Quick access system for Internet of Things sensing equipment

Abstract

The invention relates to the technical field of Internet of Things, and discloses an Internet of Things sensing device rapid access system, which comprises a device protocol feature extraction module, a device protocol feature extraction module and a device protocol feature extraction module, the protocol grammar and semantic analysis module is used for receiving the unique identifier and the physical feature, constructing a protocol grammar graph and extracting a semantic context of a protocol; the protocol reverse generation module is used for receiving a protocol syntax graph and a semantic context and converting analyzed protocol features into an equipment driving program, and the quantum optimization and dynamic configuration module is used for receiving the equipment driving program, optimizing equipment resource configuration in a protocol analysis process according to an optimization algorithm and outputting an optimization result. According to the invention, by adopting the technical scheme of clock skew calculation and radio frequency characteristic processing, the effects of accurate equipment clock synchronization and identity confirmation are achieved, the problem of low equipment access efficiency is solved, and the response speed of the network is improved.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to a rapid access system for IoT sensing devices. Background Technology

[0002] With the rapid development of IoT technology, more and more IoT sensing devices are being widely used in various fields. In smart homes, users can remotely control devices in their homes via mobile phones or voice assistants to achieve functions such as smart lighting, temperature control, and security, thereby improving the convenience and comfort of life. In terms of smart city construction, IoT sensing devices use sensor networks to achieve real-time monitoring of traffic flow, ambient air quality, and municipal facilities, promoting the optimal allocation of resources and improving the efficiency of urban management.

[0003] However, regarding device clock synchronization, many solutions rely solely on the Network Time Protocol (NTP). While NTP can provide some time synchronization, in high-latency network environments, time deviations can be significant, leading to inaccurate data processing. This is particularly insufficient in fast-response application scenarios. Secondly, traditional device authentication methods are mostly based on static features, such as MAC addresses or serial numbers. These methods are vulnerable to forgery and replay attacks, lacking true security guarantees. Because the uniqueness of devices is not fully exploited, the authentication effect is often unsatisfactory. Furthermore, existing protocol parsing technologies typically rely on fixed formats, lacking flexibility. With the diversification of devices and protocols, the accuracy and efficiency of parsing are greatly challenged. Once the protocol format changes, existing parsing solutions become ineffective, resulting in huge maintenance costs. Finally, for device resource management, existing methods often employ static configuration. Under conditions of large load fluctuations, this approach cannot quickly adapt to changes in demand, leading to resource waste and performance degradation. The lack of dynamic adjustment capabilities makes devices inadequate when dealing with complex environments. Summary of the Invention

[0004] The purpose of this invention is to address the problems of low device access efficiency, insufficient parsing accuracy, inadequate resource utilization, and delayed anomaly identification in a system for rapid access of IoT sensing devices.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A system for rapid access of IoT sensing devices includes: The device protocol feature extraction module is used to obtain the physical characteristics of IoT devices and generate unique identifiers; The protocol syntax and semantic parsing module receives unique identifiers and physical features, constructs a protocol syntax graph, and extracts the semantic context of the protocol. The protocol reverse engineering module receives the protocol syntax diagram and semantic context, and converts the parsed protocol features into a device driver. The quantum optimization and dynamic configuration module receives the device driver, optimizes the device resource configuration during the protocol parsing process according to the optimization algorithm, and outputs the optimization results. The anomaly monitoring and countermeasure detection module receives optimization results, monitors the operating status of the equipment in real time and detects potential anomalies, and outputs the monitoring data. The system management and interface module receives monitoring data and adjusts the device's access policies and configurations.

[0006] Preferably, the device protocol feature extraction module includes: The clock skew calculation unit calculates the clock skew based on a comparison between the device clock and the gateway clock; The radio frequency (RF) feature processing unit obtains RF features by analyzing the difference between the RF signal emitted by the device and the reference signal; The ID generation unit generates a unique identifier based on clock skew and radio frequency characteristics.

[0007] Preferably, the protocol syntax and semantic parsing module includes: The graph construction unit represents protocol fields as nodes and relationships between nodes as edges; The LSTM unit captures timing features by receiving the output of the constructed protocol syntax graph; The entropy calculation unit defines the weight of edges by calculating entropy values ​​in order to determine the interactions between fields.

[0008] Preferably, the protocol reverse generation module includes: The protocol description generation unit generates a description file that can be used for device operation based on the parsed protocol features; The driver generation unit extracts protocol features and generates corresponding device drivers. The parameter adjustment unit dynamically adjusts various parameters of the driver program based on device characteristics.

[0009] Preferably, the quantum optimization and dynamic configuration module includes: The model building unit constructs a quantum optimization model based on the protocol parsing results. The QUBO model characterizes the optimization problem of the device's resource allocation and protocol characteristics. The quantum sampling unit solves the optimization problem of the QUBO model and outputs the optimized resource allocation. The results feedback unit provides the optimization results to the subsequent monitoring module for dynamic configuration adjustment.

[0010] Preferably, the anomaly monitoring and anti-detection module includes: The sample generation unit generates adversarial examples based on normal data streams to test the robustness of the system. The behavior monitoring unit analyzes real-time data from the device and identifies abnormal situations. The alarm generation unit generates alarm information based on real-time analysis results to notify the system management module.

[0011] Preferably, the system management and interface module includes: The registration unit generates a unique identifier for newly connected devices and adds them to the device management list. The scheduling unit automatically adjusts the device access strategy based on real-time monitoring results. The interface unit provides an open API interface to support data interaction with third-party applications.

[0012] Preferably, the Hamiltonian H of the QUBO model constructed by the quantum optimization and dynamic configuration module is: H = -∑ i< j A ij x i x j +B∑ i x i ; Where H represents the Hamiltonian, represents the energy function of the system, and A ij Let x represent an element of a symmetric matrix. i The i-th binary variable B represents a bias term related to the device state, ∑ i<j ∑ represents the double summation over all i and j. i x i This represents the summation over all i.

[0013] Preferably, the process by which the sample generation unit generates adversarial examples is as follows: in, G(z|θ) represents the generated adversarial example. g ) represents a generator model that accepts a random variable z as input and generator parameters θ. g z represents a random variable in the latent space, ~ indicates that z is generated according to a specific distribution, and N(0,Σ) represents sampling from a multivariate normal distribution with a mean of 0 and a covariance of Σ, where 0 represents the mean vector and Σ represents the covariance matrix.

[0014] Preferably, the process by which the behavior monitoring unit generates an abnormal alarm is as follows: Where, p anomaly K represents the probability of an anomaly, and K represents the sample size. This represents the summation over k from 1 to K, used to calculate the mean of all samples, D(x⊙M). k () represents the discriminant function, which receives input data x and behavioral feature template M. k The element-wise product of , where x represents the input data, represents the current device behavior feature or state to be detected, and M... k This represents the k-th behavioral feature template.

[0015] In summary, the present invention has at least one of the following beneficial technical effects: 1. This invention employs clock deviation calculation and radio frequency feature processing technology to achieve accurate device clock synchronization and identity verification. Compared with existing technologies that rely on static information for device identification, this invention solves the problem of low device access efficiency and improves network response speed.

[0016] 2. This invention achieves efficient parsing of protocol fields and capture of timing features through an innovative scheme of protocol parsing and graph construction. Compared with traditional parsing methods, it solves the problem of insufficient parsing accuracy, significantly improves the system's ability to understand protocols, and better supports device operation.

[0017] 3. This invention adopts a quantum optimization model and dynamic configuration technology to achieve intelligent management of equipment resources. Compared with the static configuration method in the prior art, it solves the problem of insufficient resource utilization and ensures that the equipment can maintain optimal performance when the load changes, thereby significantly improving the adaptability of the system.

[0018] 4. This invention, through its anomaly monitoring and anti-detection module, possesses the capability for real-time monitoring and anomaly alarms. Compared to previous methods that relied solely on simple thresholds, it solves the problem of anomaly identification delay, ensures the security and stability of the equipment, and provides a better protection for the overall system. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a schematic diagram of the device protocol feature extraction module of the present invention; Figure 3 This is a schematic diagram of the protocol syntax and semantic parsing module of the present invention; Figure 4 This is a schematic diagram of the protocol reverse generation module of the present invention; Figure 5 This is a schematic diagram of the quantum optimization and dynamic configuration module of the present invention; Figure 6 This is a schematic diagram of the anomaly monitoring and countermeasure detection module of the present invention; Figure 7 This is a schematic diagram of the system management and interface module of the present invention. Detailed Implementation

[0020] The following is in conjunction with the appendix Figure 1 -Appendix Figure 7 The present invention will be further described in detail below.

[0021] This invention provides a rapid access system for IoT sensing devices, comprising a device protocol feature extraction module, a protocol syntax and semantic parsing module, a protocol reverse generation module, a quantum optimization and dynamic configuration module, an anomaly monitoring and adversarial detection module, and a system management and interface module. The device protocol feature extraction module includes a clock deviation calculation unit, a radio frequency feature processing unit, and an ID generation unit. Specific implementation details are as follows: First, the clock skew calculation unit is responsible for calculating the clock skew based on the comparison between the device clock and the gateway clock.

[0022] The specific calculation formula is as follows: Δt=T device -T gateway ; Where Δt represents the clock skew, T device T represents the system clock of the device. gateway This indicates the current timestamp of the gateway.

[0023] This calculation ensures time synchronization between the device and the gateway, providing a precise time basis for subsequent data processing. Then, the RF signature processing unit acquires RF signatures by analyzing the differences between the RF signals emitted by the device and the reference signal.

[0024] The process includes the following steps: Signal acquisition: Capturing signals emitted by the device using a radio frequency antenna.

[0025] Signal enhancement: Utilizes signal processing algorithms to increase the strength of the effective signal and suppress noise interference.

[0026] Feature extraction: The difference between the radio frequency signal emitted by the computing device and the preset reference signal. The formula is: RF feature =Signal device -Signal reference ; Among them, RF feature Indicates radio frequency characteristics, Signal device This refers to the radio frequency signals emitted by the device, and the real-time data captured. reference This indicates a preset reference signal.

[0027] The above process can effectively identify the unique radio frequency characteristics of a device for identity verification.

[0028] Next, the ID generation unit generates a unique identifier based on the calculated clock offset and RF characteristics. The generation formula is as follows: ID = F(Δt, RF) feature ); Where ID represents the generated unique identifier, F(Δt,RF) feature ) represents the function that generates the identifier, and Δt represents the clock skew RF. feature Indicates radio frequency characteristics.

[0029] The function F can be used in the following form: F(Δt,RF feature = Hash(Δt + RF) feature ); Where F represents the function that generates the identifier, Δt represents the clock offset between the device and the gateway, and RF feature This indicates the radio frequency characteristics of the device, and Hash represents a hash function used to convert input values ​​into unique identifiers.

[0030] This process ensures that each device ID is unique, avoids conflicts, and effectively tracks device status.

[0031] The protocol syntax and semantic parsing module of this invention includes a graph construction unit, an LSTM unit, and an entropy calculation unit, and the specific implementation is as follows: The graph building unit represents the protocol field as a graph structure, where the protocol field is represented as a node, and the relationship between nodes is represented as an edge.

[0032] The specific implementation steps are as follows: Collect protocol field information and construct a node set V. Each node v i This represents a protocol field, for example: V = {v1, v2, ..., v...} n}; Where V represents the set of nodes, v n This represents the nth variable.

[0033] Define the relationships between nodes, and construct an edge set E by analyzing protocol rules to represent the mutual dependencies between fields.

[0034] The definition of an edge is: E = {(v i ,v j ) | Field v i With field v j Relationship exists. Where E represents the set of relationships between nodes, (v i ,v j ) indicates field v i and field vj The edge.

[0035] Finally, the graph structure can be expressed mathematically as follows: G = (V, E); Where G represents the protocol graph structure, V represents the set of nodes for the protocol fields, and E represents the relationship between the fields.

[0036] The LSTM unit captures temporal features through the protocol syntax graph constructed from the input. In this section, a Long Short-Term Memory (LSTM) network is used to process the output of the protocol graph to extract temporal information.

[0037] The basic formula for LSTM is as follows: h t =f(Uh t-1 +Wx t +b); Among them, h t Let f represent the hidden state vector at the current time step, f represent the activation function, U represent the hidden layer weight matrix, W represent the input weight matrix, and x represent the hidden state vector. t Let b represent the input vector at the current time step, and b represent the bias term.

[0038] The LSTM unit can effectively capture the temporal dependencies between protocol fields, further enhancing the accuracy of protocol parsing. The entropy calculation unit calculates the entropy values ​​between protocol fields to define the weights of edges and determine the interactions between fields.

[0039] The formula for calculating entropy is: Where H(X) represents the entropy of the random variable X, p(x) i ) represents node x i The probability distribution is given by , where n represents the total number of nodes, i.e., the number of protocol fields.

[0040] The entropy calculation unit determines the interaction strength between protocol fields by calculating entropy values, and assigns weights to edges in the graph accordingly. High entropy values ​​indicate strong interdependence between fields, and the edge weights are increased accordingly, which helps to enhance the learning effect of subsequent models.

[0041] The protocol reverse engineering module of this invention includes a protocol description generation unit, a driver generation unit, and a parameter adjustment unit, and the specific implementation is as follows: The protocol description generation unit is responsible for generating a description file that can be used for device operation based on the parsed protocol features. The specific steps are as follows: Input protocol characteristics: These characteristics include the parsed field types, field lengths, default values, and hierarchical relationships between fields. These characteristics form the basis of the protocol description.

[0042] Generating the description file: This involves using template filling or serialization methods to convert protocol features into device-recognizable operation commands. The generated description file can be expressed by the formula: D = G(P) features ,T); Where D represents the generated protocol description file, G represents the description generation function, and P features This represents the set of protocol features for the input, including fields, field types, and relationships. T represents a template or format.

[0043] This description file can provide specific operating instructions for the device, such as command format and parameter settings, thereby improving the standardization of device operation.

[0044] The driver generation unit is responsible for extracting protocol features and generating the corresponding device driver. The implementation steps include: Extract protocol commands: Identify the protocol commands required for device operation, including the selection of communication interfaces and data transmission formats.

[0045] Driver generation: Using a predefined driver template, a driver suitable for the target device is built based on the extracted instructions. This generation process can be represented by the following formula: DR=H(P commands ,C); Where DR represents the generated device driver, H represents the driver generation function, and P represents the driver generation function. commands This represents the extracted set of protocol commands, and C represents the driver code template, which standardizes the structure of the driver program.

[0046] This driver enables effective communication with the device, ensuring that the device correctly processes instructions according to the predetermined protocol.

[0047] The parameter adjustment unit dynamically adjusts various parameters of the driver program based on device characteristics to optimize device performance.

[0048] The implementation steps include: Receive device status information: Obtain real-time device characteristic parameters, such as processor load (CPU). load ), memory utilization usage Network latency, etc.

[0049] Calculate and adjust driver parameters: Based on device characteristics, optimize key parameters in the driver by adjusting algorithms: P adjusted =J(P current ,S); Among them, P adjusted This represents the adjusted driving parameters, where J represents the parameter tuning function, and P represents the adjusted driving parameters. currentThis represents the current driving parameters, and S represents the current device status information set, reflecting system performance.

[0050] This unit can maximize drive performance based on the real-time status of the device, improving its response speed and accuracy.

[0051] The quantum optimization and dynamic configuration module of this invention includes a model building unit, a quantum sampling unit, and a result feedback unit, and the specific implementation is as follows: The model building unit constructs a quantum optimization model based on the protocol parsing results. The specific implementation steps are as follows: Collect protocol parsing results: Analyze the device resource characteristics (such as computing power, memory, network bandwidth, etc.) and required parameters during the protocol parsing process to form the basic data for resource configuration. Set parameters: R = {r1, r2, ..., r} n} represents the device resource set, which contains all available resources of the device.

[0052] D = {d1, d2, ..., d} m} represents the set of equipment requirement parameters, reflecting the resource requirements stipulated in the protocol.

[0053] Constructing a QUBO model: Resource allocation and protocol characteristics are represented as a quantum quadratic optimization problem. The Hamiltonian H of the model can be expressed as: H = -∑ i<j A ij x i x j +B∑ i x i ; Where H represents the energy of the optimization problem, and A ij Indicates the cooperative or competitive relationship between equipment resources, x i Indicates whether to allocate resources (r) i For a specific task, B represents the cost of a single resource allocation.

[0054] The constructed QUBO model lays the foundation for the implementation of subsequent optimization algorithms by providing a quantumized description of the resource allocation problem.

[0055] The quantum sampling unit is responsible for solving the optimization problem of the QUBO model described above and outputting the optimized resource allocation. The implementation steps include: Quantum solver input: The constructed QUBO model is input into the quantum computing platform for optimization and solution.

[0056] Execute quantum optimization algorithms: Use quantum optimization algorithms such as variable quantum feature solver (VQE) or quantum annealing (QA) to solve the QUBO model.

[0057] Output optimization results: Obtain the optimal resource allocation combination, and output it in the following format: x opt =argmin(H); Where, x opt H represents the optimal allocation state of each resource, and H represents the Hamiltonian.

[0058] This step ensures optimal allocation of equipment resources, which can significantly improve system performance and response speed.

[0059] The results feedback unit is responsible for providing the optimization results to the subsequent monitoring module for dynamic configuration adjustment. The specific steps are as follows: Receive optimization results: Obtain the optimal resource configuration output by the quantum sampling unit.

[0060] Dynamic configuration adjustment: Apply the optimization results to the device and monitor the configuration effect in real time to ensure that the device performance meets the expected goals.

[0061] The dynamic adjustment process can be expressed by the formula: R dynamic =K(x) opt ,S); Among them, R dynamic This represents the dynamically adjusted configuration parameters, K represents the dynamic adjustment function, S represents the currently monitored status data, and x represents the dynamically adjusted configuration parameters. opt This indicates the optimal allocation state of resources.

[0062] Dynamic configuration adjusts resource allocation based on real-time status to ensure optimal device performance under different load conditions.

[0063] The anomaly monitoring and adversarial detection module of this invention includes a sample generation unit, a behavior monitoring unit, and an alarm generation unit, and the specific implementation is as follows: The sample generation unit generates adversarial examples based on normal data streams to test the robustness of the system. The specific implementation steps are as follows: Normal data stream monitoring: The normal data flow of the real-time monitoring system is N = {n1, n2, ..., n} k}; Where N represents the set of all nodes in the system, used to describe protocol fields and device characteristics, n k This represents the k-th sample or data point.

[0064] Adversarial examples are generated using adversarial generative algorithms (such as Generative Adversarial Networks, GANs). The generation of can be expressed by the following formula: in, G(z|θ) represents the generated adversarial example. g ) represents a generator model that accepts a random variable z as input and generator parameters θ. g z represents a random variable in the latent space, ~ indicates that z is generated according to a specific distribution, and N(0,Σ) represents sampling from a multivariate normal distribution with a mean of 0 and a covariance of Σ, where 0 represents the mean vector and Σ represents the covariance matrix.

[0065] The generated adversarial examples can effectively test the system's ability to detect anomalies, ensuring that the system has a certain degree of robustness.

[0066] The behavior monitoring unit analyzes real-time data from the device and identifies abnormal situations. The specific steps include: Real-time data acquisition: Real-time reception of data streams D = {d1, d2, ..., d...} provided by various sensors and components. m}, where each d m This refers to the status data of a device at a certain moment, which may include information such as CPU utilization, memory usage, and network bandwidth.

[0067] Anomaly detection algorithm: Calculate the anomaly probability for each data point using the following formula: Where, p anomaly K represents the probability of an anomaly, and K represents the sample size. This represents the summation over k from 1 to K, used to calculate the mean of all samples, D(x⊙M). k () represents the discriminant function, which receives input data x and behavioral feature template M. k The element-wise product of , where x represents the input data, represents the current device behavior feature or state to be detected, and M... k This represents the k-th behavioral feature template.

[0068] This algorithm allows the system to assess the normality of equipment status in real time, thereby promptly identifying potential problems.

[0069] The alarm generation unit is responsible for generating alarm information based on real-time analysis results to notify the system management module. The specific steps are as follows: Receive anomaly detection results: Obtain real-time analysis results from the behavior monitoring unit, including p for each data point. anomaly value.

[0070] Alarm message generation: If an anomaly is detected, a corresponding alarm message is generated based on the real-time anomaly probability.

[0071] The generation process can be represented by the following decision rules: Where L represents the generated alarm information, T high A "high-risk alert" is triggered when the degree of abnormality exceeds this value. low This value indicates that a level of abnormality below this threshold is considered "normal".

[0072] Finally, the generated alarm information is output to the system management module to facilitate timely action.

[0073] The system management and interface module of this invention includes a registration unit, a scheduling unit, and an interface unit, and the specific implementation is as follows: The registration unit is responsible for registering newly connected devices. The specific implementation steps are as follows: Generate a unique identifier: Generate a unique identifier ID for each newly connected device. This identifier is generated based on device information (such as MAC address and serial number) and can use a hash function H to ensure uniqueness, represented as: ID = H(D info ); Where ID represents the device's unique identifier, H represents the hash function, and D... info This indicates relevant information about the device.

[0074] Add to Device Management List: Add the generated unique identifier and its related device information (D) to the device management list (L). devices , The update process is represented as follows: L devices =L devices ∪{(ID,D)}; Among them, L devices This represents the device management list, and D represents other attributes of the device.

[0075] This unit ensures that new equipment can be easily stored, facilitating subsequent management and providing rapid equipment identification capabilities.

[0076] The scheduling unit automatically adjusts the device access strategy based on real-time monitoring results. The specific implementation steps are as follows: Real-time monitoring data reception: Receives real-time device status data S = {s1, s2, ..., s...} from the monitoring module. m}, where each s j Indicators representing the operating status of a device, such as CPU utilization, memory usage, and network latency.

[0077] Decision Algorithm: Based on the current monitoring status, the access policy is automatically adjusted using a policy update algorithm. The update of the access policy can be expressed by the following formula: P′=f(S,P); Where P′ represents the updated access policy, S represents the real-time monitoring status dataset, P represents the current access policy, defines the device's access rules and priorities, and f represents the policy update function.

[0078] Decision-making algorithms can employ simple rule engines or complex machine learning models to achieve optimal policy adjustments.

[0079] The interface unit provides an open API to support data interaction with third-party applications. The specific implementation steps are as follows: Define API interfaces: Establish standardized data interaction interfaces to ensure that external applications can access the system via HTTP or other protocols. API functions can include device registration, status queries, policy updates, etc., as shown in the example below: API = {POST→ / register,GET→ / status,PUT→ / strategy}; Among them, API stands for Application Programming Interface, POST stands for the interface used for new device registration, GET stands for the interface used for querying device status, and PUT stands for the interface used for updating access policies.

[0080] These API interfaces allow external programs to easily exchange data with the system.

[0081] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A system for rapid access to IoT sensing devices, characterized in that, include: The device protocol feature extraction module is used to obtain the physical characteristics of IoT devices and generate unique identifiers; The protocol syntax and semantic parsing module receives unique identifiers and physical features, constructs a protocol syntax graph, and extracts the semantic context of the protocol. The protocol reverse engineering module receives the protocol syntax diagram and semantic context, and converts the parsed protocol features into a device driver. The quantum optimization and dynamic configuration module receives the device driver, optimizes the device resource configuration during the protocol parsing process according to the optimization algorithm, and outputs the optimization results. The anomaly monitoring and countermeasure detection module receives optimization results, monitors the operating status of the equipment in real time and detects potential anomalies, and outputs the monitoring data. The system management and interface module receives monitoring data and adjusts the device's access policies and configurations.

2. The IoT sensing device rapid access system according to claim 1, characterized in that, The device protocol feature extraction module includes: The clock skew calculation unit calculates the clock skew based on a comparison between the device clock and the gateway clock; The radio frequency (RF) feature processing unit obtains RF features by analyzing the difference between the RF signal emitted by the device and the reference signal; The ID generation unit generates a unique identifier based on clock skew and radio frequency characteristics.

3. The rapid access system for IoT sensing devices according to claim 1, characterized in that, The protocol syntax and semantic parsing module includes: The graph construction unit represents protocol fields as nodes and relationships between nodes as edges; The LSTM unit captures timing features by receiving the output of the constructed protocol syntax graph; The entropy calculation unit defines the weight of edges by calculating entropy values ​​in order to determine the interactions between fields.

4. The rapid access system for IoT sensing devices according to claim 1, characterized in that, The protocol reverse generation module includes: The protocol description generation unit generates a description file that can be used for device operation based on the parsed protocol features; The driver generation unit extracts protocol features and generates corresponding device drivers. The parameter adjustment unit dynamically adjusts various parameters of the driver program based on device characteristics.

5. The rapid access system for IoT sensing devices according to claim 1, characterized in that, The quantum optimization and dynamic configuration module includes: The model building unit constructs a quantum optimization model based on the protocol parsing results. The QUBO model characterizes the optimization problem of the device's resource allocation and protocol characteristics. The quantum sampling unit solves the optimization problem of the QUBO model and outputs the optimized resource allocation. The results feedback unit provides the optimization results to the subsequent monitoring module for dynamic configuration adjustment.

6. The rapid access system for IoT sensing devices according to claim 1, characterized in that, The anomaly monitoring and countermeasure detection module includes: The sample generation unit generates adversarial examples based on normal data streams to test the robustness of the system. The behavior monitoring unit analyzes real-time data from the device and identifies abnormal situations. The alarm generation unit generates alarm information based on real-time analysis results to notify the system management module.

7. The IoT sensing device rapid access system according to claim 1, characterized in that, The system management and interface module includes: The registration unit generates a unique identifier for newly connected devices and adds them to the device management list. The scheduling unit automatically adjusts the device access strategy based on real-time monitoring results. The interface unit provides an open API interface to support data interaction with third-party applications.

8. The rapid access system for IoT sensing devices according to claim 5, characterized in that, The Hamiltonian H of the QUBO model constructed by the quantum optimization and dynamic configuration module is: H=-∑ i<j A ij x i x j +B∑ i x i ; Where H represents the Hamiltonian, represents the energy function of the system, and A ij Let x represent an element of a symmetric matrix. i The i-th binary variable B represents a bias term related to the device state, ∑ i<j ∑ represents the double summation over all i and j. i x i This represents the summation over all i.

9. A rapid access system for IoT sensing devices according to claim 6, characterized in that, The process by which the sample generation unit generates adversarial examples is as follows: in, G(z|θ) represents the generated adversarial example. g ) represents a generator model that accepts a random variable z as input and generator parameters θ. g z represents a random variable in the latent space, ~ indicates that z is generated according to a specific distribution, and N(0,Σ) represents sampling from a multivariate normal distribution with a mean of 0 and a covariance of Σ, where 0 represents the mean vector and Σ represents the covariance matrix.

10. A rapid access system for IoT sensing devices according to claim 6, characterized in that, The process by which the behavior monitoring unit generates an abnormal alarm is as follows: Where, p anomaly K represents the probability of an anomaly, and K represents the sample size. This represents the summation over k from 1 to K, used to calculate the mean of all samples, D(x⊙M). k () represents the discriminant function, which receives input data x and behavioral feature template M. k The element-wise product of , where x represents the input data, represents the current device behavior feature or state to be detected, and M... k This represents the k-th behavioral feature template.

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