Single-phase electric energy meter data encryption transmission method and system based on edge computing

Abstract

This invention relates to the field of digital information transmission, and more particularly to a method and system for encrypted transmission of single-phase electricity meter data based on edge computing. The method includes: calculating a fluctuation coefficient; adaptively selecting the number of encryption rounds for the AES encryption algorithm based on the fluctuation coefficient; extracting physical noise features; reconstructing the S-box of the AES encryption algorithm based on the physical noise features to obtain a dynamically coupled S-box; encrypting the data to be encrypted using the dynamically coupled S-box and the number of encryption rounds to obtain intermediate ciphertext; generating a dynamic strength coefficient based on the fluctuation coefficient; performing energy whitening processing on the intermediate ciphertext to obtain the final ciphertext; and performing integrity verification on the final ciphertext and transmitting it to a server. The technical solution of this invention can improve the accuracy of the transmission results and achieve a balance between data transmission security, real-time performance, and low power consumption on resource-constrained edge devices.

Description

Technical Field

[0001] This invention relates to the field of digital information transmission. In particular, it relates to a method and system for encrypted data transmission from single-phase energy meters based on edge computing. Background Technology

[0002] With the development of smart grids and edge computing, the secure transmission of data from single-phase electricity meters is becoming increasingly important. In typical application scenarios such as new energy battery testing, electricity data exhibits a clear alternation between stable and abrupt changes: under stable operating conditions such as constant current / constant voltage testing, the sequences of voltage, current, and active power are highly time-correlated, exhibit highly repetitive patterns, and have extremely low entropy values. This makes the commonly used standard AES-128 algorithm prone to forming predictable differential feature paths, which are highly vulnerable to differential cryptanalysis attacks.

[0003] The existing Chinese patent application document with publication number CN120761678A discloses a novel split-type single-phase smart energy meter for testing with new energy batteries. On resource-constrained edge devices, the energy data generated under stable operating conditions exhibits high time correlation, pattern repetition, and low entropy characteristics, which makes the AES encryption algorithm easy to form predictable differential paths and thus vulnerable to differential cryptanalysis attacks. At the same time, the fixed high number of encryption rounds results in high computational overhead and high power consumption, leading to low accuracy of the encrypted transmission results. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention provides solutions in the following aspects.

[0005] In a first aspect, the present invention provides a method for encrypted transmission of single-phase energy meter data based on edge computing, comprising: acquiring current sequence, voltage sequence, and active power sequence of a single-phase energy meter during a preset sampling period; performing high-pass filtering on the current sequence to obtain a current ripple component sequence; calculating a loss equivalent impedance sequence based on the current sequence and voltage sequence; calculating a fluctuation coefficient based on the active power sequence and current ripple component sequence; adaptively selecting the number of encryption rounds of the AES encryption algorithm based on the fluctuation coefficient; extracting physical noise features based on the loss equivalent impedance sequence; reconstructing the S-box of the AES encryption algorithm based on the physical noise features to obtain a dynamically coupled S-box; encrypting the data to be encrypted using the dynamically coupled S-box and the number of encryption rounds to obtain intermediate ciphertext; generating a dynamic strength coefficient based on the fluctuation coefficient; performing energy whitening processing on the intermediate ciphertext to obtain final ciphertext; and performing integrity verification on the final ciphertext and transmitting it to a server.

[0006] Preferably, the calculation of the fluctuation coefficient includes: calculating the first-order difference absolute value sequence of the active power sequence, and calculating the mean of the first-order difference absolute value sequence as a first mean; calculating the range of the active power sequence, and calculating the first sum of the range and a preset constant, and taking the ratio of the first mean to the first sum as a first ratio; calculating the product of the first ratio and the obtained power-related weights as a first product; calculating the root mean square of the current ripple component sequence, and taking the ratio of the root mean square to the maximum current measurable by a single-phase energy meter as a second ratio; calculating the first difference between the constant 1 and the power-related weights; taking the product of the first difference and the second ratio as a second product; and taking the sum of the first product and the second product as the fluctuation coefficient.

[0007] Preferably, the step of adaptively selecting the number of encryption rounds for the AES encryption algorithm based on the fluctuation coefficient includes: comparing the fluctuation coefficient with a preset first threshold and a second threshold, wherein the first threshold is less than the second threshold; in response to the fluctuation coefficient being less than the first threshold, selecting the AES-128 encryption algorithm and setting the number of encryption rounds to 10; in response to the fluctuation coefficient being not less than the first threshold and less than the second threshold, selecting the AES-192 encryption algorithm and setting the number of encryption rounds to 12; and in response to the fluctuation coefficient being not less than the second threshold and not greater than 1, selecting the AES-256 encryption algorithm and setting the number of encryption rounds to 14.

[0008] Preferably, the extraction of physical noise features based on the loss equivalent impedance sequence includes: taking the lower 8 bits of the binary equivalent impedance value at any sampling time in the loss equivalent impedance sequence, constructing a transformation sequence from the lower 8 bits of the binary equivalent impedance values ​​at all sampling times in the preset sampling period, shifting the lower 8 bits of the binary equivalent impedance value at each sampling time in the transformation sequence according to the shift amount to obtain a shift sequence, and performing a continuous XOR operation on all sampling times in the shift sequence to obtain a first result, wherein the shift amount is the remainder of the sampling time number and 8; interleaving and splicing the current sequence and voltage sequence in the preset sampling period according to the sampling time order to obtain a spliced ​​sequence, converting the spliced ​​sequence into a binary sequence, performing a lightweight hash operation on the binary sequence to obtain a hash fingerprint, and taking the lower 8 bits of the binary representation of the hash fingerprint as a second result; and using the value obtained by XORing the first result and the second result as the physical noise feature.

[0009] Preferably, the step of reconstructing the S-box of the AES encryption algorithm based on the physical noise features to obtain the dynamically coupled S-box includes: traversing integer indices from 0 to 255 to obtain the basic value at the corresponding index in the S-box of the AES encryption algorithm; performing a bitwise XOR operation on the basic value and the physical noise features to obtain an intermediate transformation value; using the remainder of the physical noise features modulo 8 as the shift bit, performing a left circular shift operation on the intermediate transformation value to obtain the shift result; and using the shift result as the mapping value corresponding to the currently traversed index in the dynamically coupled S-box.

[0010] Preferably, the step of generating a dynamic intensity coefficient based on the fluctuation coefficient and performing energy whitening processing on the intermediate ciphertext to obtain the final ciphertext includes: linearly mapping the fluctuation coefficient to a dynamic intensity coefficient in integer form; splitting the intermediate ciphertext into bytes; and for any byte, performing an XOR operation between the byte and the dynamic intensity coefficient to obtain the corresponding byte of the final ciphertext.

[0011] Preferably, the step of performing integrity verification on the final ciphertext and transmitting it to the server includes: using the CRC32 algorithm to verify the final ciphertext and generate a check code; packaging the final ciphertext and the check code into an encrypted data packet; and publishing the encrypted data packet to the server based on the MQTT protocol or the CoAP protocol.

[0012] Secondly, the present invention also provides a single-phase energy meter data encryption transmission system based on edge computing, comprising: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned single-phase energy meter data encryption transmission method based on edge computing is implemented.

[0013] The present invention has the following effects:

[0014] This invention breaks through the limitations of traditional fixed encryption strategies. By calculating the fluctuation coefficient, it adaptively adjusts the number of AES encryption rounds, reducing computational overhead under stable data conditions and enhancing security strength under severe fluctuations. This achieves an optimal balance between edge device resources and security, effectively extending the device's lifespan.

[0015] This invention innovatively utilizes loss equivalent impedance sequences to extract physical noise features and reconstruct a dynamic S-box, deeply fusing hardware physical noise with electrical data fingerprints. This effectively increases the entropy of the key space, blocking differential cryptanalysis attack paths based on low-entropy plaintext at the core algorithm level, preventing replay and cloning attacks. Through energy whitening processing based on fluctuation coefficients, the diffusion and resistance to statistical attacks of the ciphertext are further enhanced, solving the problem of a single ciphertext pattern under stable operating conditions. Finally, by combining a lightweight communication protocol and integrity verification, it ensures that high-security transmission requirements are met at the edge of resource-constrained single-phase energy meters, while also maintaining low power consumption and real-time performance. Attached Figure Description

[0016] Figure 1 This is a flowchart of a single-phase energy meter data encryption transmission method based on edge computing, according to an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0018] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] Reference Figure 1 The method for encrypted transmission of single-phase energy meter data based on edge computing includes steps S1-S4, as follows:

[0020] S1: Collect the current sequence, voltage sequence and active power sequence of a single-phase energy meter during a preset sampling period, perform high-pass filtering on the current sequence to obtain the current ripple component sequence, and calculate the loss equivalent impedance sequence based on the current sequence and voltage sequence.

[0021] In one embodiment, the current sequence, voltage sequence, and active power sequence of a single-phase energy meter are synchronously collected during a preset sampling period. The current sequence is then subjected to high-pass filtering to remove low-frequency fundamental components and extract the current ripple component sequence, which characterizes high-frequency noise. This current ripple component sequence reflects the switching power supply noise and battery polarization characteristics during the charging and discharging process of the new energy battery. Based on the current and voltage sequences, calculations are performed to obtain the loss equivalent impedance sequence, which reflects the line loss characteristics. This loss equivalent impedance sequence encompasses cable contact resistance information within the single-phase energy meter structure and is dynamically affected by the physical state of the buffer mechanism. Specifically, the ratio of voltage to current at any sampling moment is taken as the loss equivalent impedance at that sampling moment; the current at any sampling moment is the real-time current flowing through the circuit; and the voltage at any sampling moment is the voltage drop (including line voltage drop) measured at the sampling point.

[0022] Thus, we can obtain the current sequence, voltage sequence, active power sequence, current ripple component sequence, and loss equivalent impedance sequence.

[0023] S2: Calculate the fluctuation coefficient based on the active power sequence and the current ripple component sequence, and adaptively select the number of encryption rounds of the AES encryption algorithm according to the fluctuation coefficient.

[0024] In one embodiment, the first-order difference absolute value sequence is calculated for the active power sequence, and the mean of the first-order difference absolute value sequence is taken as the first mean to characterize the frequency of active power changes. Simultaneously, the range of the active power sequence is calculated, and the range is summed with a preset constant to obtain the first sum value to prevent anomalies caused by a denominator of 0 (exemplarily, the preset constant is 0.001). The ratio of the first mean to the first sum value is then used as the first ratio to characterize the normalized relative fluctuation of active power. The first ratio is multiplied by the obtained power-related weights to obtain the first product characterizing the contribution of power fluctuations.

[0025] The root mean square (RMS) value of the current ripple component sequence is calculated, and the ratio of the RMS value to the maximum measurable current of a single-phase energy meter is used as the second ratio to characterize the normalization level of the high-frequency ripple intensity. At the same time, the difference between the constant 1 and the power-related weight is calculated as the current ripple-related weight, and this weight is multiplied by the second ratio to obtain the second product characterizing the contribution of ripple noise.

[0026] Summing the first and second products yields a fluctuation coefficient ranging from 0 to 1. This fluctuation coefficient integrates the macroscopic fluctuation characteristics of active power with the microscopic noise characteristics of current ripple. A larger value indicates more drastic changes in the current load conditions and a more complex electromagnetic environment, thus providing a quantitative basis for the subsequent adaptive selection of the number of encryption rounds.

[0027] The process of obtaining the power-related weights is a well-known technique to those skilled in the art. Specifically, it includes obtaining the power-related weights by combining the coefficients of variation of the power sequence and the impedance sequence in history, so as to give more importance to the feature terms that are less stable in historical distribution. That is, first calculate the sum of the coefficients of variation of the power sequence and the coefficients of variation of the impedance sequence, and use the ratio of the coefficients of variation of the power sequence to the sum as the power-related weights.

[0028] The fluctuation coefficient is compared with a preset first threshold and a second threshold, wherein the first threshold is less than the second threshold.

[0029] In response to a volatility coefficient less than a first threshold, the AES-128 encryption algorithm is selected, with 10 encryption rounds. In response to a volatility coefficient not less than the first threshold and less than a second threshold, the AES-192 encryption algorithm is selected, with 12 encryption rounds. In response to a volatility coefficient not less than the second threshold and not greater than 1, the AES-256 encryption algorithm is selected, with 14 encryption rounds. For example, the first threshold is set to 0.3, and the second threshold is set to 0.8.

[0030] By comparing the calculated fluctuation coefficient with a preset first threshold and a second threshold, the number of encryption rounds is adaptively selected. When the power environment is extremely stable, causing the fluctuation coefficient to be less than the first threshold, the AES-128 encryption algorithm is selected and the number of encryption rounds is set to 10 to minimize the computational overhead of the edge computing device. When the load fluctuates drastically, causing the fluctuation coefficient to be no less than the second threshold, the AES-256 encryption algorithm is selected and the number of encryption rounds is set to 14 to provide maximum security and ensure encryption strength. When the fluctuation coefficient is in the intermediate state between the first and second thresholds, the AES-192 encryption algorithm is selected and the number of encryption rounds is set to 12. This dynamically adjusts the encryption strategy according to the changes in the fluctuation coefficient, achieving an optimal balance between computing resources and security strength while ensuring data transmission security.

[0031] S3: Extract physical noise features based on the loss equivalent impedance sequence, and reconstruct the S-box of the AES encryption algorithm based on the physical noise features to obtain the dynamically coupled S-box.

[0032] It should be noted that, in response to the distinct "stable to abrupt" alternation characteristics of power data streams in new energy battery testing or daily power consumption scenarios, an adaptive encryption mechanism is established: Under stable operating conditions such as constant current / constant voltage testing, given the single data pattern, high repetition, and low plaintext entropy, using a fixed high number of encryption rounds would result in severe waste of computing power and theoretically, easily form predictable differential paths. In this case, the system reduces the number of encryption rounds based on a lower fluctuation coefficient to optimize computational overhead, and reconstructs the dynamically coupled S-box using physical noise features extracted based on loss equivalent impedance sequences, thereby destroying potential differential attack paths at the algorithm core level, thus maintaining necessary security while reducing resource consumption. During periods of drastic load switching, given the high entropy and strong randomness of the data itself, and corresponding to critical test conditions, the system triggers a higher number of encryption rounds based on a higher fluctuation coefficient, further enhancing security strength to protect highly sensitive data. Ultimately, this achieves adaptive encrypted transmission that dynamically balances computational overhead and security strength based on the real-time characteristics of the data.

[0033] In one embodiment, all sampling moments in the loss equivalent impedance sequence within a preset sampling period are traversed, and the lower 8 bits of the loss equivalent impedance value at each sampling moment are extracted to construct a transformation sequence. Then, the shift amount is determined according to the remainder of the sampling moment number and 8. The corresponding shift operation is performed on the lower 8 bits of the loss equivalent impedance value at each sampling moment in the transformation sequence to obtain a shift sequence. Continuous XOR operations are performed on the data of all sampling moments in the shift sequence to accumulate and obtain a first result, thereby capturing the randomness of impedance perturbations. At the same time, the current sequence and voltage sequence within the same preset sampling period are interleaved and spliced ​​according to the sampling time order to obtain a spliced ​​sequence. After converting the spliced ​​sequence into a binary sequence, a lightweight hash operation is performed to obtain a hash fingerprint, and the lower 8 bits of the hash fingerprint are extracted as a second result to bind the electrical characteristics of the current batch. Finally, the first result and the second result are XORed, and the generated value is the physical noise feature. The physical noise feature integrates hardware mechanical noise and electrical data fingerprint, which is used for subsequent reconstruction of the dynamically coupled S-box.

[0034] For example, a certain sampling point loss equivalent impedance The original value is ,in, This indicates that the number is in hexadecimal, and its decimal value is 31, simulating a small impedance fluctuation. First, the lower 8 bits of the binary equivalent impedance of the loss are taken, i.e. (binary is) The lower 8 bits of the binary representation are chosen because the higher bits of the loss equivalent impedance mainly reflect the DC resistance of the cable, which is relatively stable; while the lower bits contain high-entropy instantaneous physical noise. Furthermore, the purpose of using 8 bits is for subsequent XOR masking and cyclic shifting of the S-box. Specifically, the S-box contains 256 entry indices (0~255), each entry being an 8-bit byte, which can well match byte-level operations. If a mode with fewer or more than 8 bits is used, additional expansion or truncation is required.

[0035] Sampling points The sampling number is 3, and the displacement is the remainder of 3 divided by 8, which is 3. Therefore, the binary... Left circular shift by 3 bits .

[0036] The XOR result after shifting all sampling points is: The lower 8 bits of the CRC32 for the voltage-current interleaved sequence are .

[0037] Final physical noise characteristics XOR equal (The decimal value is 153).

[0038] It should be explained that the introduction of lightweight hashing to generate hash fingerprints from the interleaved binary sequence of voltage and current sequences aims to address the insufficient entropy of mechanical noise extracted solely from the loss equivalent impedance sequence under stable operating conditions, and to avoid reduced randomness due to weak impedance jitter or periodic interference. By performing hashing on the electrical data of the current data batch, the generated physical noise features can be tightly bound to the specific electrical content of the data batch, ensuring that the reconstruction of each batch of dynamically coupled S-boxes relies on real-time data features, thereby effectively preventing attackers from using historical data to carry out replay attacks. At the same time, the introduction of hash fingerprints significantly supplements the entropy of physical noise features, enhances the unpredictability of random numbers, and makes the final generated physical noise features possess both hardware physical uniqueness and dynamic correlation at the data content level, providing a high-entropy random source for the secure reconstruction of dynamically coupled S-boxes, thus building a physical security barrier against differential attacks at the core of the algorithm.

[0039] The algorithm iterates through all integer indices from 0 to 255, reading the base value at the corresponding index in the AES encryption algorithm S-box one by one to establish an initial mapping benchmark. Then, it performs a bitwise XOR operation between the base value and an 8-bit physical noise feature to obtain an intermediate transformation value that introduces hardware physical randomness, thus breaking the fixed nonlinear structure of the standard S-box. Next, it calculates the remainder of the physical noise feature modulo 8 as the shift bit, performs a left circular shift operation on the intermediate transformation value, and obtains a shifted result with dynamic bit obfuscation, ensuring that even if the base value is the same, differences will occur due to different shift amounts. Finally, it stores the shifted result as the mapping value corresponding to the currently traversed index in the dynamically coupled S-box, completing the full reconstruction of the standard S-box and generating a dynamic lookup table that deeply integrates the hardware physical noise features of the current batch. This ensures that the byte replacement rules for each data batch are unique and unpredictable, effectively blocking differential attack paths from the core transformation level of the algorithm.

[0040] S4: Encrypt the data to be encrypted using the dynamically coupled S-box and the number of encryption rounds to obtain intermediate ciphertext; generate a dynamic strength coefficient based on the fluctuation coefficient, perform energy whitening processing on the intermediate ciphertext to obtain the final ciphertext; perform integrity verification on the final ciphertext and transmit it to the server.

[0041] In one embodiment, AES encryption is performed on the data to be encrypted using a dynamically coupled S-box reconstructed based on physical noise features and an adaptively selected number of encryption rounds. The byte substitution transformation uses a dynamically coupled S-box to incorporate hardware physical randomness, thereby generating intermediate ciphertext. Subsequently, based on the fluctuation coefficient calculated above, it is converted into an integer form of dynamic strength coefficient through linear mapping. The intermediate ciphertext is then split into bytes. For any byte after splitting, a bitwise XOR operation is performed with the dynamic strength coefficient to complete energy whitening processing, thereby enhancing the diffusion and resistance to statistical attacks of the ciphertext, and obtaining the final ciphertext.

[0042] The CRC32 algorithm is used to perform integrity verification calculation on the final ciphertext, generate the corresponding check code, and package the final ciphertext and the check code to form a complete encrypted data packet. Finally, based on lightweight IoT communication protocols such as MQTT or CoAP, the encrypted data packet is published to the server to complete the secure transmission and integrity verification of the data, ensuring that the server can receive and verify the encrypted power data, thereby achieving high-security, low-overhead encrypted transmission of power data under the condition of limited edge computing resources.

[0043] The edge computing-based single-phase energy meter data encryption transmission system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the edge computing-based single-phase energy meter data encryption transmission method according to the first aspect of the present invention.

[0044] The edge computing-based single-phase energy meter data encryption transmission system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces. Their setup and functions are known in the art and will not be described in detail here.

[0045] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A single-phase electric energy meter data encryption transmission method based on edge computing, characterized in that, include: The current sequence, voltage sequence and active power sequence of a single-phase energy meter are collected during a preset sampling period. The current sequence is high-pass filtered to obtain the current ripple component sequence. The loss equivalent impedance sequence is calculated based on the current sequence and voltage sequence. The fluctuation coefficient is calculated based on the active power sequence and the current ripple component sequence, and the number of encryption rounds of the AES encryption algorithm is adaptively selected according to the fluctuation coefficient. Physical noise features are extracted based on the loss equivalent impedance sequence, and the S-box of the AES encryption algorithm is reconstructed based on the physical noise features to obtain a dynamically coupled S-box. The data to be encrypted is encrypted using a dynamically coupled S-box and a number of encryption rounds to obtain intermediate ciphertext. The dynamic intensity coefficient is generated based on the fluctuation coefficient, and the energy whitening process is performed on the intermediate ciphertext to obtain the final ciphertext. The final encrypted text is then verified for integrity and transmitted to the server. The method for extracting physical noise features based on the loss equivalent impedance sequence includes: taking the lower 8 bits of the binary representation of the loss equivalent impedance value at any sampling moment in the loss equivalent impedance sequence, constructing a transformation sequence from the lower 8 bits of the binary representation of the loss equivalent impedance values ​​at all sampling moments in the preset sampling period, shifting the lower 8 bits of the binary representation of the loss equivalent impedance value at each sampling moment in the transformation sequence according to a shift amount to obtain a shift sequence, and performing a continuous XOR operation on all sampling moments in the shift sequence to obtain a first result, where the shift amount is the remainder of the sampling moment number divided by 8; interleaving and splicing the current sequence and voltage sequence of the preset sampling period according to the sampling time order to obtain a spliced ​​sequence, converting the spliced ​​sequence into a binary sequence, performing a lightweight hash operation on the binary sequence to obtain a hash fingerprint, and taking the lower 8 bits of the binary representation of the hash fingerprint as a second result; and using the value obtained by XORing the first result and the second result as the physical noise feature. The process of reconstructing the S-box of the AES encryption algorithm based on physical noise features to obtain a dynamically coupled S-box includes: traversing integer indices from 0 to 255 to obtain the base value at the corresponding index in the S-box of the AES encryption algorithm; performing a bitwise XOR operation between the base value and the physical noise features to obtain an intermediate transformation value; using the remainder of the physical noise features modulo 8 as the shift bit, performing a left circular shift operation on the intermediate transformation value to obtain the shift result; and using the shift result as the mapping value corresponding to the currently traversed index in the dynamically coupled S-box.

2. The edge computing-based single-phase electric energy meter data encryption transmission method according to claim 1, characterized in that, The calculation of the volatility coefficient includes: Calculate the first-order difference absolute value sequence of the active power sequence, and calculate the mean of the first-order difference absolute value sequence as the first mean; calculate the range of the active power sequence, and calculate the first sum of the range and a preset constant, and use the ratio of the first mean to the first sum as the first ratio; calculate the product of the first ratio and the obtained power-related weights as the first product; Calculate the root mean square of the current ripple component sequence, and use the ratio of the root mean square to the maximum current measurable by a single-phase energy meter as the second ratio; calculate the first difference between the constant 1 and the power-related weight; and use the product of the first difference and the second ratio as the second product. The sum of the first and second products is used as the fluctuation coefficient.

3. The edge computing-based single-phase electric energy meter data encryption transmission method according to claim 1, characterized in that, The step of adaptively selecting the number of encryption rounds for the AES encryption algorithm based on the volatility coefficient includes: The fluctuation coefficient is compared with a preset first threshold and a second threshold, wherein the first threshold is less than the second threshold; In response to the fluctuation coefficient being less than the first threshold, the AES-128 encryption algorithm is selected, and the number of encryption rounds is set to 10 rounds. In response to the fluctuation coefficient being not less than the first threshold and less than the second threshold, the AES-192 encryption algorithm is selected, and the number of encryption rounds is set to 12 rounds. In response to the fluctuation coefficient being no less than the second threshold and no greater than 1, the AES-256 encryption algorithm is selected, and the number of encryption rounds is set to 14 rounds.

4. The method for encrypted transmission of single-phase energy meter data based on edge computing according to claim 1, characterized in that, The step of generating a dynamic intensity coefficient based on the fluctuation coefficient and performing energy whitening processing on the intermediate ciphertext to obtain the final ciphertext includes: The fluctuation coefficient is linearly mapped to the dynamic intensity coefficient in integer form; The intermediate ciphertext is split into bytes. For any byte, an XOR operation is performed between the byte and the dynamic strength coefficient to obtain the corresponding byte of the final ciphertext.

5. The method for encrypted transmission of single-phase energy meter data based on edge computing according to claim 1, characterized in that, The step of performing integrity verification on the final ciphertext and transmitting it to the server includes: The CRC32 algorithm is used to verify the final ciphertext and generate a check code. The final ciphertext and the check code are then packaged together to form an encrypted data packet. Encrypted data packets are published to the server based on the MQTT or CoAP protocol.

6. A single-phase energy meter data encryption transmission system based on edge computing, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the edge computing-based data encryption transmission method for single-phase energy meters according to any one of claims 1-5.

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