Software and hardware integrated encryption communication and real-time monitoring system for Internet of Things equipment

Abstract

The invention relates to the technical field of Internet of Things security. According to the software and hardware integrated encryption communication and real-time monitoring system for the Internet of Things equipment, hardware-level acceleration processing is carried out on key generation and signature operation of a post-quantum encryption algorithm through a hardware acceleration module built in an Internet of Things terminal, and an encrypted data stream is generated; performing dynamic priority allocation processing on the encryption calculation task and the real-time control task through a mixed task scheduling engine of an edge node to generate a task execution sequence; performing encryption task unloading decision processing, and generating an encryption task execution position instruction; performing cooperative processing of key management and a computing power distribution strategy to generate a dynamic resource scheduling instruction; and carrying out lossless anomaly detection processing on the encrypted data stream to generate a real-time security alarm signal, so as to achieve the technical effects of improving encrypted communication and real-time monitoring capability of the Internet of Things equipment, meeting real-time performance and security requirements, and reducing resource waste and security protection gaps.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things security, and particularly to a software and hardware integrated encryption communication and real-time monitoring system for Internet of Things devices. Background Art

[0002] With the rapid popularization of Internet of Things technology, the demand for data security and real-time response in scenarios such as intelligent industrial devices, urban perception networks, and remote medical terminals is becoming increasingly urgent. As the core capabilities of Internet of Things systems, encryption communication and real-time monitoring directly affect the collaborative efficiency between devices and the reliability against attacks, and have become key technical challenges in the field of Internet of Things security.

[0003] However, related technologies rely on classical encryption algorithms such as RSA or ECC, resulting in security risks under quantum computing attacks, high consumption of hardware resources, and difficulty in meeting real-time requirements; task scheduling mostly adopts static priority strategies, without fully considering the dynamic resource competition problems between encryption computing tasks and device control tasks, thus leading to a sharp increase in control instruction latency; encryption traffic anomaly detection technologies mostly rely on the analysis of plaintext after data decryption, resulting in increased computing overhead and introducing the risk of side-channel attacks. Summary of the Invention

[0004] Based on this, it is necessary to provide a software and hardware integrated encryption communication and real-time monitoring system for Internet of Things devices to address the above technical problems, so as to achieve the technical effects of improving the encryption communication and real-time monitoring capabilities of Internet of Things devices, meeting real-time and security requirements, and reducing resource waste and security protection gaps.

[0005] In a first aspect, the present application provides a software and hardware integrated encryption communication and real-time monitoring system for Internet of Things devices, and the system includes:

[0006] A post-quantum encryption hardware acceleration module, configured to perform hardware-level acceleration processing on the key generation and signature operations of post-quantum encryption algorithms through the hardware acceleration module built in the Internet of Things terminal, and generate encrypted data streams;

[0007] A dynamic task scheduling module, configured to perform dynamic priority allocation processing on encryption computing tasks and real-time control tasks through the hybrid task scheduling engine of the edge node based on the encrypted data stream, and generate a task execution sequence;

[0008] An intelligent offloading decision module, configured to perform encryption task offloading decision processing according to the network status monitoring result between the Internet of Things terminal and the edge node, and generate an encryption task execution location instruction;

[0009] The resource collaborative scheduling module is used to perform collaborative processing of key management and computing power allocation strategies based on the encrypted task execution location instruction through the correlation analysis between the key life cycle stage and the computing resource occupancy, and generate dynamic resource scheduling instructions;

[0010] The real-time security monitoring module is used to perform lossless anomaly detection processing on the encrypted data stream through the encrypted domain feature extraction processor built in the Internet of Things terminal, and generate real-time security warning signals.

[0011] Furthermore, according to the network status monitoring result between the Internet of Things terminal and the edge node, perform encrypted task offloading decision processing to generate encrypted task execution location instructions, including:

[0012] Continuously monitor the packet loss rate and signal strength of the wireless channel quality between the Internet of Things terminal domain edge nodes to generate a network stability evaluation result;

[0013] Based on the network stability evaluation result, split the encrypted computing task into redundant coding subtasks to generate a set of redundant coding subtasks;

[0014] Send the set of redundant coding subtasks to the edge node for parallel computing processing through multi-path transmission to generate encrypted task execution location instructions.

[0015] Furthermore, based on the network stability evaluation result, split the encrypted computing task into redundant coding subtasks to generate a set of redundant coding subtasks, including:

[0016] Perform stateless splitting processing on the computing logic of the encrypted computing task to generate a set of independent operation subtasks;

[0017] Based on the preset redundant coding rule, perform redundant coding processing on each independent operation subtask to generate redundant coding subtasks;

[0018] Perform additional check information processing on the redundant coding subtasks to generate a set of redundant coding subtasks.

[0019] Furthermore, based on the encrypted task execution location instruction, perform collaborative processing of key management and computing power allocation strategies through the correlation analysis between the key life cycle stage and the computing resource occupancy, and generate dynamic resource scheduling instructions, including:

[0020] Call the distributed computing cluster of the edge node for high-intensity computing processing during the key generation stage to generate an initial key pool;

[0021] During the key activation stage, based on the initial key pool, perform high-frequency signature operation processing through the hardware acceleration module to generate real-time encrypted data packets;

[0022] In the key update phase, based on the changed part of the initial key pool, incremental calculation processing is performed to generate dynamic resource scheduling instructions.

[0023] Further, in the key update phase, based on the changed part of the initial key pool, incremental calculation processing is performed to generate dynamic resource scheduling instructions, including:

[0024] Perform differential hashing processing on the changed part of the initial key pool to generate a set of change identifiers;

[0025] Based on the set of change identifiers, perform local indexing processing on the associated key fragments in the key pool to generate a set of key fragments to be updated;

[0026] Perform incremental encryption processing on the set of updated key fragments through a hardware acceleration module to generate dynamic resource scheduling instructions.

[0027] Further, through the encryption domain feature extraction processor built into the Internet of Things terminal, perform lossless anomaly detection processing on the encrypted data stream to generate real-time security warning signals, including:

[0028] Deploy a coprocessor directly connected to the hardware acceleration module in the Internet of Things terminal to generate the deployment status of the encryption domain feature extraction coprocessor;

[0029] Based on the deployment status of the encryption domain feature extraction coprocessor, perform statistical feature extraction processing on the timing distribution and load length of the encrypted data stream to generate a traffic pattern baseline;

[0030] Based on the traffic pattern baseline, compare the current encrypted data stream features with the traffic pattern baseline for deviation calculation to generate real-time security warning signals.

[0031] Further, based on the traffic pattern baseline, compare the current encrypted data stream features with the traffic pattern baseline for deviation calculation to generate real-time security warning signals, including:

[0032] Perform real-time statistical feature extraction processing on the current packet interval time and load length of the encrypted data stream to generate the current data stream feature vector;

[0033] Based on the timing distribution probability in the traffic pattern baseline, perform deviation calculation processing on the current data stream feature vector to generate a deviation index;

[0034] When the deviation index exceeds the preset threshold range, perform replay attack determination processing to generate real-time security warning signals.

[0035] Further, based on the encrypted data stream, perform dynamic priority allocation processing on the encrypted computing tasks and real-time control tasks through the hybrid task scheduling engine of the edge node to generate a task execution sequence, including:

[0036] Build a dynamic priority queue based on the real-time operating system of the edge node to generate dynamic priority queue configuration parameters;

[0037] Based on the dynamic priority queue configuration parameters, perform labeled classification processing on the encrypted computing tasks and real-time control tasks to generate a task priority list;

[0038] When an emergency signal of the real-time control task is detected, based on the task priority list, perform forced interruption processing on the resource allocation of the encrypted computing task to generate a task execution sequence.

[0039] In a second aspect, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program. It is characterized in that when the processor executes the computer program, the steps of any method in the first aspect of the present application are implemented.

[0040] In a third aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. It is characterized in that when the computer program is executed by a processor, the steps of any method in the first aspect of the present application are implemented.

[0041] The technical solutions provided by the present application include the following technical effects: By providing a software and hardware integrated encryption communication and real-time monitoring system for Internet of Things devices, including: a post-quantum encryption hardware acceleration module, which is used to perform hardware-level acceleration processing on the key generation and signature operations of the post-quantum encryption algorithm through the hardware acceleration module built in the Internet of Things terminal to generate an encrypted data stream; a dynamic task scheduling module, which is used to perform dynamic priority allocation processing on the encrypted computing tasks and real-time control tasks through the hybrid task scheduling engine of the edge node based on the encrypted data stream to generate a task execution sequence; an intelligent offloading decision module, which is used to perform encrypted task offloading decision processing based on the network status monitoring result between the Internet of Things terminal and the edge node to generate an encrypted task execution location instruction; a resource collaborative scheduling module, which is used to perform collaborative processing of key management and computing power allocation strategies based on the encrypted task execution location instruction through the correlation analysis of the key life cycle stage and the computing resource occupancy to generate a dynamic resource scheduling instruction; a real-time security monitoring module, which is used to perform lossless anomaly detection processing on the encrypted data stream through the encrypted domain feature extraction processor built in the Internet of Things terminal to generate a real-time security warning signal, so as to achieve the technical effects of improving the encrypted communication and real-time monitoring capabilities of Internet of Things devices, meeting the real-time and security requirements, and reducing resource waste and security protection gaps. Description of the Drawings

[0042] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0043] Figure 1 It is a structural diagram of a software and hardware integrated encryption communication and real-time monitoring system for Internet of Things devices in an embodiment of the present invention;

[0044] Figure 2 It is a flowchart of generating a real-time security warning signal by comparing the deviation between the current encrypted data stream characteristics and the traffic pattern baseline based on the traffic pattern baseline in an embodiment of the present invention. Detailed implementation manners

[0045] In order to make the above objects, features, and advantages of the present application more obvious and understandable, the following will make a detailed description of the specific implementation manners of the present application with reference to the drawings. Many specific details are set forth in the following description to fully understand the present application. However, the present application can be implemented in many other ways different from those described herein. Those skilled in the art can make similar improvements without departing from the connotation of the application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0046] As Figure 1 shown, the present application provides a software and hardware integrated encryption communication and real-time monitoring system 100 for Internet of Things devices. The system includes:

[0047] A post-quantum encryption hardware acceleration module 101, which is used to perform hardware-level acceleration processing on the key generation and signature operations of the post-quantum encryption algorithm through the hardware acceleration module built in the Internet of Things terminal to generate an encrypted data stream.

[0048] Specifically, in the Internet of Things (IoT) terminal, the post-quantum encryption hardware acceleration module 101 accelerates the key generation process of the post-quantum encryption algorithm through means such as optimizing algorithms and parallel computing. This module can specifically design circuits or utilize hardware resources such as FPGAs to quickly complete complex matrix operations and polynomial sampling, reducing the key generation time. For the signature process of the post-quantum encryption algorithm, this module also plays a crucial role. In the limited hardware resource environment of the IoT terminal, the post-quantum encryption hardware acceleration module 101 needs to reasonably allocate computing resources, including the scheduling of different computing units, the management of storage resources, and the optimization of data transmission. Through hardware-level resource management and optimization, it is ensured that while accelerating the encryption operation, it will not have a negative impact on other functions of the terminal device. This module also enhances the security of the encryption process through physical security mechanisms. For example, measures such as anti-tampering design and secure key storage are adopted to prevent physical attacks and side-channel attacks, ensuring that the encryption operation is executed in a secure hardware environment and further enhancing the security of the entire IoT system. Through the above technical logic and steps, the post-quantum encryption hardware acceleration module 101 can effectively accelerate the key generation and signature operations of the post-quantum encryption algorithm in IoT devices, improve the efficiency and security of encrypted communication, and at the same time adapt to the characteristics of resource constraints in IoT terminals.

[0049] The dynamic task scheduling module 102 is used to perform dynamic priority assignment processing on encryption computing tasks and real-time control tasks through the hybrid task scheduling engine of the edge node based on the encrypted data stream, generating a task execution sequence.

[0050] Specifically, a dynamic priority queue is constructed on the real-time operating system of the edge node, and queue parameters are configured to provide an infrastructure for subsequent task scheduling. The encryption computing tasks and real-time control tasks are labeled according to the task type and urgency, generating a task priority list to clarify the task execution order. When an emergency signal of the real-time control task is detected, based on the task priority list, the resource allocation of the encryption computing task is interrupted to ensure that the high-priority real-time control task can obtain the required resources in a timely manner and execute, generating the final task execution sequence. Through the above technical logic and steps, the dynamic task scheduling module 102 can achieve efficient dynamic scheduling of encryption computing tasks and real-time control tasks on the edge node based on the encrypted data stream, ensuring the timely execution of tasks and the efficient operation of the system, while meeting the real-time and security requirements in the IoT scenario.

[0051] The intelligent offloading decision module 103 is used to perform encryption task offloading decision processing based on the network status monitoring results between the IoT terminal and the edge node, generating an encryption task execution location instruction.

[0052] Specifically, continuously monitor the packet loss rate and signal strength of the wireless channel quality between the IoT terminal and the edge node to generate a network stability evaluation result. This step is the basis for offloading decisions and evaluates the reliability of the current network by monitoring key indicators of the network. Based on the network stability evaluation result, perform stateless segmentation on the encrypted computing task to generate a set of independent operator tasks. Then, perform redundant coding processing on each independent operator task based on a preset redundant coding rule to generate redundant coding subtasks and attach check information to form a set of redundant coding subtasks. Send the set of redundant coding subtasks to the edge node for parallel computing processing through multi-path transmission. This step utilizes the multi-path characteristics of the network to improve the efficiency and reliability of task execution. According to the results of the above steps, perform encrypted task offloading decision processing to generate an encrypted task execution location instruction. Through the above technical logic and steps, the intelligent offloading decision module 103 can reasonably make encrypted task offloading decisions according to the real-time network status monitoring results, optimize the task execution location, thereby improving the overall performance and reliability of the IoT system.

[0053] The resource collaborative scheduling module 104 is used to perform collaborative processing of key management and computing power allocation strategies based on the encrypted task execution location instruction through the correlation analysis between the key life cycle stage and the computing resource occupancy, and generate a dynamic resource scheduling instruction.

[0054] Specifically, after receiving the instruction of the encryption task execution location, the resource collaborative scheduling module first determines the life cycle stage of the key. If it is in the key generation stage, the resource collaborative scheduling module 104 will call the distributed computing cluster of the edge node to perform high-intensity computing processing to generate the initial key pool. This step utilizes the computing resources of the edge node to ensure the efficiency and security of key generation. When the key enters the activation stage, based on the initial key pool, the resource collaborative scheduling module 104 performs high-frequency signature operation processing through the hardware acceleration module to generate real-time encrypted data packets. This stage makes full use of the hardware acceleration ability to improve the speed of signature operations and ensure the real-time nature of encrypted communication. In the key update stage, the resource collaborative scheduling module 104 performs incremental calculation processing on the changed part of the initial key pool to generate dynamic resource scheduling instructions. This includes performing differential hashing processing on the changed part to generate a set of change identifiers; based on the set of change identifiers, performing local indexing processing on the associated key fragments in the key pool to generate a set of key fragments to be updated; and then performing incremental encryption processing on the set of updated key fragments through the hardware acceleration module to generate dynamic resource scheduling instructions. This step reduces the consumption of computing resources through incremental updates, while ensuring the timely update and security of the key. During the above process, the resource collaborative scheduling module 104 also performs collaborative processing of key management and computing power allocation strategies according to the computing resource occupancy. By analyzing the resource requirements in different stages, computing resources are reasonably allocated to ensure the efficient execution of the encryption task and avoid resource waste.

[0055] Through the above technical logic and steps, the resource collaborative scheduling module 104 can dynamically adjust the resource allocation and key management strategy according to the instruction of the encryption task execution location and the key life cycle stage, improving the encryption communication efficiency and resource utilization rate of IoT devices.

[0056] The real-time security monitoring module 105 is used to perform lossless anomaly detection processing on the encrypted data stream through the encryption domain feature extraction processor built in the IoT terminal to generate real-time security alarm signals.

[0057] Specifically, the real-time security monitoring module 105 collects encrypted data streams through the encryption domain feature extraction processor built into the Internet of Things terminal. This processor is directly connected to the hardware acceleration module and can efficiently obtain key information in the encrypted data stream. Based on the deployment status of the encryption domain feature extraction coprocessor, statistical feature extraction processing is performed on the timing distribution and load length of the encrypted data stream to generate a traffic pattern baseline. This step establishes a behavior model of normal traffic by analyzing the timing characteristics and load characteristics of the data stream. The characteristics of the current encrypted data stream are compared with the previously established traffic pattern baseline for deviation processing. This includes real-time statistical feature extraction of the current packet interval time and load length to generate a feature vector of the current data stream; based on the timing distribution probability in the traffic pattern baseline, the deviation index of the current data stream feature vector is calculated. When the deviation index exceeds the preset threshold range, replay attack determination processing is performed to generate a real-time security warning signal. Through the above technical logic and steps, the real-time security monitoring module 105 can perform efficient lossless anomaly detection on encrypted data streams, timely discover and alarm potential security threats, and ensure the communication security of the Internet of Things system.

[0058] The embodiment of the present application provides a software and hardware integrated encryption communication and real-time monitoring system for Internet of Things devices, including: a post-quantum encryption hardware acceleration module for performing hardware-level acceleration processing on the key generation and signature operations of the post-quantum encryption algorithm through the hardware acceleration module built into the Internet of Things terminal to generate an encrypted data stream; a dynamic task scheduling module for performing dynamic priority allocation processing on encrypted computing tasks and real-time control tasks through the hybrid task scheduling engine of the edge node based on the encrypted data stream to generate a task execution sequence; an intelligent offloading decision module for performing encrypted task offloading decision processing based on the network status monitoring result between the Internet of Things terminal and the edge node to generate an encrypted task execution location instruction; a resource collaborative scheduling module for performing collaborative processing of key management and computing power allocation strategies through the correlation analysis of the key life cycle stage and computing resource occupancy based on the encrypted task execution location instruction to generate a dynamic resource scheduling instruction; a real-time security monitoring module for performing lossless anomaly detection processing on the encrypted data stream through the encryption domain feature extraction processor built into the Internet of Things terminal to generate a real-time security warning signal, so as to achieve the technical effects of improving the encryption communication and real-time monitoring capabilities of Internet of Things devices, meeting the requirements of real-time and security, and reducing resource waste and security protection gaps.

[0059] Further, performing encrypted task offloading decision processing based on the network status monitoring result between the Internet of Things terminal and the edge node to generate an encrypted task execution location instruction includes:

[0060] Continuously monitoring the packet loss rate and signal strength of the wireless channel between the Internet of Things terminal domain edge node to generate a network stability evaluation result;

[0061] Based on the network stability evaluation results, split the encrypted computing task into redundant coding subtasks to generate a set of redundant coding subtasks;

[0062] Send the set of redundant coding subtasks to edge nodes for parallel computing processing through multi-path transmission to generate an encrypted task execution location instruction.

[0063] Specifically, continuously monitor the wireless channel quality between the IoT terminal and the edge node, focus on analyzing key indicators such as packet loss rate and signal strength, process the collected data through statistical analysis and machine learning algorithms to generate an evaluation result reflecting network stability. Based on the network stability evaluation result, perform stateless splitting on the encrypted computing task to generate a set of independent operation subtasks. Then, based on the preset redundant coding rules, perform redundant coding processing on each independent operation subtask to generate redundant coding subtasks, and attach check information to form a set of redundant coding subtasks. Send the set of redundant coding subtasks to multiple edge nodes for parallel computing processing through the multi-path transmission protocol. Utilize the distributed computing power of the edge nodes to improve the task execution efficiency, and at the same time improve the reliability and fault tolerance of data transmission through multi-path transmission. According to the results of the above steps, combined with the current network status and the computing resource situation of the edge nodes, perform encrypted task offloading decision processing to generate an encrypted task execution location instruction.

[0064] Through the above technical logic and steps, the intelligent offloading decision module 103 can reasonably make an encrypted task offloading decision according to the real-time network status monitoring results, optimize the task execution location, thereby improving the overall performance and reliability of the IoT system.

[0065] Furthermore, based on the network stability evaluation results, split the encrypted computing task into redundant coding subtasks to generate a set of redundant coding subtasks, including:

[0066] Perform stateless splitting processing on the computing logic of the encrypted computing task to generate a set of independent operation subtasks;

[0067] Based on the preset redundant coding rules, perform redundant coding processing on each independent operation subtask to generate redundant coding subtasks;

[0068] Perform check information attachment processing on the redundant coding subtasks to generate a set of redundant coding subtasks.

[0069] Specifically, the computational logic of the encryption computing task is processed in a stateless manner to generate a set of independent operator subtasks. This step ensures that each subtask can be executed independently without being affected by the states of other subtasks, thereby improving the scalability and flexibility of the task. Based on a preset redundant coding rule, each independent operator subtask is processed with redundant coding to generate redundant coding subtasks. The redundant coding subtasks are processed by attaching check information to generate a set of redundant coding subtasks. This step ensures the integrity and correctness of the data during transmission and execution by adding check information, further enhancing the robustness of the system. Through the above technical logic and steps, the encryption computing task can be effectively split into redundant coding subtasks and the corresponding set can be generated, laying a foundation for subsequent multi-path transmission and parallel computing processing, thereby improving the overall performance and reliability of the Internet of Things system.

[0070] Further, based on the encryption task execution location instruction, through the correlation analysis of the key life cycle stage and the computing resource occupancy, the collaborative processing of the key management and computing power allocation strategy is carried out to generate a dynamic resource scheduling instruction, including:

[0071] In the key generation stage, the distributed computing cluster of the edge node is called to perform high-intensity computing processing to generate an initial key pool;

[0072] In the key activation stage, based on the initial key pool, the hardware acceleration module is used to perform high-frequency signature operation processing to generate real-time encrypted data packets;

[0073] In the key update stage, based on the changed part of the initial key pool, incremental computing processing is carried out to generate a dynamic resource scheduling instruction.

[0074] Specifically, after receiving the encryption task execution location instruction, if the key is in the generation stage, the resource collaborative scheduling module will call the distributed computing cluster of the edge node to perform high-intensity computing processing to generate an initial key pool. This step utilizes the computing resources of the edge node to ensure the efficiency and security of key generation. When the key enters the activation stage, based on the initial key pool, the hardware acceleration module is used to perform high-frequency signature operation processing to generate real-time encrypted data packets. This stage makes full use of the hardware acceleration ability to improve the speed of the signature operation and ensure the real-time nature of encrypted communication. In the key update stage, based on the changed part of the initial key pool, incremental computing processing is carried out to generate a dynamic resource scheduling instruction. This includes performing differential hashing processing on the changed part to generate a set of change identifiers; based on the set of change identifiers, performing local indexing processing on the associated key fragments in the key pool to generate a set of key fragments to be updated; and then using the hardware acceleration module to perform incremental encryption processing on the set of updated key fragments to generate a dynamic resource scheduling instruction.

[0075] This step reduces the consumption of computing resources through incremental updates, while ensuring the timely update and security of the key. Through the above technical logic and steps, the resource collaborative scheduling module 104 can dynamically adjust the resource allocation and key management strategy according to the execution location instruction of the encryption task and the key life cycle stage, improving the encryption communication efficiency and resource utilization rate of IoT devices.

[0076] Furthermore, in the key update stage, based on the changed part of the initial key pool, incremental calculation processing is performed to generate dynamic resource scheduling instructions, including:

[0077] Perform differential hashing on the changed part of the initial key pool to generate a set of change identifiers;

[0078] Based on the set of change identifiers, perform local indexing on the key fragments associated in the key pool to generate a set of key fragments to be updated;

[0079] Perform incremental encryption processing on the set of updated key fragments through the hardware acceleration module to generate dynamic resource scheduling instructions.

[0080] Specifically, perform differential hashing on the changed part of the initial key pool to generate a set of change identifiers. This step quickly identifies the specific parts that have changed in the key pool by calculating the hash value of the changed part, providing accurate positioning for subsequent local updates. Based on the generated set of change identifiers, perform local indexing on the key fragments associated with it in the key pool to generate a set of key fragments to be updated. Through local indexing, the key fragments that need to be updated can be retrieved and extracted efficiently, avoiding full-scale scanning of the entire key pool, thereby saving computing resources and time. Then, perform incremental encryption processing on the set of updated key fragments through the hardware acceleration module to generate dynamic resource scheduling instructions. Through the above technical logic and steps, the incremental calculation in the key update stage can be processed efficiently, generating dynamic resource scheduling instructions, thereby optimizing resource allocation and improving the efficiency and security of key management.

[0081] Furthermore, through the encryption domain feature extraction processor built into the IoT terminal, perform lossless anomaly detection processing on the encrypted data stream to generate real-time security alarm signals, including:

[0082] Deploy a coprocessor directly connected to the hardware acceleration module in the IoT terminal to generate the deployment status of the encryption domain feature extraction coprocessor;

[0083] Based on the deployment status of the encryption domain feature extraction coprocessor, perform statistical feature extraction processing on the time series distribution and load length of the encrypted data stream to generate a traffic pattern baseline;

[0084] Based on the traffic pattern baseline, compare the characteristics of the current encrypted data stream with the traffic pattern baseline for deviation processing to generate real-time security warning signals.

[0085] Specifically, deploy a coprocessor directly connected to the hardware acceleration module in the IoT terminal to generate the deployment status of the encryption domain feature extraction coprocessor. This step ensures that the coprocessor can efficiently obtain key information in the encrypted data stream, providing a basis for subsequent feature extraction and anomaly detection. Based on the deployment status of the encryption domain feature extraction coprocessor, perform statistical feature extraction processing on the timing distribution and load length of the encrypted data stream to generate a traffic pattern baseline. This step analyzes the timing and load characteristics of the data stream to establish a behavior model of normal traffic, providing a reference standard for anomaly detection. Compare the characteristics of the current encrypted data stream with the pre-established traffic pattern baseline for deviation processing to generate real-time security warning signals. This includes performing real-time statistical feature extraction on the current packet interval time and load length of the current data stream to generate a current data stream feature vector; calculating the deviation index of the current data stream feature vector based on the timing distribution probability in the traffic pattern baseline. When the deviation index exceeds the preset threshold range, perform a replay attack determination process to generate real-time security warning signals. This step monitors the changes in the data stream characteristics in real time, promptly discovers and warns of potential security threats, and ensures the communication security of the IoT system.

[0086] Through the above technical logic and steps, the real-time security monitoring module 105 can perform efficient lossless anomaly detection on the encrypted data stream, promptly discover and warn of potential security threats, and ensure the communication security of the IoT system.

[0087] In one embodiment, as Figure 2 shown, based on the traffic pattern baseline, comparing the characteristics of the current encrypted data stream with the traffic pattern baseline for deviation processing to generate real-time security warning signals includes:

[0088] S201: Perform real-time statistical feature extraction processing on the current packet interval time and load length of the encrypted data stream to generate a current data stream feature vector;

[0089] S202: Calculate the deviation of the current data stream feature vector based on the timing distribution probability in the traffic pattern baseline to generate a deviation index;

[0090] S203: When the deviation index exceeds the preset threshold range, perform a replay attack determination process to generate real-time security warning signals.

[0091] Specifically, real-time statistical feature extraction processing is performed on the current packet interval time and payload length of the encrypted data stream to generate the current data stream feature vector. This step captures the dynamic characteristics of the data stream by monitoring the arrival time and payload size of the data packets, providing the basic data for subsequent deviation calculation. Deviation calculation: Based on the temporal distribution probability in the traffic pattern baseline, deviation calculation processing is performed on the current data stream feature vector to generate a deviation index. Among them, by comparing the current data stream features with the probability distribution in the baseline model, the deviation index is calculated to quantify the difference degree between the current data stream and the normal mode. When the deviation index exceeds the preset threshold range, replay attack determination processing is performed to generate a real-time security warning signal. This step determines whether the current data stream is abnormal through the set threshold. Once the threshold is exceeded, a security warning is immediately triggered to notify the administrator to take measures in a timely manner to prevent potential security threats. Through the above technical logic and steps, the feature changes of the encrypted data stream can be monitored in real time, potential security threats can be discovered and warned in a timely manner, and the communication security of the Internet of Things system can be ensured.

[0092] Furthermore, based on the encrypted data stream, the hybrid task scheduling engine of the edge node performs dynamic priority allocation processing on the encrypted computing task and the real-time control task to generate a task execution sequence, including:

[0093] Based on the real-time operating system of the edge node, a dynamic priority queue is constructed to generate dynamic priority queue configuration parameters;

[0094] Based on the dynamic priority queue configuration parameters, the encrypted computing task and the real-time control task are classified by tagging to generate a task priority list;

[0095] When an emergency signal of the real-time control task is detected, based on the task priority list, the resource allocation of the encrypted computing task is forcibly interrupted to generate a task execution sequence.

[0096] Specifically, on the real-time operating system of the edge node, a queue that can be dynamically adjusted according to task priorities is constructed, and dynamic priority queue configuration parameters are generated. This step ensures that tasks can be sorted according to their urgency and importance, providing the infrastructure for subsequent task scheduling. Based on the dynamic priority queue configuration parameters, the encrypted computing tasks and real-time control tasks are classified and tagged to generate a task priority list. By defining a task structure and a custom comparator, dynamic priority sorting of tasks can be achieved to ensure that high-priority tasks can be executed first. When an emergency signal of the real-time control task is detected, based on the task priority list, the resource allocation of the encrypted computing task is forcibly interrupted to generate a task execution sequence. This step dynamically adjusts the resource allocation by monitoring the urgency of tasks in real time, ensuring that critical tasks can obtain the required resources and be executed in a timely manner, thereby improving the response speed and reliability of the system.

[0097] Through the above technical logic and steps, it is possible to achieve efficient dynamic scheduling of encrypted computing tasks and real-time control tasks on the edge node based on the encrypted data stream, ensuring the timely execution of tasks and the efficient operation of the system, while meeting the real-time and security requirements in the Internet of Things scenario.

[0098] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are displayed in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least some of the steps or stages in other steps or other steps.

[0099] In one embodiment, the present application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps in the above system embodiments are implemented.

[0100] In one embodiment, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above system embodiments are implemented.

[0101] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the descriptions in the method embodiments. The device embodiments described above are merely illustrative. The components described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present disclosure solution. A person of ordinary skill in the art can understand and implement it without creative efforts.

[0102] The above embodiments only represent several implementation manners of the embodiments of the present application. The descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of the embodiments of the application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the embodiments of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. A software and hardware integrated encrypted communication and real-time monitoring system for IoT devices, characterized in that: The system comprises: Post-quantum encryption hardware acceleration module, which is used to perform hardware-level acceleration processing on the key generation and signature operations of the post-quantum encryption algorithm through the hardware acceleration module built into the IoT terminal to generate encrypted data streams; A dynamic task scheduling module is used to dynamically allocate priorities for encryption computing tasks and real-time control tasks based on the encrypted data stream through a hybrid task scheduling engine of an edge node to generate a task execution sequence; An intelligent unloading decision module, used to perform encryption task unloading decision processing according to the network status monitoring result between the Internet of Things terminal and the edge node, and generate an encryption task execution position instruction; A resource collaborative scheduling module is used to perform collaborative processing of key management and computing power allocation strategy based on the encryption task execution position instruction, through the correlation analysis of the key life cycle stage and computing resource occupancy, to generate dynamic resource scheduling instructions; The real-time security monitoring module is used to perform lossless anomaly detection processing on the encrypted data stream through the encryption domain feature extraction processor built into the Internet of Things terminal to generate a real-time security alarm signal.

2. The software and hardware integrated encryption communication and real-time monitoring system for IoT devices according to claim 1 is characterized in that: The method of performing encryption task offloading decision processing according to the network status monitoring result between the Internet of Things terminal and the edge node and generating an encryption task execution position instruction includes: Continuously monitor the packet loss rate and signal strength of the wireless channel quality between the edge nodes in the IoT terminal domain to generate a network stability evaluation result; Based on the network stability evaluation result, splitting the encryption computing task into redundant encoding subtasks to generate a redundant encoding subtask set; The redundant encoding subtask set is sent to the edge node through multi-path transmission for parallel computing processing to generate the encryption task execution position instruction.

3. The software and hardware integrated encryption communication and real-time monitoring system for Internet of Things devices according to claim 2 is characterized in that: The step of splitting the encryption computing task into redundant coding subtasks based on the network stability evaluation result and generating a redundant coding subtask set includes: Performing stateless segmentation processing on the computing logic of the encryption computing task to generate a set of independent computing subtasks; Based on a preset redundant coding rule, performing redundant coding processing on each of the independent operation subtasks to generate redundant coding subtasks; Additional verification information processing is performed on the redundant coding subtask to generate the redundant coding subtask set.

4. The software and hardware integrated encryption communication and real-time monitoring system for IoT devices according to claim 1, characterized in that: The execution position instruction based on the encryption task, through the correlation analysis between the key life cycle stage and the computing resource occupancy, performs the coordinated processing of key management and computing power allocation strategy, and generates a dynamic resource scheduling instruction, including: In the key generation phase, the distributed computing cluster of the edge node is called to perform high-intensity computing processing to generate an initial key pool; In the key activation phase, based on the initial key pool, the hardware acceleration module performs high-frequency signature operation processing to generate a real-time encrypted data packet; In the key update phase, incremental calculation processing is performed based on the changed part of the initial key pool to generate the dynamic resource scheduling instruction.

5. The software and hardware integrated encryption communication and real-time monitoring system for IoT devices according to claim 4 is characterized in that: In the key update phase, performing incremental calculation processing based on the changed part of the initial key pool to generate the dynamic resource scheduling instruction includes: Performing differential hashing on the changed part of the initial key pool to generate a set of change identifiers; Based on the change identifier set, locally indexing the key fragments associated in the key pool to generate a set of key fragments to be updated; The hardware acceleration module performs incremental encryption processing on the update key fragment set to generate the dynamic resource scheduling instruction.

6. The software and hardware integrated encryption communication and real-time monitoring system for IoT devices according to claim 1, characterized in that: The encrypted data stream is subjected to lossless anomaly detection processing by the encryption domain feature extraction processor built into the IoT terminal to generate a real-time security alarm signal, including: Deploy a coprocessor directly connected to the hardware acceleration module in the IoT terminal, and generate a deployment state of the encryption domain feature extraction coprocessor; Based on the deployment status of the encryption domain feature extraction coprocessor, statistical feature extraction processing is performed on the timing distribution and load length of the encrypted data stream to generate a traffic pattern baseline; Based on the traffic pattern baseline, the deviation degree comparison process is performed between the current encrypted data flow characteristics and the traffic pattern baseline to generate the real-time security alarm signal.

7. The software and hardware integrated encryption communication and real-time monitoring system for Internet of Things devices according to claim 6 is characterized in that: The step of comparing the deviation between the current encrypted data flow feature and the traffic pattern baseline based on the traffic pattern baseline to generate the real-time security alarm signal includes: Performing real-time statistical feature extraction processing on the current packet interval time and load length of the encrypted data stream to generate a current data stream feature vector; Based on the time series distribution probability in the traffic pattern baseline, performing deviation calculation processing on the current data flow feature vector to generate a deviation index; When the deviation index exceeds a preset threshold range, a replay attack determination process is performed to generate the real-time security alarm signal.

8. The software and hardware integrated encryption communication and real-time monitoring system for Internet of Things devices according to claim 1 is characterized in that: Based on the encrypted data stream, the hybrid task scheduling engine of the edge node performs dynamic priority allocation processing on the encrypted computing task and the real-time control task to generate a task execution sequence, including: Based on the real-time operating system of the edge node, a dynamic priority queue is constructed and configuration parameters of the dynamic priority queue are generated; Based on the dynamic priority queue configuration parameters, the encryption computing tasks and the real-time control tasks are subjected to label classification processing to generate a task priority list; When an emergency signal of the real-time control task is detected, resource allocation of the encryption computing task is forcibly interrupted based on the task priority list to generate the task execution sequence.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the hardware-software integrated encryption communication and real-time monitoring system for Internet of Things devices described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the hardware-software integrated encryption communication and real-time monitoring system for Internet of Things devices described in any one of claims 1 to 8 are implemented.

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