Electric energy meter data encryption transmission method and device based on edge calculation

Abstract

The invention relates to the field of data processing, in particular to an electric energy meter data encryption transmission method and device based on edge computing, and the method comprises the steps: obtaining and preprocessing a historical electric energy meter data sequence of a user and a to-be-transmitted data sequence, presetting a window length range, traversing and analyzing the correlation of adjacent data sequences in each window, and calculating the stability. And selecting the window length corresponding to the maximum stability value as the representative window length, and calculating the deviation degree between the data sequence to be transmitted and the electric energy meter data sequence in the corresponding time period in the window as the sensitivity. And judging whether the key is regenerated or not based on the sensitivity, and encrypting and transmitting the to-be-transmitted data sequence after the key is generated. According to the method, whether the secret key is updated or not is dynamically judged by adaptively selecting the window length, calculating the deviation distance, combining the stability confidence coefficient and the correlation weight and setting the sensitivity threshold value, the efficiency is improved while the data transmission safety is ensured, and the data transmission safety is ensured.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more particularly to a method and apparatus for encrypted transmission of electricity meter data based on edge computing. Background Technology

[0002] As a critical metering device in the power system, the accuracy and security of electricity meter data are paramount. In modern power systems, encryption technology is typically employed to ensure the confidentiality, integrity, and availability of electricity meter data during transmission. Currently, the AES encryption algorithm is a commonly used method for encrypting electricity meter data transmission. It effectively prevents data from being stolen or tampered with during transmission, ensuring the security and reliability of power system operation data and providing a fundamental guarantee for the stable operation and accurate metering of the power system.

[0003] However, existing technologies using AES encryption to encrypt and transmit electricity meter data have a significant drawback: the lack of a robust key update mechanism. Electricity meters often operate online for extended periods, and over time, the keys may age, gradually reducing their security. Furthermore, if a key is leaked, the security of the entire data transmission is severely threatened. Current systems lack automatic and secure key update mechanisms under such circumstances, which undoubtedly increases data security risks and poses a threat to the safe operation of the power system.

[0004] If, to compensate for the shortcomings of the aforementioned key update mechanism, the key is updated with each encrypted transmission, new problems arise. Frequent key updates require constant key negotiation and distribution, which significantly increases the computational burden of encryption and decryption. Simultaneously, it leads to a substantial increase in communication load, not only reducing data transmission efficiency but also potentially placing unnecessary pressure on the power system's communication network, thus affecting the normal operation of the entire system. Summary of the Invention

[0005] To address the problem that frequent key updates in existing technologies, while compensating for the shortcomings of existing mechanisms, increase the workload of encryption and decryption operations and communication, reduce data transmission efficiency, put pressure on power system communication networks, and thus affect the normal operation of the system, this invention provides solutions in the following aspects.

[0006] In the first aspect, the edge computing-based method for encrypted transmission of electricity meter data includes: acquiring historical electricity meter data sequences and data sequences to be transmitted for each user and preprocessing them; setting a preset window length range for the preprocessed historical electricity meter data sequences, and traversing and analyzing the correlation between adjacent sets of electricity meter data sequences within each window length range, calculating the stability of the historical electricity meter data sequences corresponding to each user for each window length; selecting the maximum value of the stability as the representative window length for the corresponding user, and using the deviation between the data sequence to be transmitted and the electricity meter data sequences of the corresponding time period within the representative window length as the sensitivity; determining whether to regenerate the key based on the sensitivity, and after key generation, using the key to encrypt the data sequence to be transmitted, and transmitting the encrypted data to ensure the security of the electricity meter data during transmission.

[0007] By acquiring and preprocessing historical electricity meter data sequences and data sequences to be transmitted for each user, and then analyzing the correlation between adjacent sets of electricity meter data sequences within a preset window length, the stability of the historical electricity meter data sequences for the corresponding user is calculated. The window length corresponding to the maximum stability value is selected as the representative window length for that user, and the deviation between the data sequence to be transmitted and the electricity meter data for the corresponding time period within this representative window length is quantified as sensitivity. Based on this sensitivity, it is determined whether a new key needs to be generated. After key generation, the key is used to encrypt the data sequence to be transmitted, and then the encrypted data is transmitted, thereby ensuring the security of electricity meter data during transmission. This method not only improves the security of data transmission but also optimizes the efficiency of data processing and the flexibility of the system by adaptively selecting the window length and dynamically adjusting the key management strategy. It improves the security of data transmission, reduces unnecessary key update operations, reduces computational and communication load, improves the overall efficiency of the system, enhances the adaptability and flexibility of the system, and ensures the accuracy and real-time performance of data processing.

[0008] Preferably, the preprocessing step includes: The historical electricity meter data sequence and the data sequence to be transmitted are cleaned, invalid data is deleted or corrected, missing data is filled in, and the data is aligned according to the time axis.

[0009] Preferably, the stability is calculated using the following methods: Calculate the Pearson correlation coefficient between the electricity meter data sequences of every two adjacent days within the window length, and calculate the mean and standard deviation of the Pearson correlation coefficient. The ratio between the mean of the Pearson correlation coefficient and the standard deviation of the Pearson correlation coefficient is used as the stability of the user's historical electricity meter data sequence within the window length.

[0010] Preferably, the sensitivity is calculated in the following ways: The stability of the historical electricity meter data sequence of the user corresponding to the window length is used as the confidence level. The shortest path distance between each data sequence to be transmitted and the electricity meter data sequence of the corresponding time period within the representative window length is calculated. The mean of the Pearson correlation coefficient between each electricity meter data sequence within the representative window length and the electricity meter data sequences of the two adjacent days is calculated and 1 is added to obtain the correlation weight. Calculate the mean of the Pearson correlation coefficients of the electricity meter data sequences for each adjacent two days within the representative window length, and add 1 to obtain the overall correlation strength; Calculate the product of the shortest path distance and the relevance weight, then divide by the overall relevance strength. Sum the results for each day within the representative window length, multiply the sum by stability, and then normalize.

[0011] Preferably, the step of determining whether to regenerate the key based on sensitivity includes: When the sensitivity is less than or equal to a preset threshold, it indicates that the data sequence to be transmitted does not pose a significant privacy risk, and the original key remains unchanged. Conversely, when the sensitivity is greater than the preset threshold, it indicates that the data sequence to be transmitted poses a high privacy risk, and the key needs to be regenerated before transmission.

[0012] Preferably, the step of regenerating the key before transmission includes: The key of the previous transmission of the data sequence to be transmitted is used as the initial random seed, and a salt value is randomly generated to obtain a pseudo-random key. The pseudo-random key is concatenated with the sensitivity, hash value and required key length of the corresponding data sequence to be transmitted, and the HKDF algorithm is used again to generate the final key.

[0013] Preferably, the key update mechanism includes the following steps: When the sensitivity of the data sequence to be transmitted exceeds a preset threshold, the system will trigger a key update mechanism to regenerate the key, thereby enhancing the security of data transmission. When the sensitivity of the data sequence to be transmitted in the next time period is lower than or equal to the preset threshold, the key generated in the previous time period will continue to be used for data encryption to maintain the continuity and efficiency of data transmission. Conversely, if the sensitivity of the data sequence to be transmitted in the next time period exceeds the preset threshold again, the system will restart the key update mechanism to regenerate the key, ensuring the security of data transmission.

[0014] Secondly, an edge computing-based encrypted transmission device for electricity meter data is characterized by comprising a data acquisition module, an encryption processing module, a signal transmission module, and a signal receiving module. The data acquisition module is mounted on the electricity meter and is used to acquire electricity meter data. It includes a collector 1 with an interface at its bottom. The encryption processing module is used to encrypt the acquired electricity meter data and includes an encryptor 6 connected to the collector 1 via the interface. The signal transmission module includes a base 2 and a transmitting antenna 3. The interface is connected to the base 2, and a transmitter is housed within the base 2, connected to the transmitting antenna 3. The signal receiving module includes a concentrator 4. The transmitting antenna 3 is wirelessly connected to the receiving antenna 5 of the concentrator 4. The concentrator 4 contains a receiver, a memory, and a processor for receiving the encrypted electricity meter data. The concentrator 4 has a display screen and data viewing / setting buttons for displaying and viewing the encrypted data. The memory stores computer program instructions, which, when executed by the processor, implement any of the edge computing-based encrypted transmission methods for electricity meter data.

[0015] The present invention has the following effects: 1. This invention comprehensively improves the security and efficiency of electricity data transmission by adaptively selecting the window length, calculating the deviation distance using the DTW algorithm, combining stability confidence and correlation weights of adjacent day data sequences, and setting a data sensitivity threshold to determine whether a key update is needed. It not only accurately captures differences in electricity usage habits among different users but also effectively identifies abnormal electricity usage behavior, ensuring the accuracy and real-time nature of data processing. By dynamically adjusting the key management strategy, the system can flexibly adjust according to the data sensitivity of different time periods, enhancing the system's adaptability and flexibility.

[0016] 2. This invention effectively enhances data transmission security by dynamically assessing the sensitivity of the data sequence to be transmitted and determining whether to update the key accordingly. When the data sensitivity exceeds a preset threshold, the system triggers a key update mechanism to generate a new key for the highly sensitive data, thereby ensuring the security of this data during transmission and effectively preventing privacy leaks. Simultaneously, the regenerated key is correlated with the data's sensitivity characteristic value, enabling dynamic adjustment of encryption strength to ensure data transmission security.

[0017] 3. This invention avoids unnecessary key update operations by continuing to use the key generated in the previous time period when the data sensitivity is below or equal to a preset threshold. This reduces computational and communication load and improves the overall efficiency of the system. It balances the security and efficiency of data transmission, ensuring the efficient operation of the system. Furthermore, by generating keys in a chain, a logical association is formed, facilitating the tracking of key update history and ensuring the traceability of key management. Attached Figure Description

[0018] Figure 1 This is a flowchart of steps S1-S4 in the edge computing-based encrypted transmission method for electricity meter data according to an embodiment of the present invention.

[0019] Figure 2 This is a structural block diagram of the energy meter data encryption transmission device based on edge computing according to an embodiment of the present invention. Detailed Implementation

[0020] 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.

[0021] Reference Figure 1 The method for encrypted transmission of electricity meter data based on edge computing includes steps S1-S4, as follows: S1: Obtain the historical electricity meter data sequence and the data sequence to be transmitted for each user and perform preprocessing.

[0022] The preprocessing steps include: The historical electricity meter data sequence and the data sequence to be transmitted are cleaned, invalid data is deleted or corrected, missing data is filled in, and the data is aligned according to the time axis. The preprocessed data is then initially segmented according to the timestamp.

[0023] In this embodiment, the electricity meter collects electricity consumption data every minute. The electricity meter data includes: timestamp, electricity consumption, power, and load information. Data from multiple smart meters is aggregated by a concentrator. The concentrator is responsible for collecting data from multiple electricity meters and performing preliminary processing.

[0024] In other words, the data sequence to be transmitted is transmitted once per hour. The historical electricity meter data sequence is segmented by hour to obtain the historical electricity meter data corresponding to the time period of the data sequence to be transmitted. The data sequence to be transmitted can be processed independently and in parallel, thereby improving the processing speed. Independently encrypting the data for each hour can increase the overall security of the data and avoid the risks brought by a single key. Segmented transmission can reduce the amount of data transmitted in a single transmission, reduce transmission latency, and dynamically adjust the transmission strategy according to the data volume and network status to optimize data transmission efficiency.

[0025] In the encrypted transmission of electricity meter data, the commonly used AES (Advanced Encryption Standard) encryption algorithm, while providing basic data protection, lacks an automatic and intelligent key update mechanism. If all data shares a single key, security is low, and data leakage is easily risked. While changing the key with each transmission can improve security, it increases the amount of encryption computation and communication load. Therefore, it is necessary to quantify the sensitivity characteristics of the transmitted data to determine whether a key change is needed. This ensures data security while avoiding unnecessary key update operations. The specific steps are as follows: S2: Set a preset window length range for the preprocessed historical electricity meter data sequence, and iterate through and analyze the correlation between two adjacent sets of electricity meter data sequences within each window length range, and calculate the stability of the historical electricity meter data sequence for each user corresponding to each window length.

[0026] Stability can be calculated in the following ways: Calculate the Pearson correlation coefficient between the electricity meter data sequences of every two adjacent days within the window length, and calculate the mean and standard deviation of the Pearson correlation coefficient. The ratio between the mean of the Pearson correlation coefficient and the standard deviation of the Pearson correlation coefficient is used as the stability of the user's historical electricity meter data sequence within the window length.

[0027] Specifically, stability satisfies the following relationship: ; In the formula, This indicates that the length of the representative window is... The stability of the historical electricity meter data sequence for users. This indicates that the length of the representative window is... At that time, the mean of the Pearson correlation coefficient of the electricity meter data series for every two consecutive days, This indicates that the window length is... At that time, the standard deviation of the Pearson correlation coefficient of the electricity meter data series for every two consecutive days, This indicates that the length of the representative window is... Time Tianhe Di The Pearson correlation coefficient of the electricity meter data series can reflect the correlation between electricity consumption series of two adjacent days. The higher the correlation, the more similar the user's electricity consumption behavior is on those two adjacent days.

[0028] For example, in this embodiment, the window length range The maximum value is 30 days and the minimum value is 3 days. In other words, electricity meter data is usually recorded in a finer time granularity, such as hours or minutes. However, this fine-grained data may be too complex to analyze the overall electricity consumption behavior of users and is difficult to directly reflect the long-term electricity consumption pattern of users. By using days as the unit, this fine-grained data can be aggregated to form a more representative and operable dataset. Furthermore, data measured in days is more readily used in practical applications such as electricity billing, electricity planning, and anomaly detection, providing sufficient information while avoiding the complexity and computational burden caused by overly fine-grained data.

[0029] Within a preset window length range, each possible window length is iterated sequentially, gradually increasing the window length until the maximum window length is reached, to evaluate the stability and consistency of user power consumption behavior under each window length. Specifically, when the window length... Calculate the Pearson correlation coefficients for days 1 and 2, and days 2 and 3, respectively; when the window length is... The Pearson correlation coefficients for days 1 and 2, 2 and 3, and 3 and 4 are calculated separately, and so on. The window length with the highest stability value is selected, as this length best reflects the stability and consistency of the user's electricity consumption behavior. Since the user's historical electricity meter data sequences have different characteristics, such as seasonality, periodicity, and random fluctuations, different window lengths can capture different data characteristics.

[0030] Conversely, an inappropriate window length may lead to the over-amplification or neglect of data fluctuations, thus affecting the accuracy of data analysis. By selecting the optimal window length, the sensitivity of each data segment can be assessed more accurately. Furthermore, choosing a suitable window length can reduce unnecessary calculations and improve the overall efficiency of the system.

[0031] S3: Select the maximum value of stability as the representative window length for the corresponding user, and use the degree of deviation between the data sequence to be transmitted and the energy meter data sequence for the corresponding time period within the representative window length as the sensitivity.

[0032] The methods for calculating sensitivity include: The stability of the historical electricity meter data sequence of the user corresponding to the window length is used as the confidence level. The shortest path distance between each data sequence to be transmitted and the electricity meter data sequence of the corresponding time period within the window length is calculated. The mean of the Pearson correlation coefficient between the electricity meter data sequence of each day within the window length and the electricity meter data sequences of the two adjacent days is calculated and 1 is added to obtain the correlation weight. Calculate the mean of the Pearson correlation coefficients of the electricity meter data sequences for each adjacent two days within the representative window length, and add 1 to obtain the overall correlation strength; Calculate the product of the shortest path distance and the relevance weight, then divide by the overall relevance strength. Sum the results for each day within the representative window length, multiply the sum by stability, and then normalize.

[0033] Specifically, sensitivity satisfies the following relationship: ; In the formula, Indicates the first Sensitivity of the segment of data sequence to be transmitted This indicates that the length of the representative window is... The stability of the historical electricity meter data sequence of the user at that time, as a basis for utilizing the first The degree of deviation of the meter's data from historical data quantifies its sensitivity confidence value; the higher the stability of the user's historical electricity consumption data, the higher the confidence value. This indicates the length of the window. Indicates the first The sequence of data to be transmitted and the first segment of historical data The shortest path distance of the electricity meter data sequence for the corresponding time period. Indicates the first in historical data The mean of the Pearson correlation coefficient between the daily electricity meter data series and the data series of two adjacent days. This indicates that the length of the representative window is... The mean of the Pearson correlation coefficient of the electricity meter data series for every two consecutive days. This indicates that the length of the representative window is... Time Tianhe Di Pearson correlation coefficient of the data series of electricity meters Represented by natural numbers An exponential function with base 0. This represents the normalization function.

[0034] In other words, the DTW (Dynamic Time Warping) algorithm effectively handles nonlinear scaling between time series. It amplifies distance differences through an exponential function, making the impact of less similar data on the final result more significant. By calculating the correlation between adjacent days' data, it assesses the stability of daily data; higher correlation indicates greater similarity to adjacent days' data, resulting in higher stability. This assigns higher weights to data with higher deviations, making the sensitivity feature value more accurately reflect data anomalies. Furthermore, by evaluating the correlation of data within the entire window, the overall stability of the data within the window can be determined. Higher overall correlation indicates stronger stability of the data within the window.

[0035] By analyzing the Pearson correlation coefficient and stability index of users' historical electricity meter data, the representative window length of the historical electricity meter data sequence is adaptively selected, thereby accurately capturing the differences in electricity consumption habits among different users and effectively avoiding the baseline deviation problem caused by a fixed window length. Furthermore, the deviation distance between each segment of the data to be transmitted and the historical data is calculated using the DTW algorithm, and the sensitivity feature value of each data segment is quantitatively evaluated by combining the stability confidence and the Pearson correlation weight of adjacent days' electricity meter data. This not only identifies abnormal electricity consumption behavior but also incorporates the user's historical electricity consumption data trend into the sensitivity assessment, ensuring the accuracy and real-time nature of the assessment results. In addition, a data sensitivity threshold is set to determine whether a key update is needed, cleverly balancing the security and efficiency of electricity data transmission. On the one hand, it avoids the security risks caused by all transmitted data sharing a single key; on the other hand, it also solves the high load problem caused by updating the key for each transmission.

[0036] S4: Based on sensitivity, determine whether to regenerate the key. After key generation, use the key to encrypt the data sequence to be transmitted, and then transmit the encrypted data to ensure the security of the electricity meter data during transmission.

[0037] When the sensitivity is less than or equal to a preset threshold, it indicates that the data sequence to be transmitted does not pose a significant privacy risk, and the original key remains unchanged. Conversely, when the sensitivity is greater than the preset threshold, it indicates that the data sequence to be transmitted poses a high privacy risk, and the key needs to be regenerated before transmission.

[0038] Based on the evaluation results of sensitivity feature values, the key management strategy is dynamically adjusted, which avoids the high load problem caused by frequently updating keys indiscriminately for all data segments, and ensures that data segments with high privacy risks can be adequately protected before transmission.

[0039] In this embodiment, the preset threshold is 0.6, which can be adjusted according to specific circumstances.

[0040] After calculating the sensitivity features of each data sequence to be transmitted and determining the key update requirements, further analysis of the key generation method is necessary. If a completely random key is used for updating, since this randomly generated key is unrelated to the characteristics of the electricity consumption data, there will be a lack of connection between the key update process and the data sensitivity, making on-demand encryption based on data sensitivity impossible. To solve this problem, this embodiment uses the sensitivity of the data sequence to be transmitted to assist in key generation, thereby ensuring the security of the encryption process. For the key generation method, the HKDF (HMAC-based Key Derivation Function) algorithm is selected. This is a key derivation function based on HMAC (Hash-based Message Authentication Code), whose operation includes two stages: extraction and expansion, ensuring that a high-entropy and secure key is derived from the initial input material. The specific operation steps are as follows: The key of the previous transmission of the data sequence to be transmitted is used as the initial random seed, and a salt value is randomly generated to obtain a pseudo-random key. The pseudo-random key is concatenated with the sensitivity, hash value and required key length of the corresponding data sequence to be transmitted, and the HKDF algorithm is used again to generate the final key.

[0041] By introducing the HKDF algorithm, the final key is generated by combining the sensitivity features of the data sequence segment to be transmitted with the data hash value. This key generation method dynamically binds the key to the privacy risks and content characteristics of the current data segment, thus avoiding the problem of disconnect between the key and data sensitivity in traditional methods. Simultaneously, the key used in the previous transmission is used as the initial random seed, and a randomly generated salt value is introduced to calculate the pseudo-random key. This reduces the dependence on the edge node random number generator and minimizes computational redundancy caused by repeated random number generation. Furthermore, by introducing a random salt value, the randomness of each key extraction process is ensured, effectively avoiding potential weaknesses arising from key chain dependence.

[0042] In this embodiment, the HMAC function uses the SHA-256 hash algorithm to ensure the security and fixed length of the output key.

[0043] When the sensitivity of the data sequence to be transmitted exceeds a preset threshold, the system will trigger a key update mechanism to regenerate the key, thereby enhancing the security of data transmission. When the sensitivity of the data sequence to be transmitted in the next time period is lower than or equal to the preset threshold, the key generated in the previous time period will continue to be used for data encryption to maintain the continuity and efficiency of data transmission. Conversely, if the sensitivity of the data sequence to be transmitted in the next time period exceeds the preset threshold again, the system will restart the key update mechanism to regenerate the key, ensuring the security of data transmission.

[0044] By dynamically assessing the sensitivity of the data sequence to be transmitted and determining whether to update the key accordingly, the security of data transmission is effectively enhanced. When the data sensitivity exceeds a preset threshold, the system triggers a key update mechanism to generate a new key for the highly sensitive data, thereby ensuring the security of this data during transmission and effectively preventing privacy leaks. Meanwhile, for data with sensitivity below or equal to the threshold, the existing key continues to be used, avoiding unnecessary key update operations, reducing computational and communication load, and improving the overall efficiency of the system. Furthermore, this method establishes a logical association between keys through chained key generation, facilitating the tracking of key update history and ensuring the traceability of key management. Overall, this method optimizes the key management process and improves the system's operational efficiency and reliability while ensuring data transmission security.

[0045] This invention also provides a data encryption transmission device for electricity meters based on edge computing. For example... Figure 2 As shown, the device includes a data acquisition module, an encryption processing module, a signal transmission module, and a signal receiving module; The device comprises the following components: a data acquisition module mounted on the electricity meter for collecting electricity meter data, including a collector 1 with an interface at its bottom; an encryption module for encrypting the collected electricity meter data, including an encryptor 6 connected to the collector 1 via the interface; a signal transmission module including a base 2 and a transmitting antenna 3, with the interface connected to the base 2, and a transmitter housed within the base 2 connected to the transmitting antenna 3; and a signal receiving module including a concentrator 4, with the transmitting antenna 3 wirelessly connected to the receiving antenna 5 of the concentrator 4. The concentrator 4 contains a receiver, a memory, and a processor for receiving the encrypted electricity meter data. The concentrator 4 also includes a display screen and data viewing / setting buttons for displaying and viewing the encrypted data. The memory stores computer program instructions, which, when executed by the processor, implement the edge computing-based electricity meter data encryption transmission method according to the first aspect of the present invention. The device also includes other components well-known to those skilled in the art, such as a communication bus and a communication interface, the configuration and functions of which are known in the art and will not be described further here.

[0046] 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 method for encrypted transmission of electricity meter data based on edge computing, characterized in that, include: Acquire and preprocess the historical electricity meter data sequences and data sequences to be transmitted for each user. The preprocessed historical electricity meter data sequence is given a preset window length range, and the correlation between two adjacent sets of electricity meter data sequences within each window length is analyzed. The stability of the historical electricity meter data sequence of each user corresponding to each window length is calculated. The maximum value of stability is selected as the representative window length for the corresponding user, and the degree of deviation between the data sequence to be transmitted and the energy meter data sequence of the corresponding time period within the representative window length is used as the sensitivity. Based on sensitivity assessment, a key is regenerated. After key generation, the key is used to encrypt the data sequence to be transmitted, and the encrypted data is then transmitted to ensure the security of the electricity meter data during transmission.

2. The method for encrypted transmission of electricity meter data based on edge computing according to claim 1, characterized in that, The preprocessing steps include: The historical electricity meter data sequence and the data sequence to be transmitted are cleaned, invalid data is deleted or corrected, missing data is filled in, and the data is aligned according to the time axis.

3. The method for encrypted transmission of electricity meter data based on edge computing according to claim 1, characterized in that, The stability is calculated using the following methods: Calculate the Pearson correlation coefficient between the electricity meter data sequences of every two adjacent days within the window length, and calculate the mean and standard deviation of the Pearson correlation coefficient. The ratio between the mean of the Pearson correlation coefficient and the standard deviation of the Pearson correlation coefficient is used as the stability of the user's historical electricity meter data sequence within the window length.

4. The method for encrypted transmission of electricity meter data based on edge computing according to claim 1, characterized in that, The sensitivity is calculated in the following ways: The stability of the historical electricity meter data sequence of the user corresponding to the window length is used as the confidence level. The shortest path distance between each data sequence to be transmitted and the electricity meter data sequence of the corresponding time period within the window length is calculated. The mean of the Pearson correlation coefficient between the electricity meter data sequence of each day within the window length and the electricity meter data sequences of the two adjacent days is calculated and 1 is added to obtain the correlation weight. Calculate the mean of the Pearson correlation coefficients of the electricity meter data sequences for each adjacent two days within the representative window length, and add 1 to obtain the overall correlation strength; Calculate the product of the shortest path distance and the relevance weight, then divide by the overall relevance strength. Sum the results for each day within the representative window length, multiply the sum by stability, and then normalize.

5. The method for encrypted transmission of electricity meter data based on edge computing according to claim 1, characterized in that, The sensitivity-based determination of whether to regenerate the key includes: When the sensitivity is less than or equal to a preset threshold, it indicates that the data sequence to be transmitted does not pose a significant privacy risk, and the original key remains unchanged. Conversely, when the sensitivity is greater than the preset threshold, it indicates that the data sequence to be transmitted poses a high privacy risk, and the key needs to be regenerated before transmission.

6. The method for encrypted transmission of electricity meter data based on edge computing according to claim 5, characterized in that, The step of regenerating the key before transmission includes: The key of the previous transmission of the data sequence to be transmitted is used as the initial random seed, and a salt value is randomly generated to obtain a pseudo-random key. The pseudo-random key is concatenated with the sensitivity, hash value and required key length of the corresponding data sequence to be transmitted, and the HKDF algorithm is used again to generate the final key.

7. The method for encrypted transmission of electricity meter data based on edge computing according to claim 5, characterized in that, The key update mechanism includes the following steps: When the sensitivity of the data sequence to be transmitted exceeds a preset threshold, the system will trigger a key update mechanism to regenerate the key in order to enhance the security of data transmission. If the sensitivity of the data sequence to be transmitted in the next time period is lower than or equal to a preset threshold, the key generated in the previous time period will continue to be used for data encryption to maintain the continuity and efficiency of data transmission. Conversely, if the sensitivity of the data sequence to be transmitted in the next time period exceeds the preset threshold again, the system will restart the key update mechanism to regenerate the key and ensure the security of data transmission.

8. An energy meter data encryption transmission device based on edge computing, characterized in that, It includes a data acquisition module, an encryption processing module, a signal transmission module, and a signal receiving module; The data acquisition module is installed on the electricity meter and is used to collect the electricity meter data. It includes a collector 1, and the bottom of the collector 1 is provided with an interface. The encryption processing module is used to encrypt the collected electricity meter data, including the encryptor 6, which is connected to the collector 1 through an interface. The signal transmission module includes a base 2 and a transmitting antenna 3. The interface is connected to the base 2. A transmitter is installed inside the base 2 and the transmitter is connected to the transmitting antenna 3. The signal receiving module includes a concentrator 4, and a transmitting antenna 3 is wirelessly connected to the receiving antenna 5 of the concentrator 4. The concentrator 4 is equipped with a receiver, a memory, and a processor for receiving encrypted electricity meter data. The concentrator 4 is equipped with a display screen and a data retrieval and setting button for displaying and retrieving encrypted data. The memory stores computer program instructions, which, when executed by the processor, implement the edge computing-based electricity meter data encryption transmission method according to any one of claims 1-7.

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