Power grid data aggregation method and system, electronic equipment and storage medium

Abstract

The invention provides a power grid data aggregation method and system, electronic equipment and a storage medium. According to the power grid data aggregation method, a trusted authentication mechanism carries out system parameter initialization and key distribution. And the intelligent electric meter encrypts the collected power data to obtain a symmetric ciphertext of the power data, and sends the symmetric ciphertext of the power data to the edge gateway according to a preset time period. And the edge gateway converts the symmetric ciphertext into a homomorphic ciphertext and sends the homomorphic ciphertext to a control center. And the control center performs statistical analysis on the homomorphic ciphertext, generates an analysis result and sends the analysis result to the management server. And the management server adjusts the power grid data according to the analysis result. Therefore, the data in the communication process is encrypted by using the encryption algorithm, the security of the data is ensured, the fine-grained analysis of the encrypted data is supported, and the robustness of the system is improved.

Description

Technical Field

[0001] The present application relates to the technical field of data aggregation, and in particular to a power grid data aggregation method, system, electronic device and storage medium. Background Art

[0002] With the widespread application of technologies such as artificial intelligence, 5G communications, and big data in various fields, smart grids are gradually replacing traditional power grids to provide efficient, reliable, and uninterrupted energy supply for homes and businesses. As the next generation of power grids, smart grids use advanced information and communication technologies to achieve two-way communication between users and service providers, and efficiently coordinate the production, distribution, transmission, and control of power resources.

[0003] In the prior art, it is easy for attackers to obtain data transmitted in the power grid system channel or perform corresponding tampering operations through illegal means, thereby launching active or passive attacks, which poses a great security risk. Summary of the invention

[0004] In view of this, the present application provides a power grid data aggregation method, system, electronic device and storage medium to solve the problem of major safety hazards in the prior art.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] The first aspect of the present application discloses a power grid data aggregation method, which is applied to a power grid data aggregation system, wherein the power grid data aggregation system includes a management server, a trusted authentication authority, a control center, a smart meter, and an edge gateway; wherein the power grid data aggregation method includes:

[0007] The trusted certification authority performs system parameter initialization and key distribution;

[0008] The smart meter encrypts the collected power data to obtain a symmetric ciphertext of the power data, and sends the symmetric ciphertext of the power data to the edge gateway according to a preset time period;

[0009] The edge gateway converts the symmetric ciphertext into a homomorphic ciphertext and sends it to the control center;

[0010] The control center performs statistical analysis on the homomorphic ciphertext, generates analysis results, and sends them to the management server;

[0011] The management server adjusts the power grid data according to the analysis result.

[0012] Optionally, in the above method, the trusted certification authority performs system parameter initialization and key distribution, including:

[0013] Determining a maximum circuit depth based on a set of fine-grained analysis operation types published by the management server;

[0014] A key is generated by a homomorphic encryption algorithm and the key is sent to each of the smart meters.

[0015] Optionally, in the above solution, the smart meter encrypts the collected power data to obtain a symmetric ciphertext of the power data, including:

[0016] Collect raw power data and compress it;

[0017] The compressed power data is encrypted using a symmetric encryption algorithm to obtain the symmetric ciphertext.

[0018] Optionally, in the above method, the edge gateway converts the symmetric ciphertext into a homomorphic ciphertext, including:

[0019] Determine if there is a faulty smart meter;

[0020] If a faulty smart meter is determined, fault recovery is performed on the faulty smart meter;

[0021] If it is determined that there is no faulty smart meter, performing aggregate verification on the symmetric ciphertext;

[0022] If the aggregate verification passes, the symmetric ciphertext is converted into homomorphic ciphertext using a homomorphic encryption algorithm.

[0023] Optionally, in the above method, after the smart meter encrypts the collected power data to obtain the symmetric ciphertext of the power data, the method further includes:

[0024] The homomorphic ciphertext is quadratically expanded based on a set of fine-grained analysis operation types published by the management server.

[0025] Optionally, in the above method, the control center performs statistical analysis on the homomorphic ciphertext to generate analysis results, including:

[0026] Verifying the legitimacy of the homomorphic ciphertext;

[0027] If the verification is successful, homomorphic addition is performed on the homomorphic ciphertext to obtain an aggregate vector;

[0028] Decrypting the aggregate vector to obtain a decryption result vector;

[0029] Based on the decryption result vector, processing is performed according to a preset processing strategy to obtain analysis results under different processing strategies; wherein the processing strategies include basic statistical quantity calculation, billing strategy modification, tiered pricing and deep learning.

[0030] The management server adjusts the power grid data according to the analysis result, including:

[0031] With respect to the analysis result of the basic statistical quantity calculation, backing up the analysis result of the basic statistical quantity calculation into the power consumption database;

[0032] For the analysis result of the billing policy revision, the billing policy is revised according to the analysis result of the billing policy revision;

[0033] For the analysis result of the step-by-step pricing, the analysis result of the step-by-step pricing is backed up in a pricing database, and the pricing weight is adjusted according to the usage of various types of power resources in the current system;

[0034] With respect to the analysis results of the deep learning, a gradient descent algorithm is executed according to the analysis results of the deep learning to optimize the pre-built model.

[0035] The second aspect of the present application discloses a power grid data aggregation system, which includes a management server, a trusted authentication authority, a control center, a smart meter, and an edge gateway;

[0036] The trusted authentication authority is used to perform system parameter initialization and key distribution;

[0037] The smart meter is used to encrypt the collected power data to obtain a symmetric ciphertext of the power data, and send the symmetric ciphertext of the power data to the edge gateway according to a preset time period;

[0038] The edge gateway is used to convert the symmetric ciphertext into a homomorphic ciphertext and send it to the control center;

[0039] The control center is used to perform statistical analysis on the homomorphic ciphertext, generate analysis results, and send them to the management server;

[0040] The management server is used to adjust the power grid data according to the analysis result.

[0041] The third aspect of the present application discloses an electronic device, comprising:

[0042] one or more processors;

[0043] a storage device having one or more programs stored thereon;

[0044] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of the first aspects of the present invention.

[0045] A fourth aspect of the present application discloses a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method described in any one of the first aspects of the present invention is implemented.

[0046] It can be seen from the above technical scheme that a power grid data aggregation method provided by the present application is applied to a power grid data aggregation system, wherein a trusted certification body performs system parameter initialization and key distribution. The smart meter encrypts the collected power data to obtain the symmetric ciphertext of the power data, and sends the symmetric ciphertext of the power data to the edge gateway according to a preset time period. The edge gateway converts the symmetric ciphertext into a homomorphic ciphertext and sends it to the control center. The control center performs statistical analysis on the homomorphic ciphertext, generates analysis results, and sends them to the management server. The management server adjusts the power grid data according to the analysis results. It can be seen that the present application uses an encryption algorithm to encrypt the data in the communication process, ensures the security of the data, and supports fine-grained analysis of the secret data, thereby improving the robustness of the system. Solve the problem of large security risks in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0048] Figure 1 A flowchart of a power grid data aggregation method disclosed in an embodiment of the present application;

[0049] Figure 2 A schematic diagram of a power grid data aggregation system disclosed in an embodiment of the present application;

[0050] Figure 3 A comparison chart of the computational overhead of the power grid data aggregation method disclosed in the embodiment of the present application and the prior art on a resource-constrained terminal device;

[0051] Figure 4 A comparison chart of the calculation overhead in the control center between the power grid data aggregation method disclosed in the embodiment of the present application and the prior art;

[0052] Figure 5 A comparison chart of the communication overhead between the power grid data aggregation method disclosed in the embodiment of the present application and the prior art on the smart meter-edge device side;

[0053] Figure 6 A comparison chart of the communication overhead between the power grid data aggregation method disclosed in the embodiment of the present application and the prior art on the edge device-control center side;

[0054] Figure 7 A schematic diagram of an electronic device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] In this application, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0057] Furthermore, in this document, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0058] As can be seen from the background technology, in the prior art, attackers can easily obtain data transmitted in the power grid system channel or perform corresponding tampering operations through illegal means, thereby launching active attacks or passive attacks, which poses a great security risk.

[0059] In view of this, the present application provides a power grid data aggregation method, system, electronic device and storage medium to solve the problem of major safety hazards in the prior art.

[0060] The present application embodiment provides a power grid data aggregation method, which is applied to a power grid data aggregation system, such as Figure 1 As shown, specifically including:

[0061] S101. The trusted certification authority performs system parameter initialization and key distribution.

[0062] It should be noted that the power grid data aggregation system can be found in Figure 2 , including management server, trusted certification authority, control center, smart meter, and edge gateway.

[0063] Trusted certification authorities are used to initialize system parameters and distribute keys;

[0064] The smart meter is used to encrypt the collected power data to obtain the symmetric ciphertext of the power data, and send the symmetric ciphertext of the power data to the edge gateway according to a preset time period;

[0065] The edge gateway is used to convert the symmetric ciphertext into homomorphic ciphertext and send it to the control center;

[0066] The control center is used to perform statistical analysis on homomorphic ciphertext, generate analysis results, and send them to the management server;

[0067] The management server is used to adjust the power grid data according to the analysis results.

[0068] Among them, the trusted authentication agency is responsible for system parameter initialization and key distribution, and assists the management server in distributing fine-grained analysis tasks and assists smart meters in generating and distributing zero and random numbers (performed in the offline phase, without the need to establish a secure channel between smart meters). After completing the above parameter generation and distribution operations in the initialization phase, the trusted authentication agency goes offline and comes back online only when and if there is a major adjustment in the system topology to complete key distribution and regeneration of zero and random numbers.

[0069] Optionally, in another embodiment of the present application, an implementation of step S101 may include:

[0070] The maximum circuit depth is determined based on a set of fine-grained analysis operation types published by the management server.

[0071] The key is generated through a homomorphic encryption algorithm and sent to each smart meter.

[0072] It should be noted that system parameter initialization and key distribution mainly include a fine-grained operation negotiation sub-phase, a related parameter and key generation sub-phase, and a random number distribution sharing sub-phase.

[0073] (1) Fine-grained operation negotiation sub-phase: The management server determines the data analysis type Among them, MEAN, QMEAN, HMEAN, VAR, STD, BSC, DP, and DL represent the arithmetic mean, quadratic mean, harmonic mean, variance, standard deviation, and three types of advanced statistical requirements: Billing Strategy Correction (BSC), Differential Pricing (DP), and Deep Learning (DL). Trusted certification authorities are generally considered to be fully trusted institutions, responsible for generating and broadcasting public parameters during the initialization phase, generating encryption key pairs and signature key pairs, and distributing them to corresponding entities through secure channels. The trusted certification authority determines the maximum value of the required circuit depth based on the set of data analysis types selected by the management server. Where S represents the set of fine-grained data analysis types published by the management server, S i It is a subset of S, L represents the required circuit depth, and then the type set S is broadcast to the edge gateway.

[0074] (2) Related parameters and key generation sub-stage:

[0075] (a) The trusted certification authority generates the corresponding parameters of the BGV algorithm PP1 = {params1, params2, ..., params j :=(R,d j ,n j ,q j ,x j ,N j )←E.Setup(1 λ ,1 (j+1)·μ )}, where PP1 represents the system parameters required by the BGV algorithm, E.Setup(1 λ ,1 (j+1)·μ ) represents the initialization setting function, λ represents the algorithm receiving security parameters, 1 λ represents the random seed bit length, j represents the upper limit of the number of fully homomorphic operations, R = Z[x] / (x d +1) indicates the domain, d j represents the degree of the ring, n j represents the dimension, q j represents an odd modulus, χ j represents the noise distribution, N j =n j polylog(q j );

[0076] (b) Pasta corresponding parameters The parameter S represents the S-box required to generate the PASTA-π permutation. feistel (·) and S cube (·) and the affine mapping Aj,N,i (·) and the order matrix

[0077] (c) FALCON-based sequential aggregation signature related parameters PP3 = {enc, dec, H1, H2, Order SM ,Order EG ,Order S ' M}, where enc represents the encoding function, dec represents the decoding function, H1 and H2 represent two types of hash functions, and Order SM Indicates the order of signature aggregation, Order S ' M Indicates the backup signature sequence in case of system failure;

[0078] (d) Public parameters required by threshold secret sharing algorithm Where p represents a prime number, n represents the number of all parties involved in secret sharing, represents n different random vectors;

[0079] (e) Data dimension conversion parameter PP5 = {q, Q, θ}, where q represents a prime number, Q represents the product of k pairs of coprime primes, and θ represents a vector;

[0080] (f) The threshold value of the number of new users during the user registration phase PP6 = K;

[0081] A trusted certification authority generates a BGV fully homomorphic public-private key pair in represents the public key, Represents the private key, Pasta symmetric key K SE , perform homomorphic operations on the symmetric key and convert the ciphertext C KHE Sent to the edge gateway to generate meter aggregation signature and verify public-private key pair Generate gateway aggregate signature and verify public and private key pairs

[0082] (3) Random number distribution sharing sub-phase, such as Figure 4 As shown, the random number distribution sharing sub-phase included in the initialization phase of the present invention includes the following steps:

[0083] Step 1: The control center generates corresponding random numbers for each set of electric meters in different areas And send it to a trusted certification body;

[0084] Step 2: The trusted authentication agency receives the random number and counts the number of electricity meters in the area n SM , perform random number splitting, the calculation formula is as follows:

[0085]

[0086] The meter receives a random number r ij as a secret to be shared;

[0087] Step 3: The smart meter selects a random integer to construct a vector a, and generates a shared vector that satisfies the following calculation formula:

[0088] ss i,j =( (j) ,a>+e i )modp;

[0089] Among them, ss i,j Indicates the electric meter SM i Send to meter SM j The secret fragments of I (j) represents a random vector, e i Indicates noise;

[0090] Step 4: The electricity meters complete the sending and receiving of their respective random numbers, receive their own shared secret shares and store them in the database.

[0091] S102: The smart meter encrypts the collected power data to obtain a symmetric ciphertext of the power data, and sends the symmetric ciphertext of the power data to the edge gateway according to a preset time period.

[0092] It should be noted that the smart meter encrypts the collected power data to obtain the symmetric ciphertext of the power data, and regularly sends the symmetric ciphertext of the power data to the edge gateway according to a preset time period, so that the edge gateway reports the encrypted multi-dimensional power data to the control center.

[0093] Optionally, in another embodiment of the present application, an implementation of step S102 may include:

[0094] Collect raw power data and compress it.

[0095] The compressed power data is encrypted using a symmetric encryption algorithm to obtain a symmetric ciphertext.

[0096] It should be noted that the smart meter collects the original power data and compresses it into one-dimensional data. The compression operation is performed by the following formula:

[0097] m i =d i,1 θ1+d i,2 θ2+...+d i,k θ k modQ;

[0098] where d i =(d i,1 ​,d i,2 ,...,d i,k ) indicates smart meter SM i The raw power data collected, θ i =(θ1,θ2,...,θ k ) represents a vector;

[0099] The smart meter then uses random numbers to mask the data and generate m i =m i +r ij .

[0100] The processed data is then encoded into a vector to be encrypted, and the PASTA symmetric encryption algorithm is executed to generate a ciphertext vector

[0101] Finally, refer to the signature aggregation order, receive the corresponding signature private key, generate the signature σ, and encapsulate it into the following data packet:

[0102] D i =C i ||T i ||σ;

[0103] Where || represents the cascade relationship, T i Indicates the current time.

[0104] S103. The edge gateway converts the symmetric ciphertext into homomorphic ciphertext and sends it to the control center.

[0105] It should be noted that the edge gateway acts as an intermediate device between the smart meter and the control center, receives the symmetric ciphertext uploaded by the smart meter, and performs hybrid homomorphic encryption operations to convert it into homomorphic ciphertext. The homomorphic ciphertext is evaluated according to the evaluation tasks distributed by the management server in the initialization phase, which facilitates subsequent statistical analysis.

[0106] Optionally, in another embodiment of the present application, an implementation of step S103 may include:

[0107] Determine if there is a faulty smart meter.

[0108] If it is determined that a faulty smart meter exists, fault recovery is performed on the faulty smart meter.

[0109] If it is determined that there is no faulty smart meter, aggregate verification is performed on the symmetric ciphertext.

[0110] If the aggregate verification passes, the homomorphic encryption algorithm is used to convert the symmetric ciphertext into homomorphic ciphertext.

[0111] It should be noted that the edge gateway processes the data sent by the smart meter, specifically including: fault recovery sub-stage and conversion evaluation sub-stage.

[0112] Failure recovery sub-phase:

[0113] Step 1: If the edge gateway does not receive all data packets sent by the meters, it is determined that some smart meters are faulty, and the system enters the fault recovery sub-stage;

[0114] Step 2: The edge gateway broadcasts the identity set S of the faulty meters. After receiving the identity, the normally working meters use the lattice-based secret sharing algorithm to share the identity in the full-rank grid. and the target vector t′, and the CVP approximation algorithm is performed to recover the corresponding secret ss j ;

[0115] The lattice secret sharing algorithm approximates the CVP problem through the Babai nearest plane algorithm to restore the shared secret. Through public parameter generation, shared secret generation and definition of full-rank square matrix, the CVP approximation algorithm is executed on the lattice and target vector to achieve the purpose of recovering the secret.

[0116] Step 3: The edge gateway completes the restoration of the random numbers corresponding to all faulty meters, encrypts the random number summation result using the fully homomorphic encryption public key and stores the ciphertext, calculated according to the following formula:

[0117]

[0118] Among them C FTHE represents ciphertext, FHE.Enc(·) represents the encryption algorithm in the fully homomorphic encryption algorithm BVG, PP1 represents the necessary system parameters of the BGV algorithm, Represents the BVG fully homomorphic algorithm public key, Indicates that the edge gateway has completed the restoration of the random numbers corresponding to all faulty meters.

[0119] Conversion Evaluation Sub-Phase:

[0120] Step 1: If the gateway control area does not fail or the failure recovery is successfully completed, the system enters the conversion evaluation sub-phase;

[0121] Step 2: The edge gateway receives the sequential aggregate signature uploaded by the normal meter cluster, selects the corresponding public key set and the corresponding data packet set, inputs them into the verification function, and executes the signature generation algorithm in reverse order to obtain x0←dec(z0,α1), where x0←0 n ,α1←0 n-k , x0 represents the decoding function result generated by the aggregate signature algorithm in the FALCON-based sequential aggregate signature. If x0=0 n If established, it means that the aggregation verification has passed;

[0122] Step 3: If the verification is successful, use the fully homomorphic public key to encrypt the received message and execute the following algorithm to generate the ciphertext:

[0123]

[0124] Step 4: Determine that the decryption evaluation function is the Pasta decryption algorithm, and call the homomorphic evaluation function to perform the decryption evaluation operation on the fully homomorphic ciphertext:

[0125]

[0126] where FHE.Eval(·) represents the decryption evaluation operation, Represents the BVG fully homomorphic public key, Dec KHE (·) represents the decryption algorithm, represents a fully homomorphic ciphertext;

[0127] Step 5: If a failure occurs in the previous stage, the final encryption result should also be added with the ciphertext corresponding to the missing random number to generate the final ciphertext HEC i =HEC i +C FTHE .

[0128] Optionally, in another embodiment of the present application, the above step S103 may further include:

[0129] The homomorphic ciphertext is expanded exponentially based on the set of fine-grained analysis operation types published by the management server.

[0130] It should be noted that the edge gateway determines the specific expansion power to be performed according to the fine-grained analysis type set S broadcast by the management server in the initialization phase, and the square expansion is used as an example to explain:

[0131] Step 1: The edge gateway successfully obtains the homomorphic ciphertext HEC of the normal working smart meter data i , calling the homomorphic multiplication operation to square the ciphertext, the formula is as follows:

[0132]

[0133] Where FHE.Mult(·) represents the homomorphic multiplication operation;

[0134] Step 2: The edge gateway obtains the power expansion result and packages it into a ciphertext processing result, which is submitted according to the following formula:

[0135]

[0136] Step 3: The edge gateway calls the signature algorithm according to the aggregation order and combines its own signature private key to generate a sequential aggregation signature σ′. The gateway constructs the data packet Dj =PC j ||T j ||σ′.

[0137] S104. The control center performs statistical analysis on the homomorphic ciphertext, generates analysis results, and sends them to the management server.

[0138] It should be noted that the control center performs statistical analysis on homomorphic ciphertext, generates analysis results, and sends them to the management server to evaluate basic statistics and advanced analysis results to provide support for the management server's decision-making.

[0139] Optionally, in another embodiment of the present application, an implementation of the above step S104 may include:

[0140] Verify the legitimacy of homomorphic ciphertext.

[0141] If the verification is successful, homomorphic addition is performed on the homomorphic ciphertext to obtain the aggregate vector.

[0142] Decrypt the aggregate vector to obtain a decrypted result vector.

[0143] Based on the decryption result vector, processing is performed according to the preset processing strategy to obtain analysis results under different processing strategies; among them, the processing strategies include basic statistical quantity calculation, billing strategy modification, tiered pricing and deep learning.

[0144] It should be noted that the control center receives the data packets uploaded by the edge gateway and verifies the legitimacy of the timestamp and signature in turn;

[0145] If the above verification passes, homomorphic addition is performed on the ciphertext to obtain the final aggregate vector

[0146] The control center performs the decryption operation to obtain the decryption result vector Calculated by the following formula:

[0147]

[0148] in represents the final aggregate vector, s Lmax represents the maximum value of the required circuit depth under vector s;

[0149] Perform random number elimination and use the Chinese remainder theorem to restore arbitrary dimensional data d l =E modq i ,1≤l≤k.

[0150] The control center obtains the high-power aggregation results of data in any dimension and performs the following fine-grained analysis operations:

[0151] Basic statistics calculation: for data dimension u, control center combines The basic statistics are calculated using the following formula:

[0152] MEAN=d u / n SM

[0153]

[0154] Billing strategy modification: For billing strategies s≥3, the control center decrypts and obtains the corresponding power data billing results M under each strategy sum,1 ,M sum,2 ,…,M sum,s , calculate the sum of squares between groups and the sum of squares within groups to conduct hypothesis test evaluation; after the evaluation is completed, the control center compares the evaluation results with the threshold F C Compare and correct the result D CC =(R price ) is sent to the management server, and the calculation formula is as follows:

[0155]

[0156] Tiered pricing: The control center decrypts and packages the tiered pricing results D cc =(p1,…,p k ), sent to the management server for subsequent adjustment;

[0157] Deep learning: The control center completes the data UD corresponding to dimension u u After the restoration, perform Taylor expansion on the function to be evaluated and construct the approximate result, and package all the approximate results D CC =(Eva1,…,Eva k ) and sent to the management server for subsequent adjustments.

[0158] S105. The management server adjusts the power grid data according to the analysis results.

[0159] It should be noted that the management server is connected to the control center. During the initialization phase, it will issue fine-grained analysis tasks, receive analysis results uploaded by the control center, and adjust the grid data according to the analysis results, such as adjusting electricity prices. The management server is connected to two secure databases, the electricity consumption database and the pricing database, which are used to save regional electricity consumption records and billing records, respectively, to achieve macro-control of the overall electricity of large-scale smart grids.

[0160] Optionally, in another embodiment of the present application, an implementation of the above step S105 may include:

[0161] With respect to the analysis results of the basic statistical calculations, the analysis results of the basic statistical calculations are backed up in the electricity consumption database.

[0162] For the analysis result of the billing policy revision, the billing policy is revised according to the analysis result of the billing policy revision.

[0163] The analysis results of the ladder pricing are backed up in the pricing database, and the pricing weights are adjusted according to the usage of various types of power resources in the current system.

[0164] For the analysis results of deep learning, the gradient descent algorithm is executed according to the analysis results of deep learning to optimize the pre-built model.

[0165] It should be noted that the management server makes corresponding adjustments based on the analysis results uploaded by the control center. If the analysis results of the basic statistical calculation are received, the management server backs up the analysis results in the power consumption database, and the arithmetic mean and variance serve as important references for measuring regional power consumption.

[0166] If the analysis result of the billing policy modification is received, the management server will update the billing policy according to the evaluation value uploaded by the control center and the threshold F C The comparison result is used to modify the billing strategy and broadcast a more reasonable billing strategy PS new .

[0167] If the analysis result of the tiered pricing is received, the management server backs up the pricing result in the pricing database and adjusts the pricing weight according to the usage of various power resources in the current system, and broadcasts the new tiered pricing weight (ω′1,…,ω′ k ).

[0168] If the analysis results of deep learning are received, the management server executes a gradient descent algorithm according to the analysis results of deep learning to optimize the pre-built model to improve the accuracy of the model.

[0169] In a power grid data aggregation method provided by the present application, it is applied to a power grid data aggregation system, wherein a trusted certification authority performs system parameter initialization and key distribution. The smart meter encrypts the collected power data to obtain the symmetric ciphertext of the power data, and sends the symmetric ciphertext of the power data to the edge gateway according to a preset time period. The edge gateway converts the symmetric ciphertext into a homomorphic ciphertext and sends it to the control center. The control center performs statistical analysis on the homomorphic ciphertext, generates analysis results, and sends them to the management server. The management server adjusts the power grid data according to the analysis results. It can be seen that the present application uses an encryption algorithm to encrypt data in the communication process, ensures the security of the data, and supports fine-grained analysis of secret data, thereby improving the robustness of the system. Solve the problem of major security risks in the prior art.

[0170] In another embodiment of the present application, a user dynamic joining and revocation stage is designed to realize dynamic changes in the system topology:

[0171] (1) Dynamic joining: When a new user registers, the trusted authentication authority needs to be awakened to complete the public parameter and key distribution. The dynamic joining operation is divided into the following steps:

[0172] Step 1: The trusted authentication authority sends the random number r' to the new user and the control center simultaneously through a secure channel. ik , the control center securely records the random numbers corresponding to newly registered users and removes them during the final data restoration process;

[0173] Step 2: If the number of new user registrations exceeds the threshold K, the entire system re-executes zero and random number allocation;

[0174] (2) Dynamic revocation:

[0175] Step 1: The meter to be cancelled sends a data packet containing Pasta symmetric encryption ciphertext and timestamp to the edge gateway

[0176] Step 2: The edge gateway performs ciphertext type conversion and forwards it to the control center;

[0177] Step 3: The control center decrypts and recovers the random number r jj And remove it from its own database.

[0178] In order to verify the feasibility of the present invention, the test results and related operation overhead of the designed privacy-preserving data aggregation protocol under simulation are shown and explained below. The relevant configuration is shown in Table 1:

[0179] Table 1

[0180]

[0181] like Figure 3 As shown, the smart meter in the present invention only needs to perform one Pasta symmetric encryption operation and one FALCON signature operation to generate a sequential aggregate signature. The calculation overhead on the meter side is much smaller than other mainstream solutions such as MMSDA, and is more suitable for smart meter devices widely deployed in resource-constrained environments in large-scale smart grids.

[0182] like Figure 4 As shown, the control center of the present invention needs to execute the FALCON aggregate signature verification algorithm once, and then execute P·n MD BGV homomorphic addition operations and P BGV homomorphic decryption operations; although the scheme designed by the present invention has a larger overhead on the control center side than other schemes, the scheme achieves high data availability at the cost of less computational overhead growth.

[0183] like Figure 5 and Figure 6 As shown, in the scheme designed by the present invention, the terminal device only needs to transmit symmetric ciphertext and post-quantum signature to the middle-layer gateway, and the communication cost between the meter and the gateway is low. Although the communication cost is higher than the mainstream scheme of using traditional public key cryptography system to implement data encryption operation, the proposed scheme has anti-quantum characteristics and higher security. The scheme designed by the present invention concentrates a large number of data analysis operations on the middle-layer devices, provides support for fine-grained analysis operations in the grid at the cost of large communication overhead between the gateway and the control center, and improves the availability of power data.

[0184] The present invention combines a hybrid homomorphic encryption system with a data mask to better ensure the confidentiality and privacy of the collected and summarized electricity consumption data. The fine-grained analysis operation performed by the middle-layer device is based on homomorphic ciphertext, and the analysis conclusion does not disclose any confidential information of the electricity consumption data. Sensitive data is masked by a random mask. Under the assumption that the smart meter cannot communicate directly during the execution of the scheme, other smart meters and external adversaries cannot obtain the data mask, and passive adversaries cannot restore the mask without collecting a specified number of secrets. The present invention uses the Pasta algorithm and the BGV algorithm to form a hybrid homomorphic encryption system to ensure the confidentiality of power data. In addition, the scheme uses a sequential aggregate signature based on FALCON to ensure the authenticity and integrity of user power data, and can resist key recovery attacks, signature forgery attacks, hybrid attacks combining the middle meet algorithm and the key recovery algorithm, and algebraic attacks against the rich algebraic structure of FALCON. The present invention introduces a timestamp mechanism to resist active attacks such as tampering attacks, replay attacks, and man-in-the-middle attacks, and has certain fault tolerance characteristics, supporting rapid recovery from faults.

[0185] Another embodiment of the present application further provides an electronic device, such as Figure 7 As shown, specifically including:

[0186] One or more processors 701.

[0187] The storage device 702 stores one or more programs.

[0188] When one or more programs are executed by one or more processors 701 , the one or more processors 701 implement any one of the methods in the above embodiments.

[0189] Another embodiment of the present application further provides a computer storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, any one of the methods in the above embodiments is implemented.

[0190] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0191] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0192] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for aggregating power grid data, characterized in that: Applied to a power grid data aggregation system, the power grid data aggregation system includes a management server, a trusted authentication agency, a control center, a smart meter, and an edge gateway; wherein the power grid data aggregation method includes: The trusted certification authority performs system parameter initialization and key distribution; The smart meter encrypts the collected power data to obtain a symmetric ciphertext of the power data, and sends the symmetric ciphertext of the power data to the edge gateway according to a preset time period; The edge gateway converts the symmetric ciphertext into a homomorphic ciphertext and sends it to the control center; The control center performs statistical analysis on the homomorphic ciphertext, generates analysis results, and sends them to the management server; The management server adjusts the power grid data according to the analysis result.

2. The method according to claim 1, characterized in that The trusted authentication authority performs system parameter initialization and key distribution, including: Determining a maximum circuit depth based on a set of fine-grained analysis operation types published by the management server; A key is generated by a homomorphic encryption algorithm and the key is sent to each of the smart meters.

3. The method according to claim 1, characterized in that The smart meter encrypts the collected power data to obtain a symmetric ciphertext of the power data, including: Collect raw power data and compress it; The compressed power data is encrypted using a symmetric encryption algorithm to obtain the symmetric ciphertext.

4. The method according to claim 1, characterized in that The edge gateway converts the symmetric ciphertext into a homomorphic ciphertext, including: Determine if there is a faulty smart meter; If a faulty smart meter is determined, fault recovery is performed on the faulty smart meter; If it is determined that there is no faulty smart meter, performing aggregate verification on the symmetric ciphertext; If the aggregate verification passes, the symmetric ciphertext is converted into homomorphic ciphertext using a homomorphic encryption algorithm.

5. The method according to claim 1, characterized in that The smart electric meter encrypts the collected power data to obtain the symmetric ciphertext of the power data, and further includes: The homomorphic ciphertext is quadratically expanded based on a set of fine-grained analysis operation types published by the management server.

6. The method according to claim 1, characterized in that The control center performs statistical analysis on the homomorphic ciphertext to generate analysis results, including: Verifying the legitimacy of the homomorphic ciphertext; If the verification is successful, homomorphic addition is performed on the homomorphic ciphertext to obtain an aggregate vector; Decrypting the aggregate vector to obtain a decryption result vector; Based on the decryption result vector, processing is performed according to a preset processing strategy to obtain analysis results under different processing strategies; wherein the processing strategies include basic statistical quantity calculation, billing strategy modification, tiered pricing and deep learning.

7. The method according to claim 1, characterized in that The management server adjusts the power grid data according to the analysis result, including: With respect to the analysis result of the basic statistical quantity calculation, backing up the analysis result of the basic statistical quantity calculation into the power consumption database; For the analysis result of the billing policy revision, the billing policy is revised according to the analysis result of the billing policy revision; For the analysis result of the step-by-step pricing, the analysis result of the step-by-step pricing is backed up in a pricing database, and the pricing weight is adjusted according to the usage of various types of power resources in the current system; With respect to the analysis results of the deep learning, a gradient descent algorithm is executed according to the analysis results of the deep learning to optimize the pre-built model.

8. A power grid data aggregation system, characterized in that: The power grid data aggregation system includes a management server, a trusted authentication agency, a control center, a smart meter, and an edge gateway; The trusted authentication authority is used to perform system parameter initialization and key distribution; The smart meter is used to encrypt the collected power data to obtain a symmetric ciphertext of the power data, and send the symmetric ciphertext of the power data to the edge gateway according to a preset time period; The edge gateway is used to convert the symmetric ciphertext into a homomorphic ciphertext and send it to the control center; The control center is used to perform statistical analysis on the homomorphic ciphertext, generate analysis results, and send them to the management server; The management server is used to adjust the power grid data according to the analysis result.

9. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored thereon, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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