Electronic information product intelligent safety monitoring system based on Internet of Things perception

Abstract

The invention discloses an electronic information product intelligent security monitoring system based on Internet of Things perception, and belongs to the technical field of Internet of Things, the system comprises a perception layer, an encryption layer, a storage layer, a decision layer and an application layer, a one-time key is generated for each session through an ECDHE elliptic curve key exchange protocol, and even if one key is cracked, the security of the electronic information product is ensured. The security of other sessions is not influenced, the dynamic defense capability of the system is greatly improved, the ECDHE algorithm is combined with the large prime number p and the original root g to carry out key exchange, the client and the server can generate a high-strength shared key based on relatively small secret integers c and s, the long-term security of data is ensured, and the security of the system is improved through quantum data encryption. Abnormal behaviors are detected according to statistical analysis of quantum entanglement and measurement results, the security state of a communication link can be monitored in real time, and the unpredictability and security of a key are greatly enhanced through combination of a dynamic key and quantum data encryption.

Description

Technical Field

[0001] The present invention belongs to the technical field of Internet of Things, and specifically refers to an intelligent safety monitoring system for electronic information products based on Internet of Things perception. Background Art

[0002] With the advancement of IoT technology, more and more physical devices are connected to the Internet, realizing the interconnection between devices. IoT integrates sensors, actuators, software and other technologies to enable objects to collect, exchange data and perform operations based on the data. This technical foundation provides a wide range of data sources and remote control capabilities for intelligent monitoring systems.

[0003] However, the existing intelligent security monitoring system for electronic information products still has certain defects. The existing intelligent security monitoring system for electronic information products relies on static key encryption or a weaker dynamic key generation mechanism, which means that once the key is cracked, the communication security of the entire system will be seriously threatened. There is no efficient data storage architecture, which leads to slow processing of real-time monitoring data and limited analysis of historical data, affecting the response speed and decision-making efficiency of the system. It relies on passive security strategies, that is, investigation and response are started only after the security incident occurs, lacks active threat identification and prediction capabilities, has a long response time, and is difficult to achieve immediate prevention and efficient processing of security incidents, which reduces the overall protection efficiency of the system. Therefore, an intelligent security monitoring system for electronic information products based on Internet of Things perception is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent security monitoring system for electronic information products based on Internet of Things perception to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent security monitoring system for electronic information products based on Internet of Things perception, comprising a perception layer, an encryption layer, a storage layer, a decision layer and an application layer;

[0006] The perception layer captures the real-time status information of electronic information products by integrating sensors, cameras, RFID, GPS and sensor gateways, and converts physical signals into transmittable data packets to ensure that real-time and accurate actual status information can be obtained;

[0007] The encryption layer encrypts data using encryption algorithms during data transmission and storage, and performs identity authentication when accessing data to ensure data security;

[0008] The storage layer is used to store and manage large amounts of data that have passed through the encryption layer. The local database is used to quickly access frequently queried data, reduce network latency, and perform local encrypted storage of sensitive information to ensure that the security and availability of key data are not affected even in unstable network conditions.

[0009] The decision layer obtains and analyzes the data from the storage layer, and the warning protocol module monitors abnormal behaviors in the network in real time, predicts failures, automatically executes safety measures according to preset rules, and quickly triggers the alarm mechanism;

[0010] The application layer converts the analysis results of the decision-making layer into intuitive reports and charts and displays them to users. Users can monitor the system status, configuration, alarms, view historical record information and perform interactive operations through the interface.

[0011] Wherein, the encryption layer includes a security protocol module and a recording protocol module;

[0012] The specific operation method of the encryption layer includes the following steps:

[0013] A1: Dynamic key negotiation: The client and server initiate a connection, the server sends a digital certificate, and their public keys are exchanged through the ECDHE elliptic curve.

[0014] A2: Dynamic key generation: The client and server use each other's public key and their own private key to calculate a shared key, and generate a session key from the shared key through a key derivation function;

[0015] A3: Quantum data encryption: quantum state transmission generates a shared quantum key, which is combined with the session key;

[0016] A4: Two-way identity authentication: The client encrypts a random challenge with the session key and sends it to the server. The server encrypts a challenge and sends it back to the client to verify the identities of both parties.

[0017] A5: Data fragmentation encapsulation: The original data transmitted by the perception layer is divided into data fragments, and a header is added to each data fragment;

[0018] A6: MAC authentication: The MAC key in the session key is used to calculate a message authentication code for each data slice, and the calculated MAC value is appended to the header of the corresponding data slice;

[0019] A7: Data encryption: Each data shard is kept confidential through the encryption key in the session key;

[0020] A8: Data transmission: The encrypted data is divided into pieces and encapsulated in a format and sent to the receiver through the network. The receiver decapsulates and decrypts the data according to the corresponding protocol and verifies the MAC.

[0021] Among them, for A2, it is assumed that the client C and the server S use a large prime number p and a primitive root g;

[0022] The client selects a random secret integer c (1 < c < p - 1), and calculates the public value A as A = g c mod p and sends it to the server;

[0023] The server selects a random secret integer s (1 < s < p - 1), and calculates the public value B as B = g s mod p and sends it to the client;

[0024] The formula for the client to calculate the shared key is:

[0025] K C = B c mod p (1),

[0026] In formula (1), K C represents the shared key of the client, C represents the client, and c represents the private key of the client;

[0027] The formula for the server to calculate the shared key is:

[0028] K S = B s mod p (2),

[0029] In formula (2), K S represents the shared key of the server, S represents the server, and s represents the private key of the server. According to the properties of constant power operations, the K C = K S ;

[0030] Through the shared key K = K C = K S as the output, the session key SK is generated through the key derivation function, and the formula is:

[0031] SK = HKDF(K, salt, info) (3),

[0032] In formula (3), HKDF represents the key derivation function, K represents the shared key, salt represents the salt value, and info represents the context information.

[0033] Among them, in A3, the client prepares the quantum state through the polarization direction and sends it to the server. The server measures the received quantum state through the polarization basis. The client and the server publicly select the measurement basis through the classical channel, retain the quantum bits measured on the same basis as the key bits, detect abnormal behavior through quantum entanglement and statistical analysis of measurement results, and finally obtain the quantum key QK after error correction and privacy amplification;

[0034] The obtained quantum key QK is combined with the session key SK to generate the final key for data encryption. The implementation formula is:

[0035] SK′=Combine(SK,QK),

[0036] In the formula, SK′ represents the final key, and the Combine function represents combining SK and QK.

[0037] By adopting advanced encryption technologies such as dynamic key negotiation, quantum data encryption and two-way identity authentication, the security of data during transmission and storage has been greatly improved. The introduction of quantum keys has further enhanced the encryption strength, enabling the system to resist the possible cracking threats posed by quantum computers. The dynamic generation and security of keys are ensured through ECDHE and key exchange protocols based on discrete logarithm problems of large prime numbers. At the same time, HKDF further enhances the randomness and diversity of keys. The introduction of quantum keys and their combination with session keys increase the difficulty for attackers to crack.

[0038] Wherein, the storage layer is divided into a cache access layer, a distributed layer and a local database;

[0039] The cache access layer and the basic database are used to store the system's configuration information, metadata, and core business data, providing basic operation support for the monitoring system; the real-time database is used to quickly store and retrieve real-time monitoring data from the encryption layer to ensure the timeliness and immediacy of the data; the historical database is used to store processed historical data for a long time, which is used for trend analysis, audit tracking, and compliance checks to help identify long-term patterns and anomalies; the time series database is used to optimize time series data, efficiently store and query data with timestamps, and record changes in device status over time; data backup and recovery performs regular data backup according to the set time to ensure timely data recovery; and adaptive data compression automatically adjusts the algorithm according to the content of the data block to perform adaptive data compression.

[0040] Among them, the distributed layer, Hadoop is used for data processing and analysis of large amounts of data; cloud storage dynamically adjusts storage capacity according to actual needs for cross-regional data sharing; data servers are used to manage data storage and access, and integrate data caching mechanisms to speed up frequent access to data;

[0041] The local database is used to store operation logs, temporary data and critical data with extremely high real-time requirements. When the network is unstable, it temporarily stores important emergency data to ensure that data is not lost.

[0042] The storage layer enables the system to process massive data, support big data analysis, and promote efficient use of resources and cross-regional data sharing through the deployment of the distributed layer, especially the application of Hadoop and cloud storage.

[0043] The decision layer includes data preprocessing, data analysis engine, rule engine, prediction model, response execution module and warning protocol;

[0044] The data preprocessing is connected to the application layer through an API interface to perform data preprocessing and format conversion on the received data packets; the data analysis engine extracts the characteristics of electronic information products from the data and the processed data, analyzes the data through cluster analysis, and compares the data in combination with the storage layer to obtain the final analysis results; the rule engine sets the security rule threshold and performs rule matching on the results of the data analysis engine analysis, and issues an early warning notification to the application layer when the matching result exceeds the set threshold.

[0045] Among them, the prediction model predicts future security incidents based on the storage layer data through a time series model, and evaluates the severity and possibility of potential security according to the model prediction results; the response execution module automatically executes the response according to the results of the rule engine and the prediction model.

[0046] The decision-making layer integrates functions such as data preprocessing, deep analysis, rule engine and prediction model, realizes accurate monitoring and prediction of the status of electronic information products, can actively discover potential security threats and respond quickly to reduce risks. Through the implementation of the prediction model, the system can predict security incidents in advance, and the automated operation of the response execution module reduces the need for manual intervention and improves the response speed. At the same time, the warning protocol ensures the timely communication of key information and enhances the emergency response capability.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. In order to solve the problem of insufficient security during data transmission, the present invention generates a one-time key for each session through the ECDHE elliptic curve key exchange protocol. Even if a key is cracked, it will not affect the security of other sessions, greatly improving the dynamic defense capability of the system. Through the ECDHE algorithm combined with a large prime number p and a primitive root g for key exchange, the client and the server can generate a high-strength shared key based on relatively small secret integers c and s, ensuring the long-term security of the data;

[0049] 2. The present invention uses quantum data encryption to detect abnormal behavior based on quantum entanglement and statistical analysis of measurement results. It can also monitor the security status of communication links in real time. By combining dynamic keys and quantum data encryption, the unpredictability and security of keys are greatly enhanced.

[0050] 3. The present invention optimizes the storage and access efficiency of data through the hierarchical design of the storage layer, ensures the rapid response of real-time monitoring data, meets the needs of long-term storage and historical data analysis, and improves the reliability and resource utilization efficiency of the system;

[0051] 4. The present invention includes active security response capabilities based on rules and predictive models through data preprocessing and in-depth analysis at the decision-making layer. This enables the system to automatically discover potential threats, predict security incidents, and take immediate measures, significantly shortening the response time, reducing manual intervention, and improving the efficiency and accuracy of responding to security incidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a structural schematic diagram of an intelligent safety monitoring system for electronic information products based on Internet of Things perception according to the present invention;

[0053] Figure 2 This is an operation flow chart of feature analysis of an intelligent safety monitoring system for electronic information products based on Internet of Things perception according to the present invention;

[0054] Figure 3 The present invention is an operation flow chart of the security protocol layer of an intelligent security monitoring system for electronic information products based on Internet of Things perception. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Example

[0057] See also Figure 1-Figure 3 As shown, the present invention provides a technical solution: including a perception layer, an encryption layer, a storage layer, a decision layer and an application layer;

[0058] The perception layer captures the real-time status information of electronic information products by integrating sensors, cameras, RFID, GPS and sensor gateways, and converts physical signals into transmittable data packets to ensure that real-time and accurate actual status information can be obtained;

[0059] The encryption layer encrypts data using encryption algorithms during data transmission and storage, and performs identity authentication when accessing data to ensure data security;

[0060] The storage layer is used to store and manage large amounts of data that have passed through the encryption layer. The local database is used to quickly access frequently queried data, reduce network latency, and perform local encrypted storage of sensitive information to ensure that the security and availability of key data are not affected even in unstable network conditions.

[0061] The decision layer obtains and analyzes the data from the storage layer, and the warning protocol module monitors abnormal behaviors in the network in real time, predicts failures, automatically executes safety measures according to preset rules, and quickly triggers the alarm mechanism;

[0062] The application layer converts the analysis results of the decision-making layer into intuitive reports and charts and displays them to users. Users can monitor the system status, configuration, alarms, view historical record information and perform interactive operations through the interface.

[0063] Wherein, the encryption layer includes a security protocol module and a recording protocol module;

[0064] The specific operation method of the encryption layer includes the following steps:

[0065] A1: Dynamic key negotiation: The client and server initiate a connection, the server sends a digital certificate, and their public keys are exchanged through the ECDHE elliptic curve.

[0066] A2: Dynamic key generation: The client and server use each other's public key and their own private key to calculate a shared key, and generate a session key from the shared key through a key derivation function;

[0067] A3: Quantum data encryption: quantum state transmission generates a shared quantum key, which is combined with the session key;

[0068] A4: Two-way identity authentication: The client encrypts a random challenge with the session key and sends it to the server. The server encrypts a challenge and sends it back to the client to verify the identities of both parties.

[0069] A5: Data fragmentation encapsulation: The original data transmitted by the perception layer is divided into data fragments, and a header is added to each data fragment;

[0070] A6: MAC authentication: The MAC key in the session key is used to calculate a message authentication code for each data slice, and the calculated MAC value is appended to the header of the corresponding data slice;

[0071] A7: Data encryption: Each data shard is kept confidential through the encryption key in the session key;

[0072] A8: Data Transmission: The encrypted data is fragmented and encapsulated in a format, and then sent to the receiving party through the network. The receiving party unpacks, decrypts the data according to the corresponding protocol, and verifies the MAC.

[0073] Among them, for A2, it is assumed that the client C and the server S use a large prime number p and a primitive root g;

[0074] The client selects a random secret integer c (1 < c < p - 1), calculates the public value A as A = g c mod p and sends it to the server;

[0075] The server selects a random secret integer s (1 < s < p - 1), calculates the public value B as B = g s mod p and sends it to the client;

[0076] The formula for the client to calculate the shared key is:

[0077] K C = B c mod p (1),

[0078] In formula (1), K C represents the client's shared key, C represents the client, and c represents the client's private key;

[0079] The formula for the server to calculate the shared key is:

[0080] K S = B s mod p (2),

[0081] In formula (2), K S represents the server's shared key, S represents the server, and s represents the server's private key. According to the properties of constant power operations, the K C = K S ;

[0082] Through the shared key K = K C = K S as the output, the session key SK is generated through the key derivation function. The implementation formula is:

[0083] SK = HKDF(K, salt, info) (3),

[0084] In formula (3), HKDF represents the key derivation function, K represents the shared key, salt represents the salt value, and info represents the context information.

[0085] Among them, in A3, the client prepares the quantum state through the polarization direction and sends it to the server. The server measures the received quantum state through the polarization basis. The client and the server publicly select the measurement basis through the classical channel, retain the quantum bits measured on the same basis as the key bits, detect abnormal behavior through quantum entanglement and statistical analysis of measurement results, and finally obtain the quantum key QK after error correction and privacy amplification;

[0086] The obtained quantum key QK is combined with the session key SK to generate the final key for data encryption. The implementation formula is:

[0087] SK′=Combine(SK,QK),

[0088] In the formula, SK′ represents the final key, and the Combine function represents combining SK and QK.

[0089] By adopting advanced encryption technologies such as dynamic key negotiation, quantum data encryption and two-way identity authentication, the security of data during transmission and storage has been greatly improved. The introduction of quantum keys has further enhanced the encryption strength, enabling the system to resist the possible cracking threats posed by quantum computers. The dynamic generation and security of keys are ensured through ECDHE and key exchange protocols based on discrete logarithm problems of large prime numbers. At the same time, HKDF further enhances the randomness and diversity of keys. The introduction of quantum keys and their combination with session keys increase the difficulty for attackers to crack.

[0090] Wherein, the storage layer is divided into a cache access layer, a distributed layer and a local database;

[0091] The cache access layer and the basic database are used to store the system's configuration information, metadata, and core business data, providing basic operation support for the monitoring system; the real-time database is used to quickly store and retrieve real-time monitoring data from the encryption layer to ensure the timeliness and immediacy of the data; the historical database is used to store processed historical data for a long time, which is used for trend analysis, audit tracking, and compliance checks to help identify long-term patterns and anomalies; the time series database is used to optimize time series data, efficiently store and query data with timestamps, and record changes in device status over time; data backup and recovery performs regular data backup according to the set time to ensure timely data recovery; and adaptive data compression automatically adjusts the algorithm according to the content of the data block to perform adaptive data compression.

[0092] Among them, the distributed layer, Hadoop is used for data processing and analysis of large amounts of data; cloud storage dynamically adjusts storage capacity according to actual needs for cross-regional data sharing; data servers are used to manage data storage and access, and integrate data caching mechanisms to speed up frequent access to data;

[0093] The local database is used to store operation logs, temporary data, and critical data with extremely high real-time requirements. In the case of unstable network conditions, it temporarily stores important and urgent data to ensure data is not lost.

[0094] Through the deployment of the distributed layer in the storage layer, especially the application of Hadoop and cloud storage, the system can process massive amounts of data, support big data analysis, and promote the effective utilization of resources and cross-regional data sharing.

[0095] Among them, the decision-making layer includes data preprocessing, data analysis engine, rule engine, prediction model, response execution module, and warning protocol;

[0096] The data preprocessing is connected to the application layer through the API interface to perform data preprocessing and format conversion on the received data packets; the data analysis engine extracts the characteristics of electronic information products from the processed data, analyzes the data through cluster analysis, and combines with the storage layer for data comparison to obtain the final analysis result; the rule engine sets the security rule threshold, performs rule matching on the results analyzed by the data analysis engine, and issues a warning to notify the application layer when the matching result exceeds the set threshold.

[0097] Among them, the prediction model predicts future security events through the time series model based on the data in the storage layer, and evaluates the severity and possibility of potential security according to the model prediction results; the response execution module automatically executes responses according to the results of the rule engine and the prediction model.

[0098] The decision-making layer integrates functions such as data preprocessing, in-depth analysis, rule engine, and prediction model, realizes precise monitoring and prediction of the status of electronic information products, can actively discover potential security threats and respond quickly, reduce risks. Through the implementation of the prediction model, the system can predict security events in advance, and the automated operation of the response execution module reduces the need for manual intervention, improves the response speed. At the same time, the warning protocol ensures the timely transmission of key information and enhances the emergency handling ability.

[0099] In this example, specifically: for A2, it is assumed that the client C and the server S use a large prime number p and a primitive root g;

[0100] The client selects a random secret integer c (1 < c < p - 1), and calculates the public value A as A = g c mod p and sends it to the server;

[0101] The server selects a random secret integer s (1 < s < p - 1), and calculates the public value B as B = g s mod p and sends it to the client;

[0102] The client calculates the shared key implementation formula as follows:

[0103] K C =B c modp(1),

[0104] In formula (1), K C represents the shared key of the client, C represents the client, and c represents the private key of the client;

[0105] The server calculates the shared key implementation formula as follows:

[0106] K S =B s modp(2),

[0107] In formula (2), K S represents the shared key of the server, S represents the server, s represents the private key of the server, and according to the properties of constant power operation, K C =K S ;

[0108] By sharing the key K = K C =K S As output, the session key SK is generated through the key derivation function, which is implemented as:

[0109] SK=HKDF(K,salt,info)(3),

[0110] In formula (3), HKDF represents the key derivation function, K represents the shared key, salt represents the salt value, and info represents the context information.

[0111] In this example, specifically: A3, the client prepares the quantum state through the polarization direction and sends it to the server, the server measures the received quantum state through the polarization basis, the client and the server publicly select the measurement basis through the classical channel, retain the quantum bits measured on the same basis as the key bits, detect abnormal behavior through quantum entanglement and statistical analysis of measurement results, and finally obtain the quantum key QK after error correction and privacy amplification;

[0112] The obtained quantum key QK is combined with the session key SK to generate the final key for data encryption. The implementation formula is:

[0113] SK′=Combine(SK,QK),

[0114] In the formula, SK′ represents the final key, and the Combine function represents combining SK and QK.

[0115] By adopting advanced encryption technologies such as dynamic key negotiation, quantum data encryption and two-way identity authentication, the security of data during transmission and storage has been greatly improved. The introduction of quantum keys has further enhanced the encryption strength, enabling the system to resist the possible cracking threats posed by quantum computers. The dynamic generation and security of keys are ensured through ECDHE and key exchange protocols based on discrete logarithm problems of large prime numbers. At the same time, HKDF further enhances the randomness and diversity of keys. The introduction of quantum keys and their combination with session keys increase the difficulty for attackers to crack.

[0116] Working principle: The sensor layer continuously collects status information of electronic information products through integrated sensors, cameras, RFID tags, GPS modules and other components, and converts this physical information into digital data packets through the sensor gateway;

[0117] According to the data transmission process, the client and the server first conduct dynamic key negotiation, use the ECDHE elliptic curve to exchange public keys, and combine quantum data encryption technology to generate a shared quantum key. Combined with the session key, it includes dynamic key generation, quantum key exchange, two-way identity authentication, data sharding encapsulation, MAC authentication and data encryption; the encrypted data is stored in a hierarchical structure, including a cache access layer, a distributed layer and a local database for fast access, big data processing, long-term archiving and data recovery. The decision-making layer obtains data from the storage layer for in-depth analysis and processing. The data preprocessing module formats and cleans the received data. The data analysis engine conducts in-depth mining of the data, combines the rule engine and the prediction model to evaluate the security situation, automatically identify abnormal behavior and predict potential risks. Once a security risk is discovered, take action immediately through the response execution module, and trigger an alarm through the warning protocol and notify the application layer at the same time. The application layer presents the analysis results of the decision-making layer in a user-friendly form, allowing users to intuitively monitor the system status, configure system settings, view alarms and historical records, and perform necessary interactive operations.

[0118] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0119] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An intelligent safety monitoring system for electronic information products based on Internet of Things perception, characterized in that: Includes perception layer, encryption layer, storage layer, decision layer and application layer; The perception layer captures the real-time status information of electronic information products by integrating sensors, cameras, RFID, GPS and sensor gateways, and converts physical signals into transmittable data packets to ensure that real-time and accurate actual status information can be obtained; The encryption layer encrypts data using encryption algorithms during data transmission and storage, and performs identity authentication when accessing data to ensure data security; The storage layer is used to store and manage large amounts of data that have passed through the encryption layer, and is used to quickly access frequently queried data, reduce network latency, and ensure that the security and availability of key data are not affected even in unstable network conditions; The decision layer obtains and analyzes the data from the storage layer, and the warning protocol module monitors abnormal behaviors in the network in real time, predicts failures, automatically executes safety measures according to preset rules, and quickly triggers the alarm mechanism; The application layer converts the analysis results of the decision-making layer into intuitive reports and charts and displays them to users. Users can monitor the system status, configuration, alarms, view historical record information and perform interactive operations through the interface.

2. According to claim 1, an intelligent safety monitoring system for electronic information products based on Internet of Things perception is characterized by: The encryption layer includes a security protocol module and a recording protocol module; The specific operation method of the encryption layer includes the following steps: A1: Dynamic key negotiation: The client and server initiate a connection, the server sends a digital certificate, and their public keys are exchanged through the ECDHE elliptic curve. A2: Dynamic key generation: The client and server use each other's public key and their own private key to calculate a shared key, and generate a session key from the shared key through a key derivation function; A3: Quantum data encryption: quantum state transmission generates a shared quantum key, which is combined with the session key; A4: Two-way identity authentication: The client encrypts a random challenge with the session key and sends it to the server. The server encrypts a challenge and sends it back to the client to verify the identities of both parties. A5: Data fragmentation encapsulation: The original data transmitted by the perception layer is divided into data fragments, and a header is added to each data fragment; A6: MAC authentication: The MAC key in the session key is used to calculate a message authentication code for each data slice, and the calculated MAC value is appended to the header of the corresponding data slice; A7: Data encryption: Each data shard is kept confidential through the encryption key in the session key; A8: Data transmission: The encrypted data is divided into pieces and encapsulated in a format and sent to the receiver through the network. The receiver decapsulates and decrypts the data according to the corresponding protocol and verifies the MAC.

3. According to claim 2, an intelligent safety monitoring system for electronic information products based on Internet of Things perception is characterized in that: A2, assuming that the client C and the server S use a large prime number p and a primitive root g; The client selects a random secret integer c (1 < c < p - 1), calculates the public value A as A = g c mod p and sends it to the server; The server selects a random secret integer s (1 < s < p - 1), calculates the public value B as B = g s mod p and sends it to the client; The client calculates the shared key implementation formula as follows: K C =B c modp(1), In formula (1), K C represents the shared key of the client, C represents the client, and c represents the private key of the client; The server calculates the shared key implementation formula as follows: K S =B s modp(2), In formula (2), K S represents the shared key of the server, S represents the server, s represents the private key of the server, and according to the properties of constant power operation, K C =K S ; By sharing the key K = K C =K S As output, the session key SK is generated through the key derivation function, which is implemented as: SK=HKDF(K,salt,info)(3), In formula (3), HKDF represents the key derivation function, K represents the shared key, salt represents the salt value, and info represents the context information.

4. According to claim 2, an intelligent safety monitoring system for electronic information products based on Internet of Things perception is characterized in that: In A3, the client prepares the quantum state through polarization direction and sends it to the server. The server measures the received quantum state through polarization basis. The client and the server publicly select the measurement basis through the classical channel, retain the quantum bits measured on the same basis as the key bits, detect abnormal behavior through quantum entanglement and statistical analysis of measurement results, and finally obtain the quantum key QK after error correction and privacy amplification. The obtained quantum key QK is combined with the session key SK to generate the final key for data encryption. The implementation formula is: SK′=Combine(SK,QK), In the formula, SK′ represents the final key, and the Combine function represents combining SK and QK.

5. According to claim 1, an intelligent safety monitoring system for electronic information products based on Internet of Things perception is characterized by: The storage layer is divided into a cache access layer, a distributed layer and a local database; The cache access layer and the basic database are used to store system configuration information, metadata and core business data, providing basic operation support for the monitoring system; The real-time database is used to quickly store and retrieve real-time monitoring data from the encryption layer to ensure the timeliness and immediacy of the data. The historical database is used to store processed historical data for a long time for trend analysis, audit tracking and compliance checks to identify long-term patterns and anomalies. The time series database is used to optimize time series data, efficiently store and query data with timestamps, and record changes in device status over time. Data backup and recovery: Perform regular data backup according to the set time to ensure timely data recovery; Adaptive data compression automatically adjusts the algorithm according to the content of the data block to perform adaptive data compression.

6. The intelligent safety monitoring system for electronic information products based on Internet of Things perception according to claim 5 is characterized in that: In the distributed layer, Hadoop is used to process and analyze large amounts of data; cloud storage dynamically adjusts storage capacity according to actual needs and is used for cross-regional data sharing; data servers are used to manage data storage and access, and integrate data caching mechanisms to speed up frequent access to data; The local database is used to store operation logs, temporary data and critical data with extremely high real-time requirements. When the network is unstable, it temporarily stores important emergency data to ensure that data is not lost.

7. The intelligent security monitoring system for electronic information products based on Internet of Things perception according to claim 1 is characterized in that: The decision layer includes data preprocessing, data analysis engine, rule engine, prediction model, response execution module and warning protocol; The data preprocessing is connected to the application layer through an API interface to perform data preprocessing and format conversion on the received data packets; the data analysis engine extracts the characteristics of electronic information products from the data and the processed data, analyzes the data through cluster analysis, and compares the data in combination with the storage layer to obtain the final analysis results; the rule engine sets the security rule threshold and performs rule matching on the results of the data analysis engine analysis, and issues an early warning notification to the application layer when the matching result exceeds the set threshold.

8. The intelligent safety monitoring system for electronic information products based on Internet of Things perception according to claim 7 is characterized in that: The prediction model predicts future security events based on the storage layer data through a time series model, and evaluates the severity and possibility of potential security events based on the model prediction results; The response execution module automatically executes responses based on the results of the rule engine and prediction model.

Images