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

Abstract

The present application relates to the field of data processing, especially to an electric energy meter data encryption transmission method and device based on edge computing, the method comprising: obtaining and preprocessing the historical electric energy meter data sequence and the to-be-transmitted data sequence of a user, presetting a window length range and traversing and analyzing the correlation of adjacent data sequences in each window, and calculating the stability. The window length corresponding to the maximum stability value is selected as the representative window length, and the deviation degree of the to-be-transmitted data sequence from the electric energy meter data sequence in the corresponding period in the window is calculated as the sensitivity. Whether to regenerate the key is judged based on the sensitivity, the to-be-transmitted data sequence is encrypted and transmitted after the key generation is completed. The present application dynamically judges whether to update the key by adaptively selecting the window length, calculating the deviation distance, combining the stability confidence and the correlation weight, and setting the sensitivity threshold, thereby improving the efficiency while ensuring the safety of data transmission and ensuring the safety of data transmission.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing. In particular, it relates to an edge computing-based power meter data encryption transmission method and device. BACKGROUND

[0002] As a key metering device of the power system, the accuracy and security of the data of the power meter are crucial. In modern power systems, the transmission of power meter data often uses encryption technology to ensure the confidentiality, integrity and availability of the data. Currently, the AES encryption algorithm is a commonly used method for power meter data encryption transmission. It can effectively prevent data from being stolen or tampered with during transmission, ensuring the security and reliability of the power system operation data, and providing a basic guarantee for the stable operation and accurate metering of the power system.

[0003] However, there is a significant defect in the prior art when using the AES encryption algorithm to encrypt the data of the power meter for transmission. That is, there is a lack of a perfect key update mechanism. The power meter often needs to be online for a long time. As time goes on, the key may become old, and its security will gradually decrease. Moreover, once the key is leaked, the security of the entire data transmission will be seriously threatened. However, the current system lacks an automatic and secure key update method in this case, which undoubtedly greatly increases the risk of data security and poses a hidden danger to the safe operation of the power system.

[0004] If the key is updated every time for encryption transmission in order to make up for the defect of the above key update mechanism, a new problem will arise. Frequent key updates require constant key negotiation and distribution, which will increase a large amount of encryption and decryption operations. At the same time, it will also cause a significant increase in communication load, which will not only reduce the efficiency of data transmission, but also may cause unnecessary pressure on the communication network of the power system, affecting the normal operation of the entire system. SUMMARY

[0005] To solve the problem in the prior art that frequent key updates can make up for the defects of the existing mechanism, but will increase a large amount of encryption and decryption operations and communication load, reduce the efficiency of data transmission, and bring pressure to the communication network of the power system, thereby affecting the normal operation of the system, the present application provides solutions in the following aspects.

[0006] In a first aspect, the method for encrypting transmission of electric energy meter data based on edge computing comprises: obtaining and preprocessing historical electric energy meter data sequences and to-be-transmitted data sequences of each user; presetting a window length range for the preprocessed historical electric energy meter data sequences, and traversing and analyzing the correlation between adjacent two groups of electric energy meter data sequences in each window length in the preset window length range to calculate the stability of the historical electric energy meter data sequences of each user corresponding to each window length; selecting the maximum value of the stability as the representative window length of the corresponding user, and taking the deviation between the to-be-transmitted data sequence and the electric energy meter data sequence in the corresponding period in the representative window length as the sensitivity; judging whether to regenerate a key based on the sensitivity, and after the key is generated, encrypting the to-be-transmitted data sequence by using the key, and transmitting the encrypted data to ensure the security of the electric energy meter data in the transmission process.

[0007] By obtaining and preprocessing the historical electric energy meter data sequences and to-be-transmitted data sequences of each user, then traversing and analyzing the correlation between adjacent two groups of electric energy meter data sequences in each window length in the preset window length range, the stability of the historical electric energy meter data sequences of each user is calculated. The window length corresponding to the maximum value of the stability is selected as the representative window length of the user, and the deviation between the to-be-transmitted data sequence and the electric energy meter data in the corresponding period in the representative window length is quantified as the sensitivity. Whether to regenerate a key is judged based on the sensitivity, and after the key is generated, the to-be-transmitted data sequence is encrypted by using the key, and then the encrypted data is transmitted, thereby ensuring the security of the electric energy meter data in the transmission process. 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. The security of data transmission is improved, unnecessary key update operations are reduced, the calculation and communication load is reduced, the overall efficiency of the system is improved, the adaptability and flexibility of the system are enhanced, and the accuracy and real-time performance of data processing are ensured.

[0008] Preferably, the preprocessing step comprises:

[0009] The historical electric energy meter data sequences and to-be-transmitted data sequences are data cleaned, invalid data is deleted or corrected, missing data is filled, and alignment is performed according to the time axis.

[0010] Preferably, the stability calculation method comprises:

[0011] The Pearson correlation coefficient between each adjacent two days of electric energy meter data sequences in the window length is calculated, and the mean and standard deviation of the Pearson correlation coefficient are calculated. The ratio between 1 and the mean of the Pearson correlation coefficient and the standard deviation of the Pearson correlation coefficient is taken as the stability of the historical electric energy meter data sequences of the user in the window length.

[0012] Preferably, the sensitivity is calculated in the following way:

[0013] The stability of the historical power meter data sequence of the user corresponding to the window length is taken as the confidence, the shortest path distance between each piece of to-be-transmitted data sequence and the power meter data sequence representing the corresponding time period within the window length is calculated, the mean of the Pearson correlation coefficients between each power meter data sequence within the window length and the power meter data sequences of the adjacent previous and next two days is calculated, and 1 is added to obtain the correlation weight;

[0014] The mean of the Pearson correlation coefficients between each adjacent two-day power meter data sequence within the window length is calculated, and 1 is added to obtain the overall correlation strength;

[0015] The product of the shortest path distance and the correlation weight is calculated, and then divided by the overall correlation strength, and the results of each day within the window length are summed up, multiplied by the stability, and then normalized.

[0016] Preferably, the sensitivity is calculated in the following way:

[0017] When the sensitivity is less than or equal to a preset threshold, it indicates that the to-be-transmitted data sequence does not have significant privacy risks, and the original key is maintained; otherwise, when the sensitivity is greater than the preset threshold, it indicates that the to-be-transmitted data sequence has high privacy risks, and the key needs to be regenerated before transmission.

[0018] Preferably, the step of regenerating the key before transmission includes:

[0019] The key of the last transmission data of the to-be-transmitted data sequence is taken as the initial random seed, a salt value is randomly generated to obtain a pseudo-random key, the pseudo-random key is spliced with the sensitivity, the hash value and the required key length of the corresponding to-be-transmitted data sequence, and the HKDF algorithm is used again to generate the final key.

[0020] Preferably, the key update mechanism includes the following steps:

[0021] In response to the sensitivity of the to-be-transmitted data sequence exceeding the preset threshold, the system will trigger the key update mechanism to regenerate the key to enhance the security of data transmission; in response to the sensitivity of the to-be-transmitted data sequence in the next time period being lower than or equal to the preset threshold, the key generated in the last time period is continued to be used for data encryption to maintain the continuity and efficiency of data transmission; on the contrary, if the sensitivity of the to-be-transmitted data sequence in the next time period exceeds the preset threshold again, the system will start the key update mechanism again to regenerate the key to ensure the security of data transmission.

[0022] In a second aspect, the device for encrypted transmission of electric energy meter data based on edge computing comprises a data acquisition module, an encryption processing module, a signal transmission module, and a signal receiving module. The data acquisition module is arranged on the electric energy meter and is used to acquire electric energy meter data of the electric energy meter, and comprises an acquisition device 1, the bottom end of the acquisition device 1 is provided with an interface; the encryption processing module is used to perform encryption processing on the acquired electric energy meter data, and comprises an encryption device 6, the encryption device 6 is connected with the acquisition device 1 through the interface; the signal transmission module comprises a base 2 and a transmission antenna 3, the interface is connected with the base 2, a transmitter is arranged in the base 2, and the transmitter is connected with the transmission antenna 3; the signal receiving module comprises a concentrator 4, the transmission antenna 3 is wirelessly connected with a receiving antenna 5 of the concentrator 4, a receiver, a memory, and a processor are arranged in the concentrator 4, and are used to receive the encrypted electric energy meter data; the concentrator 4 is provided with a display screen and a data review setting button, and is used to display and review the encrypted transmission data; the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method for encrypted transmission of electric energy meter data based on edge computing is realized.

[0023] The present application has the following effects:

[0024] 1. The present application selects the window length adaptively, calculates the deviation distance using the DTW algorithm, combines the stability confidence and the correlation weight of adjacent data sequences, and sets the data sensitivity threshold to determine whether the key needs to be updated, thereby comprehensively improving the security and efficiency of power consumption data transmission. It can not only accurately capture the differences in power consumption habits of different users, but also effectively identify abnormal power consumption behavior, ensuring the accuracy and real-time performance 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 adaptability and flexibility of the system.

[0025] 2. The present application dynamically evaluates the sensitivity of the data sequence to be transmitted and decides whether to update the key accordingly, effectively enhancing the security of data transmission. When the data sensitivity exceeds the preset threshold, the system triggers the key update mechanism to generate a new key for high-sensitivity data, thereby ensuring the security of these data during transmission and effectively preventing privacy leakage. At the same time, the newly generated key is related to the sensitivity characteristic value of the data, which can dynamically adjust the encryption strength to ensure the security of data transmission.

[0026] 3. The present application continues to use the key generated in the previous time period when the data sensitivity is less than or equal to the preset threshold, avoiding unnecessary key update operations, reducing the calculation and communication load, and improving the overall efficiency of the system. It balances the security and efficiency of data transmission, ensuring efficient operation of the system. In addition, by generating the key in a chain, a logical association is formed, which facilitates tracking the key update history and ensures the traceability of key management. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is a method flowchart of steps S1-S4 in the electric energy meter data encryption transmission method based on edge computing according to an embodiment of the present application.

[0028] Figure 2 is a structural block diagram of the electric energy meter data encryption transmission device based on edge computing according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments of the present application.

[0030] Referring to Figure 1 The electric energy meter data encryption transmission method based on edge computing includes steps S1-S4, and specifically as follows:

[0031] S1: Obtain the historical electric energy meter data sequence and the to-be-transmitted data sequence of each user and perform preprocessing.

[0032] The preprocessing step includes:

[0033] The historical electric energy meter data sequence and the to-be-transmitted data sequence are subjected to data cleaning, invalid data is deleted or corrected, missing data is filled, and alignment is performed according to the time axis. The preprocessed data is preliminarily segmented according to the time stamp.

[0034] In this embodiment, the electric energy meter collects power consumption data every minute, and the electric energy meter data includes time stamp, power consumption, power, and load information. The data of multiple smart electric energy meters are collected through a concentrator. The concentrator is responsible for collecting data from multiple electric energy meters and performing preliminary processing.

[0035] That is, the to-be-transmitted data sequence is transmitted once per hour, and the historical electric energy meter data sequence is segmented by hour to obtain the historical electric energy meter data of the time period corresponding to the to-be-transmitted data sequence. The to-be-transmitted data sequence can be processed independently and in parallel, thereby improving the processing speed. Independent encryption of each hour of data can increase the overall security of the data, avoid the risk brought by a single key, segmented transmission can reduce the amount of data transmitted at a time, reduce transmission delay, and dynamically adjust the transmission strategy according to the data volume and network state to optimize the data transmission efficiency.

[0036] In the data encryption transmission of the electric energy meter, the commonly used AES (Advanced Encryption Standard) encryption algorithm can provide basic data protection, but lacks an automatic and intelligent key update mechanism. If all data share a key, the security is low and the risk of data leakage is easy to occur. If the key is changed every time, although the security can be improved, a large amount of encryption operation and communication load will be increased. Therefore, the sensitivity characteristics of the transmission data need to be quantified to determine whether the key needs to be changed, so as to ensure the data security and avoid unnecessary key update operation. The specific steps are as follows:

[0037] S2: The preprocessed historical electric energy meter data sequence is preset with a window length range, and the correlation between adjacent two groups of electric energy meter data sequences in each window length in the preset window length range is analyzed and traversed. The stability of the historical electric energy meter data sequence of each window length corresponding to the user is calculated.

[0038] The calculation method of stability includes:

[0039] The Pearson correlation coefficient between each adjacent two days of electric energy meter data sequences in the window length is calculated, and the mean and standard deviation of the Pearson correlation coefficient are calculated. The ratio between 1 and the mean of the Pearson correlation coefficient and the standard deviation of the Pearson correlation coefficient is taken as the stability of the historical electric energy meter data sequence of the user in the window length.

[0040] Specifically, the stability satisfies the following relationship:

[0041] ;

[0042] In the formula, represents the stability of the historical electric energy meter data sequence of the user when the window length is represents the mean of the Pearson correlation coefficient of each adjacent two days of electric energy meter data sequences when the window length is represents the standard deviation of the Pearson correlation coefficient of each adjacent two days of electric energy meter data sequences when the window length is represents the Pearson correlation coefficient of the electric energy meter data sequence of the th day and the th day when the window length is , which can reflect the correlation of the adjacent two days of power consumption sequences. The higher the correlation is, the more similar the user's power consumption behavior in the adjacent two days is.

[0043] For example, in the embodiment, the window length range is ​​​that is, the maximum value is 30 days and the minimum value is 3 days, that is, the electric energy meter data is usually recorded in a fine time granularity, such as hours or minutes, but these fine-grained data may be too complex to directly reflect the long-term power consumption mode of the user when analyzing the overall power consumption behavior of the user, and the data in units of days can aggregate these fine-grained data to form a more representative and operable data set;

[0044] In addition, the data in units of days is easier to be used in practical applications, such as electricity fee calculation, power consumption planning and anomaly detection, and can provide sufficient information while avoiding the complexity and computational burden caused by too fine-grained data.

[0045] In the preset window length range, each possible window length is sequentially traversed, and the window length is gradually increased until the maximum window length is reached, so as to evaluate the stability and consistency of the power consumption behavior of the user under each window length. Specifically, when the window length is , the Pearson correlation coefficients of the 1st day and the 2nd day, the 2nd day and the 3rd day are calculated respectively; when the window length is , the Pearson correlation coefficients of the 1st day and the 2nd day, the 2nd day and the 3rd day, the 3rd day and the 4th day are calculated respectively, and so on. The window length with the maximum stability value is selected, which can best reflect the stability and consistency of the power consumption behavior of the user. Since the historical electric energy meter data sequence of the user has different characteristics, such as seasonality, periodicity and random fluctuation, different window lengths can capture different data characteristics.

[0046] On the contrary, if the selected window length is not appropriate, the fluctuation of the data may be overestimated or ignored, thereby affecting the accuracy of the data analysis. By selecting the optimal window length, the sensitivity of each piece of data can be more accurately evaluated. In addition, selecting a suitable window length can reduce unnecessary calculations and improve the overall efficiency of the system.

[0047] S3: Selecting the maximum value of the stability as the representative window length of the corresponding user, and taking the deviation degree between the to-be-transmitted data sequence and the electric energy meter data sequence in the corresponding time period in the representative window length as the sensitivity.

[0048] The calculation method of the sensitivity includes:

[0049] Taking the stability of the historical electric energy meter data sequence of the user corresponding to the window length as the confidence, calculating the shortest path distance between each to-be-transmitted data sequence and the electric energy meter data sequence in the corresponding time period in the representative window length; calculating the mean value of the Pearson correlation coefficients of the electric energy meter data sequence of each day in the representative window length and the adjacent previous and subsequent two days, and adding 1 to obtain the correlation weight;

[0050] The mean value of the Pearson correlation coefficient of the electric energy meter data sequence of each adjacent two days within the window length is calculated, and 1 is added to obtain the overall correlation strength;

[0051] The product of the shortest path distance and the correlation weight is calculated, and then divided by the overall correlation strength. The results of each day within the window length are summed, multiplied by the stability, and then normalized.

[0052] Specifically, the sensitivity satisfies the following relationship:

[0053] ;

[0054] In the formula, represents the sensitivity of the data sequence to be transmitted in the first segment, represents the stability of the historical electric energy meter data sequence of the user when the window length is , which is a confidence value quantifying the sensitivity of the deviation of the electric energy meter data in the first segment from the historical data. The higher the stability of the user's historical electricity consumption data, the higher the confidence; represents the mean value of the Pearson correlation coefficient of the electric energy meter data sequence of each adjacent two days within the window length, represents the shortest path distance between the data sequence to be transmitted in the first segment and the electric energy meter data sequence in the historical data corresponding to the time period of the first day, represents the mean value of the Pearson correlation coefficient of the electric energy meter data sequence of the first day in the historical data and the data sequences of the adjacent two days, represents the mean value of the Pearson correlation coefficient of the electric energy meter data sequence of each adjacent two days within the window length, represents the Pearson correlation coefficient of the electric energy meter data sequence of the first day and the first day within the window length, represents the Pearson correlation coefficient of the electric energy meter data sequence of the first day and the first day within the window length, represents the exponential function with the natural number as the base, represents the normalization function.

[0055] That is, the DTW (Dynamic Time Warping) algorithm can effectively handle the nonlinear stretching between time series, amplify the distance difference through the exponential function, so that the data with lower similarity has a more significant impact on the final result; by calculating the correlation of adjacent day data, the stability of each day data is evaluated, the higher the correlation, the more similar the data of adjacent days, the higher the stability, and the higher the weight of the data with higher deviation, so that the sensitivity characteristic value more accurately reflects the abnormality of the data; by evaluating the correlation of the data in the entire window, the overall stability of the data in the window can be determined. The higher the overall correlation, the stronger the stability of the data in the window.

[0056] By analyzing the Pearson correlation coefficient and stability index of the user's historical electric energy meter data, the representative window length of the historical electric energy meter data sequence is adaptively selected, so that the differences in power consumption habits of different users can be accurately captured, and the baseline deviation problem caused by the fixed window length can be effectively avoided. Further, with the help of the DTW algorithm, the deviation distance of each piece of data sequence to be transmitted from the historical data is calculated, and the stability confidence and the Pearson correlation weight of the adjacent day electric energy meter data are combined to quantitatively evaluate the sensitivity characteristic value of each piece of data. Not only can abnormal power consumption behavior be identified, but also the historical power consumption data trend of the user can be integrated into the sensitivity evaluation to ensure the accuracy and real-time of the evaluation result. In addition, by setting a data sensitivity threshold, it is determined whether the key needs to be updated, and the security and efficiency of the power consumption data transmission are balanced. On the one hand, the security risks caused by all transmission data sharing a single key are avoided; on the other hand, the high load problem caused by updating the key every time is also solved.

[0057] S4: Based on the sensitivity, it is judged whether to regenerate the key. After the key is generated, the data sequence to be transmitted is encrypted by using the key, and the encrypted data is transmitted to ensure the security of the electric energy meter data in the transmission process.

[0058] When the sensitivity is less than or equal to the preset threshold, it indicates that the data sequence to be transmitted does not have significant privacy risks, and the original key is maintained. Otherwise, when the sensitivity is greater than the preset threshold, it indicates that the data sequence to be transmitted has high privacy risks, and the key needs to be regenerated before transmission.

[0059] According to the evaluation result of the sensitivity characteristic value, the key management strategy is dynamically adjusted, which not only avoids the high load problem caused by frequent updating of all data segments without distinction, but also ensures that data segments with high privacy risks can be fully protected before transmission.

[0060] In this embodiment, the preset threshold is 0.6, which can be adjusted according to specific circumstances.

[0061] After the sensitivity characteristic value of each segment of the data sequence to be transmitted is calculated and the judgment of the key update requirement is completed, it is necessary to further analyze the key generation method. If the key is generated in a completely random manner for updating, since such a randomly generated key has no association with the characteristics of the power consumption data, this will lead to a lack of connection between the key update process and the sensitivity of the data, and the on-demand encryption based on the sensitivity of the data cannot be realized. In order to solve this problem, the embodiment assists in generating the key according to the sensitivity of the data sequence to be transmitted, so as to ensure the security of the encryption process. On the key generation method, the HKDF (HMAC-based Key Derivation Function) algorithm is selected, which is a key derivation function based on HMAC (Hash-based Message Authentication Code). The operation process includes two stages of extraction and expansion, which can ensure that the key with high entropy and security is derived from the initial input material. The specific operation steps are as follows:

[0062] The key of the last transmission data of the data sequence to be transmitted is used as an initial random seed, and a salt value is randomly generated to obtain a pseudo-random key. The pseudo-random key is spliced with the sensitivity, hash value and required key length corresponding to the data sequence to be transmitted, and the HKDF algorithm is used again to generate the final key.

[0063] By introducing the HKDF algorithm, the sensitivity characteristic value of the data sequence to be transmitted and the data hash value are combined to generate the final key. This key generation method can dynamically bind the key with the privacy risk and content characteristics of the current data segment, thereby avoiding the problem that the key is disconnected with the data sensitivity in the traditional method. At the same time, the key used for the last transmission is used as an initial random seed, and a randomly generated salt value is introduced to calculate the pseudo-random key. The dependence on the random number generator of the edge node is reduced, and the calculation redundancy caused by repeated generation of random numbers is reduced. In addition, by introducing the random salt value, the randomness of each key extraction process is ensured, and potential weaknesses that may be caused by the chain dependence of the key are effectively avoided.

[0064] In the embodiment, the HMAC function adopts the SHA-256 hash algorithm to ensure the security and fixed length of the output key.

[0065] In response to the sensitivity of the data sequence to be transmitted exceeding the preset threshold, the system triggers the key update mechanism to regenerate the key to enhance the security of data transmission; in response to the sensitivity of the data sequence to be transmitted in the next time period being lower than or equal to the preset threshold, the key generated in the last time period is continued 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 start the key update mechanism again to regenerate the key to ensure the security of data transmission.

[0066] By dynamically evaluating the sensitivity of the data sequence to be transmitted and deciding whether to update the key accordingly, the security of data transmission is effectively enhanced. When the data sensitivity exceeds the preset threshold, the system triggers the key update mechanism to generate a new key for high-sensitivity data, thereby ensuring the security of these data during transmission and effectively preventing privacy leakage. At the same time, for data with a sensitivity lower than or equal to the threshold, the existing key is continued to be used, avoiding unnecessary key update operations, reducing the computational and communication load, and improving the overall efficiency of the system. In addition, the method establishes a logical association between the keys through chain generation of the keys, facilitating tracking of the key update history and ensuring the traceability of key management. Overall, this method ensures the security of data transmission while optimizing the key management process, improving the operational efficiency and reliability of the system.

[0067] The application also provides an edge computing-based electric energy meter data encryption transmission device. Figure 2 As shown in the figure, the device comprises a data acquisition module, an encryption processing module, a signal transmitting module and a signal receiving module.

[0068] The data acquisition module is arranged on the electric energy meter and is used for acquiring electric energy meter data of the electric energy meter, and comprises an acquirer 1, the bottom end of the acquirer 1 is provided with an interface; the encryption processing module is used for performing encryption processing on the acquired electric energy meter data, and comprises an encryptor 6, the encryptor 6 is connected with the acquirer 1 through the interface; the signal transmitting module comprises a base 2 and a transmitting antenna 3, the interface is connected with the base 2, the base 2 is provided with a transmitter inside, and the transmitter is connected with the transmitting antenna 3; the signal receiving module comprises a concentrator 4, the transmitting antenna 3 is wirelessly connected with a receiving antenna 5 of the concentrator 4, and the concentrator 4 is provided with a receiver, a memory and a processor inside and is used for receiving the encrypted electric energy meter data, the concentrator 4 is provided with a display screen and a data checking setting button and is used for displaying and checking the encrypted transmission data; the memory stores computer program instructions, and the computer program instructions are executed by the processor to realize the edge computing-based electric energy meter data encryption transmission method according to the first aspect of the application. The device further comprises a communication bus, a communication interface and other components familiar to those skilled in the art, the arrangement and functions of which are known in the art, and thus will not be described here.

[0069] It should be noted that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the inventive concept, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application patent should be subject to the appended claims.

Claims

1. An edge-computing-based power meter data encryption transmission method, characterized in that, The method comprises the following steps: obtaining historical electric energy meter data sequences and to-be-transmitted data sequences of each user and pre-processing the same; presetting a window length range for the pre-processed historical electric energy meter data sequences, and analyzing the correlation between adjacent two groups of electric energy meter data sequences in each window length in the preset window length range, and calculating the stability of the historical electric energy meter data sequences of each user corresponding to each window length; selecting the maximum value of the stability as the representative window length of the corresponding user, and taking the deviation between the to-be-transmitted data sequence and the electric energy meter data sequence in the corresponding time period in the representative window length as the sensitivity; judging whether to regenerate the key based on the sensitivity, after the key is generated, the to-be-transmitted data sequence is encrypted by using the key, and the encrypted data is transmitted, so as to ensure the security of the electric energy meter data in the transmission process.

2. The edge computing based electric energy meter data encryption transmission method according to claim 1, characterized in that, The pre-processing step comprises the following steps: data cleaning is performed on the historical electric energy meter data sequences and the to-be-transmitted data sequences, invalid data is deleted or corrected, missing data is filled, and alignment is performed according to the time axis. 3.The edge computing based electric energy meter data encryption transmission method according to claim 1, characterized in that, The stability calculation method comprises the following steps: the Pearson correlation coefficient between the electric energy meter data sequences of each adjacent two days in the window length is calculated, the mean and the standard deviation of the Pearson correlation coefficient are calculated, and the ratio between 1 and the mean of the Pearson correlation coefficient and the standard deviation of the Pearson correlation coefficient is taken as the stability of the historical electric energy meter data sequences of the user in the window length.

4. The edge computing based electric energy meter data encryption transmission method according to claim 1, characterized in that, The sensitivity calculation method comprises the following steps: the stability of the historical electric energy meter data sequences of the user corresponding to the window length is taken as the confidence, the shortest path distance between each to-be-transmitted data sequence and the electric energy meter data sequence in the corresponding time period in the representative window length is calculated; the mean of the Pearson correlation coefficients of the electric energy meter data sequences of each day in the representative window length and the adjacent previous and subsequent two days is calculated, and 1 is added to obtain the correlation weight; the mean of the Pearson correlation coefficients of the electric energy meter data sequences of each adjacent two days in the representative window length is calculated, and 1 is added to obtain the overall correlation strength; the product of the shortest path distance and the correlation weight is calculated, and then divided by the overall correlation strength, and the sum of the results of each day in the representative window length is calculated, multiplied by the stability, and then normalized.

5. The edge computing based electric energy meter data encryption transmission method according to claim 1, characterized in that, The judgment of whether to regenerate the key based on the sensitivity comprises the following steps: when the sensitivity is less than or equal to a preset threshold, it indicates that the to-be-transmitted data sequence does not exist significant privacy risk, and the original key is maintained unchanged, otherwise, when the sensitivity is greater than the preset threshold, it indicates that the to-be-transmitted data sequence exists higher privacy risk, and the key needs to be regenerated before transmission.

6. The edge computing based electric energy meter data encryption transmission method according to claim 5, characterized in that, The step of regenerating the key before transmission comprises the following steps: the key of the last transmission data of the to-be-transmitted data sequence is taken as an initial random seed, a salt value is randomly generated to obtain a pseudo-random key, the pseudo-random key is spliced with the sensitivity, the hash value and the required key length of the corresponding to-be-transmitted data sequence, and the HKDF algorithm is used again to generate a final key.

7. The edge computing based electric energy meter data encryption transmission method according to claim 5, characterized in that, The key update mechanism comprises the following steps: when the sensitivity of the to-be-transmitted data sequence exceeds the preset threshold, the system will trigger the key update mechanism to regenerate the key, so as 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 the preset threshold, the data encryption is continued using the key generated in the last time period to maintain the continuity and efficiency of data transmission; on the contrary, if the sensitivity of the data sequence to be transmitted in the next time period exceeds the preset threshold again, the key update mechanism is started again to regenerate the key, ensuring the security of data transmission.

8. The edge computing-based electric energy meter data encryption transmission device is characterized in that, The system comprises a data acquisition module, an encryption processing module, a signal transmitting module and a signal receiving module. The data acquisition module is arranged on the electric energy meter and is used for acquiring electric energy meter data of the electric energy meter and comprises an acquisition device 1, the bottom end of the acquisition device 1 is provided with an interface; The encryption processing module is used for encrypting the acquired electric energy meter data and comprises an encryption device 6, the encryption device 6 is connected with the acquisition device 1 through the interface; The signal transmitting module comprises a base 2 and a transmitting antenna 3, the interface is connected with the base 2, the base 2 is provided with a transmitter, and the transmitter is connected with the transmitting antenna 3; The signal receiving module comprises a concentrator 4, the transmitting antenna 3 is wirelessly connected with a receiving antenna 5 of the concentrator 4, the concentrator 4 is provided with a receiver, a memory and a processor inside, is used for receiving the encrypted electric energy meter data, the concentrator 4 is provided with a display screen and a data checking setting button, is used for displaying and checking the encrypted and transmitted data; the memory stores computer program instructions, when the computer program instructions are executed by the processor, the electric energy meter data encryption transmission method based on edge computing according to any one of claims 1-7 is realized.

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