End-to-end transmission method for energy power data dense state trusted flow
Patent Information
- Application Number
- CN202611385322.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-09-08
- Publication Date
- 2026-10-16
AI Technical Summary
[0004]本申请提供面向能源电力数据密态可信流转的端到端传输方法，以解决能源电力数据加密过程的控制参数取值不合理，导致加密效果不佳的问题，所采用的技术方案具体如下：
本申请根据标准数据块内能源电力数据的值域范围和集中分布状态，计算标准数据块的特征复杂度，标准数据块的特征复杂度越大，标准数据块内能源电力数据呈现出的重复模式或者集中分布状态越为显著、可压缩性高，越需要施加更强的混沌扰动来消除数据统计特征，考虑到不同能源电力数据对数据安全的要求不同，计算标准数据块的重要性，进而根据相邻标准数据块的特征复杂度、重要性，以及相邻标准数据块内能源电力数据的自相关性和变异系数，确定标准数据块的融合特征值、自适应控制参数；最后，根据标准数据块的特征复杂度、自相关性、变异系数和重要性，确定混沌加密算法的初始种子值，并完成各标准数据块的动态混沌加密，进而借助安全通信链路，完成密态可信流转的端到端传输，解决能源电力数据加密过程的控制参数取值不合理，导致加密效果不佳的问题。
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Figure CN122932949A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data security technology, specifically to an end-to-end transmission method for the secure and reliable flow of energy and power data. Background Technology
[0002] In power business scenarios such as smart grid dispatching, load forecasting, and equipment operation and maintenance, massive amounts of diverse energy and power data need to be frequently circulated and shared among multiple entities on the generation, grid, and user sides. Energy and power data is characterized by its large volume, complex types, and strong correlations; its transmission security directly affects the stable operation of power infrastructure and the overall security of the power grid. Generally, a stream encryption algorithm based on Logistic chaotic mapping is used to strengthen the security of energy and power data, ensuring the integrity and confidentiality of power data transmission.
[0003] However, the control parameters in the stream encryption algorithm based on Logistic chaotic mapping Using fixed values cannot adapt to the dynamic encryption requirements of power data with different characteristics. When the control parameter value is too large, the Logistic chaotic mapping trajectory will enter the periodic window prematurely or converge to a fixed point, resulting in a significant shortening of the generated key stream period and a sharp decline in random performance, making it impossible to provide long-term secure encryption key support for massive power data. When the control parameter value is too small, the chaotic mapping is in a weak chaotic state, and the autocorrelation of the generated key stream is significantly enhanced, which cannot effectively mask the statistical characteristics and temporal structure of the original data. The encrypted ciphertext still retains obvious patterns of the original data, has weak anti-decryption capabilities, and cannot meet the high-security transmission encryption requirements of energy and power data. Summary of the Invention
[0004] This application provides an end-to-end transmission method for the secure and reliable flow of energy and power data, to solve the problem of poor encryption effect caused by unreasonable control parameter values in the energy and power data encryption process. The specific technical solution adopted is as follows: One embodiment of this application provides an end-to-end transmission method for the secure and reliable flow of energy and power data, the method comprising the following steps: Collect different types of energy and electricity data, and divide the data into standard data blocks according to the same type of energy and electricity data; Based on the values of all energy and power data in the standard data block, the feature complexity of the standard data block is calculated. Combining the autocorrelation and coefficient of variation of energy and power data in adjacent standard data blocks, the importance of the standard data block is calculated. Based on the feature complexity and importance of adjacent standard data blocks, as well as the autocorrelation and coefficient of variation of energy and power data in adjacent standard data blocks, the fusion feature value of the standard data block is determined. Based on the fusion feature value, the adaptive control parameters of the standard data block are obtained. Based on the characteristic complexity, autocorrelation, coefficient of variation and importance of the standard data blocks, the initial seed value of the chaotic encryption algorithm is determined, and the dynamic chaotic encryption of each standard data block is completed. Then, with the help of a secure communication link, the end-to-end transmission of the encrypted trusted flow is completed.
[0005] Furthermore, the standard data block is a fixed-length block of fixed preset bytes.
[0006] Furthermore, the method for calculating the feature complexity of the standard data block is as follows: The ratio of the maximum value to the average value of all energy and electricity data in the standard data block is denoted as the value range of the standard data block. The ratio of the value range of the standard data block to the information entropy of the normalized values of all energy and electricity data in the standard data block is denoted as the feature complexity of the standard data block.
[0007] Furthermore, the method for calculating the importance of the standard data block is as follows: The normalized values of the feature complexity, the absolute value of the autocorrelation, and the normalized value of the coefficient of variation of the standard data block are arranged in order to obtain the feature vector of the standard data block. The average value of the absolute values of the cosine similarity between the feature vectors of the standard data block and the feature vectors of the preset number of adjacent standard data blocks is recorded as the feature similarity of the standard data block. The product of the difference between the feature similarity of the number 1 and the feature similarity of the standard data block and the preset weight factor is recorded as the importance of the standard data block.
[0008] Furthermore, the method for determining the fusion feature value of the standard data block is as follows: Based on the feature complexity, autocorrelation, coefficient of variation and importance of all standard data blocks in the sliding history cache window, construct the feature matrix of the last standard data block in the sliding history cache window; Based on the complexity, autocorrelation, coefficient of variation, and importance of all features within the feature matrix, calculate the first, second, third, and fourth feature differences of the standard data block. The sum of these four feature differences is denoted as the fifth feature difference of the standard data block. The ratios of these four feature differences to the fifth feature difference are then denoted as the first, second, third, and fourth weighting factors of the standard data block, respectively. The first, second, third, and fourth weighting factors of the standard data block are used as weights for the normalized values of feature complexity, absolute autocorrelation, coefficient of variation, and importance of the standard data block, respectively. The normalized values of feature complexity, absolute autocorrelation, coefficient of variation, and importance of the standard data block are weighted and summed. The normalized value of the weighted sum is recorded as the fusion feature value of the standard data block.
[0009] Furthermore, the method for constructing the feature matrix is as follows: The normalized values of feature complexity, absolute value of autocorrelation, normalized value of coefficient of variation, and normalized value of importance of the standard data block are arranged in order to establish the feature row vector of the standard data block. According to the order of all standard data blocks in the sliding history cache window, the feature row vectors of all standard data blocks in the sliding history cache window are arranged in order from top to bottom to establish the feature matrix of the last standard data block in the sliding history cache window.
[0010] Furthermore, the calculation methods for the first feature difference, second feature difference, third feature difference, and fourth feature difference of the standard data block are as follows: The sum of the eigenvalues of the normalized values of all feature complexities within the feature matrix is denoted as the first eigenvalue of the standard data block; the sum of the eigenvalues of the absolute values of all autocorrelation within the feature matrix is denoted as the second eigenvalue of the standard data block; the sum of the eigenvalues of the normalized values of all coefficients of variation within the feature matrix is denoted as the third eigenvalue of the standard data block; and the sum of the eigenvalues of the normalized values of all importance within the feature matrix is denoted as the fourth eigenvalue of the standard data block.
[0011] Furthermore, the adaptive control parameter of the standard data block is the sum of the product of the fused feature value of the standard data block and the preset adjustment parameter and the preset initial control parameter.
[0012] Furthermore, the method for determining the initial seed value is as follows: The normalized values of the feature complexity, absolute value of autocorrelation, normalized value of coefficient of variation, and normalized value of importance of the standard data block are concatenated into a string. The underlying binary sequence corresponding to the hash value of the concatenated string is obtained. The binary sequence is converted into a decimal integer, and the decimal integer is divided by... The calculation result is used as the initial seed value for the chaotic encryption algorithm, where, The default total number of bits in the underlying binary sequence corresponding to the hash value.
[0013] Furthermore, before dynamic chaotic encryption, boundary verification is performed on the initial seed value of the chaotic encryption algorithm. The boundary verification method is as follows: when the initial seed value is less than or equal to the preset minimum threshold, or when the initial seed value is greater than or equal to the difference between 1 and the preset minimum threshold, the initial seed value is reset to the default value of 0.5.
[0014] The beneficial effects of this application are: This application calculates the feature complexity of standard data blocks based on the value range and concentrated distribution of energy and power data within them. The greater the feature complexity, the more significant the repetitive patterns or concentrated distribution of the energy and power data within the block, and the higher its compressibility. This necessitates stronger chaotic perturbations to eliminate statistical characteristics. Considering the different data security requirements of various energy and power data, the importance of standard data blocks is calculated. Then, based on the feature complexity and importance of adjacent standard data blocks, as well as the autocorrelation and coefficient of variation of energy and power data within adjacent standard data blocks, the fusion feature value and adaptive control parameters of the standard data blocks are determined. Finally, based on the feature complexity, autocorrelation, coefficient of variation, and importance of the standard data blocks, the initial seed value of the chaotic encryption algorithm is determined, and dynamic chaotic encryption of each standard data block is completed. Then, using a secure communication link, end-to-end transmission of the encrypted and trusted flow is achieved, solving the problem of poor encryption results caused by unreasonable control parameter values in the energy and power data encryption process. Attached Figure Description
[0015] Figure 1 This is a schematic flowchart of an end-to-end transmission method for energy and power data secure and reliable flow, provided in one embodiment of this application. Detailed Implementation
[0016] Please see Figure 1 The diagram illustrates an end-to-end transmission method for trusted, encrypted flow of energy and power data according to an embodiment of this application. The method includes the following steps: Step S001: Collect different types of energy and electricity data, and divide the same type of energy and electricity data into standard data blocks.
[0017] With the increasing demand for cross-entity flow of energy and power data, privacy-preserving computation technologies such as federated learning and secure multi-party computation are widely used in scenarios such as heterogeneous data collaboration between power and transportation sectors, site selection and capacity assessment of charging facilities, and electric vehicle carrying capacity assessment of power distribution networks. In these scenarios, each participant needs to upload local model parameters or encryption features to a central node or global aggregator. These participants include power companies, transportation companies, and charging facility operators. The transmission of this encrypted data throughout the entire "device-gateway-center" link also faces security threats such as eavesdropping, tampering, and replay attacks. However, existing stream encryption algorithms based on Logistic chaotic mapping only focus on end-to-end encryption of a single data stream, failing to consider the needs of encrypted data transmission involving multiple participants, multiple rounds, and heterogeneous data types in privacy-preserving computation scenarios. They lack the ability to adapt to the differentiated data transmission characteristics in different privacy-preserving computation tasks, making it difficult to support the secure flow of the entire process of privacy-preserving computation of energy and power data.
[0018] Collect different types of energy and power data, including: operation and control data, user-side data, power data, equipment status data, meteorological data, and load forecast results data.
[0019] The operational control data includes switch status, voltage telemetry, current telemetry, and power telemetry acquired through the SCADA system, collected periodically in polling cycles of 5 seconds; user-side data includes user electricity consumption, voltage qualification rate, and power outage records acquired by AMI smart meters, reported in batches every 30 minutes; power data includes three-phase voltage phasors, frequency, and phase angle data, sampled at a frequency of 60Hz; equipment status data includes transformer temperature and vibration, collected every 10 seconds; meteorological data includes temperature, humidity, and wind speed, collected every 5 minutes; and load forecasting results are acquired through the load forecasting system, collected every 30 minutes.
[0020] Different types of energy and power data are uploaded to the edge gateway. The edge gateway performs protocol parsing on the different types of energy and power data, converting the protocol messages reported by the devices into structured business data, and further performing data cleaning to filter out abnormal, duplicate, and invalid data, outputting preprocessed energy and power data. The preprocessed energy and power data is then divided into blocks to obtain standard data blocks. In this embodiment, fixed-length blocks are used, with each standard data block fixed at 1024 bytes. When the data volume is insufficient, a PKCS#7 padding mechanism is used to supplement it.
[0021] At this point, the standard data block has been obtained.
[0022] Step S002: Calculate the feature complexity of the standard data block based on the values of all energy and power data in the standard data block. Combine the autocorrelation and coefficient of variation of energy and power data in adjacent standard data blocks to calculate the importance of the standard data block. Based on the feature complexity and importance of adjacent standard data blocks, as well as the autocorrelation and coefficient of variation of energy and power data in adjacent standard data blocks, determine the fusion feature value of the standard data block. Based on the fusion feature value, obtain the adaptive control parameters of the standard data block.
[0023] Electricity load data in energy and power systems typically exhibits periodicity, resulting in a large number of repeated bytes in the plaintext. After XOR processing, these repeated plaintext bytes leave detectable statistical bias characteristics in the ciphertext, reducing the security of the encrypted data. Furthermore, these repeated plaintext bytes are highly compressible; when the data stream is attacked, attackers can indirectly deduce relevant characteristics of the plaintext by leveraging the ciphertext's compression ratio, further increasing the risk of plaintext information leakage. Therefore, a stronger chaotic perturbation effect is needed, i.e., increasing the control parameters. The value of is chosen to make the byte distribution of the key stream as uniform as possible, converting low-entropy plaintext containing a lot of repetitive content into high-entropy ciphertext.
[0024] The ratio of the maximum value to the average value of all energy and electricity data in the standard data block is denoted as the value range of the standard data block. The ratio of the value range of the standard data block to the information entropy of the normalized values of all energy and electricity data in the standard data block is denoted as the feature complexity of the standard data block.
[0025] In the process of calculating the ratio, to avoid the denominator being zero, a preset value needs to be added to the denominator. An example of this preset value is... This embodiment uses the maximum-minimum normalization method to calculate the normalized value. In the maximum-minimum normalization method, when the difference between the maximum and minimum values is 0, the sum of the difference between the maximum and minimum values and a preset non-zero small constant is used as the denominator to calculate the normalized value. In this embodiment, the preset non-zero small constant is set to a value of [value missing]. In practical applications, implementers may use other methods of existing technology, such as the tanh function and the sigmoid function, to calculate the normalized value, which are not limited here; the calculation of information entropy is a well-known technique and will not be elaborated further.
[0026] The greater the feature complexity of the standard data block, the larger the value range of the standard data block, and the smaller the information entropy, the more significant the repetitive pattern or concentrated distribution state of the energy and electricity data in the standard data block, and the higher the compressibility, the more necessary it is to apply stronger chaotic perturbations to eliminate the statistical features of the data.
[0027] Significant autocorrelation exists among different energy and power data within a standard data block. For example, load data for the energy and power system during morning and evening hours exhibits a monotonic trend, and the corresponding plaintext displays autocorrelation. This trend-based plaintext can be linearly predicted, increasing the probability of information leakage when data transmission is attacked. Similarly, energy and power data during off-peak load periods at night is highly concentrated, unevenly distributed in bytes, and shows similarities between adjacent energy and power data, also exhibiting strong autocorrelation. Therefore, due to its inherent physical characteristics, power load data generally exhibits high autocorrelation, meaning it possesses a predictable temporal structure, making it highly susceptible to being deciphered and leading to data leakage.
[0028] Therefore, the autocorrelation of energy and electricity data within the standard data block is calculated, and the degree of linear correlation of energy and electricity data within the standard data block in the time series dimension is evaluated by the autocorrelation.
[0029] The closer the autocorrelation value of energy and power data within a standard data block is to 1, the higher the similarity between adjacent energy and power data within the standard data block; the closer the autocorrelation value of energy and power data within a standard data block is to 0, the higher the randomness of adjacent energy and power data within the standard data block.
[0030] Control parameters The larger the value of , the lower the autocorrelation of the generated chaotic sequence, the closer it is to ideal white noise, and the better it can mask the temporal structure of the plaintext. That is, the greater the autocorrelation of the energy and electricity data within the standard data block, the lower the autocorrelation characteristics required of the key stream, and the larger the control parameter needs to be set. The value of .
[0031] While the feature complexity and autocorrelation of standard data blocks are used to measure the overall statistical characteristics of energy and power data, they fail to reflect the dispersion of these data values. For example, voltage data during periods of stable base load tends to range from 220±2V, while load data during periods of peak-valley alternation spans from 30MW to 120MW. Although the autocorrelation and feature complexity of these two types of data are similar, their dispersion differs significantly, leading to different requirements for key stream diffusion. Therefore, it is necessary to evaluate the dispersion of energy and power data values within standard data blocks.
[0032] Calculate the normalized value of the coefficient of variation of energy and electricity data within the standard data block. The coefficient of variation is used to evaluate the dispersion of the values of energy and electricity data within the standard data block. The smaller the coefficient of variation, the more concentrated the values of energy and electricity data within the standard data block are, and stronger key stream diffusion is required to cover the entire value range.
[0033] It should be noted that this embodiment uses the maximum-minimum normalization method to calculate the normalized value. In the maximum-minimum normalization method, when the difference between the maximum and minimum values is 0, the sum of the difference between the maximum and minimum values and a preset non-zero small constant is used as the denominator to calculate the normalized value. In this embodiment, the preset non-zero small constant is set to a value of [value missing]. In practical applications, implementers may use other methods from existing technologies, such as the tanh function or the sigmoid function, to calculate the normalized value; no restrictions are imposed here.
[0034] Different energy and power data have different data security requirements. For example, user privacy data in energy and power systems is highly private and requires security protection, while temperature data is public data and has lower security requirements.
[0035] The normalized values of the feature complexity, the absolute value of the autocorrelation, and the normalized value of the coefficient of variation of the standard data block are arranged in order to obtain the feature vector of the standard data block. The average value of the absolute values of the cosine similarity between the feature vectors of the standard data block and the feature vectors of the preset number of adjacent standard data blocks is recorded as the feature similarity of the standard data block. The product of the difference between the feature similarity of the number 1 and the feature similarity of the standard data block and the preset weight factor is recorded as the importance of the standard data block.
[0036] To meet the millisecond-level real-time processing requirements, the edge gateway is configured with a sliding history cache window. In this embodiment, the sliding history cache window is configured to support the capacity of a standard data block. The preset quantity is set to 32. In this embodiment, the preset quantity is 31, that is, the preset quantity is 32. The difference between the number 1 and the value of the weight factor should be greater than 0 and less than or equal to 1. In this embodiment, the weight factor is set to 1. The calculation of cosine similarity is a well-known technique and will not be described in detail here.
[0037] The capacity of the sliding history cache window relative to standard data blocks. The larger the value of , the higher the stability of the weight estimation results, but the slower the response speed to changes in data burst patterns; the capacity of the sliding history cache window for standard data blocks. The smaller the value, the stronger the adaptability to data burst patterns, but the greater the fluctuation of the weight result. Implementers can select the optimal parameter within the range of values greater than or equal to 32 and less than or equal to 128, based on the actual business scenario and data characteristics.
[0038] The normalized values of feature complexity, absolute value of autocorrelation, normalized value of coefficient of variation, and normalized value of importance of the standard data block are arranged in order to establish the feature row vector of the standard data block. According to the order of all standard data blocks in the sliding history cache window, the feature row vectors of all standard data blocks in the sliding history cache window are arranged in order from top to bottom to establish the feature matrix of the last standard data block in the sliding history cache window.
[0039] Calculate the information entropy of each data point within the feature matrix of the standard data block. For any data point within the feature matrix of the standard data block, the difference between the number 1 and the information entropy of that data is denoted as the feature difference value. Calculate the information entropy of the normalized values of all feature complexities, the information entropy of the absolute values of all autocorrelation, the information entropy of the normalized values of all coefficients of variation, and the information entropy of the normalized values of all importance within the feature matrix of the standard data block. Record the differences between the number 1 and the information entropy of each of these values as the first, second, third, and fourth feature differences of the standard data block. The sum of these four feature differences is recorded as the fifth feature difference of the standard data block. The ratios of the first, second, third, and fourth feature differences of the data block to the fifth feature difference are denoted as the first, second, third, and fourth weighting factors of the standard data block, respectively. These weighting factors are then used as weights for the normalized values of feature complexity, absolute autocorrelation, coefficient of variation, and importance of the standard data block, respectively. A weighted sum is then calculated on these weighted sums, and the normalized value of the weighted sum is denoted as the fused feature value of the standard data block.
[0040] The calculation of information entropy is a well-known technique and will not be elaborated further. The smaller the information entropy of the data, the more important the information it provides, the higher the distinguishability of the data for feature recognition, and the greater the weight that should be assigned to the data.
[0041] It should be noted that this embodiment uses the maximum-minimum normalization method to calculate the normalized value. In the maximum-minimum normalization method, when the difference between the maximum and minimum values is 0, the sum of the difference between the maximum and minimum values and a preset non-zero small constant is used as the denominator to calculate the normalized value. In this embodiment, the preset non-zero small constant is set to a value of [value missing]. In practical applications, implementers may use other methods from existing technologies, such as the tanh function or the sigmoid function, to calculate the normalized value; no restrictions are imposed here.
[0042] The sum of the product of the fused characteristic value of the standard data block and the preset adjustment parameter and the preset initial control parameter is denoted as the adaptive control parameter of the standard data block.
[0043] The initial control parameter should be greater than or equal to 3.6 and less than or equal to 4. In this embodiment, the initial control parameter is 3.6 and the adjustment parameter is 0.4.
[0044] At this point, the adaptive control parameters for the standard data block are obtained.
[0045] Step S003: Based on the characteristic complexity, autocorrelation, coefficient of variation and importance of the standard data blocks, determine the initial seed value of the chaotic encryption algorithm, and complete the dynamic chaotic encryption of each standard data block. Then, with the help of a secure communication link, complete the end-to-end transmission of the encrypted trusted flow.
[0046] The normalized values of the feature complexity, absolute value of autocorrelation, normalized value of coefficient of variation, and normalized value of importance of the standard data block are concatenated into a string. The underlying binary sequence corresponding to the hash value of the concatenated string is obtained. The binary sequence is converted into a decimal integer, and the decimal integer is divided by... The calculation result is used as the initial seed value for the chaotic encryption algorithm, and the calculation result is in A continuous, smooth floating-point number within an interval. Wherein, The default total number of bits in the underlying binary sequence corresponding to the hash value.
[0047] The above processing allows for proportional scaling of long hash integer results, transforming them into... For continuous and smooth floating-point numbers within a given interval, the more uniform the numerical distribution of the initial seed value in a chaotic encryption algorithm, the better the randomness of the seed.
[0048] Boundary checks are performed on the initial seed value of the chaotic encryption algorithm. Specifically, when the initial seed value is less than or equal to a preset minimum threshold, or when the initial seed value is greater than or equal to the difference between 1 and the preset minimum threshold, the initial seed value is reset to the default value of 0.5 to ensure that the initial seed value falls within the effective chaotic open interval of the Logistic map. Internally, it avoids the performance degradation of the key stream caused by hash collisions.
[0049] Among them, the value of the minimum threshold should be greater than or equal to and less than or equal to In this embodiment, the value of the minimum threshold is... .
[0050] Dynamic chaotic encryption of each standard data block is achieved through a chaotic encryption algorithm. The ciphertext data block, along with its corresponding encryption parameters, sequence number, timestamp, and integrity verification information, is encapsulated into a transmission unit. Using existing secure communication links, such as a low-latency chained protocol combining quantum key distribution with post-quantum encryption algorithms, the encapsulated transmission unit is sent to the receiving end. Upon receiving the transmission unit, the receiving end performs signature and sequence number integrity verification. Then, based on a pre-defined key negotiation mechanism and the encryption parameters attached to the transmission unit, each standard data block is independently decrypted and reassembled sequentially to recover the original plaintext. Simultaneously, the data block hash digest and data flow record are stored on the blockchain for evidence, achieving end-to-end traceability of the transmission process and completing the entire process of trusted encrypted data transfer.
[0051] The encryption process is a well-known technology and will not be described in detail here.
[0052] This completes the end-to-end transmission of energy and power data in a dense and reliable manner.
Claims
1. An end-to-end transmission method for dense and reliable flow of energy and power data, characterized in that, The method includes the following steps: Collect different types of energy and electricity data, and divide the data into standard data blocks according to the same type of energy and electricity data; Based on the values of all energy and power data in the standard data block, the feature complexity of the standard data block is calculated. Combining the autocorrelation and coefficient of variation of energy and power data in adjacent standard data blocks, the importance of the standard data block is calculated. Based on the feature complexity and importance of adjacent standard data blocks, as well as the autocorrelation and coefficient of variation of energy and power data in adjacent standard data blocks, the fusion feature value of the standard data block is determined. Based on the fusion feature value, the adaptive control parameters of the standard data block are obtained. Based on the characteristic complexity, autocorrelation, coefficient of variation and importance of the standard data blocks, the initial seed value of the chaotic encryption algorithm is determined, and the dynamic chaotic encryption of each standard data block is completed. Then, with the help of a secure communication link, the end-to-end transmission of the encrypted trusted flow is completed.
2. The end-to-end transmission method for dense and reliable flow of energy and power data according to claim 1, characterized in that, The standard data block is a fixed-length block of fixed preset bytes.
3. The end-to-end transmission method for dense and reliable flow of energy and power data according to claim 1, characterized in that, The method for calculating the feature complexity of the standard data block is as follows: The ratio of the maximum value to the average value of all energy and electricity data in the standard data block is denoted as the value range of the standard data block. The ratio of the value range of the standard data block to the information entropy of the normalized values of all energy and electricity data in the standard data block is denoted as the feature complexity of the standard data block.
4. The end-to-end transmission method for dense and reliable flow of energy and power data according to claim 1, characterized in that, The method for calculating the importance of the standard data block is as follows: The normalized values of the feature complexity, the absolute value of the autocorrelation, and the normalized value of the coefficient of variation of the standard data block are arranged in order to obtain the feature vector of the standard data block. The average value of the absolute values of the cosine similarity between the feature vectors of the standard data block and the feature vectors of the preset number of adjacent standard data blocks is recorded as the feature similarity of the standard data block. The product of the difference between the feature similarity of the number 1 and the feature similarity of the standard data block and the preset weight factor is recorded as the importance of the standard data block.
5. The end-to-end transmission method for dense and reliable flow of energy and power data according to claim 1, characterized in that, The method for determining the fusion feature value of the standard data block is as follows: Based on the feature complexity, autocorrelation, coefficient of variation and importance of all standard data blocks in the sliding history cache window, construct the feature matrix of the last standard data block in the sliding history cache window; Based on the complexity, autocorrelation, coefficient of variation, and importance of all features within the feature matrix, calculate the first, second, third, and fourth feature differences of the standard data block. The sum of these four feature differences is denoted as the fifth feature difference of the standard data block. The ratios of these four feature differences to the fifth feature difference are then denoted as the first, second, third, and fourth weighting factors of the standard data block, respectively. The first, second, third, and fourth weighting factors of the standard data block are used as weights for the normalized values of feature complexity, absolute autocorrelation, coefficient of variation, and importance of the standard data block, respectively. The normalized values of feature complexity, absolute autocorrelation, coefficient of variation, and importance of the standard data block are weighted and summed. The normalized value of the weighted sum is recorded as the fusion feature value of the standard data block.
6. The end-to-end transmission method for dense and reliable flow of energy and power data according to claim 5, characterized in that, The method for constructing the feature matrix is as follows: The normalized values of feature complexity, absolute value of autocorrelation, normalized value of coefficient of variation, and normalized value of importance of the standard data block are arranged in order to establish the feature row vector of the standard data block. According to the order of all standard data blocks in the sliding history cache window, the feature row vectors of all standard data blocks in the sliding history cache window are arranged in order from top to bottom to establish the feature matrix of the last standard data block in the sliding history cache window.
7. The end-to-end transmission method for dense and reliable flow of energy and power data according to claim 6, characterized in that, The calculation methods for the first feature difference, second feature difference, third feature difference, and fourth feature difference of the standard data block are as follows: The sum of the eigenvalues of the normalized values of all feature complexities within the feature matrix is denoted as the first eigenvalue of the standard data block; the sum of the eigenvalues of the absolute values of all autocorrelation within the feature matrix is denoted as the second eigenvalue of the standard data block; the sum of the eigenvalues of the normalized values of all coefficients of variation within the feature matrix is denoted as the third eigenvalue of the standard data block; and the sum of the eigenvalues of the normalized values of all importance within the feature matrix is denoted as the fourth eigenvalue of the standard data block.
8. The end-to-end transmission method for dense and reliable flow of energy and power data according to claim 1, characterized in that, The adaptive control parameter of the standard data block is the sum of the product of the fused feature value of the standard data block and the preset adjustment parameter and the preset initial control parameter.
9. The end-to-end transmission method for dense and reliable flow of energy and power data according to claim 1, characterized in that, The method for determining the initial seed value is as follows: The normalized values of the feature complexity, absolute value of autocorrelation, normalized value of coefficient of variation, and normalized value of importance of the standard data block are concatenated into a string. The underlying binary sequence corresponding to the hash value of the concatenated string is obtained. The binary sequence is converted into a decimal integer, and the decimal integer is divided by... The calculation result is used as the initial seed value for the chaotic encryption algorithm, where, The default total number of bits in the underlying binary sequence corresponding to the hash value.
10. The end-to-end transmission method for dense and reliable flow of energy and power data according to claim 1, characterized in that, Before dynamic chaotic encryption, boundary verification is performed on the initial seed value of the chaotic encryption algorithm. The boundary verification method is as follows: when the initial seed value is less than or equal to the preset minimum threshold, or when the initial seed value is greater than or equal to the difference between 1 and the preset minimum threshold, the initial seed value is reset to the default value of 0.5.