A secure quantum encryption transmission method for power internet of things

Abstract

The application discloses a kind of security quantum encryption transmission methods for power internet of things, it is related to the field of encrypted communication, including: collecting power grid data, simplifying power grid data to output simplified version power grid data as transmission target;Traversal simplified version power grid data, based on the symbol distribution state in simplified version power grid data, the random number column digit of quantum random number generator generated is designed;The application ensures data quality by washing, completing and space alignment to power grid data, realizes data simplification by character mapping and digital processing, effectively reduces transmission data volume, improves transmission efficiency, simultaneously, based on the symbol distribution of simplified data, the digit of quantum random number is designed, combined with entropy value verification and Pearson correlation coefficient analysis, guarantee the high randomness and security of random number column.

Description

Technical Field

[0001] This invention relates to the field of encrypted communication technology, specifically to a secure quantum encrypted transmission method for the power Internet of Things. Background Technology

[0002] Power data records encompass the entire process of power generation, transmission, distribution, and consumption, including real-time and historical data such as power generation, load, and voltage. This data can be used for grid dispatching, energy consumption analysis, and fault early warning, helping to optimize power allocation.

[0003] Patent application No. 202111353509.0 discloses an IoT-based method for encrypted transmission of power monitoring images. The specific steps are as follows: Step 1: Combine the image variance with the mean absolute gradient to obtain weight coefficients, and use a multi-decomposition method to smooth pixels around the edges, achieving foreground and background separation. Step 2: Use a single-qubit gate circuit to encrypt the foreground region of the image. Step 3: After the foreground region information is encrypted, the foreground contour information still exists. An encrypted training framework is used to help hide the contour information: Since the number of selected training frames is far less than the number of video frames, it is impossible to perform a one-to-one correspondence between them. Therefore, a sequence is specified for the training frames so that the foreground of the training framework is used to hide the contour information in that sequence. Step 4: Use a background coverage coefficient to encrypt the background image, calculate a digital key, and randomly select an image to construct the required image key. Step 5: Complete the encryption of the foreground image, and save the digital key and image key to achieve encrypted image transmission. This application aims to "optimize the monitoring capabilities of existing online power monitoring systems and provide a new encryption method for power monitoring image transmission scenarios."

[0004] However, in scenarios where quantum encryption is used to transmit text and numerical power grid data, there is still room for improvement in the application of quantum encryption technology to enhance data transmission security.

[0005] To address this, a secure quantum-encrypted transmission method for the power Internet of Things is proposed. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a secure quantum encrypted transmission method for the power Internet of Things, which can effectively solve the problems of the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions;

[0008] This invention discloses a secure quantum-encrypted transmission method for the power Internet of Things, comprising:

[0009] The process involves: collecting power grid data, simplifying the data, and outputting a simplified version of the power grid data as the transmission target; traversing the simplified power grid data and designing the number of bits in the random number sequence generated by a quantum random number generator based on the symbol distribution state in the simplified power grid data; obtaining the design result of the number of bits in the random number sequence generated by the quantum random number generator and applying the design result to the quantum random number generator to output a random number sequence with the corresponding number of bits; selecting an encryption algorithm and preprocessing the output random number sequence to obtain the random number sequence used by the selected encryption algorithm; applying the selected encryption algorithm combined with the random number sequence to encrypt the simplified power grid data and transmitting it to a preset receiving end.

[0010] Furthermore, the power grid data originates from various transmission, storage, and distribution equipment in the power network. Each set of power grid data is simultaneously marked with the name and number of its source transmission, storage, and distribution equipment during collection. Before performing multimodal simplification, the power grid data undergoes simultaneous cleaning, completion, and spatiotemporal alignment processing to ensure that each set of power grid data collected is unique and complete relative to all power grid data.

[0011] The simplification of the power grid data refers to the simplification of the data volume.

[0012] In the process of multimodal simplification, the power grid data that has been cleaned, completed, and spatiotemporally aligned is simultaneously set with a data collection space or a data collection time interval. The set data collection space or time interval is used to select the corresponding power grid data from all collected power grid data that has been cleaned, completed, and spatiotemporally aligned as the simplification processing target.

[0013] Furthermore, the simplified processing logic for the power grid data is as follows:

[0014] Traverse the power grid data, extract all character information from the power grid data, remove duplicate characters from the character information to obtain a character information set without duplicate characters, and create a character mapping table based on the character information set that maps characters to numbers;

[0015] Based on the character information in the power grid data, the corresponding number is queried in the character mapping table, and the queried number is used to replace the corresponding character information in the power grid data. The number of numbers replaced by the query result in the power grid data, 'a', is compared with the number of numbers originally contained in the power grid data, 'b'.

[0016] When a > b, add parentheses to all the original numbers contained in the power grid data; when a < b, add parentheses to all the numbers in the power grid data that were replaced by the query results; when a = b, add parentheses to either all the original numbers contained in the power grid data or all the numbers in the power grid data that were replaced by the query results, to simplify the power grid data into a simplified version consisting only of numbers and parentheses.

[0017] Furthermore, the symbol distribution state in the simplified power grid data, i.e., the distance between each group of brackets in the simplified power grid data, is represented by the symbol distribution state according to the arrangement order of the brackets in the simplified power grid data. ;

[0018] This indicates the number of numbers between the first set of parentheses and the next set of parentheses in the simplified power grid data. This indicates the number of numbers between the parentheses appearing in the second group and the parentheses appearing in the third group in the simplified power grid data.

[0019] The number of bits in the random number sequence generated by the quantum random number generator is:

[0020] ;

[0021] In the formula: The number of bits in a random number sequence generated by a quantum random number generator; for The maximum and minimum values; The timestamp value for the current operation; The timestamp in seconds for the current operation. It is a constant;

[0022] in, , Taking the timestamp 12:36:59 as an example, =36, =59, constant The value range is (0, 1]. The more numbers and parentheses in the simplified power grid data, the larger the constant. The larger the value, the better; conversely, the smaller the value, the more likely it is to be a constant. The smaller the value.

[0023] Furthermore, before the random number sequence output by the quantum random number generator is submitted to the preprocessing operation, the randomness characteristic parameters of the random number sequence are verified by a preset security threshold. The preprocessing operation is performed only when the randomness characteristic parameters meet the preset security threshold; otherwise, the random number sequence is regenerated by the quantum random number generator and verified.

[0024] Among them, the randomness characteristic parameter includes at least the entropy value.

[0025] Furthermore, the entropy value of the random number sequence is calculated using the following formula:

[0026] ;

[0027] In the formula: This represents the number of distinct digits in the sequence. Let be the frequency of occurrence of the i-th digit; The Pearson correlation coefficient between the numbers in the sequence and their position indices; This is the theoretical maximum correlation coefficient; The number of unique patterns; The length of the number sequence; This is the preset length of the local mode window;

[0028] in, ∈[-1,1], the unique pattern refers to the pattern in The number of times the following random number sequence appears in a unique case.

[0029] Furthermore, the Pearson correlation coefficient between the numbers in the sequence and the position index. The calculation formula is:

[0030] ;

[0031] In the formula: This is the value corresponding to the index of the j-th position; The mean of the sequence; This represents the average of the location indices.

[0032] Furthermore, the preprocessing operations for the random number sequence generated by the quantum random number generator include:

[0033] Configure a quantum state reference group containing 3 orthogonal basis vectors, with each basis vector corresponding to a set of quantum state parameters; divide the original sequence output by the quantum random number generator into sub-blocks of 1024 bits each, obtain the quantum state parameters of each sub-block through a quantum state measurement device, and retain the sub-blocks that match the basis vector parameters of any one of the reference groups with a degree ≥95% to form the initial sequence;

[0034] A processing module consisting of one frequency counter and one adjustment operation unit is constructed. The frequency counter counts the number of occurrences of "0" and "1" bits in the initial sequence in real time. The adjustment operation unit outputs the adjustment coefficient based on the statistical results. The value of each bit in the initial sequence is modulo 2 with the adjustment coefficient to make the difference between the proportion of "0" and "1" in the processed sequence ≤ 0.001.

[0035] Set up a transformation matrix consisting of three chaotic equations with a dimension of 64×64. Divide the dynamically balanced sequence into data blocks of 64 bits each. Input the data blocks into the transformation matrix and perform row shift and column XOR operations in sequence. The row shift amount is the output value of the chaotic equation, and the column XOR object is randomly specified by the chaotic equation. Repeat this process three times to obtain the disturbed sequence.

[0036] A 128-bit quantum key and a 128-bit classical key are generated. The quantum key is output by the quantum key generator, and the classical key is generated by the key derivation function. The perturbed sequence is added bit by bit with the quantum key, and then the result is encrypted with the classical key in groups of 32 bits each. The encryption rounds are 8, forming an intermediate encryption sequence.

[0037] Configure a variable hash module that supports 128-bit, 256-bit, and 512-bit output. This module contains three hash functions with different bit lengths. Based on the key length requirements of the target encryption algorithm, select the corresponding hash function to process the intermediate encryption sequence and output a random number sequence that meets the length requirements.

[0038] Furthermore, the adjustment operation unit takes the difference Δ between the counts of "0" and "1" bits counted by the frequency counter as input;

[0039] When Δ>0, the output coefficient decreases linearly as the absolute value of Δ increases. The initial value is 0.99, and the coefficient decreases by 0.05 for every 100 units of Δ, down to a minimum of 0.01.

[0040] When Δ < 0, the output coefficient increases linearly with the increase of the absolute value of Δ. The initial value is 0.01, and the coefficient increases by 0.05 for every 100 units of |Δ|, up to a maximum of 0.99.

[0041] When Δ=0, the output is fixed at 0.5.

[0042] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects:

[0043] This invention provides a secure quantum encryption transmission method for the power Internet of Things. During the execution of this method, the power grid data is cleaned, completed, and spatiotemporally aligned to ensure data quality. Then, the data is simplified through character mapping and digital processing, which effectively reduces the amount of data transmitted and improves transmission efficiency. At the same time, the number of quantum random numbers is designed based on the symbol distribution of the simplified data. Combined with entropy verification and Pearson correlation coefficient analysis, the high randomness and security of the random number sequence are guaranteed.

[0044] Furthermore, preprocessing techniques such as quantum state reference group screening, 0-1 bit adjustment, and chaotic transformation are used to adapt random numbers to encryption algorithms, enhancing encryption adaptability. Finally, combined with quantum encryption technology, data encryption transmission is achieved, comprehensively improving the security, efficiency, and reliability of power Internet of Things data transmission, and meeting the high requirements of power systems for data real-time performance and confidentiality. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0046] Figure 1 This is a flowchart illustrating a secure quantum-encrypted transmission method for the power Internet of Things. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0048] The present invention will be further described below with reference to embodiments. Example:

[0049] This embodiment presents a secure quantum-encrypted transmission method for the power Internet of Things, such as... Figure 1 As shown, it includes:

[0050] Collect power grid data, simplify the power grid data, and output the simplified power grid data as the transmission target;

[0051] The power grid data comes from various transmission, storage, and distribution equipment in the power network. Each set of power grid data is simultaneously marked with the name and number of the transmission, storage, and distribution equipment from which it originates during the acquisition process. Before performing multimodal simplification, the power grid data undergoes cleaning, completion, and spatiotemporal alignment processing to ensure that each set of power grid data acquired is unique and complete relative to all power grid data.

[0052] Simplifying power grid data means simplifying the amount of data.

[0053] Among them, when the cleaned, completed, and spatiotemporally aligned power grid data is simplified in a multimodal manner, a data collection space or data collection time interval is set simultaneously. The set data collection space or data collection time interval is used to pick out the corresponding power grid data from all the collected and cleaned, completed, and spatiotemporally aligned power grid data as the simplification processing target.

[0054] It should be noted that the completion and spatiotemporal alignment processing of power grid data before performing multimodal simplification includes:

[0055] For data completion processing, for example, when data is missing due to equipment failure, communication interruption, etc., if a small amount of data is missing (missing percentage ≤ 5%), linear interpolation can be used to estimate the missing value based on the linear relationship between data at adjacent time points; or mean imputation can be used to impute the missing value by averaging the data from several time periods before and after the missing value. For cases with a larger amount of missing data (missing percentage > 5%), machine learning-based methods can be used, such as building a Long Short-Term Memory (LSTM) network model to predict and impute missing data by learning the time-series characteristics of historical data; or generative adversarial networks (GANs) can be used to generate data with characteristics similar to the real data to fill the gaps. Furthermore, when multiple related data sources exist, information can be obtained from other data sources based on the correlation between data to impute missing data, ensuring the integrity of power grid data and providing a reliable data foundation for subsequent power system analysis, state estimation, load forecasting, and other applications.

[0056] In terms of time alignment, precise timestamps can be added to the data collected from each data source, and time synchronization technologies such as the Global Positioning System (GPS) or Network Time Protocol (NTP) can be used to ensure that the data collected by different devices at different times are consistent on the time base. For time deviations caused by transmission delays, time series interpolation, sliding window and other algorithms are used for calibration to make the data fit a unified time section and meet the needs of real-time monitoring and analysis of the power grid.

[0057] In terms of spatial alignment, the spatial coordinates involved in various power grid data, such as the geographical locations of substations and transmission lines, are transformed into a unified geographic information system (GIS) spatial reference coordinate system. For differences in scale and projection methods among spatial data from different sources, mathematical methods such as coordinate transformation and affine transformation are used for adjustment and matching. Through feature point extraction and matching, the correspondence between different spatial datasets is determined, thereby achieving the fusion and alignment of spatial structure and attribute data, providing a precise data foundation for power grid spatial analysis and planning.

[0058] The purpose of its completion is to ensure the integrity of power grid data, while the purpose of spatiotemporal alignment is to provide differentiated storage conditions for power grid data in order to meet the adaptive selective retrieval of subsequent power grid data;

[0059] The simplified processing logic for power grid data is as follows:

[0060] Traverse the power grid data, extract all character information from the power grid data, remove duplicate characters from the character information to obtain a character information set without duplicate characters, and create a character mapping table based on the character information set that maps characters to numbers;

[0061] Based on the character information in the power grid data, the corresponding number is queried in the character mapping table, and the queried number is used to replace the corresponding character information in the power grid data. The number of numbers replaced by the query result in the power grid data, 'a', is compared with the number of numbers originally contained in the power grid data, 'b'.

[0062] When a > b, add parentheses to all the original numbers contained in the power grid data; when a < b, add parentheses to all the numbers in the power grid data that are replaced by the query results; when a = b, add parentheses to either all the original numbers contained in the power grid data or all the numbers in the power grid data that are replaced by the query results, to simplify the power grid data into a simplified version consisting only of numbers and parentheses.

[0063] Traverse simplified power grid data, and design the number of bits in the random number sequence generated by a quantum random number generator based on the symbol distribution state in the simplified power grid data;

[0064] In simplified power grid data, the symbol distribution state refers to the distance between each group of brackets. The symbol distribution state is represented according to the order of the brackets in the simplified power grid data as follows: ;

[0065] This indicates the number of numbers between the first set of parentheses and the next set of parentheses in the simplified power grid data. This indicates the number of numbers between the parentheses appearing in the second group and the parentheses appearing in the third group in the simplified power grid data.

[0066] The number of bits in the random number sequence generated by the quantum random number generator is:

[0067] ;

[0068] In the formula: The number of bits in a random number sequence generated by a quantum random number generator; for The maximum and minimum values; The timestamp value for the current operation; The timestamp in seconds for the current operation. It is a constant;

[0069] in, , Taking the timestamp 12:36:59 as an example, =36, =59, constant The value range is (0, 1]. The more numbers and parentheses in the simplified power grid data, the larger the constant. The larger the value, the better; conversely, the smaller the value, the more likely it is to be a constant. The smaller the value;

[0070] The above formula determines the number of bits in a random number sequence by taking into account the simplified symbol distribution characteristics of the power grid data, the time factor, and the data volume characteristics.

[0071] Specifically, the maximum and minimum values ​​of the number of digits between brackets in each group of simplified power grid data are used to capture the local structural features of the data; the timestamp values ​​of minutes, hours, and seconds of the operation are introduced to incorporate dynamic time factors to enhance randomness; the constant is dynamically adjusted according to the total number of digits and brackets in the simplified power grid data. The larger the data volume, the larger the constant value, so that the number of digits in the random number sequence is adapted to the data scale. The final number of digits can match the requirements of power grid data transmission encryption for the length of random numbers, ensuring the correlation between encryption strength and data characteristics.

[0072] Obtain the design result of the number of bits in the random number sequence generated by the quantum random number generator, and apply the design result to the quantum random number generator to output a random number sequence with the corresponding number of bits;

[0073] Before the random number sequence output by the quantum random number generator is submitted to the preprocessing operation, the randomness characteristic parameters of the random number sequence are verified by a preset security threshold. The preprocessing operation is performed only when the randomness characteristic parameters meet the preset security threshold. Otherwise, the random number sequence is regenerated by the quantum random number generator and verified.

[0074] Among them, the randomness characteristic parameter includes at least the entropy value;

[0075] The formula for calculating the entropy of a random number sequence is:

[0076] ;

[0077] In the formula: This represents the number of distinct digits in the sequence. Let be the frequency of occurrence of the i-th digit; The Pearson correlation coefficient between the numbers in the sequence and their position indices; This is the theoretical maximum correlation coefficient; The number of unique patterns; The length of the number sequence; This is the preset length of the local mode window;

[0078] in, ∈[-1,1], the unique pattern refers to the pattern in The number of times the following random number sequence appears in a unique case;

[0079] The above formula comprehensively evaluates the randomness of a random number sequence from multiple dimensions. It reflects the diversity of number distribution by statistically analyzing the number of different types of numbers in the sequence and their frequency of occurrence. It introduces the Pearson correlation coefficient and its theoretical maximum correlation coefficient between the numbers in the sequence and their position indices to measure whether there is a position-dependent regularity in the sequence. It also assesses the uniqueness of local data patterns by combining the number of unique patterns, sequence length, and local pattern window length. The combined effect of these multi-dimensional indicators comprehensively quantifies the unpredictability of the random number sequence, providing a scientific basis for verifying whether random numbers meet the encryption security threshold and ensuring that the random numbers used for encryption have sufficient randomness to resist attacks.

[0080] Examples:

[0081] The number sequence is [0,1,2,3,4,4,4], If the value is 2, then we have [0,1], [1,2], [2,3], [3,4], [4,4], and [4,4]. Therefore, the unique pattern contains [0,1], [1,2], [2,3], and [3,4], which means... =4;

[0082] Pearson correlation coefficient between numbers and position indices in a sequence The calculation formula is:

[0083] ;

[0084] In the formula: This is the value corresponding to the index of the j-th position; The mean of the sequence; The mean of the location indices;

[0085] The above formula assesses whether there is a positional correlation in a random number sequence by calculating the degree of linear correlation between the number and its position index. The formula quantifies the degree of linear dependence of the numerical value on the position by the relationship between the corresponding value of the position index, the mean of the sequence, and the mean of the position index. The result is limited to the range of [-1,1], which makes it easy to intuitively judge the strength of the correlation.

[0086] If the correlation coefficient is close to 0, it indicates that there is no significant linear correlation between the number and the position, and the sequence is more random. If it deviates from 0, it indicates that there is a certain regularity and further optimization is needed. This provides a specific indicator for verifying the randomness of the random number sequence and ensures that the random number is not easily cracked by positional patterns during encryption.

[0087] Select an encryption algorithm, and preprocess the output random number sequence to match the encryption algorithm to obtain the random number sequence used by the selected encryption algorithm;

[0088] Preprocessing operations for random number sequences generated by a quantum random number generator include:

[0089] Configure a quantum state reference group containing 3 orthogonal basis vectors, with each basis vector corresponding to a set of quantum state parameters; divide the original sequence output by the quantum random number generator into sub-blocks of 1024 bits each, obtain the quantum state parameters of each sub-block through a quantum state measurement device, and retain the sub-blocks that match the basis vector parameters of any one of the reference groups with a degree ≥95% to form the initial sequence;

[0090] A processing module consisting of one frequency counter and one adjustment operation unit is constructed. The frequency counter counts the number of occurrences of "0" and "1" bits in the initial sequence in real time. The adjustment operation unit outputs the adjustment coefficient based on the statistical results. The value of each bit in the initial sequence is modulo 2 with the adjustment coefficient to make the difference between the proportion of "0" and "1" in the processed sequence ≤ 0.001.

[0091] Set up a transformation matrix consisting of three chaotic equations with a dimension of 64×64. Divide the dynamically balanced sequence into data blocks of 64 bits each. Input the data blocks into the transformation matrix and perform row shift and column XOR operations in sequence. The row shift amount is the output value of the chaotic equation, and the column XOR object is randomly specified by the chaotic equation. Repeat this process three times to obtain the disturbed sequence.

[0092] A 128-bit quantum key and a 128-bit classical key are generated. The quantum key is output by the quantum key generator, and the classical key is generated by the key derivation function. The perturbed sequence is added bit by bit with the quantum key, and then the result is encrypted with the classical key in groups of 32 bits each. The encryption rounds are 8, forming an intermediate encryption sequence.

[0093] Configure a variable hash module that supports 128-bit, 256-bit, and 512-bit output. This module contains three hash functions with different bit lengths. Based on the key length requirements of the target encryption algorithm, the corresponding hash function is selected to process the intermediate encryption sequence and output a random number sequence that meets the length requirements.

[0094] The adjustment operation unit takes the difference Δ between the counts of "0" and "1" bits counted by the frequency counter as input;

[0095] When Δ>0, the output coefficient decreases linearly as the absolute value of Δ increases. The initial value is 0.99, and the coefficient decreases by 0.05 for every 100 units of Δ, down to a minimum of 0.01.

[0096] When Δ < 0, the output coefficient increases linearly with the increase of the absolute value of Δ. The initial value is 0.01, and the coefficient increases by 0.05 for every 100 units of |Δ|, up to a maximum of 0.99.

[0097] When Δ=0, the output is fixed at 0.5;

[0098] The following explanation is provided regarding the chaotic equation, key derivation function, and hash function mentioned above:

[0099] Chaos equations:

[0100] All three chaotic equations are designed by combining the characteristics of power grid data from the Internet of Things (IoT) with quantum properties. The first is a nonlinear mapping equation based on grid voltage fluctuation parameters, using the average number of digits between brackets in a simplified grid data set as the initial input. It incorporates a 16-bit random number output from a quantum random number generator as a perturbation term, and introduces a grid frequency fluctuation coefficient (range 0.01-0.05) during the iteration process. The second is an iterative equation incorporating the instantaneous change rate of current, using the timestamp fraction T as the initial parameter, and adding vacuum fluctuation sampling values ​​(obtained by a quantum measurement device) in each iteration. The third is a differential equation based on the correlation between photon polarization state and grid phase angle, using a constant... (Taken from the quantum random number bit calculation formula in the document) is the proportionality coefficient. The range of the equation solution is strictly limited to 1-63 (to match the row shift requirement of the 64×64 transformation matrix). The iteration step size is dynamically adjusted by the total number of digits in the simplified power grid data to ensure that the output value is evenly distributed.

[0101] Key derivation function:

[0102] The input to the key derivation function is a 64-bit random number, generated from the symbol distribution (the arrangement characteristics of brackets and numbers) in the simplified power grid data. The function includes three rounds of transformation: the first round of permutation operation constructs a permutation table based on the position index of brackets in the power grid data, and the permutation rules are dynamically adjusted according to the ratio of the hour value to the second value of the timestamp; the second round of substitution operation replaces specific bits in the 32-bit blocks, and the substitution table is constructed by the 64-bit sequence output by the quantum random number generator and the hash value of the power grid device number; the third round of diffusion operation diffuses the results of the first two rounds to 128 bits by introducing the total number of numbers in the simplified power grid data as a diffusion factor, and the 8 round parameters of each transformation are all taken from the random number sequence generated by the quantum random number generator.

[0103] Hash function:

[0104] Three hash functions with different bit widths are adapted to the encryption requirements of the power Internet of Things (IoT): In the 16 rounds of compression operations of the 128-bit hash function, the obfuscation operation is designed based on the distribution density of brackets in the simplified power grid data, and the parameter for each round of obfuscation is the millisecond value of the power grid data acquisition timestamp; the diffusion operation dynamically adjusts the diffusion range by combining the entropy value of the output sequence of the quantum random number generator (calculated according to the entropy formula). The 256-bit hash function adds 8 rounds of nonlinear transformation on the basis of the 128-bit function. Its transformation factor is the XOR result of the power grid device number and the quantum key, and each round of transformation introduces the maximum consecutive occurrence of numbers in the simplified power grid data as an adjustment parameter. The 512-bit hash function contains a parallel processing structure consisting of two independent 256-bit processing units (corresponding to "parallel processing of two 256-bit hash functions" above). The inputs of the two units are the first and second halves of the intermediate encryption sequence, respectively. The input segmentation rule is determined based on the total number of brackets in the simplified power grid data (even brackets result in equal segmentation, while odd brackets result in one extra bit in the first half). The fusion operation is designed based on the spatiotemporal alignment characteristics of the power grid data (taken from the power grid data preprocessing requirements in the document). The outputs of the two 256-bit processing units are fused into a 512-bit result using a quantum state interference algorithm. The initial vector is the verified result of a 64-bit random number sequence generated by the quantum random number generator in the document.

[0105] The simplified power grid data is encrypted using the selected encryption algorithm combined with a random number sequence and then transmitted to a preset receiving end.

[0106] In the above embodiments, the method first cleans and completes the power grid data and simplifies the data volume to reduce the transmission burden; then, it designs the number of quantum random numbers based on the simplified data symbol distribution, verifies and preprocesses them to adapt to the encryption algorithm; finally, it encrypts and transmits the data. This ensures the uniqueness and integrity of the data, improves the security of the random numbers and the adaptability of the encryption, efficiently guarantees the security and stability of power Internet of Things data transmission, and meets the core requirements of the power system for data transmission.

[0107] The following is an application example of the method described in the above embodiments:

[0108] I. Power Grid Data Acquisition and Preprocessing

[0109] A regional power IoT system needs to transmit key operational data from a 110kV substation within its jurisdiction. The data collection targets include three main transformers (numbered T1-001, T1-002, and T1-003), two sets of switchgear (numbered K1-001 and K1-002), and one energy storage device (numbered B1-001). The data collection time is from 9:00 AM to 9:05 AM on July 24, 2025. The source device name and number for each data set will be simultaneously labeled, for example, "T1-001: 2025-07-24 09:00, Voltage 220kV, Current 600A, Temperature 42℃" and "K1-001: 2025-07-24 09:00, Switch Status Closed, Load 30MW".

[0110] Preprocess the collected raw data:

[0111] Cleaning: Remove erroneous or duplicate data such as "T1-002: 2025-07-24 09:03, abnormal voltage value";

[0112] Complete: Add the missing "energy storage capacity 85%" data for B1-001 at 9:02;

[0113] Spatiotemporal alignment: Unify the timestamps of all data to the hour of every minute (e.g., 9:00, 9:01, etc.) to ensure consistency in the time dimension of the data. Combine this with the distribution location of the equipment to further divide the power data corresponding to the equipment, ensuring consistency in the spatial dimension of the data.

[0114] II. Simplified Processing of Power Grid Data

[0115] 1. Character Map Creation

[0116] Traverse the preprocessed power grid data, extract all character information (such as "transformer, voltage, kV, closed, capacity", etc.), remove duplicates to obtain the character set, and create a character-number mapping table:

[0117] Transformer → 1, Voltage → 2, kV → 3, Current → 4, A → 5, Temperature → 6, ℃ → 7, Switch → 8, Status → 9, Closed → 10, Load → 11, MW → 12, Energy Storage → 13, Capacity → 14, % → 15.

[0118] 2. Character substitution and bracket marking

[0119] Replace the characters in the original data with numbers from the mapping table. For example, replace "T1-001:2025-07-2409:00,voltage220kV,current600A,temperature42℃" with "T1-001:2025-07-2409:00,22203,46005,6427".

[0120] The number of numbers after the replacement, a (numbers in the mapping table: 2, 3, 4, 5, 6, 7, a total of 6) and the number of numbers in the original table, b (220, 600, 42, a total of 3), are compared. Since a > b, parentheses are added to the original numbers. The final simplified data is "T1-001: 2025-07-2409:00, 2 (220) 3, 4 (600) 5, 6 (42) 7".

[0121] III. Bit Design of Quantum Random Number Generator

[0122] 1. Symbol Distribution State Analysis

[0123] In the simplified data, the brackets are (220), (600), and (42). Calculate the number of numbers between the brackets in order:

[0124] The numbers between (220) and (600) are 3 and 4, and the quantity X1 = 2;

[0125] The numbers between (600) and (42) are 5 and 6, and the quantity is X2=2.

[0126] Therefore, in the symbol distribution state, MAX(X)=2 and MIN(X)=2.

[0127] 2. Digit Calculation

[0128] The current timestamp is 9:05:10, where the minute value T=5 and the second value t=10; due to the moderate data volume, constants are used. Take 0.6. Calculate the number of digits in the random number sequence according to the formula:

[0129] S = 6 bits.

[0130] IV. Quantum Random Number Generation and Verification

[0131] 1. Generate a random number sequence

[0132] Based on the design results, the quantum random number generator outputs a 6-bit random number sequence "101001".

[0133] 2. Randomness verification

[0134] The frequency of occurrence of different digit types k=2 (0 and 1) in the sequence ;

[0135] Pearson correlation coefficient r: The mean of position indices 1-6 is f=3.5, the sequence mean is x=0.5, and the calculated r=0.03;

[0136] Theoretical maximum correlation coefficient max=1, local pattern window L=2, number of unique patterns S=3 ("10", "01", "00"), sequence length N=6;

[0137] The entropy value H = 0.97, which is higher than the safety threshold of 0.8, so the verification is successful.

[0138] V. Preprocessing of Random Number Series

[0139] 1. Sub-block filtering

[0140] The 6-bit sequence is treated as a sub-block, and its quantum state parameters are obtained through a quantum state measurement device. The matching degree with the three preset orthogonal basis vectors is 96% (≥95%), and the sub-block is retained.

[0141] 2. Frequency Adjustment

[0142] The frequency counter counts that "0" appears 3 times and "1" appears 3 times, Δ=0, and the adjustment coefficient is fixed at 0.5; after each bit is modulo 2 with 0.5, the difference in the proportion of "0" and "1" is 0, which meets the requirements.

[0143] 3. Transformation and Encryption

[0144] A 64×64 transformation matrix is ​​constructed, and the sequence is padded with 58 bits (total 64 bits) and then three row shifts and column XOR operations are performed to generate a scrambled sequence. Combining a 128-bit quantum key and a classical key, after eight rounds of 32-bit block encryption, a 128-bit random number sequence adapted to the AES algorithm is output.

[0145] VI. Encrypted Transmission

[0146] The AES-128 encryption algorithm is selected, and a preprocessed random number sequence is used as the key to encrypt the simplified power grid data, generating the ciphertext "E7F3...A2D9", which is then transmitted to the preset receiving end (regional power dispatch center) to complete the secure transmission process.

[0147] In summary, the methods described in the above embodiments ensure data quality by cleaning, completing, and aligning the power grid data in time and space. Data simplification is achieved through character mapping and digital processing, effectively reducing the amount of data transmitted and improving transmission efficiency. Furthermore, the number of quantum random numbers is designed based on the symbol distribution of the simplified data, and combined with entropy verification and Pearson correlation coefficient analysis to ensure the high randomness and security of the random number sequence. In addition, preprocessing techniques such as quantum state reference group screening, 0-1 bit adjustment, and chaotic transformation are used to adapt the random numbers to encryption algorithms, enhancing encryption adaptability. Finally, quantum encryption technology is combined to achieve encrypted data transmission, comprehensively improving the security, efficiency, and reliability of power Internet of Things (IoT) data transmission, and meeting the high requirements of power systems for data real-time performance and confidentiality.

[0148] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A secure quantum-encrypted transmission method for the power Internet of Things, characterized in that, include: Collect power grid data, simplify the power grid data, and output the simplified power grid data as the transmission target; Traverse simplified power grid data, and design the number of bits in the random number sequence generated by a quantum random number generator based on the symbol distribution state in the simplified power grid data; The symbol distribution state in the simplified power grid data refers to the distance between each group of brackets in the simplified power grid data. The symbol distribution state is represented according to the arrangement order of the brackets in the simplified power grid data as follows: ; This indicates the number of numbers between the first set of parentheses and the next set of parentheses in the simplified power grid data. This indicates the number of numbers between the parentheses appearing in the second group and the parentheses appearing in the third group in the simplified power grid data. The number of bits in the random number sequence generated by the quantum random number generator is: ； In the formula: The number of bits in a random number sequence generated by a quantum random number generator; for The maximum and minimum values; The timestamp value for the current operation; The timestamp in seconds for the current operation. It is a constant; in, , Taking the timestamp 12:36:59 as an example, =36, =59, constant The value range is (0, 1]. The more numbers and parentheses in the simplified power grid data, the larger the constant. The larger the value, the better; conversely, the smaller the value, the more likely it is to be a constant. The smaller the value; Obtain the design result of the number of bits in the random number sequence generated by the quantum random number generator, and apply the design result to the quantum random number generator to output a random number sequence with the corresponding number of bits; Select an encryption algorithm, and preprocess the output random number sequence to match the encryption algorithm to obtain the random number sequence used by the selected encryption algorithm; The simplified power grid data is encrypted using the selected encryption algorithm combined with a random number sequence and then transmitted to a preset receiving end.

2. The secure quantum-encrypted transmission method for the power Internet of Things according to claim 1, characterized in that, The power grid data comes from various transmission, storage, and distribution equipment in the power network. Each set of power grid data is simultaneously marked with the name and number of the transmission, storage, and distribution equipment from which it originates during the collection process. Before performing multimodal simplification, the power grid data is simultaneously cleaned, completed, and spatiotemporally aligned to ensure that each set of power grid data collected is unique and complete relative to all power grid data. The simplification of the power grid data refers to the simplification of the data volume. In the process of multimodal simplification, the power grid data that has been cleaned, completed, and spatiotemporally aligned is simultaneously set with a data collection space or a data collection time interval. The set data collection space or time interval is used to select the corresponding power grid data from all collected power grid data that has been cleaned, completed, and spatiotemporally aligned as the simplification processing target.

3. The secure quantum encrypted transmission method for the power Internet of Things according to claim 1, characterized in that, The simplified data volume processing logic for the power grid data is as follows: Traverse the power grid data, extract all character information from the power grid data, remove duplicate characters from the character information to obtain a character information set without duplicate characters, and create a character mapping table based on the character information set that maps characters to numbers; Based on the character information in the power grid data, the corresponding number is queried in the character mapping table, and the queried number is used to replace the corresponding character information in the power grid data. The number of numbers replaced by the query result in the power grid data, 'a', is compared with the number of numbers originally contained in the power grid data, 'b'. When a > b, add parentheses to all the original numbers contained in the power grid data; When a < b, add parentheses to all numbers in the power grid data that are replaced by the query results; when a = b, add parentheses to either all numbers originally contained in the power grid data or all numbers in the power grid data that are replaced by the query results, to simplify the power grid data into a simplified version consisting only of numbers and parentheses.

4. A secure quantum-encrypted transmission method for the power Internet of Things according to claim 1, characterized in that, Before the random number sequence output by the quantum random number generator is submitted to the preprocessing operation, the randomness characteristic parameters of the random number sequence are verified by a preset security threshold. The preprocessing operation is performed only when the randomness characteristic parameters meet the preset security threshold; otherwise, the random number sequence is regenerated by the quantum random number generator and verified. Among them, the randomness characteristic parameter includes at least the entropy value.

5. A secure quantum-encrypted transmission method for the power Internet of Things according to claim 4, characterized in that, The entropy value of the random number sequence is calculated using the following formula: ； In the formula: This represents the number of distinct digits in the sequence. Let be the frequency of occurrence of the i-th digit; The Pearson correlation coefficient between the numbers in the sequence and their position indices; This is the theoretical maximum correlation coefficient; The number of unique patterns; The length of the number sequence; This is the preset length of the local mode window; in, ∈[-1,1], the unique pattern refers to the pattern in The number of times the following random number sequence appears in a unique case.

6. A secure quantum-encrypted transmission method for the power Internet of Things according to claim 5, characterized in that, The Pearson correlation coefficient between the numbers and position indices in the sequence. The calculation formula is: ； In the formula: This is the value corresponding to the index of the j-th position; The mean of the sequence; This represents the average of the location indices.

7. A secure quantum-encrypted transmission method for the power Internet of Things according to claim 1, characterized in that, Preprocessing operations for random number sequences generated by a quantum random number generator include: Configure a quantum state reference group containing 3 orthogonal basis vectors, with each basis vector corresponding to a set of quantum state parameters; divide the original sequence output by the quantum random number generator into sub-blocks of 1024 bits each, obtain the quantum state parameters of each sub-block through a quantum state measurement device, and retain the sub-blocks that match the basis vector parameters of any one of the reference groups with a degree ≥95% to form the initial sequence; A processing module consisting of one frequency counter and one adjustment operation unit is constructed. The frequency counter counts the number of occurrences of "0" and "1" bits in the initial sequence in real time. The adjustment operation unit outputs the adjustment coefficient based on the statistical results. The value of each bit in the initial sequence is modulo 2 with the adjustment coefficient to make the difference in the proportion of "0" and "1" in the processed sequence ≤ 0.

001. Set up a transformation matrix consisting of three chaotic equations with a dimension of 64×64. Divide the dynamically balanced sequence into data blocks of 64 bits each. Input the data blocks into the transformation matrix and perform row shift and column XOR operations in sequence. The row shift amount is the output value of the chaotic equation, and the column XOR object is randomly specified by the chaotic equation. Repeat this process three times to obtain the disturbed sequence. A 128-bit quantum key and a 128-bit classical key are generated. The quantum key is output by the quantum key generator, and the classical key is generated by the key derivation function. The perturbed sequence is added bit by bit with the quantum key, and then the result is encrypted with the classical key in groups of 32 bits each. The encryption rounds are 8, forming an intermediate encryption sequence. Configure a variable hash module that supports 128-bit, 256-bit, and 512-bit output. This module contains three hash functions with different bit lengths. Based on the key length requirements of the target encryption algorithm, select the corresponding hash function to process the intermediate encryption sequence and output a random number sequence that meets the length requirements.

8. A secure quantum-encrypted transmission method for the power Internet of Things according to claim 7, characterized in that, The adjustment operation unit takes the difference Δ between the "0" and "1" bit counts statistically analyzed by the frequency counter as input; When Δ>0, the output coefficient decreases linearly as the absolute value of Δ increases. The initial value is 0.99, and the coefficient decreases by 0.05 for every 100 units of Δ, down to a minimum of 0.

01. When Δ < 0, the output coefficient increases linearly with the increase of the absolute value of Δ. The initial value is 0.01, and the coefficient increases by 0.05 for every 100 units of |Δ|, up to a maximum of 0.

99. When Δ=0, the output is fixed at 0.5.

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