Power distribution iot terminal management method and system based on quantum cloud code and electronic equipment

Abstract

The application relates to the technical field of electric power data transmission, and discloses a power distribution Internet of Things terminal management method and system based on quantum cloud codes and electronic equipment. The method comprises the following steps: obtaining a quantum cloud code seed by confusing original data through a random number generator, obtaining a quantum cloud code sequence through fragmentation encryption, forming a terminal device grouping key according to a preset rule, generating a device identity characteristic code in combination with running state information, establishing a data transmission channel according to the characteristic code, forming a secure communication link through session key encryption and verification, collecting running data in the communication link, obtaining a standardized data set through cleaning and normalization, mapping and outputting device behavior characteristics, dynamically adjusting operation permissions to build a management system, performing regular snapshots and distributed backup, forming disaster recovery backup data to establish a recovery strategy. The application realizes the safe authentication, dynamic permission allocation and intelligent management of terminal devices, and significantly improves the safety and management efficiency of the system.

Description

Technical Field

[0001] The present application relates to the field of power data transmission, and in particular to a method, system and electronic equipment for managing power distribution IoT terminals based on quantum cloud code. Background Art

[0002] With the rapid development of smart grid technology, the number of IoT (Internet of Things) terminal devices in distribution automation systems has exploded. These devices undertake critical tasks such as data collection, equipment monitoring, and fault diagnosis, playing a vital role in the safe and stable operation of power systems. Existing distribution IoT terminal management primarily relies on traditional key management and access control methods, using fixed keys for identity authentication and static permission allocation mechanisms to manage terminal device access rights. Regular backups are also used to ensure system data security.

[0003] However, existing technologies have numerous shortcomings. First, traditional key management methods lack security and are vulnerable to various network attacks. Second, static permission allocation mechanisms cannot dynamically adjust based on the operating status of terminal devices, which can easily lead to problems with over- or under-allocation of permissions. Third, traditional backup methods often use centralized storage, which poses a single point of failure risk and low recovery efficiency. Furthermore, existing terminal management methods lack in-depth analysis and evaluation of device behavior, making it difficult to promptly detect and address abnormal behavior, posing a potential threat to system security. Summary of the Invention

[0004] This application provides a method, system, and electronic device for managing power distribution IoT terminals based on quantum cloud code. These methods address the technical issues of poor system security and low management efficiency in existing power distribution IoT terminal management, which arise from the lack of a dynamic security authentication mechanism and intelligent permission management system. By introducing quantum cloud code technology, combined with quantum encryption and dynamic permission management, secure authentication, dynamic permission allocation, and intelligent management of terminal devices are achieved, significantly improving system security and management efficiency.

[0005] In a first aspect, the application provides a power distribution Internet of Things terminal management method based on quantum cloud code, which comprises: performing confusion processing on original data by a random number generator to obtain a quantum cloud code seed, performing sharding encryption on the quantum cloud code seed to obtain a quantum cloud code sequence, performing grouping matching on the quantum cloud code sequence according to a preset rule to form a terminal device grouping key; extracting hardware identification data of a terminal device based on the terminal device grouping key, combining terminal running state information to perform feature synthesis on the hardware identification data to generate a device identity feature code; establishing a data transmission channel according to the device identity feature code, performing encryption processing on the data transmission channel by a session key to establish an encrypted data channel, performing integrity checking on the encrypted data channel to form a secure communication link; collecting terminal running data in the secure communication link, performing data cleaning and normalization processing on the terminal running data to obtain a standardized data set, performing feature mapping according to the standardized data set to output device behavior features; performing hierarchical evaluation on terminal access permissions according to the device behavior features, dynamically adjusting terminal operation permissions through the hierarchical evaluation results to construct a permission management system; periodically snapshotting system running states according to the permission management system, performing distributed backup on system snapshot data to form disaster recovery backup data, and establishing a system recovery strategy according to the disaster recovery backup data.

[0006] In a second aspect, the application provides a power distribution Internet of Things terminal management system based on quantum cloud code, which comprises:

[0007] An encryption module is configured to perform confusion processing on original data by a random number generator to obtain a quantum cloud code seed, perform sharding encryption on the quantum cloud code seed to obtain a quantum cloud code sequence, and perform grouping matching on the quantum cloud code sequence according to a preset rule to form a terminal device grouping key.

[0008] An extraction module is configured to extract hardware identification data of a terminal device based on the terminal device grouping key, combine terminal running state information to perform feature synthesis on the hardware identification data, and generate a device identity feature code.

[0009] An establishment module is configured to establish a data transmission channel according to the device identity feature code, perform encryption processing on the data transmission channel by a session key to establish an encrypted data channel, perform integrity checking on the encrypted data channel to form a secure communication link.

[0010] The cleaning module is used for collecting terminal running data in the secure communication link, performing data cleaning and normalization processing on the terminal running data, obtaining a standardized data set, performing feature mapping according to the standardized data set, and outputting device behavior features;

[0011] The grading module is used for grading and evaluating terminal access permissions according to the device behavior features, dynamically adjusting terminal operation permissions through the grading and evaluation results, and constructing a permission management system.

[0012] The snapshot module is used for periodically snapshotting system running states according to the permission management system, performing distributed backup on system snapshot data, forming disaster recovery backup data, and establishing a system recovery strategy according to the disaster recovery backup data.

[0013] The third aspect of the application provides an electronic device, the memory stores machine readable instructions executable by the processor, when the electronic device runs, the processor and the memory communicate through the bus, and the machine readable instructions are executed by the processor to perform the steps of the power distribution Internet of Things terminal management method based on quantum cloud code.

[0014] In the technical scheme provided by the application, the original data is processed by the random number generator to achieve high randomness and unpredictability of the data, effectively preventing data from being stolen and tampered with. The quantum cloud code seed is processed by using the fragmentation encryption technology, which improves the security and reliability of the data. The terminal device grouping key is generated and distributed to realize accurate identification and classification management of the terminal device, and the security control ability of the system is enhanced. The hardware identification data is extracted based on the terminal device grouping key, and the device identity feature code is generated by combining the terminal running state information, which ensures the uniqueness and verifiability of the device identity. The data transmission channel is encrypted by using the session key and subjected to integrity check to form a secure communication link, which ensures the security and integrity of data transmission. The terminal running data in the secure communication link is collected and processed, and the standardized data set is obtained by data cleaning and normalization processing, which improves the quality and usability of the data. The device behavior features are accurately output by performing feature mapping according to the standardized data set, which provides a reliable basis for subsequent permission management. The terminal access permissions are graded and evaluated according to the device behavior features, the dynamic adjustment of the permissions is realized, and a flexible and effective permission management system is constructed. Through the periodic snapshotting and distributed backup mechanism, perfect disaster recovery backup data is formed to ensure the recoverability of the system data. The system recovery strategy is established according to the disaster recovery backup data to improve the reliability and continuous operation ability of the system. The overall scheme realizes the secure access, reliable communication, dynamic management and effective recovery of the power distribution Internet of Things terminal, and significantly improves the security, reliability and management efficiency of the power distribution Internet of Things network. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings.

[0016] Figure 1 An embodiment of the power distribution Internet of Things terminal management method based on quantum cloud code in the present application;

[0017] Figure 2 An embodiment of the power distribution Internet of Things terminal management system based on quantum cloud code in the present application;

[0018] Figure 3 The structure diagram of the electronic device in the embodiment of the present application. DETAILED DESCRIPTION

[0019] The embodiment of the present application provides a power distribution Internet of Things terminal management method, system and electronic device based on quantum cloud code. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "comprising" or "having" and any variation thereof is intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0020] For the sake of understanding, the specific process of the embodiment of the present application will be described below. Please refer to Figure 1 An embodiment of the power distribution Internet of Things terminal management method based on quantum cloud code in the present application includes:

[0021] Step S101, the original data is processed by a random number generator, a quantum cloud code seed is obtained, the quantum cloud code seed is encrypted by fragmentation, a quantum cloud code sequence is obtained, the quantum cloud code sequence is matched according to a preset rule, and a terminal device grouping key is formed;

[0022] Step S102, the hardware identification data of the terminal device is extracted based on the terminal device grouping key, and the hardware identification data is synthesized in combination with the terminal running state information to generate a device identity feature code.

[0023] Step S103: Establish a data transmission channel based on the device identity code, encrypt the data transmission channel using the session key, establish an encrypted data channel, perform integrity check on the encrypted data channel, and form a secure communication link;

[0024] Step S104: collecting terminal operation data in the secure communication link, performing data cleaning and normalization on the terminal operation data to obtain a standardized data set, performing feature mapping based on the standardized data set, and outputting device behavior features;

[0025] Step S105: Perform a hierarchical assessment of terminal access rights based on device behavior characteristics, dynamically adjust terminal operation rights based on the hierarchical assessment results, and build a rights management system;

[0026] Step S106: Take regular snapshots of the system operation status according to the authority management system, perform distributed backup of the system snapshot data to form disaster recovery backup data, and establish a system recovery strategy based on the disaster recovery backup data.

[0027] It is understandable that the execution subject of this application can be a power distribution IoT terminal management system based on quantum cloud code, or a terminal or server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0028] Specifically, the raw data is obfuscated using a random number generator. This obfuscation process employs a nonlinear transformation algorithm to convert the input raw data into a random sequence. In the power distribution IoT network, raw data contains information such as terminal device operating parameters, communication status, and resource usage. During the data obfuscation process, each value in the raw data is randomly permuted and nonlinearly transformed to generate a quantum cloud code seed. This quantum cloud code seed is a highly random and unpredictable key sequence used for subsequent secure authentication and encrypted communications. The generated quantum cloud code seed is then encrypted using a dynamic sharding strategy, dividing the quantum cloud code seed into multiple sub-segments based on data structure characteristics. Each sub-segment has a different weight and security level, and each sub-segment is independently encrypted using a symmetric encryption algorithm to ensure data confidentiality. The encrypted sub-segments are combined to form a quantum cloud code sequence, which contains complete terminal device authentication information and security policies. When grouping and matching quantum cloud code sequences, terminal devices are classified based on their functional attributes, security level, access rights, and other characteristics, and corresponding key groups are assigned to different types of terminal devices. Group matching utilizes a multi-dimensional evaluation mechanism, comprehensively considering factors such as the device's computing power, communication requirements, and security requirements, ultimately generating a terminal device group key. This terminal device group key is used for subsequent identity authentication and data encryption. Based on the obtained terminal device group key, the terminal device's hardware identification data is extracted. This hardware identification data includes the device's unique identification code, hardware configuration information, and operating environment parameters. Hardware identification data extraction utilizes a layered scanning strategy, collecting device features from the hardware layer to the application layer. Simultaneously, combined with real-time monitoring data such as terminal operational status, including CPU utilization, memory usage, and network traffic, hardware identification data is synthesized to generate a signature code that uniquely identifies the device. The generated device identity signature code serves as the basis for establishing secure communication. Through signature verification and key negotiation, a data transmission channel is established. This data transmission channel utilizes a multi-layer encryption mechanism, using session keys for real-time encryption of transmitted data. Session keys are dynamically generated through a key exchange protocol to ensure the security of the communication process. After establishing the encrypted data channel, packet checksumming and integrity verification are performed to establish a stable and reliable secure communication link. Terminal operational data transmitted over the secure communication link is collected, including device operational status, performance indicators, and abnormal events. The collected raw data may contain noise, redundancy, outliers, and other issues, necessitating data cleaning. The data cleaning process includes steps such as outlier detection, data completion, and redundancy removal. The cleaned data is then normalized to bring data of different dimensions into the same scale, forming a standardized dataset.

[0029] Conduct a hierarchical assessment of terminal access rights based on device behavioral characteristics. The assessment process considers multiple dimensions, including the device's access history, resource usage, and abnormal behavior. By establishing an assessment indicator system, conduct a quantitative analysis of device access behavior and generate hierarchical assessment results. Dynamically adjust terminal operating permissions based on the assessment results, and build a permissions management system with multiple security levels. In accordance with the established permissions management system, regularly take snapshots of the system's operating status, recording key data such as system configuration information, operating parameters, and permissions allocation. System snapshot data uses a distributed storage strategy, distributing data across multiple backup nodes to improve data reliability and availability. The resulting disaster recovery backup data is used for system failure recovery and status backtracking, and a system recovery strategy, including recovery timing and priority strategies, is established accordingly.

[0030] For example, when a power distribution terminal device is connected to the system, it first receives a set of raw data, including the device model XM-2024, MAC address, and CPU serial number CPU-8A76. This data is converted into a 128-bit quantum cloud code seed through random number obfuscation. This seed is then encrypted into eight 16-bit pieces, each piece forming a quantum cloud code sequence. The terminal device group key KEY-M789 is generated based on the device's security level (Level-3) and function type (monitoring). This key extracts hardware identification data and combines it with operational status information such as the current CPU utilization rate of 75%, memory utilization of 60%, and network bandwidth utilization of 45% to generate a device identity signature. The system then establishes an encrypted communication link. After cleaning and normalizing the collected terminal data, the system detects three unauthorized access attempts against the device in the past 24 hours. The system automatically downgrades its permission level by one level and takes hourly status snapshots, which are stored on three distributed storage nodes for disaster recovery.

[0031] In the embodiments of the present application, a random number generator is used to obfuscate the original data, achieving a high degree of randomness and unpredictability, effectively preventing data theft and tampering. Sharded encryption technology is used to process quantum cloud code seeds, improving data security and reliability. The generation and distribution of terminal device group keys enables accurate identification and classification management of terminal devices, enhancing the system's security control capabilities. Hardware identification data is extracted based on the terminal device group key, and a device identity feature code is generated in combination with terminal operating status information, ensuring the uniqueness and verifiability of the device identity. The established data transmission channel is encrypted using a session key and integrity checked simultaneously to form a secure communication link, ensuring the security and integrity of data transmission. Terminal operating data in the secure communication link is collected and processed, and a standardized data set is obtained through data cleaning and normalization, improving data quality and availability. Feature mapping is performed based on the standardized data set to accurately output device behavior characteristics, providing a reliable basis for subsequent permission management. Terminal access rights are graded and assessed based on device behavior characteristics, enabling dynamic adjustment of permissions and building a flexible and effective permission management system. Through regular snapshots and a distributed backup mechanism, a comprehensive disaster recovery backup database is established, ensuring the recoverability of system data. System recovery strategies are established based on this data, improving system reliability and continuous operation. The overall solution enables secure access, reliable communication, dynamic management, and effective recovery of distribution IoT terminals, significantly enhancing the security, reliability, and management efficiency of the distribution IoT network.

[0032] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0033] (1) The random number generator is used to map the operating parameters of the distribution terminal to random numbers to generate the original confusion matrix, and the random perturbation vector is constructed based on the original confusion matrix;

[0034] (2) Perform nonlinear transformation on the original data according to the random perturbation vector to obtain the initial obfuscated data stream, and reorganize the initial obfuscated data stream through piecewise linear transformation to form a quantum cloud code seed;

[0035] (3) Establish a data shard index table based on the quantum cloud code seed, and split the quantum cloud code seed through a dynamic sharding algorithm to obtain a quantum cloud code shard set;

[0036] (4) Encrypt and convert the quantum cloud code fragment set according to the symmetric encryption algorithm, output the encrypted fragment sequence, sort and combine the encrypted fragment sequence through the data reconstruction algorithm, and generate the quantum cloud code sequence;

[0037] (5) Grouping calculation is performed on the quantum cloud code sequence by hierarchical clustering to obtain a grouping feature vector, and a terminal grouping mapping table is constructed according to the grouping feature vector;

[0038] (6) Key distribution is performed on the terminal device according to the terminal grouping mapping table, and each group key parameter is calculated through a key derivation function to form a terminal device grouping key.

[0039] Specifically, the operating parameters of the power distribution terminal are subjected to random number mapping processing. The operating parameters include core operating indexes such as voltage value, current value, power factor, and harmonic content of the device. These deterministic parameter values are mapped into a random space by a random number generator to generate an original confusion matrix. The original confusion matrix is a multi-dimensional data structure, each dimension corresponding to a type of operating parameter, and the element values in the matrix are obtained by random mapping of the original parameter values. Based on the original confusion matrix, a random disturbance vector is constructed, the dimension of the disturbance vector matches the dimension of the original confusion matrix, and is used for further randomization processing of the data.

[0040] For the hierarchical clustering processing process, the following mathematical model is used:

[0041]

[0042] wherein: represents the weighted distance between the i-th and j-th samples in the quantum cloud code sequence, represents the weight coefficient of the k-th feature, represents the k-th feature value of the i-th sample, represents the k-th feature value of the j-th sample, and n represents the total number of feature dimensions.

[0043] The random disturbance vector is applied to the original data for nonlinear transformation processing. The nonlinear transformation adopts a multi-level data conversion mechanism, each level performs different degrees of disturbance on the data, and finally obtains an initial confusion data stream. This data stream needs to be subjected to segmented linear transformation for data reorganization, and the confused data is rearranged and combined to form a quantum cloud code seed with a specific structure. The quantum cloud code seed is the basis for subsequent security mechanisms, and its structure needs to meet certain randomness and unpredictability requirements. A data fragmentation index table is established based on the generated quantum cloud code seed, which records the position information and association relationship of the data fragments. The quantum cloud code seed is divided by a dynamic fragmentation algorithm, which dynamically adjusts the fragmentation size and the number of fragments according to the importance and security level of the data, and finally obtains a quantum cloud code fragment set. Each fragment contains part of the key information, and a single fragment cannot restore the complete data content.

[0044] A symmetric encryption algorithm is used to perform encryption conversion on a set of quantum cloud code shards. The encryption process uses a high-strength encryption key to independently encrypt each shard and output an encrypted shard sequence. The encrypted shards need to be sorted and combined using a data reconstruction algorithm. The data reconstruction algorithm will reassemble the scattered encrypted shards into a complete quantum cloud code sequence based on the association relationship and version information between the shards. The quantum cloud code sequence is grouped and calculated using hierarchical clustering. By calculating the similarity and association between the shards, shards with similar characteristics are grouped together to obtain a grouping feature vector. The grouping feature vector reflects the differences and internal consistency between different shard groups. Based on these feature vectors, a terminal group mapping table is constructed, which defines the correspondence between different types of terminal devices and shard groups.

[0045] Keys are assigned to each terminal device based on the terminal group mapping table, and a key derivation function is used to calculate and generate dedicated key parameters for each group of devices. The key derivation process takes into account the device's security level, functional requirements, and resource constraints, ultimately forming a complete terminal device group key system.

[0046] For example, a smart power distribution terminal monitors real-time operating parameters including voltage 220V, current 5A, power factor 0.95, and harmonic content 2.5%. These parameters are converted into a 48-bit raw confusion matrix using random number mapping, with each 12 bits corresponding to a parameter value. These values ​​are modulated by a generated random perturbation vector, resulting in a 192-bit initial obfuscated data stream through three layers of nonlinear transformation. This data is then reconstructed through eight linear transformations to form a 256-bit quantum cloud code seed. This seed is then divided into 16 slices of 16 bits each using a dynamic sharding algorithm to generate a set of quantum cloud code slices. These 16 slices are encrypted using the AES-256 algorithm and reconstructed to form a 512-bit quantum cloud code sequence. This sequence is then classified into a Class B terminal group based on security level and functional attributes through hierarchical clustering, and the corresponding group key parameter, QKEY-B396, is obtained. The entire process strictly adheres to data security requirements, ensuring secure access and reliable operation of the power distribution terminal.

[0047] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0048] (1) Perform identification analysis on the terminal device through the terminal device group key to obtain the hardware data stream, classify the data according to the hardware data stream, and form hardware identification data;

[0049] (2) Collect and count the resource occupancy indicators, communication load parameters, and power consumption values ​​of the terminal equipment to obtain the operating parameter set, and integrate the operating parameter set in time through time synchronization to generate terminal operating status information;

[0050] (3) Extract features from hardware identification data according to data dimensionality reduction to obtain a hardware feature set. Remove redundant data from the hardware feature set based on data filtering to form a hardware feature vector.

[0051] (4) Combine data based on the terminal operation status information and hardware feature vectors to obtain a multidimensional feature table, extract key values ​​from the multidimensional feature table through feature screening, and generate a core feature matrix;

[0052] (5) Perform nonlinear mapping on the core feature matrix based on feature transformation to obtain a feature space sequence, sort the feature space sequence by importance according to the variable correlation degree, and form a feature identification set;

[0053] (6) The feature identification set is encrypted and transformed through the digital signature to obtain the identity identification sequence, and the identity identification sequence is encrypted according to the encryption key to form the device identity feature code.

[0054] Specifically, the terminal device's identification information is parsed using the terminal device's group key, which serves as the decryption key to decrypt the hardware identification information stored in the device. The hardware data stream obtained after decryption contains raw data such as the device's processor information, memory information, and communication interface information. This data is categorized and organized to form structured hardware identification data based on different hardware component types. During terminal device operation, the device's resource usage indicators are collected in real time, including CPU utilization, memory utilization, and storage space usage. Communication load parameters are also monitored, including network traffic, communication latency, and packet loss rate. Furthermore, power consumption values ​​for the device are collected, including overall device power consumption and the distribution of power consumption by module. This data is statistically analyzed to generate an operating parameter set. Through timestamp alignment and sequence synchronization, the data collected at different time points is integrated into time-series terminal operating status information.

[0055] The hardware identification data is subjected to dimensionality reduction, and principal component analysis is used to extract key features to obtain a hardware feature set. The hardware feature set contains duplicate and redundant information, which is removed through data filtering techniques, retaining key feature information and ultimately forming a streamlined hardware feature vector.

[0056] For the nonlinear mapping process of feature transformation, the following mathematical model is adopted:

[0057]

[0058] in: represents the i-th eigenvalue after mapping, represents the jth eigenvalue in the input feature matrix, represents the mapping scale factor, represents the nonlinear degree adjustment parameter, Represents the weight coefficient matrix between features, represents the bias parameter, Indicates the dimension of the input features.

[0059] The terminal's operating status information is fused with the hardware feature vectors, and the combined data is stored in a multidimensional table structure to form a multidimensional feature table. Feature importance is assessed, and the most representative values ​​for the device's identity are extracted from the multidimensional feature table to construct a core feature matrix. A nonlinear mapping transformation is performed on the core feature matrix, projecting the feature data into a high-dimensional feature space to generate a feature space sequence. Based on the correlation coefficients between features, the variables in the feature space sequence are evaluated and ranked for importance, ultimately forming a feature identification set.

[0060] The signature set is encrypted and transformed using a digital signature algorithm to ensure the immutability of the signature data and generate an identity sequence. This identity sequence is then re-encrypted using a device-specific encryption key, ultimately forming a unique device identity code.

[0061] For example, the hardware information of a power distribution terminal device model QD-2024 is parsed using the terminal device group key KEY-G789. This yields a hardware data stream containing the CPU model ARM-M4 (180MHz), memory capacity 256MB, and communication interface 4G-LTE. Data classification generates hardware identification data consisting of 15 fields. Real-time data collected for the device shows a CPU utilization rate of 65%, memory utilization of 48%, communication bandwidth utilization of 35%, and total power consumption of 15W. This data is time-synchronized, sampled every 10 seconds, and accumulated for one minute to form the terminal's operating status information. Dimensionality reduction is performed on the hardware identification data, reducing the 15 features to eight key features. Duplicate information is removed to form a 6-dimensional hardware feature vector. The operating status information is combined with the feature vector to generate an 8×12 multidimensional feature table, from which the most representative 6×8 core feature matrix is ​​extracted. This matrix is ​​converted to a 24-dimensional feature space sequence using nonlinear mapping. Features with a correlation greater than 0.8 are selected based on correlation analysis to form the feature identification set. Finally, the RSA-2048 algorithm is used for digital signature to generate a 512-bit identity sequence, which is then encrypted using the AES-256 algorithm to form the final device identity code.

[0062] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0063] (1) Authenticate and verify the device identity code through data analysis, obtain channel authentication information, perform connection configuration based on the channel authentication information, and establish a data transmission channel;

[0064] (2) Periodically perform statistics on the data flow characteristics of the data transmission channel to obtain a flow characteristic table, and convert and calculate the flow characteristic table through key generation to form a session key;

[0065] (3) Encrypt the data packets of the data transmission channel according to the session key to obtain the encrypted data stream, and construct the channel of the encrypted data stream according to the data reorganization to generate the encrypted data channel;

[0066] (4) Perform checksum calculation on the data packet header of the encrypted data channel to obtain a check sequence, perform verification operation on the check sequence through data comparison, and output an integrity identifier;

[0067] (5) Calculate the reliability of the encrypted data channel according to the integrity identifier to obtain a channel evaluation value, and make optimization adjustments based on the channel evaluation value to form an optimized parameter group;

[0068] (6) The optimized parameter group is updated through channel configuration to obtain a secure channel configuration. The encrypted data channel is reconstructed according to the secure channel configuration to form a secure communication link.

[0069] Specifically, the device identity signature code is authenticated and verified through the data parsing module. The verification process includes signature code format checking, digital signature verification, and encrypted information decryption. After verification, channel authentication information is obtained. The authentication information contains key parameters such as the device's identity, security level, and communication permissions. Based on this authentication information, the communication connection is configured, and parameters such as the transmission protocol type, data packet format, and communication bandwidth are set to establish the initial data transmission channel. The established data transmission channel is monitored in real time, and data traffic characteristics in the channel are collected, including indicators such as packet size, transmission rate, delay jitter, and packet loss rate. A periodic statistical method is used to collect traffic data every minute, and the statistical results are recorded in the traffic characteristic table. The traffic characteristic table contains traffic data from multiple time windows. This data is processed using a key generation algorithm to extract random characteristic values, ultimately generating a session key for encrypted communication.

[0070] Session keys are used to encrypt and protect data packets transmitted in data transmission channels. The encryption process uses block encryption, dividing data packets into fixed-size blocks for encryption operations, generating a ciphertext data stream. The encrypted data stream is reassembled, and the encrypted data packets are reorganized according to the predetermined channel structure to construct an encrypted data channel. The encrypted data channel adds a data encryption layer to the existing data transmission channel to ensure the security of data transmission. To ensure the integrity of data transmission, each data packet transmitted in the encrypted data channel must be verified. The verification process first calculates the checksum of the data packet header and uses a cyclic redundancy check algorithm to generate a check sequence. The calculated check sequence is compared and verified with the original check value carried in the data packet. Based on the comparison result, an integrity flag is output, which contains information such as the verification status and error type.

[0071] The reliability of the encrypted data channel is quantitatively assessed based on the integrity identifier. Performance indicators such as the bit error rate, retransmission rate, and latency are calculated to generate a channel evaluation value. This value reflects the current channel transmission quality. Based on the evaluation results, channel parameters are optimized, including adjusting encryption strength, updating transmission protocols, and optimizing bandwidth allocation. Ultimately, a set of optimized parameters is generated.

[0072] Apply the optimized parameters to the channel configuration, updating configuration items such as the communication protocol, encryption algorithm, and transmission strategy to generate a new secure channel configuration scheme. Based on this configuration scheme, the original encrypted data channel is reconstructed, the channel structure is optimized, and the transmission strategy is adjusted to ultimately establish a secure and reliable communication link.

[0073] For example, the identity code of a certain power distribution terminal device is parsed and verified to confirm that it is a legal device with a security level of 3, and authentication information including the device ID (DID-8A92) and the communication authority code (AUTH-567) is obtained. Based on the authentication information, the communication parameters are configured, the transmission rate is set to 10 Mbps, the maximum packet length is set to 1500 bytes, and a data transmission channel is established. The channel traffic is counted for 5 minutes and recorded in the traffic feature table, with an average transmission rate of 8.5 Mbps, a peak rate of 9.8 Mbps, and a time delay fluctuation range of 5-15 ms. According to these traffic characteristics, a 256-bit session key (SK-789F) is generated. The transmission data is encrypted using the session key, and the data packets are grouped and encrypted in units of 64 bytes to generate an encrypted data stream. The 32-bit CRC check value is calculated for each encrypted data packet, and 100 consecutive data packets are monitored. Two check errors are found, and the integrity indicator shows a correctness rate of 98%. Based on this integrity indicator, the channel evaluation value is calculated as 0.95, triggering parameter optimization, which adjusts the encryption grouping size to 128 bytes and reduces the transmission rate to 8 Mbps, forming a new set of optimized parameters. After updating the channel configuration, the reconstructed secure communication link maintains a data correctness rate of 100% in subsequent 30 minutes of communication.

[0074] In a specific embodiment, the process of performing step S104 can specifically include the following steps:

[0075] (1) Collecting quantum state information transmitted in the secure communication link to obtain a quantum state data stream, time dividing the quantum state data stream based on quantum entanglement characteristics to form terminal operation data;

[0076] (2) Identifying noise in the terminal operation data according to a quantum state threshold to obtain a quantum noise set, and filtering the quantum noise set based on quantum state purity to generate a cleaned quantum state sequence;

[0077] (3) Performing quantum state normalization calculation on the cleaned quantum state sequence to obtain standardized quantum data, and mapping and transforming the standardized quantum data based on quantum state projection to output a standardized data set;

[0078] (4) Constructing quantum features based on the standardized data set to obtain a quantum feature combination, and performing mapping analysis on the quantum feature combination according to quantum dimensions to form a quantum state feature table;

[0079] (5) Extracting quantum key features from the quantum state feature table based on quantum correlation degree, and prioritizing the quantum key features according to quantum state weight to generate a quantum feature matrix;

[0080] (6) Perform multi-dimensional state superposition on the quantum characteristic matrix to obtain the device quantum state sequence, and integrate the device quantum state sequence through quantum state fusion to form the device behavior characteristics.

[0081] Specifically, quantum state information transmitted over secure communication links is collected in real time. Quantum state information is a quantized representation of the terminal device's operating status, containing quantum encodings of information such as the device's operating parameters, communication status, and resource usage. This information is captured by a quantum state collector and converted into a quantum state data stream. This quantum state data stream is a continuous sequence of quantum bits, each of which carries partial information about the device's state. Leveraging the properties of quantum entanglement, the quantum state data stream is segmented into time windows. The data in each time window constitutes an independent quantum state sample, ultimately forming a data set describing the terminal's operating status. The acquired terminal operating data is then subjected to noise identification and filtering. The quantum state threshold is a preset quantum state purity standard used to determine the noise component in the data. By comparing it with the quantum state threshold, quantum noise in the terminal operating data is identified. This noise includes data distortion caused by factors such as quantum decoherence and quantum measurement errors. The identified noise data is aggregated into a quantum noise set and then filtered based on the quantum state purity metric. Quantum state purity reflects the quality of the quantum state; higher purity indicates more stable quantum states. After purity filtering, high-quality quantum state data is retained to form a cleaned quantum state sequence.

[0082] The cleaned quantum state sequence is normalized to map quantum state data of different scales into a unified value range. The quantum state normalization calculation takes into account the amplitude and phase information of the quantum state to ensure that the normalized data retains quantum characteristics. The standardized quantum data is projected into a specific quantum state space through the quantum state projection operator to achieve data dimensionality reduction and feature extraction, and finally output a standardized data set. Quantum features are constructed based on the standardized data set, and multiple basis states are combined into composite quantum states through the principle of quantum state superposition to form a quantum feature combination. The feature combination is analyzed according to the quantum dimension, which describes the complexity and information capacity of the quantum state. The feature data is organized into a quantum state feature table through dimensional mapping. Each row in the table corresponds to a quantum feature, and each column represents a different quantum state parameter.

[0083] The parameters in the quantum state characteristic table are analyzed and extracted using a quantum correlation measurement method. The quantum correlation reflects the strength of interactions between different quantum states. Based on the magnitude of the correlation, the most representative quantum features are selected as quantum key features. These key features are ranked by importance according to their quantum state weights, which reflect the degree of influence of each quantum feature on device behavior. This ultimately generates a sorted quantum characteristic matrix. The quantum states in the quantum characteristic matrix are then subjected to multidimensional superposition. This coherent superposition of quantum states forms a more complex quantum state structure, resulting in a device quantum state sequence. Finally, a quantum state fusion algorithm is used to integrate the information in the quantum state sequence, extracting the features that best characterize device behavior and forming a complete device behavior signature.

[0084] For example, during operation, a smart power distribution terminal uses a quantum state collector to collect 100 qubits of state information per second, including quantum state representations of parameters such as voltage, current, and power. This quantum state information is segmented into 10ms time windows to form quantum state samples containing 10 qubits. Based on a preset quantum state threshold of 0.85, noisy data with quantum state purity below the threshold is identified. A total of 12 quantum state samples were found to be contaminated by quantum noise. Quantum purity filtering was performed to retain samples with purity greater than 0.9, resulting in 88 high-quality quantum state sequences. These sequences were quantum normalized to unify the quantum state value range to the [0, 1] interval. Dimensionality mapping was used to reduce the 10-dimensional quantum state data to 6-dimensional feature vectors, constructing a 6×88 quantum feature table. Quantum correlation was calculated, and it was found that four quantum features had correlations exceeding 0.8. These features were extracted and sorted by weight to form a 4×88 quantum feature matrix. Finally, through quantum state superposition and fusion, these features are integrated into a 256-bit device behavior feature code for subsequent device management and control.

[0085] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0086] (1) Perform quantum state statistics on quantum access frequency, quantum access duration, and quantum access resources based on device behavior characteristics to obtain quantum state access data, classify the quantum state access data according to the quantum access mode, and form a quantum access sequence;

[0087] (2) By comparing the quantum states of the quantum access sequence, quantum over-limit access, frequent quantum access, and access during quantum abnormal periods are identified to obtain a quantum anomaly set. The quantum anomaly set is graded and evaluated according to the quantum risk degree to generate a quantum risk sequence;

[0088] (3) Based on the quantum state degree in the quantum risk sequence, the terminal access rights are quantized to obtain quantum authority indicators, and the quantum authority indicators are segmented according to the quantum state threshold to form a graded evaluation result;

[0089] (4) Perform quantum state statistics on the hierarchical evaluation results to obtain the quantum authority distribution, calculate the quantum weight of the quantum authority distribution based on the quantum state ratio, and generate a quantum evaluation matrix;

[0090] (5) Dynamically adjust the terminal operation authority through the quantum evaluation matrix to obtain the quantum adjustment parameters, update the quantum adjustment parameters according to the quantum refresh cycle, and output the quantum dynamic sequence;

[0091] (6) Construct a quantum multi-level structure based on the quantum dynamic sequence to obtain a quantum hierarchy table, organize the quantum hierarchy table according to the quantum inheritance relationship, and form an authority management system.

[0092] Specifically, terminal access behavior is quantized and analyzed based on device behavior characteristics. Quantum access frequency represents the quantum state representation of the number of accesses per unit time; quantum access duration describes the quantum encoding of the duration of each access; and quantum access resources reflect the quantum state characteristics of the type of system resources accessed and the amount of usage. Quantum state statistics are calculated for these three dimensions and converted into quantum state access data. Quantum state access data is categorized and organized according to different quantum access modes, such as regular access, emergency access, and batch access, ultimately generating a quantum access sequence containing access characteristics. Quantum state comparative analysis is performed on the generated quantum access sequence, focusing on identifying three types of abnormal access behaviors: quantum over-limit access refers to access attempts that exceed the authorized scope; quantum frequent access refers to high-frequency access within a short period of time; and quantum abnormal period access refers to access requests during abnormal time periods. Identified abnormal access behaviors are grouped into a quantum anomaly set and evaluated based on a quantum risk index. Quantum risk is a comprehensive index that incorporates multiple quantum state parameters, such as the degree of anomaly, the scope of impact, and the level of hazard. This evaluation generates a quantum risk sequence, which contains risk rating information for each type of abnormal access.

[0093] Based on the risk level recorded in the quantum risk sequence, the terminal's access rights are quantized. This quantization process converts traditional discrete permission levels into continuous quantum state representations, generating quantum permission indicators. Quantum state thresholds are set as boundary conditions for permission grading, and the quantum permission indicators are segmented, dividing permissions into multiple levels to form a graded assessment result. Quantum state statistical analysis is performed on the graded assessment results to calculate the distribution of different permission levels and form a quantum permission distribution. Based on the proportion of each permission level in the overall system, the corresponding quantum state weight is calculated. The quantum state weight reflects the importance and influence of each permission level. This weight calculation generates a quantum assessment matrix, which contains a complete quantum state representation of the permission distribution.

[0094] The quantum evaluation matrix is ​​used to adjust terminal operating permissions in real time, generating quantum adjustment parameters based on the matrix's evaluation results. These parameters include the change in permission level and the direction of adjustment. These adjustment parameters are regularly updated according to a preset quantum refresh cycle to ensure the timeliness of permission adjustments. Ultimately, a quantum dynamic sequence reflecting permission changes is output. A hierarchical permission structure is constructed based on the quantum dynamic sequence, organizing different levels of permission into a quantum multi-level structure to generate a quantum hierarchy. This hierarchy is organized and optimized according to quantum inheritance relationships, which define the inclusion and derivation relationships between different permission levels, ultimately forming a complete permission management system.

[0095] For example, the device behavior characteristics of a power distribution terminal device showed a quantum access frequency of 30 per hour over the past 24 hours, with an average access duration of two minutes. These accesses primarily targeted distribution control and data collection resources. Quantum state statistics of this data revealed that 80% of these accesses were routine, 15% were emergency, and 5% were batch data accesses. Quantum state comparison revealed three quantum over-limit accesses (attempts to access high-voltage switch control permissions), five frequent quantum accesses (more than 10 requests within a minute), and two unusual accesses (operation requests at 3:00 AM). The quantum risk assessment results for these unusual accesses were quantized to a value between 0 and 1, with over-limit access risk at 0.8, frequent access risk at 0.6, and unusual access risk at 0.7. Based on the risk assessment results, the terminal permissions were quantized, converting the original five levels of permissions into quantum state representations. A quantum state threshold of 0.7 was set as the downgrade condition. After a tiered assessment, the terminal's permissions were downgraded from Level 4 to Level 3. The updated quantum permission distribution shows: Level 3 permissions account for 65%, Level 2 permissions account for 25%, and Level 1 permissions account for 10%. The permissions configuration is updated every four hours based on the quantum refresh cycle, ultimately classifying the terminal as a restricted operation in the permissions management system.

[0096] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0097] (1) Perform periodic quantum sampling of the quantum state operation status according to the authority management system to obtain quantum state samples, verify the quantum state samples according to the quantum state integrity, and form system state snapshot data;

[0098] (2) Perform quantum classification on the system state snapshot data through quantum state parameters to obtain a quantum state classification set, construct a quantum index on the quantum state classification set based on the quantum data structure, and generate a quantum snapshot index table;

[0099] (3) Perform quantum sharding on the quantum snapshot index table to obtain a quantum shard set, and perform quantum state mapping on the quantum shard set according to the quantum storage node to form a distributed storage sequence;

[0100] (4) Distribute the system snapshot data to quantum nodes according to the distributed storage sequence to obtain a quantum backup set, and verify the consistency of the quantum backup set through quantum state synchronization to form disaster recovery backup data;

[0101] (5) Perform quantum backtracking analysis on the quantum historical state based on the disaster recovery backup data to obtain the quantum state change sequence, construct quantum relationships on the quantum state change sequence based on the quantum correlation degree, and generate a quantum transfer table;

[0102] (6) The quantum state of the system recovery point is located through the quantum transfer table to obtain the quantum recovery sequence, which is sorted according to the quantum priority to form a system recovery strategy.

[0103] Specifically, according to the quantum state operating parameter requirements defined in the authority management system, periodic quantum state sampling is performed on the terminal device. Quantum state sampling collects the operating status information of the terminal device every 10 seconds, including quantized power parameters, voltage parameters, communication status and other key indicators to form the original quantum state sample. The collected quantum state samples are integrity checked to verify the validity and integrity of each quantum state, and incomplete or damaged quantum state data are eliminated to ultimately form the system state snapshot data. Quantum state parameter analysis is performed on the system state snapshot data, and the data is divided into different quantum state categories based on parameter characteristics, such as operating state, alarm state, fault state, etc., to obtain a quantum state classification set. An index relationship is established for the classified data based on the quantum data structure. The quantum data structure uses the superposition characteristics of the quantum state to encode multi-dimensional state information into quantum bits, and a quantum snapshot index table is generated by constructing a quantum index.

[0104] The quantum snapshot index table is partitioned according to the quantum sharding strategy. Each shard contains a set of related quantum state information, forming a quantum shard set. Based on the pre-configured quantum storage node distribution, the quantum shard set is mapped to different storage nodes. A mapping relationship between quantum states and physical storage locations is established, generating a distributed storage sequence. Based on the distributed storage sequence, the system snapshot data is distributed to each quantum node, with each node storing a portion of the quantum state information, forming a quantum backup set. A quantum state synchronization mechanism ensures data consistency across nodes. The quantum backup set is then checked and compared to verify data integrity and consistency, ultimately generating reliable disaster recovery backup data.

[0105] Based on disaster recovery backup data, a quantum backtracking analysis of the system's historical operating status is performed, focusing on the evolution of quantum states, recording the patterns and characteristics of state transitions, and generating a quantum state change sequence. Quantum correlations are used to calculate the correlations between different states, constructing a quantum state transition network and forming a quantum transition table. This quantum transition table is used to locate key recovery points in the system—times when the system state is stable and data is intact. These recovery points are organized into a quantum recovery sequence. The quantum recovery sequence is prioritized based on business importance and recovery priority, ultimately forming a system recovery strategy.

[0106] For example, during operation, a smart power distribution terminal samples quantum states every 10 seconds, collecting information on parameters such as voltage (220V ± 5%), current (50A ± 2A), and power factor (0.95 ± 0.02). A total of 8,640 quantum state samples were collected over a 24-hour period, and 8,500 valid samples were retained after integrity verification. These samples were classified according to operational characteristics, with 85% representing normal operation, 12% representing minor alarms, and 3% representing severe faults. A quantum index was created for the classified data, using a 4-bit code to represent the state type and an 8-bit code to represent the timestamp, forming a quantum snapshot index table. The index table was divided into 32 quantum shards, each containing approximately 266 quantum state records, distributed across eight quantum storage nodes. A quantum state synchronization mechanism ensures data consistency across nodes, with verification results showing a data synchronization rate of 99.9%. Quantum backtracking analysis of 30 days of historical data revealed that the device underwent five state transitions, leading to the establishment of a quantum transition table containing 15 key recovery points. Based on the degree of business impact, these recovery points are divided into three priorities, and a multi-level recovery strategy covering 1 hour, 6 hours, and 24 hours is constructed.

[0107] The above describes the power distribution IoT terminal management method based on quantum cloud code in the embodiment of the present application. The following describes the power distribution IoT terminal management system based on quantum cloud code in the embodiment of the present application. Figure 2In the embodiment of the present application, an embodiment of the power distribution IoT terminal management system based on quantum cloud code includes:

[0108] An encryption module is used to obfuscate the original data using a random number generator to obtain a quantum cloud code seed, perform shard encryption on the quantum cloud code seed to obtain a quantum cloud code sequence, and group and match the quantum cloud code sequence according to preset rules to form a terminal device group key;

[0109] An extraction module, configured to extract hardware identification data of a terminal device based on the terminal device group key, perform feature synthesis on the hardware identification data in combination with terminal operation status information, and generate a device identity feature code;

[0110] An establishment module is used to establish a data transmission channel according to the device identity code, encrypt the data transmission channel using a session key, establish an encrypted data channel, perform integrity check on the encrypted data channel, and form a secure communication link;

[0111] a cleaning module, configured to collect terminal operation data in the secure communication link, perform data cleaning and normalization processing on the terminal operation data to obtain a standardized data set, perform feature mapping based on the standardized data set, and output device behavior features;

[0112] A grading module is used to perform a graded assessment of terminal access rights based on the device behavior characteristics, dynamically adjust terminal operation rights based on the graded assessment results, and build a rights management system;

[0113] The snapshot module is used to take regular snapshots of the system operation status according to the authority management system, perform distributed backup of the system snapshot data to form disaster recovery backup data, and establish a system recovery strategy based on the disaster recovery backup data.

[0114] Through the collaborative efforts of the aforementioned components, a random number generator obfuscates the raw data, achieving a high degree of randomness and unpredictability, effectively preventing data theft and tampering. Sharded encryption technology is used to process quantum cloud code seeds, improving data security and reliability. The generation and distribution of terminal device group keys enables precise identification and classification management of terminal devices, enhancing the system's security control capabilities. Hardware identification data is extracted based on the terminal device group keys and combined with terminal operational status information to generate a device identity signature, ensuring the uniqueness and verifiability of the device identity. The established data transmission channel is encrypted using a session key and integrity-checked, forming a secure communication link and ensuring the security and integrity of data transmission. Terminal operational data within the secure communication link is collected and processed, and a standardized dataset is generated through data cleansing and normalization, improving data quality and usability. Feature mapping is performed based on the standardized dataset to accurately output device behavioral characteristics, providing a reliable basis for subsequent permission management. Terminal access rights are assessed in a tiered manner based on device behavioral characteristics, enabling dynamic adjustment of permissions and building a flexible and effective permission management system. Through regular snapshots and a distributed backup mechanism, a comprehensive disaster recovery backup database is established, ensuring the recoverability of system data. System recovery strategies are established based on this data, improving system reliability and continuous operation. The overall solution enables secure access, reliable communication, dynamic management, and effective recovery of distribution IoT terminals, significantly enhancing the security, reliability, and management efficiency of the distribution IoT network.

[0115] Based on the same technical concept, the embodiment of the present application also provides an electronic device. Figure 3 3 is a schematic diagram of the structure of an electronic device 300 provided in an embodiment of the present application, including a processor 301, a memory 302, and a bus 303. The memory 302 is used to store execution instructions and includes a memory 3021 and an external memory 3022. The memory 3021 is also referred to as internal memory and is used to temporarily store operation data in the processor 301 and data exchanged with an external memory 3022 such as a hard disk. The processor 301 exchanges data with the external memory 3022 through the memory 3021. When the electronic device 300 is running, the processor 301 and the memory 302 communicate via the bus 303.

[0116] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for managing power distribution IoT terminals based on quantum cloud code, characterized in that: The power distribution IoT terminal management method based on quantum cloud code includes: The original data is obfuscated by a random number generator to obtain a quantum cloud code seed, the quantum cloud code seed is encrypted in pieces to obtain a quantum cloud code sequence, and the quantum cloud code sequence is grouped and matched according to a preset rule to form a terminal device group key, including: performing random number mapping on the operating parameters of the distribution terminal by a random number generator to generate an original confusion matrix, and constructing a random perturbation vector based on the original confusion matrix; performing nonlinear transformation on the original data according to the random perturbation vector to obtain an initial obfuscated data stream, and reorganizing the initial obfuscated data stream through a piecewise linear transformation to form a quantum cloud code seed; based on the quantum The sub-cloud code seed establishes a data shard index table, and the quantum cloud code seed is segmented by a dynamic sharding algorithm to obtain a quantum cloud code shard set; the quantum cloud code shard set is encrypted and converted according to a symmetric encryption algorithm to output an encrypted shard sequence, which is sorted and combined by a data reconstruction algorithm to generate a quantum cloud code sequence; the quantum cloud code sequence is grouped and calculated by hierarchical clustering to obtain a group feature vector, and a terminal group mapping table is constructed based on the group feature vector; keys are distributed to terminal devices according to the terminal group mapping table, and key parameters of each group are calculated by a key derivation function to form a terminal device group key; Extracting the hardware identification data of the terminal device based on the terminal device group key, synthesizing the characteristics of the hardware identification data in combination with the terminal operation status information, and generating a device identity feature code; Establishing a data transmission channel according to the device identity code, encrypting the data transmission channel using a session key to establish an encrypted data channel, and performing an integrity check on the encrypted data channel to form a secure communication link; collecting terminal operation data in the secure communication link, performing data cleaning and normalization on the terminal operation data to obtain a standardized data set, performing feature mapping based on the standardized data set, and outputting device behavior features; Conduct a hierarchical assessment of terminal access rights based on the device behavior characteristics, dynamically adjust terminal operation rights based on the hierarchical assessment results, and build a rights management system; According to the authority management system, regular snapshots are taken of the system operation status, distributed backups are performed on the system snapshot data to form disaster recovery backup data, and a system recovery strategy is established based on the disaster recovery backup data.

2. The method for managing power distribution IoT terminals based on quantum cloud code according to claim 1 is characterized in that: The extracting the hardware identification data of the terminal device based on the terminal device group key, synthesizing the characteristics of the hardware identification data in combination with the terminal operation status information, and generating the device identity feature code includes: Performing identification resolution on the terminal device using the terminal device group key to obtain a hardware data stream, and performing data classification based on the hardware data stream to form hardware identification data; Collect and count the resource occupancy indicators, communication load parameters, and power consumption values ​​of the terminal device to obtain an operating parameter set, and perform time sequence integration on the operating parameter set through time synchronization to generate terminal operating status information; Extract features from the hardware identification data according to data dimensionality reduction to obtain a hardware feature set, and remove redundant data from the hardware feature set according to data filtering to form a hardware feature vector; Combining data based on the terminal operation status information and the hardware feature vector to obtain a multidimensional feature table, extracting key values ​​from the multidimensional feature table through feature screening to generate a core feature matrix; Performing nonlinear mapping on the core feature matrix according to feature transformation to obtain a feature space sequence, and ranking the feature space sequence according to variable correlation to form a feature identification set; The characteristic identification set is encrypted and transformed by a digital signature to obtain an identity identification sequence, and the identity identification sequence is encrypted according to an encryption key to form a device identity characteristic code.

3. The method for managing power distribution IoT terminals based on quantum cloud code according to claim 1 is characterized in that: The step of establishing a data transmission channel according to the device identity code, encrypting the data transmission channel using a session key to establish an encrypted data channel, and performing integrity verification on the encrypted data channel to form a secure communication link includes: Authenticate and verify the device identity code through data analysis to obtain channel authentication information, perform connection configuration based on the channel authentication information, and establish a data transmission channel; Performing periodic statistics on data flow characteristics of the data transmission channel to obtain a flow characteristic table, and converting and calculating the flow characteristic table through key generation to form a session key; Performing encryption operation on the data packets of the data transmission channel according to the session key to obtain an encrypted data stream, and performing channel construction on the encrypted data stream according to data reorganization to generate an encrypted data channel; Performing a checksum calculation on the data packet header of the encrypted data channel to obtain a check sequence, performing a verification operation on the check sequence through data comparison, and outputting an integrity identifier; Performing reliability calculation on the encrypted data channel according to the integrity identifier to obtain a channel evaluation value, and performing optimization adjustment based on the channel evaluation value to form an optimization parameter group; The optimized parameter group is updated through channel configuration to obtain a secure channel configuration, and the encrypted data channel is reconstructed according to the secure channel configuration to form a secure communication link.

4. The method for managing power distribution IoT terminals based on quantum cloud code according to claim 1 is characterized in that: The collecting of terminal operation data in the secure communication link, performing data cleaning and normalization processing on the terminal operation data to obtain a standardized data set, performing feature mapping based on the standardized data set, and outputting device behavior features, includes: Data collection is performed on quantum state information transmitted in a secure communication link to obtain a quantum state data stream, and the quantum state data stream is time-segmented using quantum entanglement characteristics to form terminal operation data; performing noise identification on the terminal operation data according to a quantum state threshold to obtain a quantum noise set, performing data filtering on the quantum noise set according to quantum state purity to generate a cleansed quantum state sequence; performing quantum state normalization calculation on the cleaned quantum state sequence to obtain standardized quantum data, performing mapping transformation on the standardized quantum data through quantum state projection, and outputting a standardized data set; Constructing quantum features based on the standardized data set to obtain a quantum feature combination, and mapping and analyzing the quantum feature combination according to quantum dimensions to form a quantum state feature table; Extracting parameters from the quantum state feature table using quantum correlation to obtain quantum key features, prioritizing the quantum key features according to quantum state weights, and generating a quantum feature matrix; The quantum characteristic matrix is ​​subjected to multi-dimensional state superposition to obtain a device quantum state sequence, and the device quantum state sequence is integrated through quantum state fusion to form device behavior characteristics.

5. The method for managing power distribution IoT terminals based on quantum cloud code according to claim 1 is characterized in that: The step of performing a hierarchical assessment of terminal access rights based on the device behavior characteristics, dynamically adjusting terminal operation rights based on the hierarchical assessment results, and constructing a rights management system includes: performing quantum state statistics on the quantum access frequency, quantum access duration, and quantum access resources according to the device behavior characteristics to obtain quantum state access data, and classifying the quantum state access data according to quantum access modes to form a quantum access sequence; By comparing the quantum states of the quantum access sequence, quantum over-limit access, frequent quantum access, and access during quantum abnormal periods are identified to obtain a quantum abnormality set, and the quantum abnormality set is graded and evaluated according to the quantum risk degree to generate a quantum risk sequence; Based on the quantum state degree in the quantum risk sequence, the terminal access rights are quantized to obtain a quantum authority index, and the quantum authority index is segmented according to the quantum state threshold to form a graded evaluation result; Performing quantum state statistics on the hierarchical evaluation results to obtain a quantum authority distribution, performing quantum weight calculation on the quantum authority distribution based on the quantum state proportion, and generating a quantum evaluation matrix; Dynamically adjust the terminal operation authority through the quantum evaluation matrix to obtain quantum adjustment parameters, update the quantum adjustment parameters according to the quantum refresh cycle, and output a quantum dynamic sequence; A quantum multi-level structure is constructed according to the quantum dynamic sequence to obtain a quantum hierarchical table, and the quantum hierarchical table is organized according to the quantum inheritance relationship to form a rights management system.

6. The method for managing power distribution IoT terminals based on quantum cloud code according to claim 5 is characterized in that: The method of taking regular snapshots of the system operation status according to the authority management system, performing distributed backup of the system snapshot data to form disaster recovery backup data, and establishing a system recovery strategy based on the disaster recovery backup data includes: Performing periodic quantum sampling on the quantum state operation state according to the authority management system to obtain quantum state samples, and verifying the quantum state samples according to quantum state integrity to form system state snapshot data; Performing quantum classification on the system state snapshot data using quantum state parameters to obtain a quantum state classification set, and constructing a quantum index on the quantum state classification set based on a quantum data structure to generate a quantum snapshot index table; Performing quantum sharding processing on the quantum snapshot index table to obtain a quantum shard set, and performing quantum state mapping on the quantum shard set according to quantum storage nodes to form a distributed storage sequence; Distributing the system snapshot data to quantum nodes according to the distributed storage sequence to obtain a quantum backup set, and verifying the consistency of the quantum backup set through quantum state synchronization to form disaster recovery backup data; Performing quantum backtracking analysis on the quantum historical state based on the disaster recovery backup data to obtain a quantum state change sequence, constructing a quantum relationship for the quantum state change sequence based on the quantum correlation degree, and generating a quantum transfer table; The quantum state of the system recovery point is located by the quantum transfer table to obtain a quantum recovery sequence, and the quantum recovery sequence is sorted according to the quantum priority to form a system recovery strategy.

7. A power distribution IoT terminal management system based on quantum cloud code, used to implement the power distribution IoT terminal management method based on quantum cloud code as described in any one of claims 1 to 6, characterized in that: The power distribution IoT terminal management system based on quantum cloud code includes: The encryption module is used to confuse the original data through a random number generator to obtain a quantum cloud code seed, perform shard encryption on the quantum cloud code seed to obtain a quantum cloud code sequence, and group match the quantum cloud code sequence according to preset rules to form a terminal device group key, including: performing random number mapping on the operating parameters of the distribution terminal through a random number generator to generate an original confusion matrix, and constructing a random perturbation vector based on the original confusion matrix; performing nonlinear transformation on the original data according to the random perturbation vector to obtain an initial obfuscated data stream, and reorganizing the initial obfuscated data stream through a piecewise linear transformation to form a quantum cloud code seed; based on The quantum cloud code seed establishes a data shard index table, and the quantum cloud code seed is segmented by a dynamic sharding algorithm to obtain a quantum cloud code shard set; the quantum cloud code shard set is encrypted and converted according to a symmetric encryption algorithm to output an encrypted shard sequence, and the encrypted shard sequence is sorted and combined by a data reconstruction algorithm to generate a quantum cloud code sequence; the quantum cloud code sequence is grouped and calculated by hierarchical clustering to obtain a group feature vector, and a terminal group mapping table is constructed based on the group feature vector; keys are distributed to terminal devices according to the terminal group mapping table, and key parameters of each group are calculated by a key derivation function to form a terminal device group key; An extraction module, configured to extract hardware identification data of a terminal device based on the terminal device group key, perform feature synthesis on the hardware identification data in combination with terminal operation status information, and generate a device identity feature code; An establishment module is used to establish a data transmission channel according to the device identity code, encrypt the data transmission channel using a session key, establish an encrypted data channel, perform integrity check on the encrypted data channel, and form a secure communication link; a cleaning module, configured to collect terminal operation data in the secure communication link, perform data cleaning and normalization processing on the terminal operation data to obtain a standardized data set, perform feature mapping based on the standardized data set, and output device behavior features; A grading module is used to perform a graded assessment of terminal access rights based on the device behavior characteristics, dynamically adjust terminal operation rights based on the graded assessment results, and build a rights management system; The snapshot module is used to take regular snapshots of the system operation status according to the authority management system, perform distributed backup of the system snapshot data to form disaster recovery backup data, and establish a system recovery strategy based on the disaster recovery backup data.

8. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate through the bus. When the machine-readable instructions are executed by the processor, the steps of the power distribution Internet of Things terminal management method based on quantum cloud code are performed as described in any one of claims 1 to 6.

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