score is stored in historical access data, providing a data foundation for trust assessment of future user visits and improving the platform's security.

[0131] Example 2

[0132] This embodiment provides a network information consultation platform based on a zero-trust architecture. The communication method for such a platform, as described above, is applied. Figure 6 As shown, it includes: identity management module, data acquisition module, trust assessment module, access management module, real-time monitoring module, and access management module;

[0133] The identity management module is used for user authentication.

[0134] The data acquisition module is used to acquire user data when identity authentication is successful;

[0135] The trust assessment module is used to input user data into the trust assessment model and output a comprehensive trust score.

[0136] The access management module is used to grant access permissions based on the overall trust level.

[0137] The real-time monitoring module is used to monitor users' inquiry behavior in real time and calculate real-time trust levels.

[0138] The access management module is used to manage access permissions based on real-time trust levels.

[0139] In this embodiment, by acquiring user data of users who have successfully authenticated their identities, calculating the user's overall trust level based on the user data to quantify the user's credit rating, and monitoring the user throughout the consultation process, the platform improves its security by combining the user's credit rating with real-time monitoring to manage user access permissions.

[0140] It should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the module division described above is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, each functional module can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0141] Example 3

[0142] This embodiment provides an electronic device 2, such as... Figure 7 As shown, there is a processor 21 and a memory 22. The memory 22 is used to store computer program code, which includes computer instructions. When the processor 21 executes the computer instructions, the electronic device executes the above-described communication method of a network information consulting platform based on a zero-trust architecture.

[0143] The electronic device 2 includes a processor 21, a memory 22, an output device 23, and an input device 24. The processor 21, memory 22, output device 23, and input device 24 are coupled together via connectors, which may include various interfaces, transmission lines, or buses, etc., and are not limited in this embodiment of the invention. It should be understood that in various embodiments of the invention, coupling refers to mutual connection through a specific method, including direct connection or indirect connection through other devices, such as through various interfaces, transmission lines, buses, etc.

[0144] The processor 21 can be one or more graphics processing units (GPUs). If the processor 21 is a GPU, the GPU can be a single-core GPU or a multi-core GPU. Optionally, the processor 21 can be a processor group composed of multiple GPUs, with the multiple processors coupled to each other via one or more buses. Optionally, the processor 21 can also be other types of processors, etc., and this embodiment of the invention is not limited thereto.

[0145] The memory 22 can be used to store computer program instructions, as well as various types of computer program code, including program code for executing the present invention. Optionally, the memory 22 may include, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM), and the memory 22 is used for related instructions and data.

[0146] Input device 24 is used to input data and / or signals, and output device 23 is used to output data and / or signals. Output device 23 and input device 24 can be independent devices or an integrated device.

[0147] This embodiment provides a computer-readable storage medium storing a computer program, which includes program instructions. When executed by a processor of an electronic device, the program instructions cause the processor to execute the aforementioned communication method of a network information consultation platform based on a zero-trust architecture.

[0148] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A communication method for a network information consultation platform based on a zero-trust architecture, characterized in that, Includes the following steps: User authentication; When identity authentication is successful, user data is retrieved; The user data is input into the trust assessment model, and the overall trust score is output, including: The user data is normalized, and the weight coefficient of each user data is calculated. The first level of trust is calculated based on the normalized user data and the weighting coefficients. Retrieve historical access data; Obtain the consultation score for each of the historical access data and calculate the time difference of the data; Calculate the second level of trust based on the consultation score and the time difference; The first trust level and the second trust level are weighted and summed to obtain the comprehensive trust level; Based on the overall trust level, access permissions are obtained, including: Analyze the consultation content to identify the consultation topics; Select a decision tree based on the consultation topic; The overall trust level is compared with the trust threshold, and the initial permissions are obtained from the decision tree based on the comparison result; Calculate the relevance between the consultation topic and the initial permissions; The initial permissions are eliminated based on the correlation to obtain the access permissions; Real-time monitoring of user inquiry behavior and calculation of real-time trust levels; Access permissions are managed based on the real-time trust level.

2. The communication method of a network information consultation platform based on a zero-trust architecture according to claim 1, characterized in that, The formula for calculating the first level of trust is as follows: ， in, As the highest level of trust, For the amount of user data, For the first Weighting coefficients for individual user data For the first Normalized values ​​of individual user data; The formula for calculating the second level of trust is as follows: ， in, As the second level of trust, The number of historical access data. The decay rate over time. For the first The time difference between the historical access data and the current access. For the first Consultation score of each historical access data; The formula for calculating the overall trust level is as follows: ， ， in, To assess overall trust levels, and These are the weighting coefficients. As the highest level of trust, This is the second level of trust.

3. The communication method of a network information consultation platform based on a zero-trust architecture according to claim 1, characterized in that, The real-time monitoring of users' consultation behavior and the calculation of real-time trust levels include the following steps: Monitor users' inquiry behavior in real time and record the number of times it is completed; When abnormal behavior is detected, the number of abnormalities is recorded and an abnormality score is obtained. The real-time trust level is calculated based on the number of completions, the number of anomalies, and the anomaly score.

4. The communication method of a network information consultation platform based on a zero-trust architecture according to claim 1, characterized in that, The formula for calculating the real-time trust level is as follows: ， in, For real-time trust levels, To assess overall trust levels, The number of types of abnormal behavior. For the first Abnormal scores for various abnormal behaviors For the first The number of abnormal behaviors. For normal score, This represents the number of consultations completed.

5. The communication method of a network information consultation platform based on a zero-trust architecture according to claim 1, characterized in that, The management of access permissions based on the real-time trust level includes the following steps: Compare the real-time trust level with the trust threshold; When the real-time trust level is less than the trust threshold, the user's consultation session is terminated; When the real-time trust level is greater than or equal to the trust threshold, the real-time difference is calculated; When the real-time difference is greater than or equal to zero, the access permission is expanded; When the real-time difference is less than zero, the access permission is reduced; When a consultation session ends, the consultation score is recorded and added to the historical access data.

6. A network information consultation platform based on a zero-trust architecture, employing the communication method of a network information consultation platform based on a zero-trust architecture as described in any one of claims 1 to 5, characterized in that, include: The module includes an identity management module, a data acquisition module, a trust assessment module, an access management module, a real-time monitoring module, and an access management module. The identity management module is used to authenticate the user's identity; The data acquisition module is used to acquire user data when identity authentication is successful; The trust assessment module is used to input the user data into the trust assessment model and output a comprehensive trust level. The access management module is used to obtain access permissions based on the comprehensive trust level; The real-time monitoring module is used to monitor users' consultation behavior in real time and calculate the real-time trust level. The access management module is used to manage the access permissions based on the real-time trust level.

7. An electronic device, characterized in that, The device includes a processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, and when the processor executes the computer instructions, the electronic device executes a communication method for a network information consultation platform based on a zero-trust architecture as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which includes program instructions that, when executed by a processor of an electronic device, cause the processor to perform a communication method for a network information consultation platform based on a zero-trust architecture as described in any one of claims 1 to 5.

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