Multi-platform data interaction safety supervision system based on artificial intelligence

Abstract

The invention discloses a multi-platform data interaction safety supervision system based on artificial intelligence, and relates to the technical field of artificial intelligence. The monitoring center is in communication connection with a data acquisition and integration module, an artificial intelligence analysis and detection module, a data encryption and protection module, an identity verification and data interaction module and a real-time monitoring and early warning module; collecting interaction data of a plurality of platforms and visitor identity data, and preprocessing the interaction data to obtain to-be-interacted data; constructing a data analysis and detection standard model, and performing analysis and detection operation on the to-be-interacted data to obtain interactive data; performing data encryption operation on the interactive data to obtain encrypted interactive data; performing identity verification operation on the identity data of the visitor, performing multi-platform data interaction operation on the encrypted interaction data by the visitor passing the identity verification, and evaluating a security state; early warning and notification are sent out according to the corresponding safety state; and the data security is improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a multi-platform data interaction security supervision system based on artificial intelligence. Background Art

[0002] With the rapid development of information technology, data plays a vital role in all walks of life. Whether it is finance, medical care, education or e-commerce, the collection, storage, processing and transmission of data have become important tasks for enterprises and institutions. In this context, multi-platform data interaction has become more and more common, but the resulting data security issues are also becoming increasingly serious.

[0003] Limitations of existing technologies:

[0004] 1. Traditional encryption technology: Although encryption technology plays an important role in data protection, it is difficult for traditional encryption technology to ensure complete data security in a multi-platform environment. The management and distribution of encryption keys is also a complex issue.

[0005] 2. Isolated security mechanisms: Many platforms operate independently and adopt independent security mechanisms, lacking unified security management and monitoring. Such isolated security mechanisms are difficult to cope with cross-platform data security threats.

[0006] 3. Manual monitoring and management: Data security management relies on manual monitoring and manual intervention, which is not only inefficient but also prone to human errors and unable to respond to complex and changing security threats in a timely manner.

[0007] Application of Artificial Intelligence in Data Security:

[0008] 1. Threat detection and prediction: AI can identify and predict potential security threats by analyzing large amounts of historical data. Machine learning algorithms can detect abnormal behavior patterns and provide timely warnings.

[0009] 2. Automated response: Artificial intelligence technology can realize automated security response, quickly handle detected security incidents, reduce human intervention, and improve response speed and accuracy.

[0010] 3. Access control and authentication: The dynamic access control mechanism based on artificial intelligence can adjust access rights in real time according to user behavior and environmental factors to improve data security.

[0011] 4. Data encryption and privacy protection: Artificial intelligence technology can optimize data encryption algorithms and privacy protection measures, and improve the security of data in multi-platform interactions.

[0012] Therefore, a multi-platform data interaction security supervision system based on artificial intelligence is now provided. Summary of the invention

[0013] In order to solve the above technical problems, the purpose of the present invention is to provide a multi-platform data interaction security supervision system based on artificial intelligence.

[0014] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a multi-platform data interaction security supervision system based on artificial intelligence, comprising a monitoring center, wherein the monitoring center is communicatively connected with a data acquisition and integration module, an artificial intelligence analysis and detection module, a data encryption and protection module, an identity authentication and data interaction module, and a real-time monitoring and early warning module;

[0015] The data collection and preprocessing module is used to collect interaction data of multiple platforms and visitor identity data, perform preprocessing operations on the interaction data, and obtain data to be interacted;

[0016] The artificial intelligence analysis and detection module is used to construct a data analysis and detection standard model, perform analysis and detection operations on the interactive data, and obtain interactive data;

[0017] The data encryption and protection module is used to perform data encryption operation on the interactive data to obtain encrypted interactive data;

[0018] The identity authentication and data interaction module is used to perform identity authentication operations on visitor identity data, and the authenticated visitor performs multi-platform data interaction operations on the encrypted interaction data, and evaluates the security status of the multi-platform data interaction operations;

[0019] The real-time monitoring and early warning module is used to monitor the security status of multi-platform data interaction operations in real time, and issue early warnings and notifications according to the corresponding security status.

[0020] Furthermore, the process of the data collection and preprocessing module collecting the interaction data of multiple platforms and the visitor identity data includes:

[0021] The data collection and preprocessing module is provided with a number of interactive data collection units and identity data collection units, and is associated with the corresponding platform interface to set the collection time;

[0022] The interactive data collection units are numbered as i=1, 2, 3, ..., n, where n is a natural number;

[0023] The identity data collection units are numbered as j=1, 2, 3, ..., m, where m is a natural number;

[0024] The interactive data collection unit collects i groups of data that need to be transmitted through the interface of the corresponding platform, and records them as interactive data;

[0025] The identity data collection unit collects j groups of data for identity authentication through the interface of the corresponding platform, and records them as visitor identity data.

[0026] Furthermore, the data acquisition and preprocessing module performs preprocessing operations on the interaction data, and the process of obtaining the data to be interacted includes:

[0027] Binary-encode the interaction data collected at the current collection time to obtain encoded interaction data, which is recorded as binary interaction data;

[0028] Binary encode the current collection time to obtain the encoded collection time, which is recorded as the binary collection time;

[0029] Generate a primary separator and a secondary separator according to the unique identification code, the binary interaction data, and the binary collection time, respectively;

[0030] The first-level separator is used to separate the unique identification code and the binary interactive data, and the second-level separator is used to separate the binary interactive data and the binary collection time, and chain binary data are formed and recorded as the data to be interacted.

[0031] Furthermore, the artificial intelligence analysis and detection module constructs a data analysis and detection standard model, performs analysis and detection operations on the interactive data, and the process of obtaining interactive data includes:

[0032] Build a standard test model based on convolutional neural network;

[0033] Acquire several groups of data to be interacted at historical collection moments, and use the data to be interacted as training sample sets, import the obtained training sample sets into a standard test model, train the standard test model, and record the trained standard test model as a data analysis and detection standard model;

[0034] According to the data analysis and detection standard model, an indifference index of the data to be interacted is obtained;

[0035] Set the indifference index threshold;

[0036] If the indifference index of the corresponding data to be interacted is greater than the indifference index threshold, it means that the data to be interacted has an abnormality;

[0037] If the indifference index of the corresponding data to be interacted is less than or equal to the indifference index threshold, the data to be interacted is recorded as interactive data.

[0038] Furthermore, the data encryption and protection module performs data encryption operation on the interactive data, and the process of obtaining the encrypted interactive data includes:

[0039] Separate the unique identification code, binary interaction data, and binary collection time according to the primary separator and the secondary separator;

[0040] Split the binary interaction data into data blocks according to the segmentation rules;

[0041] Performing security processing on the data block to generate a message authentication code MAC;

[0042] Generate a corresponding and unique initialization vector v for each data block, perform initialization operations on the data block, and ensure that each data block is unique;

[0043] Generate a corresponding and unique rotation offset k for each data block, move the characters in each data block backward by k positions according to the rotation offset k, and return the characters beyond the data block boundary to the beginning;

[0044] Reverse each character of the offset data block according to the reversal key rule to form ciphertext information;

[0045] The corresponding initialization vector v, message authentication code MAC, ciphertext information, unique identification code and binary collection time are combined to form new data, which is recorded as encrypted interaction data.

[0046] Furthermore, the process of the identity authentication and data interaction module performing identity authentication operation on the visitor identity data includes:

[0047] Obtain visitor identification data;

[0048] When data interaction is performed for the first time, the visitor identity data is registered in the corresponding database and an update cycle is set;

[0049] If the visitor's identity data is consistent according to verification within the update cycle, the visitor has the data interaction authority.

[0050] Furthermore, the process in which the identity authentication and data interaction module performs a multi-platform data interaction operation on the encrypted interaction data and evaluates the security status of the multi-platform data interaction operation includes:

[0051] Visitors who have obtained data interaction permissions can decrypt the encrypted interaction data transmitted by different platforms to obtain the original interaction data;

[0052] A multi-platform data interaction status assessment model is constructed, and the security status of the multi-platform data interaction operation process is obtained according to the multi-platform data interaction status assessment model.

[0053] Furthermore, the process of decrypting the encrypted interaction data includes:

[0054] Split the encrypted interactive data into the corresponding initialization vector v, message authentication code MAC ciphertext information, unique identification code and binary collection time;

[0055] According to the initialization vector v, each data block is initialized to ensure that each data block is unique;

[0056] Reverse each data block again according to the reversal key rule, restore the offset data block, and move the offset data block forward k positions according to the rotation offset k to obtain the original data block;

[0057] The data block after the recovery offset is processed by the secure hash algorithm to generate a new message authentication code MAC, which is compared with the original message authentication code MAC. If they are consistent, it means that the data has not been tampered with. Binary decoding is then performed to obtain the original interactive data and the decryption integrity.

[0058] Furthermore, the process of building a multi-platform data interaction status evaluation model includes:

[0059] Build a standard evaluation model based on machine learning methods;

[0060] The historical decryption completeness obtained by decrypting the encrypted interaction data by several groups of visitors is obtained, and the historical decryption completeness is used as a sample set and a training set, and input into a standard evaluation model for training and testing, and the trained and tested standard evaluation model is recorded as a multi-platform data interaction status evaluation model.

[0061] Furthermore, the process in which the real-time monitoring and early warning module monitors the security status of multi-platform data interaction in real time and issues an early warning and notification when an abnormal situation is detected includes:

[0062] Obtain the security status of multi-platform data interaction;

[0063] If the security status of the corresponding visitor performing the multi-platform data interaction operation is normal, it means that the multi-platform data interaction operation process is safe and the visitor obtains the corresponding interaction data;

[0064] If the security status of the corresponding visitor performing multi-platform data interaction operations is abnormal, it means that the multi-platform data interaction operation process is unsafe, and corresponding warnings and notifications are issued to refuse data interaction.

[0065] Compared with the prior art, the beneficial effects of the present invention are: collecting interaction data and visitor identity data of multiple platforms, performing preprocessing operations on the interaction data, and obtaining data to be interacted with; constructing a data analysis and detection standard model, performing analysis and detection operations on the interaction data to be interacted with, and obtaining interactive data; performing data encryption operations on the interactive data to obtain encrypted interaction data; performing identity authentication operations on the visitor identity data, and the authenticated visitor performs multi-platform data interaction operations on the encrypted interaction data, and evaluates the security status; and issuing warnings and notifications according to the corresponding security status; improving data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0067] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0068] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0069] like Figure 1 As shown, a multi-platform data interaction security supervision system based on artificial intelligence includes a monitoring center, and the monitoring center is communicatively connected with a data acquisition and integration module, an artificial intelligence analysis and detection module, a data encryption and protection module, an identity authentication and data interaction module, and a real-time monitoring and early warning module;

[0070] The data collection and preprocessing module is used to collect interaction data of multiple platforms and visitor identity data, perform preprocessing operations on the interaction data, and obtain data to be interacted;

[0071] The artificial intelligence analysis and detection module is used to construct a data analysis and detection standard model, perform analysis and detection operations on the interactive data, and obtain interactive data;

[0072] The data encryption and protection module is used to perform data encryption operation on the interactive data to obtain encrypted interactive data;

[0073] The identity authentication and data interaction module is used to perform identity authentication operations on visitor identity data, and the authenticated visitor performs multi-platform data interaction operations on the encrypted interaction data, and evaluates the security status of the multi-platform data interaction operations;

[0074] The real-time monitoring and early warning module is used to monitor the security status of multi-platform data interaction operations in real time, and issue early warnings and notifications according to the corresponding security status.

[0075] It should be further explained that, in the specific implementation process, the specific process of the data collection and preprocessing module collecting the interaction data of multiple platforms and the visitor identity data includes:

[0076] The data collection and preprocessing module is provided with a number of interactive data collection units and identity data collection units, and is associated with the corresponding platform interface to set the collection time;

[0077] The interactive data collection units are numbered as i=1, 2, 3, ..., n, where n is a natural number;

[0078] The identity data collection units are numbered as j=1, 2, 3, ..., m, where m is a natural number;

[0079] The interactive data collection unit collects i groups of data that need to be transmitted through the interface of the corresponding platform, and records them as interactive data;

[0080] The identity data collection unit collects j groups of data for identity authentication through the interface of the corresponding platform and records them as visitor identity data;

[0081] It should be further explained that the interface has a unique identification code and is binary; the visitor identity data includes but is not limited to IP address, MAC address, contact information and device fingerprint.

[0082] It should be further explained that, in the specific implementation process, the data acquisition and preprocessing module performs preprocessing operations on the interaction data, and the specific process of obtaining the data to be interacted includes:

[0083] Binary-encode the interaction data collected at the current collection time to obtain encoded interaction data, which is recorded as binary interaction data;

[0084] Binary encode the current collection time to obtain the encoded collection time, which is recorded as the binary collection time;

[0085] Generate a primary separator and a secondary separator according to the unique identification code, the binary interaction data, and the binary collection time, respectively;

[0086] The first-level separator is used to separate the unique identification code and the binary interactive data, and the second-level separator is used to separate the binary interactive data and the binary collection time, and chain binary data are formed and recorded as the data to be interacted.

[0087] The data collection and preprocessing module uploads the data to be interacted with to the artificial intelligence analysis and detection module, and uploads the visitor identity data to the access control and identity authentication module.

[0088] It should be further explained that, in the specific implementation process, the artificial intelligence analysis and detection module constructs a data analysis and detection standard model, performs analysis and detection operations on the interactive data, and the specific process of obtaining interactive data includes:

[0089] Build a standard test model based on convolutional neural network;

[0090] Acquire several groups of data to be interacted at historical collection moments, and use the data to be interacted as training sample sets, import the obtained training sample sets into a standard test model, train the standard test model, and record the trained standard test model as a data analysis and detection standard model;

[0091] It should be further explained that the specific process of obtaining the training sample set includes:

[0092] Obtain i groups of data to be interacted at historical collection times, and use io groups of data to be interacted at historical collection times as sample data, where o is a natural number less than i;

[0093] The data to be interacted at the remaining historical collection moments are used as the test set;

[0094] A training sample set is constructed based on the sample data and the test set.

[0095] According to the data analysis and detection standard model, an indifference index of the data to be interacted is obtained;

[0096] Set the indifference index threshold;

[0097] If the indifference index of the corresponding data to be interacted is greater than the indifference index threshold, it means that the data to be interacted is abnormal and data interaction is not allowed;

[0098] If the indifference index of the corresponding data to be interacted is less than or equal to the indifference index threshold, it means that there is no abnormality in the data to be interacted, data interaction can be performed, and the data to be interacted is recorded as interactive data.

[0099] The artificial intelligence analysis and detection module uploads the interactive data to the data encryption and protection module.

[0100] It should be further explained that, in the specific implementation process, the data encryption and protection module performs data encryption operation on the interactive data, and the specific process of obtaining the encrypted interactive data includes:

[0101] Separate the unique identification code, binary interaction data, and binary collection time according to the primary separator and the secondary separator;

[0102] Split binary interactive data into data blocks according to segmentation rules to improve data security;

[0103] It should be further explained that the segmentation rule is to segment the codes in the order of front and back and of equal size;

[0104] Perform security processing on the data block according to a secure hash algorithm to generate a message authentication code MAC;

[0105] Generate a corresponding and unique initialization vector v for each data block, perform initialization operations on the data block, and ensure that each data block is unique;

[0106] Generate a corresponding and unique rotation offset k for each data block, shift the characters in each data block backward by k positions according to the rotation offset k, and return the characters beyond the data block boundary to the beginning to improve the security of the information;

[0107] Reverse each character of the offset data block according to the reversal key rule to form ciphertext information;

[0108] It should be further explained that the reversal key rule is that the characters of the data block are reversed according to the length of five characters. When the characters of the data block are an integer multiple of five characters, they are reversed normally; when the characters of the data block are not an integer multiple of five characters, the remaining characters are not reversed;

[0109] The corresponding initialization vector v, message authentication code MAC, ciphertext information, unique identification code and binary collection time are combined to form new data, which is recorded as encrypted interaction data.

[0110] The data encryption and protection module uploads the encrypted interaction data to the identity authentication and data interaction module.

[0111] It should be further explained that, in the specific implementation process, the specific process of the identity authentication and data interaction module performing identity authentication operation on the visitor identity data includes:

[0112] Obtain visitors’ IP addresses, MAC addresses, contact information, and device fingerprints;

[0113] When data interaction is performed for the first time, the IP address, MAC address, contact information, and device fingerprint are registered in the corresponding database respectively, and an update cycle is set;

[0114] When the IP address, MAC address, contact information and device fingerprint are registered in the corresponding databases respectively, the visitor has data interaction authority by verifying that the visitor identity data is consistent within the update period.

[0115] It should be further explained that, in the specific implementation process, the identity authentication and data interaction module performs multi-platform data interaction operations on the encrypted interaction data and evaluates the security status of the multi-platform data interaction operations. The specific process includes:

[0116] Visitors who have obtained data interaction permissions can decrypt the encrypted interaction data transmitted by different platforms to obtain the original interaction data;

[0117] Constructing a multi-platform data interaction status assessment model, and obtaining the security status of the multi-platform data interaction operation process according to the multi-platform data interaction status assessment model;

[0118] It should be further explained that the safety state includes a normal state and an abnormal state;

[0119] It should be further explained that, in the specific implementation process, the specific process of decrypting the encrypted interactive data includes:

[0120] Split the encrypted interactive data into the corresponding initialization vector v, message authentication code MAC ciphertext information, unique identification code and binary collection time;

[0121] According to the initialization vector v, each data block is initialized to ensure that each data block is unique;

[0122] Reverse each data block again according to the reversal key rule, restore the offset data block, and move the offset data block forward k positions according to the rotation offset k to obtain the original data block;

[0123] The data block after the recovery offset is processed by the secure hash algorithm to generate a new message authentication code MAC, which is compared with the original message authentication code MAC. If they are consistent, it means that the data has not been tampered with. Binary decoding is then performed to obtain the original interactive data and the decryption integrity.

[0124] It should be further explained that, in the specific implementation process, the specific process of building a multi-platform data interaction status evaluation model includes:

[0125] Build a standard evaluation model based on machine learning methods;

[0126] The historical decryption completeness obtained by decrypting the encrypted interaction data by several groups of visitors is obtained, and the historical decryption completeness is used as a sample set and a training set, and input into a standard evaluation model for training and testing, and the trained and tested standard evaluation model is recorded as a multi-platform data interaction status evaluation model.

[0127] The identity authentication and data interaction module uploads the security status to the real-time monitoring and early warning module in real time.

[0128] It should be further explained that, in the specific implementation process, the real-time monitoring and early warning module monitors the security status of multi-platform data interaction in real time, and issues early warnings and notifications when abnormal situations are detected. The specific process includes:

[0129] Obtain the security status of multi-platform data interaction;

[0130] If the security status of the corresponding visitor performing the multi-platform data interaction operation is normal, it means that the multi-platform data interaction operation process is safe and the visitor obtains the corresponding interaction data;

[0131] If the security status of the corresponding visitor performing multi-platform data interaction operations is abnormal, it means that the multi-platform data interaction operation process is unsafe, and corresponding warnings and notifications are issued to refuse data interaction.

[0132] 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 multi-platform data interaction security supervision system based on artificial intelligence, including a monitoring center, characterized in that: The monitoring center is communicatively connected to a data acquisition and integration module, an artificial intelligence analysis and detection module, a data encryption and protection module, an identity authentication and data interaction module, and a real-time monitoring and early warning module; The data collection and preprocessing module is used to collect interaction data of multiple platforms and visitor identity data, perform preprocessing operations on the interaction data, and obtain data to be interacted; The artificial intelligence analysis and detection module is used to construct a data analysis and detection standard model, perform analysis and detection operations on the interactive data, and obtain interactive data; The data encryption and protection module is used to perform data encryption operation on the interactive data to obtain encrypted interactive data; The identity authentication and data interaction module is used to perform identity authentication operations on visitor identity data, and the authenticated visitor performs multi-platform data interaction operations on the encrypted interaction data, and evaluates the security status of the multi-platform data interaction operations; The real-time monitoring and early warning module is used to monitor the security status of multi-platform data interaction operations in real time, and issue early warnings and notifications according to the corresponding security status.

2. According to the artificial intelligence-based multi-platform data interaction security supervision system of claim 1, it is characterized in that: The process of the data collection and preprocessing module collecting the interaction data of multiple platforms and the visitor identity data includes: The data collection and preprocessing module is provided with a number of interactive data collection units and identity data collection units, and is associated with the corresponding platform interface to set the collection time; The interactive data collection units are numbered as i=1, 2, 3, ..., n, where n is a natural number; The identity data collection units are numbered as j=1, 2, 3, ..., m, where m is a natural number; The interactive data collection unit collects i groups of data that need to be transmitted through the interface of the corresponding platform, and records them as interactive data; The identity data collection unit collects j groups of data for identity authentication through the interface of the corresponding platform, and records them as visitor identity data.

3. According to claim 2, a multi-platform data interaction security supervision system based on artificial intelligence is characterized in that: The data acquisition and preprocessing module performs preprocessing operations on the interaction data to obtain the data to be interacted with, including: Binary-encode the interaction data collected at the current collection time to obtain encoded interaction data, which is recorded as binary interaction data; Binary encode the current collection time to obtain the encoded collection time, which is recorded as the binary collection time; Generate a primary separator and a secondary separator according to the unique identification code, the binary interaction data, and the binary collection time, respectively; The first-level separator is used to separate the unique identification code and the binary interactive data, and the second-level separator is used to separate the binary interactive data and the binary collection time, and chain binary data are formed and recorded as the data to be interacted.

4. According to claim 3, a multi-platform data interaction security supervision system based on artificial intelligence is characterized in that: The artificial intelligence analysis and detection module constructs a data analysis and detection standard model to perform analysis and detection operations on the interactive data. The process of obtaining interactive data includes: Build a standard test model based on convolutional neural network; Acquire several groups of data to be interacted at historical collection moments, and use the data to be interacted as training sample sets, import the obtained training sample sets into a standard test model, train the standard test model, and record the trained standard test model as a data analysis and detection standard model; According to the data analysis and detection standard model, an indifference index of the data to be interacted is obtained; Set the indifference index threshold; If the indifference index of the corresponding data to be interacted is greater than the indifference index threshold, it means that the data to be interacted has an abnormality; If the indifference index of the corresponding data to be interacted is less than or equal to the indifference index threshold, the data to be interacted is recorded as interactive data.

5. According to claim 4, a multi-platform data interaction safety supervision system based on artificial intelligence is characterized in that: The data encryption and protection module performs data encryption operation on the interactive data, and the process of obtaining the encrypted interactive data includes: Separate the unique identification code, binary interaction data, and binary collection time according to the primary separator and the secondary separator; Split the binary interaction data into data blocks according to the segmentation rules; Performing security processing on the data block to generate a message authentication code MAC; Generate a corresponding and unique initialization vector v for each data block, perform initialization operations on the data block, and ensure that each data block is unique; Generate a corresponding and unique rotation offset k for each data block, move the characters in each data block backward by k positions according to the rotation offset k, and return the characters beyond the data block boundary to the beginning; Reverse each character of the offset data block according to the reversal key rule to form ciphertext information; The corresponding initialization vector v, message authentication code MAC, ciphertext information, unique identification code and binary collection time are combined to form new data, which is recorded as encrypted interaction data.

6. The multi-platform data interaction safety supervision system based on artificial intelligence according to claim 5 is characterized in that: The process of the identity authentication and data interaction module performing identity authentication operation on the visitor identity data includes: Obtain visitor identification data; When data interaction is performed for the first time, the visitor identity data is registered in the corresponding database and an update cycle is set; If the visitor's identity data is consistent according to verification within the update cycle, the visitor has the data interaction authority.

7. The multi-platform data interaction safety supervision system based on artificial intelligence according to claim 6 is characterized in that: The process in which the identity authentication and data interaction module performs a multi-platform data interaction operation on the encrypted interaction data and evaluates the security status of the multi-platform data interaction operation includes: Visitors who have obtained data interaction permissions can decrypt the encrypted interaction data transmitted by different platforms to obtain the original interaction data; A multi-platform data interaction status assessment model is constructed, and the security status of the multi-platform data interaction operation process is obtained according to the multi-platform data interaction status assessment model.

8. The multi-platform data interaction safety supervision system based on artificial intelligence according to claim 7 is characterized in that: The process of decrypting encrypted interaction data includes: Split the encrypted interactive data into the corresponding initialization vector v, message authentication code MAC ciphertext information, unique identification code and binary collection time; According to the initialization vector v, each data block is initialized to ensure that each data block is unique; Reverse each data block again according to the reversal key rule, restore the offset data block, and move the offset data block forward k positions according to the rotation offset k to obtain the original data block; The data block after the recovery offset is processed by the secure hash algorithm to generate a new message authentication code MAC, which is compared with the original message authentication code MAC. If they are consistent, it means that the data has not been tampered with. Binary decoding is then performed to obtain the original interactive data and the decryption integrity.

9. The multi-platform data interaction safety supervision system based on artificial intelligence according to claim 8 is characterized in that: The process of building a multi-platform data interaction status evaluation model includes: Build a standard evaluation model based on machine learning methods; The historical decryption completeness obtained by decrypting the encrypted interaction data by several groups of visitors is obtained, and the historical decryption completeness is used as a sample set and a training set, and input into a standard evaluation model for training and testing, and the trained and tested standard evaluation model is recorded as a multi-platform data interaction status evaluation model.

10. The multi-platform data interaction safety supervision system based on artificial intelligence according to claim 9 is characterized in that: The process of the real-time monitoring and early warning module monitoring the security status of multi-platform data interaction in real time and issuing early warnings and notifications when abnormal situations are detected includes: Obtain the security status of multi-platform data interaction; If the security status of the corresponding visitor performing the multi-platform data interaction operation is normal, it means that the multi-platform data interaction operation process is safe and the visitor obtains the corresponding interaction data; If the security status of the corresponding visitor performing multi-platform data interaction operations is abnormal, it means that the multi-platform data interaction operation process is unsafe, and corresponding warnings and notifications are issued to refuse data interaction.

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