Enterprise electric energy consumption quality comprehensive portraying method based on fully homomorphic encryption technology

Abstract

The invention discloses an enterprise electric energy consumption quality comprehensive portraying method based on a fully homomorphic encryption technology, and relates to the technical field of data privacy protection and electric energy consumption evaluation, and the method comprises the steps: employing the fully homomorphic encryption technology to encrypt a multi-party data source based on a trusted privacy computing platform; carrying out joint feature index calculation on the encrypted data, and calculating an enterprise portrait evaluation index; and decrypting the enterprise portrait evaluation index to generate an enterprise electric energy consumption quality portrait. According to the method, multi-party data sources are encrypted by using a fully homomorphic encryption technology on the trusted privacy computing platform, privacy protection and security of the data in the computing process are realized, and the trusted privacy computing platform realizes effective integration among different data sources by performing joint feature index computing on the encrypted data, so that the security of the data in the computing process is improved. Through the enterprise electric energy consumption quality portrait, the enterprise can identify the electric energy consumption quality more clearly, and then adjust the operation strategy according to the portrait result.

Description

Technical Field

[0001] The present invention relates to the technical field of data privacy protection and electric energy consumption assessment, and specifically to a comprehensive profiling method for enterprise electric energy consumption quality based on fully homomorphic encryption technology. Background Art

[0002] In the current industrial and economic environment, large, medium and small enterprises face serious problems in the quality assessment of electricity consumption, mainly in terms of support for the realization of dual carbon goals, renewable energy absorption capacity and effective assessment of the efficiency of electricity utilization in economic activities. Although the tax bureau, power grid companies, green electricity trading platforms and corporate carbon emission verification agencies have accumulated a large amount of important data on electricity consumption quality, the current situation is that most of these data exist in isolated forms in various systems, forming data islands, leading to unreasonable construction of data walls and structural barriers. This phenomenon hinders the effective flow of information between different data sources, making it difficult for enterprises to achieve comprehensive and accurate electricity consumption quality assessment.

[0003] Existing methods for profiling the quality of corporate electricity consumption usually rely on a single data source for evaluation, which has several obvious shortcomings. First, the use of a single data source can easily lead to one-sided evaluation results and fail to fully reflect the actual situation of electricity consumption, making it difficult to provide decision makers with a scientific and reliable basis. Secondly, due to the lack of multi-party data sharing and linkage, the data barriers faced by enterprises in the process of resource utilization hinder the objective monitoring of the quality of economic development. In addition, when dealing with large and complex data sets, current data analysis methods lack effective privacy protection technology, resulting in a significantly increased risk of data leakage. In the process of enterprises using data to make decisions, privacy compliance issues cannot be ignored. This technical barrier limits the decision-making ability within the enterprise and also slows down the overall economic transformation to a green and sustainable one. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing electricity consumption quality portrait methods have data island problems, information flow is blocked due to data barriers, lack of effective privacy protection leads to data leakage risks, and how to achieve accurate enterprise electricity consumption quality assessment through efficient data integration and privacy protection.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for comprehensive profiling of enterprise electricity consumption quality based on fully homomorphic encryption technology, comprising encrypting multiple data sources based on a trusted privacy computing platform using fully homomorphic encryption technology; performing joint feature index calculations on the encrypted data and calculating enterprise portrait evaluation indicators; decrypting the enterprise portrait evaluation indicators to generate an enterprise electricity consumption quality portrait.

[0007] As a preferred solution of the method for comprehensive profiling of enterprise electricity consumption quality based on fully homomorphic encryption technology described in the present invention, the trusted privacy computing platform includes generating public parameters (λ, N, Q) and private key q of the fully homomorphic encryption algorithm to encrypt local data of multiple parties; wherein λ is a security parameter, N is the modulus, and Q is a random public parameter of no more than λ / 2 bits; the public parameters (λ, N, Q) are shared with multiple parties, including the tax bureau, power grid company, green electricity trading platform and enterprise carbon emission verification agency.

[0008] As a preferred solution of the enterprise power consumption quality comprehensive portrait method based on fully homomorphic encryption technology described in the present invention, wherein: the use of fully homomorphic encryption technology to encrypt multiple data sources includes calculating the calculation factors of the joint characteristic indicators through multiple subjects, performing two homomorphic encryptions on the calculation factors of the joint characteristic indicators according to the public parameters of the fully homomorphic encryption algorithm, and transmitting the calculated ciphertext to the trusted privacy computing platform; the calculation of the joint characteristic indicators by multiple subjects includes the tax bureau jointly calculating the kilowatt-hour output value ratio and the ton carbon output value ratio, the power grid company jointly calculating the green electricity consumption ratio and the kilowatt-hour output value ratio, the green electricity trading platform jointly calculating the green electricity consumption ratio, and the enterprise carbon emission verification agency jointly calculating the ton carbon output value ratio; the two homomorphic encryptions include initial encryption and secondary encryption; the initial encryption includes inputting plaintext data m, and outputting the initial encryption result C'←E of the plaintext data nc1 (m), the initial encryption algorithm, is expressed as:

[0009] E nc1 (m) = m + r 1 QqI

[0010] Among them, E nc1 is the initial encryption function, r 1 is a λ / 2-bit random number, and mod is a remainder function; the re-encryption includes inputting the initial encryption result C' and outputting the re-encryption result C←E nc2 (C'), the encryption algorithm is expressed as:

[0011] E nc2 (C') = C'(1 + r 2 q)modN=(m+r 1 Q)(1+r 2 q)modN

[0012] Among them, E nc2 is the re-encryption function, r 2 is a random number of λ bits.

[0013] As a preferred solution of the enterprise power consumption quality comprehensive portrait method based on fully homomorphic encryption technology described in the present invention, wherein: the joint feature index calculation of the encrypted data includes the trusted privacy computing platform performing joint feature index calculation according to the calculation factor of the encrypted joint feature index through multiplication linear operation; the multiplication linear operation includes defining the plaintext m 1 、m 2 And the corresponding ciphertext C 1 ←E nc2 (E nc1 (m 1 ))、C 2 ←E nc2 (E nc1 (m 2 )), satisfying the multiplication linear operation constraint, expressed as:

[0014] D ec2 =(D ec1 (C 1 C 2 ))=m 1 m 2

[0015] Among them, D ec1 is the initial decryption function, D ec2 is the re-decryption function; when the linear operation performed by the trusted privacy computing platform is Δ, if the linear operation Δ is multiplication, the corresponding ciphertext input is C 1 and C 2 , the linear operation ciphertext result is C 3 According to the multiplication homomorphism of homomorphic encryption, it can be expressed as:

[0016] C 3 =C 1 C 2

[0017] The joint characteristic indicators include the kilowatt-hour output value ratio, the ton carbon output value ratio and the green electricity consumption ratio; the kilowatt-hour output value ratio is jointly calculated by the power grid company and the tax bureau, expressed as:

[0018]

[0019] Among them, λ ddb is the kilowatt-hour output value ratio, θ dd is the electricity output value of the enterprise, Q cz is the enterprise output value counted by the tax bureau, W beis the total electricity consumption of enterprises counted by the power grid company, θ dd0 The national average kilowatt-hour output value is calculated by the grid company; the ton carbon output value ratio is jointly calculated by the enterprise carbon emission verification agency and the tax bureau, expressed as:

[0020]

[0021] Among them, λ dtb is the carbon output value ratio per ton, ρ dt is the carbon output value of the enterprise, T tp is the carbon emissions of enterprises counted by carbon emission verification agencies, ρ dt0 The national average carbon output value per ton is calculated by the enterprise carbon emission verification agency; the green electricity consumption ratio is calculated jointly by the power grid company and the green electricity trading platform, expressed as:

[0022]

[0023] Among them, λ 1vb is the green electricity consumption ratio, W 1v The green electricity consumption of enterprises is counted by the green electricity trading platform.

[0024] As a preferred solution of the enterprise power consumption quality comprehensive portrait method based on fully homomorphic encryption technology described in the present invention, wherein: the enterprise portrait evaluation index is calculated by the trusted privacy computing platform according to the joint feature index through additive linear operation; the additive linear operation includes defining the plaintext m 1 、m 2 And the corresponding ciphertext C 1 ←E nc2 (E nc1 (m 1 ))、C 2 ←E nc2 (E nc1 (m 2 )), satisfying the additive linear operation constraint, expressed as:

[0025] D ec2 =(D ec1 (C 1 +C 2 ))=m 1 +m 2

[0026] When the linear operation performed by the trusted privacy computing platform is Δ, if the linear operation Δ is addition, the corresponding ciphertext input is C 1 and C 2 , the linear operation ciphertext result is C 3 When , according to the additive homomorphism of homomorphic encryption, it can be expressed as:

[0027] C3 =C 1 +C 2

[0028] Calculate the evaluation index of enterprise power consumption quality portrait, expressed as:

[0029]

[0030] Among them, μ xf It is an evaluation index for the quality portrait of enterprise electricity consumption.

[0031] As a preferred solution of the enterprise power consumption quality comprehensive portrait method based on fully homomorphic encryption technology described in the present invention, wherein: the decryption of the enterprise portrait evaluation index includes performing two homomorphic decryptions on the enterprise portrait evaluation index through a private key q to obtain the plain text of the enterprise portrait evaluation index; the two homomorphic decryptions include initial decryption and secondary decryption; the initial decryption includes inputting the secondary encryption result C and outputting the initial decryption result C * ←D ec1 (C), the first decryption, is expressed as:

[0032] D ec1 (C) = Cmodq = (m + r 1 QmodN)(1+r 2 q)modq=m+r 1 QqI

[0033] Decryption again includes inputting the initial decryption result C * , output plaintext m←D ec2 (C * ), decrypted again, expressed as:

[0034] D ec2 (C * )=C*modQ=(m+r 1 QmodN)modQ=mmodQ

[0035] Among them, D ec2 To decrypt again.

[0036] As a preferred scheme of the method for comprehensive profiling of enterprise power consumption quality based on fully homomorphic encryption technology described in the present invention, wherein: the generating of enterprise power consumption quality portrait includes profiling the enterprise based on the plain text of enterprise portrait evaluation index according to enterprise portrait rules, and outputting the enterprise portrait result; the enterprise portrait rules include when the enterprise portrait evaluation index value is greater than 1.2, the power consumption quality is excellent; when the enterprise portrait evaluation index value is greater than or equal to 1 and less than or equal to 1.2, the power consumption quality is good; when the enterprise portrait evaluation index value is greater than or equal to 0.8 and less than or equal to 1, the power consumption quality is medium; when the enterprise portrait evaluation index value is greater than or equal to 0.6 and less than or equal to 0.8, the power consumption quality is poor; when the enterprise portrait evaluation index value is less than 0.6, the power consumption quality is extremely poor.

[0037] Another object of the present invention is to provide a comprehensive portrait system of enterprise electricity consumption quality based on fully homomorphic encryption technology, which can homomorphically encrypt the joint characteristic indicator calculation factors of multiple parties through public parameters based on a trusted privacy computing platform, and transmit them to the trusted privacy computing platform, thereby solving the problem that the current electricity consumption quality portrait technology lacks effective privacy protection.

[0038] As a preferred solution of the enterprise power consumption quality comprehensive portrait system based on fully homomorphic encryption technology described in the present invention, it includes: a trusted privacy computing platform, a homomorphic encryption module, a homomorphic decryption module, and an enterprise portrait function module; the trusted privacy computing platform is used to generate public parameters and private keys of the fully homomorphic encryption algorithm, and calculate joint feature indicators and enterprise portrait evaluation indicators based on the encrypted joint feature indicator calculation factors; the homomorphic encryption module is used to homomorphically encrypt the joint feature indicator calculation factors of multiple parties based on the public parameters of the trusted privacy computing platform, and transmit them to the trusted privacy computing platform; the homomorphic decryption module is used to decrypt the enterprise portrait evaluation indicators based on the private key of the trusted privacy computing platform to obtain the plaintext of the enterprise portrait indicators; the enterprise portrait function module is used to profile the enterprise according to the enterprise portrait rules based on the plaintext of the enterprise portrait evaluation indicators, and output the enterprise portrait results.

[0039] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for comprehensively profiling the quality of enterprise electricity consumption based on fully homomorphic encryption technology.

[0040] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for comprehensive profiling of enterprise power consumption quality based on fully homomorphic encryption technology.

[0041] Beneficial effects of the invention: The enterprise power consumption quality comprehensive portrait method based on fully homomorphic encryption technology provided by the invention realizes the privacy protection and security of data in the calculation process by encrypting multiple data sources using fully homomorphic encryption technology on a trusted privacy computing platform. This security and privacy protection not only enhances the trust of all parties, but also breaks down the barriers to information sharing between enterprises, so that multiple parties can jointly achieve a win-win situation of environmental protection and economic benefits in cooperation. By performing joint feature indicator calculations on encrypted data, the trusted privacy computing platform realizes the effective integration of different data sources. Through multi-party calculations, key indicators such as the kilowatt-hour output value ratio can be jointly generated. , tons of carbon output value ratio and green electricity consumption ratio. The accuracy and security of calculation are guaranteed by the characteristics of fully homomorphic encryption. Enterprises can obtain a more scientific portrait of the quality of electricity consumption. By decrypting the enterprise portrait evaluation indicators, the jointly calculated encrypted indicators can be converted into understandable plaintext results, so that all participants can have an intuitive insight into the actual performance of the enterprise's electricity consumption. Enterprises can more clearly identify the quality of their electricity consumption, and then adjust their business strategies according to the portrait results, such as optimizing the energy structure or improving carbon emission management, thereby improving the overall operating efficiency and social responsibility. The present invention achieves better results in terms of safety, adaptability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0043] Figure 1 An overall flow chart of a method for comprehensive profiling of enterprise electricity consumption quality based on fully homomorphic encryption technology provided for the first embodiment of the present invention.

[0044] Figure 2 A module schematic diagram of a comprehensive portrait system of enterprise electricity consumption quality based on fully homomorphic encryption technology provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0046] Example 1, reference Figure 1, which is an embodiment of the present invention, provides a comprehensive portrait method for enterprise power consumption quality based on fully homomorphic encryption technology, including:

[0047] S1: Based on a trusted privacy computing platform, use fully homomorphic encryption technology to encrypt multi-party data sources.

[0048] Furthermore, based on the trusted privacy computing platform, it includes generating public parameters (λ, N, Q) and a private key q of the fully homomorphic encryption algorithm to encrypt local data of multiple parties; where λ is a security parameter, N is a modulus, and Q is a random public parameter not exceeding λ / 2 bits; sharing the public parameters (λ, N, Q) with multiple parties, including the tax bureau, power grid company, green power trading platform, and enterprise carbon emission verification agency.

[0049] It should be noted that the trusted privacy computing platform can be a third-party platform or a platform jointly built by the power grid company, tax bureau, and banking institution with trusted privacy computing capabilities; N is the product of two randomly selected large prime numbers with λ bits.

[0050] Furthermore, using fully homomorphic encryption technology to encrypt multi-party data sources includes calculating the calculation factors of the joint feature index through multiple parties, performing two homomorphic encryptions on the calculation factors of the joint feature index according to the public parameters of the fully homomorphic encryption algorithm, and transmitting the calculated ciphertext to the trusted privacy computing platform; multiple parties calculating the joint feature index includes the tax bureau jointly calculating the electricity production value ratio per unit and the carbon production value ratio per ton, the power grid company jointly calculating the green power consumption ratio and the electricity production value ratio per unit, the green power trading platform jointly calculating the green power consumption ratio, and the enterprise carbon emission verification agency jointly calculating the carbon production value ratio per ton; the two homomorphic encryptions include primary encryption and secondary encryption; the primary encryption includes inputting plaintext data m and outputting the primary encryption result C'←E nc1 (m), the primary encryption algorithm, is expressed as:

[0051] E nc1 (m) = m + r 1 Q mod N

[0052] where E nc1 is the primary encryption function, r 1 is a random number with λ / 2 bits, and mod is the remainder function; the secondary encryption includes inputting the primary encryption result C' and outputting the secondary encryption result C←E nc2 (C'), the secondary encryption algorithm, is expressed as:

[0053] E nc2 (C') = C'(1 + r 2 q) mod N = (m + r 1 Q)(1 + r 2 q) mod N

[0054] Among them, E nc2 is the re-encryption function, r 2 is a random number of λ bits.

[0055] It should be noted that the power grid company is responsible for providing the company's electricity consumption, the green electricity trading platform is responsible for providing the company's green electricity consumption, the company's carbon emission verification agency is responsible for providing the company's carbon emissions, and the tax bureau is responsible for providing the company's revenue.

[0056] The total electricity consumption of enterprises in the past 12 months according to statistics from the power grid company be , and query the national electricity output value θ dd0 , calculate the kilowatt-hour output value ratio factor The power grid company uses the public parameters (λ, N, Q) of the fully homomorphic encryption algorithm model to Perform two encryption calculations to obtain the encryption calculation result and will Sent to the trusted privacy computing platform.

[0057] The tax bureau counts the output value of enterprises in the past 12 months. cz , obtain the power output value factor Q cz , using the public parameters (λ, N, Q) of the fully homomorphic encryption algorithm model to cz Perform two encryption calculations to obtain the encryption calculation result E(Q cz ), and E(Q cz ) is sent to the trusted privacy computing platform.

[0058] The carbon emission verification agency of the enterprise has calculated the carbon emission of the enterprise in the past 12 months. tp , and calculate the national average carbon output value per ton ρ dt0 , calculate the ton carbon output value ratio factor Using the public parameters (λ, N, Q) of the fully homomorphic encryption algorithm model Perform two encryption calculations to obtain the encryption calculation result and will Sent to the trusted privacy computing platform.

[0059] The total electricity consumption of enterprises in the past 12 months according to statistics from the power grid company be , calculate the green electricity consumption ratio factor Using the public parameters (λ, N, Q) of the fully homomorphic encryption algorithm model Perform two encryption calculations to obtain the encryption calculation result and will Sent to the trusted privacy computing platform.

[0060] Green electricity trading platform statistics the green electricity consumption of enterprises in the past 12 months W1v , get the green electricity consumption ratio factor W 1v , using the public parameters (λ, N, Q) of the fully homomorphic encryption algorithm model to 1v Perform two encryption calculations to obtain the encryption calculation result E(W 1v ), and E(W 1v ) is sent to the trusted privacy computing platform.

[0061] It should also be noted that by implementing fully homomorphic encryption technology on a trusted privacy computing platform, the data privacy of multiple parties can be effectively protected. Specifically, the process of generating public parameters (λ, N, Q) and private key q ensures the transparency and security of data during the encryption process. This step ensures that data provided by different institutions (such as tax bureaus, power grid companies, green electricity trading platforms, and carbon emission verification agencies) are integrated and calculated in an encrypted state, minimizing the risk of data leakage. Therefore, the protection of sensitive data is achieved, allowing all parties to cooperate and share information without worrying about data exposure. Ultimately, this measure not only enhances the trust between multiple parties, but also helps promote cross-sector cooperation and provides strong data support for economic and environmental governance.

[0062] S2: Calculate the joint feature indicators of the encrypted data and calculate the enterprise portrait evaluation indicators.

[0063] Furthermore, the calculation of the joint characteristic index of the encrypted data includes the trusted privacy computing platform performing the joint characteristic index calculation according to the calculation factor of the encrypted joint characteristic index through multiplication linear operation; the multiplication linear operation includes defining the plaintext m 1 、m 2 And the corresponding ciphertext C 1 ←E nc2 (E nc1 (m 1 ))、C 2 ←E nc2 (E nc1 (m 2 )), satisfying the multiplication linear operation constraint, expressed as:

[0064] D ec2 =(D ec1 (C 1 C 2 ))=m 1 m 2

[0065] Among them, D ec1 is the initial decryption function, D ec2 is the re-decryption function; when the linear operation performed by the trusted privacy computing platform is Δ, if the linear operation Δ is multiplication, the corresponding ciphertext input is C 1and C 2 The linear operation ciphertext result is C 3 When it is, according to the multiplicative homomorphism of homomorphic encryption, it is expressed as:

[0066] C 3 = C 1 C 2

[0067] The combined characteristic indicators include the ratio of output value per kilowatt-hour, the ratio of output value per ton of carbon, and the ratio of green electricity consumption; through the power grid company and the tax bureau, the ratio of output value per kilowatt-hour is jointly calculated and expressed as:

[0068]

[0069] Among them, λ ddb is the ratio of output value per kilowatt-hour, θ dd is the output value per kilowatt-hour of the enterprise, Q cz is the output value of the enterprise statistically by the tax bureau, W be is the total electricity consumption of the enterprise statistically by the power grid company, θ dd0 is the national average output value per kilowatt-hour statistically by the power grid company; through the enterprise carbon emission verification agency and the tax bureau, the ratio of output value per ton of carbon is jointly calculated and expressed as:

[0070]

[0071] Among them, λ dtb is the ratio of output value per ton of carbon, ρ dt is the output value per ton of carbon of the enterprise, T tp is the carbon emission of the enterprise statistically by the enterprise carbon emission verification agency, ρ dt0 is the national average output value per ton of carbon statistically by the enterprise carbon emission verification agency, ρ dt0 is defined as the ratio of the national GDP and the national carbon emissions; through the power grid company and the green electricity trading platform, the ratio of green electricity consumption is jointly calculated and expressed as:

[0072]

[0073] Among them, λ 1vb is the ratio of green electricity consumption, W 1v is the green electricity consumption of the enterprise statistically by the green electricity trading platform.

[0074] It should be noted that after the trusted privacy computing platform obtains and E(Q cz ), according to the formula for jointly calculating the ratio of output value per kilowatt-hour, after performing homomorphic encryption multiplication calculation, the encrypted calculation result E(λ ddb ) of the ratio of output value per kilowatt-hour λ ddb ) is obtained.

[0075] The trusted privacy computing platform obtains and E(Qcz Then, according to the formula for jointly calculating the ton carbon output value ratio, the ton carbon output value ratio λ is obtained after homomorphic encryption multiplication calculation dtb The encrypted calculation result E(λ dtb ).

[0076] Trusted Privacy Computing Platform Acquisition and E(W 1v ), the green electricity consumption ratio λ is obtained by performing homomorphic encryption multiplication according to the formula for jointly calculating the green electricity consumption ratio 1vb The encrypted calculation result E(λ 1vb ).

[0077] Furthermore, the enterprise portrait evaluation index is calculated by the trusted privacy computing platform according to the joint feature index, and the enterprise power consumption quality portrait evaluation index is calculated by additive linear operation; the additive linear operation includes defining the plaintext m 1 、m 2 And the corresponding ciphertext C 1 ←E nc2 (E nc1 (m 1 ))、C 2 ←E nc2 (E nc1 (m 2 )), satisfying the additive linear operation constraint, expressed as:

[0078] D ec2 =(D ec1 (C 1 +C 2 ))=m 1 +m 2

[0079] When the linear operation performed by the trusted privacy computing platform is Δ, if the linear operation Δ is addition, the corresponding ciphertext input is C 1 and C 2 , the linear operation ciphertext result is C 3 When , according to the additive homomorphism of homomorphic encryption, it can be expressed as:

[0080] C 3 =C 1 +C 2

[0081] Calculate the evaluation index of enterprise power consumption quality portrait, expressed as:

[0082]

[0083] Among them, μ xf It is an evaluation index for the quality portrait of enterprise electricity consumption.

[0084] It should be noted that the trusted privacy computing platform uses the enterprise power consumption quality portrait evaluation index formula to calculate the enterprise power output value ratio ciphertext E(λ ddb ), tons of carbon output value ratio ciphertext E(λ dtb ) and green electricity consumption ratio ciphertext E(λ 1vb ), perform homomorphic encryption addition operation, and obtain the ciphertext E(μ xf ).

[0085] It should also be noted that the encrypted data is jointly calculated using a trusted privacy computing platform, and multiplication linear operations are used to generate enterprise electricity consumption quality portrait evaluation indicators. This process achieves three-dimensional data analysis by collaboratively calculating key indicators such as the kilowatt-hour output value ratio, ton carbon output value ratio, and green electricity consumption ratio. Specifically, the digital joint characteristic indicators enable all parties to jointly evaluate the energy consumption and carbon emission behaviors of enterprises without decrypting the data, thereby generating more targeted management and decision-making basis. In this way, enterprises can not only obtain a more comprehensive market competitiveness analysis, but also optimize energy use and reduce carbon emissions in a targeted manner to promote sustainable development. Therefore, this step effectively improves data utilization and promotes the rapid development of data-driven decision-making.

[0086] S3: Decrypt the enterprise portrait evaluation indicators and generate the enterprise electricity consumption quality portrait.

[0087] Furthermore, decrypting the enterprise portrait evaluation index includes performing two homomorphic decryptions on the enterprise portrait evaluation index through the private key q to obtain the plain text of the enterprise portrait evaluation index; the two homomorphic decryptions include the initial decryption and the secondary decryption; the initial decryption includes inputting the secondary encryption result C and outputting the initial decryption result C * ←D ec1 (C), the first decryption, is expressed as:

[0088] D ec1 (C) = Cmodq = (m + R 1 QmodN)(1+R 2 q)modq=m+R 1 QqI

[0089] Decryption again includes inputting the initial decryption result C * , output plaintext m←D ec2 (C * ), decrypted again, expressed as:

[0090] D ec2 (C * )=C*modQ=(m+r 1 QmodN)modQ=mmodQ

[0091] Among them, D ec2 To decrypt again.

[0092] It should be noted that the ciphertext E(μ xf ), and the decrypted plaintext μ of the power consumption quality portrait evaluation index can be obtained by decrypting it twice with the private key. xf .

[0093] It is also explained that generating an enterprise's electricity consumption quality portrait includes profiling the enterprise based on the enterprise portrait evaluation index plain text and according to the enterprise portrait rules, and outputting the enterprise portrait results; the enterprise portrait rules include when the enterprise portrait evaluation index value is greater than 1.2, the electricity consumption quality is excellent; when the enterprise portrait evaluation index value is greater than or equal to 1 and less than or equal to 1.2, the electricity consumption quality is good; when the enterprise portrait evaluation index value is greater than or equal to 0.8 and less than or equal to 1, the electricity consumption quality is medium; when the enterprise portrait evaluation index value is greater than or equal to 0.6 and less than or equal to 0.8, the electricity consumption quality is poor; when the enterprise portrait evaluation index value is less than 0.6, the electricity consumption quality is extremely poor.

[0094] It should also be noted that the enterprise portrait evaluation indicators are decrypted twice through the private key. The process of obtaining the plaintext shows how to regenerate an operational enterprise electricity consumption quality portrait based on the ciphertext. After the data decryption is implemented, not only the real-time evaluation of electricity consumption quality is achieved, but also the enterprises are classified according to the set enterprise portrait rules. In this way, the enterprise can immediately feedback its electricity consumption status and take corresponding measures to optimize internal management. This efficient and dynamic evaluation mechanism enhances the adaptability of enterprises in the market environment, helps them to monitor and evaluate their own operational efficiency and environmental protection responsibilities in real time, so as to allocate resources and formulate strategies more accurately. Therefore, it realizes the continuous improvement of environmentally friendly enterprises and helps to promote the overall transformation of society to a low-carbon economy.

[0095] Example 2 is an embodiment of the present invention, which provides a method for comprehensive profiling of enterprise electricity consumption quality based on fully homomorphic encryption technology. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0096] In this embodiment, the experimental subject is selected as "ABC Company", which is a medium-sized enterprise involved in multiple industries. The purpose is to evaluate the quality of its electricity consumption and generate a corporate portrait. To ensure the accuracy and reliability of the evaluation results, the participating parties include the tax bureau, power grid company, green electricity trading platform and corporate carbon emission verification agency. All participants have prepared relevant data sources to support the implementation of the experiment.

[0097] At the beginning of the experiment, the trusted privacy computing platform generated the public parameters of the fully homomorphic encryption algorithm.

[0098] (λ, N, Q), set the security parameter λ = 128, randomly select two λ-bit large prime numbers 101 and 103, calculate their product, modulus N = 10403, and generate a random public parameter Q = 7 (not exceeding λ / 2). These public parameters are securely shared with all participants.

[0099] Next, the power grid company collected the electricity consumption data of ABC Company for the past 12 months and calculated the total electricity consumption W. be =165000kWh, and query the national electricity output valueθ dd0 = 13.8.0 yuan / kWh, to calculate the kilowatt-hour output value ratio factor, the tax bureau counted the company's total output value in the past year and obtained the result of Q cz =3,200,000 yuan, while the carbon emissions counted by the enterprise carbon emissions verification agency is T tp =62.4 tons, national average carbon output value per ton ρ dt0 The price is 22,700 yuan / ton. Based on these data, each party then performs two homomorphic encryption processes. First, the encryption results of each data are calculated using the primary encryption algorithm and sent to the trusted privacy computing platform. The power grid company calculates the power output value ratio factor based on the encrypted power consumption. The total output value of the company counted by the tax bureau is Q cz After encryption, it is also transmitted to the calculation platform. At the same time, the carbon emission verification agency calculates the ton carbon output value ratio factor

[0100] Next, the green electricity trading platform provides the company's green electricity consumption statistics for the past year, and the result is W 1v =52000kWh, and the corresponding encryption processing is carried out. All encrypted data are centrally collected on the trusted privacy computing platform for subsequent joint feature indicator calculation.

[0101] By using multiplication and addition linear operations, the trusted privacy computing platform combines multiple parameters such as the kilowatt-hour output value ratio, ton carbon output value ratio and green electricity consumption ratio to generate a corporate portrait evaluation index. Finally, the indicator is decrypted twice to obtain the plaintext value, and classified according to the corporate portrait rules, further generating an electricity consumption quality portrait of ABC Company.

[0102] Refer to Table 1 to analyze the experimental data.

[0103] Table 1 Experimental data record table

[0104]

[0105] This embodiment demonstrates the advantages and innovation of the comprehensive profiling method for enterprise electricity consumption quality based on fully homomorphic encryption technology through the contents of Table 1.

[0106] First, the data in Table 1 reveals the integration capabilities of the trusted privacy computing platform under multiple data sources. By aggregating data from power grid companies, tax bureaus, green electricity trading platforms and carbon emission verification agencies, a more comprehensive corporate portrait can be formed, making the overall assessment more scientific and accurate. For example, the kilowatt-hour output value ratio calculated by combining ABC company's total output value and electricity consumption is 1.408, and the ton carbon output value ratio is 2.259, indicating that the company's electricity utilization efficiency is at the national level and is a low-energy and low-emission enterprise. At the same time, the company's green electricity consumption ratio is 0.315, indicating that the company still has room for exploration in clean energy consumption.

[0107] Compared with the traditional model that relies on a single data source, the comprehensive application of this invention not only breaks the data island phenomenon, but also realizes the effective utilization of multi-party data under the premise of ensuring data privacy. Previous technologies often lack effective privacy protection measures, leading to obstacles to data release and sharing. The trusted privacy computing platform uses fully homomorphic encryption to enable all participants to obtain the required information without disclosing their private data. This innovation enhances the security and reliability of multi-party data collaboration.

[0108] Finally, the calculated power consumption quality evaluation index is 1.327, which is an excellent grade according to the definition, reflecting that the company's power consumption quality is maintained at a good level. It can be seen that after implementing the present invention, ABC Company has gained more valuable management insights and promoted the company to move towards efficient, green and sustainable development. Overall, this embodiment demonstrates the innovative and practical solution of forming a comprehensive portrait of corporate power consumption quality based on fully homomorphic encryption technology. Example 3, with reference to Figure 2 , as an embodiment of the present invention, provides a comprehensive portrait system of enterprise electricity consumption quality based on fully homomorphic encryption technology, including a trusted privacy computing platform, a homomorphic encryption module, a homomorphic decryption module, and an enterprise portrait function module.

[0109] The trusted privacy computing platform is used to generate the public parameters and private keys of the fully homomorphic encryption algorithm, and calculate the joint feature indicators and enterprise portrait evaluation indicators based on the calculation factors of the encrypted joint feature indicators; the homomorphic encryption module is used to homomorphically encrypt the joint feature indicator calculation factors of multiple parties based on the public parameters of the trusted privacy computing platform, and transmit them to the trusted privacy computing platform; the homomorphic decryption module is used to decrypt the enterprise portrait evaluation indicators based on the private key of the trusted privacy computing platform to obtain the plaintext of the enterprise portrait indicators; the enterprise portrait function module is used to profile the enterprise based on the plaintext of the enterprise portrait evaluation indicators and according to the enterprise portrait rules, and output the enterprise portrait results.

[0110] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0111] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0112] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0113] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A comprehensive profiling method for enterprise power consumption quality based on fully homomorphic encryption technology, characterized in that: include: Based on the trusted privacy computing platform, fully homomorphic encryption technology is used to encrypt multi-party data sources; Calculate joint feature indicators for encrypted data and calculate enterprise portrait evaluation indicators; Decrypt the enterprise portrait evaluation indicators and generate the enterprise electricity consumption quality portrait.

2. The method for comprehensive profiling of enterprise power consumption quality based on fully homomorphic encryption technology according to claim 1, characterized in that: The trusted privacy computing platform includes generating public parameters (λ, N, Q) and private key q of a fully homomorphic encryption algorithm to encrypt local data of multiple entities; Where λ is the security parameter, N is the modulus, and Q is a random public parameter not exceeding λ / 2 bits; The public parameters (λ, N, Q) are shared with multiple parties, including the tax bureau, power grid company, green electricity trading platform and corporate carbon emission verification agency.

3. The method for comprehensive profiling of enterprise power consumption quality based on fully homomorphic encryption technology as claimed in claim 2, characterized in that: The use of fully homomorphic encryption technology to encrypt multi-party data sources includes calculating the calculation factors of the joint feature indicators through multiple entities, performing two homomorphic encryptions on the calculation factors of the joint feature indicators according to the public parameters of the fully homomorphic encryption algorithm, and transmitting the calculated ciphertext to the trusted privacy computing platform; The joint characteristic indicators calculated by multiple entities include the tax bureau jointly calculating the kilowatt-hour output value ratio and ton carbon output value ratio, the power grid company jointly calculating the green electricity consumption ratio and kilowatt-hour output value ratio, the green electricity trading platform jointly calculating the green electricity consumption ratio, and the corporate carbon emission verification agency jointly calculating the ton carbon output value ratio; Double homomorphic encryption includes initial encryption and re-encryption; The initial encryption includes inputting plaintext data m and outputting the initial encryption result of the plaintext data C'←E nc1 (m), the initial encryption algorithm, is expressed as: E nc1 (m)=m+r1QmodN Among them, E nc1 is the initial encryption function, r1 is a λ / 2-bit random number, and mod is the remainder function; The re-encryption includes inputting the initial encryption result C' and outputting the re-encryption result C←E nc2 (C'), the encryption algorithm is expressed as: And nc2 (C')=C'(1+r2q)modN=(m+r1Q)(1+r2q)modN Among them, E nc2 For the re-encryption function, r2 is a random number of λ bits.

4. The method for comprehensive profiling of enterprise power consumption quality based on fully homomorphic encryption technology according to claim 3 is characterized in that: The calculation of the joint characteristic index for the encrypted data includes the trusted privacy computing platform calculating the joint characteristic index according to the calculation factor of the encrypted joint characteristic index through multiplication linear operation; The multiplication linear operation includes defining the plaintext m1, m2 and the corresponding ciphertext C1←E nc2 (E nc1 (m1))、C2←E nc2 (E nc1 (m2)), satisfying the multiplication linear operation constraint, expressed as: D ec2 =(D ec1 (C1C2))=m1m2 Among them, D ec1 is the initial decryption function, D ec2 To decrypt the function again; When the linear operation performed by the trusted privacy computing platform is Δ, if the linear operation Δ is multiplication, the corresponding ciphertext input is C1 and C2, and the linear operation ciphertext result is C3, according to the multiplication homomorphism of homomorphic encryption, it can be expressed as: C3=C1C2 The joint characteristic indicators include the output value ratio of kilowatt-hour electricity, the output value ratio of tons of carbon, and the green electricity consumption ratio; The power grid company and the tax bureau jointly calculate the kilowatt-hour output value ratio, which is expressed as: Among them, λ ddb is the kilowatt-hour output value ratio, θ dd is the electricity output value of the enterprise, Q cz is the enterprise output value counted by the tax bureau, W be is the total electricity consumption of enterprises counted by the power grid company, θ dd0 The national average kilowatt-hour output value calculated by the grid company; Through the enterprise carbon emission verification agency and the tax bureau, the ton carbon output value ratio is jointly calculated and expressed as: Among them, λ dtb is the carbon output value ratio per ton, ρ dt is the carbon output value of the enterprise, T tp is the carbon emissions of enterprises counted by carbon emission verification agencies, ρ dt0 The national average carbon output value per ton calculated by corporate carbon emission verification agencies; The green electricity consumption ratio is calculated jointly by the power grid company and the green electricity trading platform, and is expressed as: Among them, λ 1vb is the green electricity consumption ratio, W 1v The green electricity consumption of enterprises is counted by the green electricity trading platform.

5. The method for comprehensive profiling of enterprise power consumption quality based on fully homomorphic encryption technology according to claim 4, characterized in that: The calculation of the enterprise portrait evaluation index includes the trusted privacy computing platform calculating the enterprise power consumption quality portrait evaluation index according to the joint feature index through additive linear operation; Additive linear operations include defining plaintext m1, m2 and corresponding ciphertext C1←E nc2 (E nc1 (m1))、C2←E nc2 (E nc1 (m2)), satisfying the additive linear operation constraint, expressed as: D ec2 =(D ec1 (C1+C2))=m1+m2 When the linear operation performed by the trusted privacy computing platform is Δ, if the linear operation Δ is addition, the corresponding ciphertext inputs are C1 and C2, and the linear operation ciphertext result is C3, according to the additive homomorphism of homomorphic encryption, it can be expressed as: C3=C1+C2 Calculate the evaluation index of enterprise power consumption quality portrait, expressed as: Among them, μ xf It is an evaluation index for the quality portrait of enterprise electricity consumption.

6. The method for comprehensive profiling of enterprise power consumption quality based on fully homomorphic encryption technology according to claim 5, characterized in that: Decrypting the enterprise portrait evaluation index includes performing two homomorphic decryptions on the enterprise portrait evaluation index through the private key q to obtain the plain text of the enterprise portrait evaluation index; The two homomorphic decryptions include the first decryption and the second decryption; The initial decryption includes inputting the re-encrypted result C and outputting the initial decrypted result C * ←D ec1 (C), the first decryption, is expressed as: D ec1 (C)=Cmodq=(m+r1QmodN)(1+r2q)modq=m+r1QmodN Decryption again includes inputting the initial decryption result C * , output plaintext m←D ec2 (C * ), decrypted again, expressed as: D ec2 (C * )=C*modQ=(m+r1QmodN)modQ=mmodQ Among them, D ec2 To decrypt again.

7. The method for comprehensive profiling of enterprise power consumption quality based on fully homomorphic encryption technology according to claim 6, characterized in that: Generating the enterprise power consumption quality portrait includes profiling the enterprise based on the enterprise portrait evaluation index plain text and according to the enterprise portrait rules, and outputting the enterprise portrait result; The enterprise portrait rules include that when the enterprise portrait evaluation index value is greater than 1.2, the quality of electricity consumption is excellent; When the enterprise portrait evaluation index value is greater than or equal to 1 and less than or equal to 1.2, the quality of electricity consumption is good; When the enterprise portrait evaluation index value is greater than or equal to 0.8 and less than or equal to 1, the quality of electricity consumption is medium; When the enterprise portrait evaluation index value is greater than or equal to 0.6 and less than or equal to 0.8, the quality of electricity consumption is poor; When the enterprise portrait evaluation index value is less than 0.6, the quality of electricity consumption is extremely poor.

8. A system using the method for comprehensive profiling of enterprise power consumption quality based on fully homomorphic encryption technology as described in any one of claims 1 to 7, characterized in that: Including trusted privacy computing platform, homomorphic encryption module, homomorphic decryption module, and enterprise portrait function module; The trusted privacy computing platform is used to generate public parameters and private keys of the fully homomorphic encryption algorithm, calculate the joint feature index and the enterprise portrait evaluation index based on the calculation factors of the encrypted joint feature index; The homomorphic encryption module is used to homomorphically encrypt the joint characteristic index calculation factors of multiple entities based on the public parameters of the trusted privacy computing platform, and transmit them to the trusted privacy computing platform; The homomorphic decryption module is used to decrypt the enterprise portrait evaluation index based on the private key of the trusted privacy computing platform to obtain the plain text of the enterprise portrait index; The enterprise portrait function module is used to profile the enterprise based on the enterprise portrait evaluation index plain text and according to the enterprise portrait rules, and output the enterprise portrait results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for comprehensive profiling of enterprise electricity consumption quality based on fully homomorphic encryption technology described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for comprehensive profiling of enterprise electricity consumption quality based on fully homomorphic encryption technology described in any one of claims 1 to 7 are implemented.

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