Federal learning-based energy data asset right confirmation method, system and device, and medium

Abstract

The invention discloses an energy data asset right confirmation method, system and device based on federated learning and a medium, and the method comprises the steps: decrypting an encryption model parameter sent by each energy data node to obtain a model node parameter, the model node parameter being obtained by optimizing a local model based on local energy data by each energy data node, aggregating the model node parameters to obtain global model parameters, constructing a global model based on the global model parameters, calculating a data contribution degree of each energy data node according to the global model, generating an asset right confirmation certificate corresponding to each energy data node based on the data contribution degree, according to the method, the data contribution degree of each node is accurately quantified in a distributed energy environment, the right of energy data assets is accurately confirmed while the data security and privacy are ensured, and the data leakage risk generated in the process of analyzing and processing multi-source heterogeneous data is effectively avoided.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, system, device and medium for energy data asset rights confirmation based on federated learning. Background Art

[0002] With the development of the trend of energy data assetization, in the current energy industry, the existing data collection system is usually participated by multiple stakeholders, including power generation companies, grid operators, users, etc. These participants collect, store and process data independently, and the resource rights confirmation mechanism can determine who owns the data and how to reasonably allocate the right to use it. At present, due to the lack of unified data management and ownership definition standards, the ownership of energy data has become complicated, and traditional rights confirmation methods are difficult to meet the ownership requirements of multi-source heterogeneous data in distributed scenarios. In addition, as more and more personal and corporate energy consumption information is digitally recorded, the risk of privacy leakage increases during the analysis and processing of multi-source heterogeneous data. Summary of the invention

[0003] The main purpose of the present invention is to provide a method, system, device and medium for energy data asset rights confirmation based on federated learning, aiming to solve the technical problems that the existing technology cannot accurately confirm the rights of energy data assets in a distributed energy environment, and there is a great risk of privacy leakage during data processing and analysis.

[0004] To achieve the above-mentioned object, the present invention provides an energy data asset rights confirmation method based on federated learning, the method is applied to a central node in an energy data asset rights confirmation system based on federated learning, the energy data asset rights confirmation system based on federated learning comprises the central node and a plurality of energy data nodes, the central node is communicatively connected with each energy data node; The method comprises the following steps: Decrypting the encrypted model parameters sent by each energy data node to obtain model node parameters, where the model node parameters are obtained by optimizing the local model based on local energy data by each energy data node; The model node parameters are aggregated to obtain global model parameters, and a global model is constructed based on the global model parameters. The global model parameters are aggregated based on the following formula: in, is the global model parameter, Represents energy data node The model node parameters, Energy Data Node The amount of data; Calculate the data contribution of each energy data node according to the global model; An asset confirmation certificate corresponding to each energy data node is generated based on the data contribution, and the asset confirmation certificate is used to determine the data authority of the energy data node, and the data authority includes data ownership authority and data use authority.

[0005] Optionally, calculating the data contribution of each energy data node according to the global model includes: The initial contribution of each energy data node is calculated according to the global model: in, Represents energy data node The initial contribution of Represents energy data node The contribution of model node parameters to the performance improvement of the global model, Indicates the contribution of the model node parameters of all energy data nodes to the performance improvement of the global model, is a smoothing factor, which is used to balance the contribution of historical rounds with the contribution of the current round. Indicates the current round; Acquire raw energy data provided by each energy data node, and pre-process the raw energy data to obtain target energy data; The initial contribution is adjusted based on the target energy data corresponding to each energy data node to obtain a data contribution.

[0006] Optionally, adjusting the initial contribution based on the target energy data corresponding to each energy data node to obtain the data contribution includes: Performing time analysis on the target energy data corresponding to each energy data node to determine peak load period data and normal period data in the target energy data; Determine the time period weight of each target energy data based on the peak load time period data and the normal time period data; Perform feature analysis on the target energy data corresponding to each energy data node to determine the energy type and data type corresponding to each target energy data; Determine a characteristic weight of each target energy data according to the energy type and data type; Conduct node importance analysis on each energy data node to determine the node importance of each energy data node; Determine the node weight of each energy data node based on the node importance; The initial contribution is adjusted according to the time period weight, the feature weight and the node weight to obtain the data contribution.

[0007] Optionally, the acquiring original energy data provided by each energy data node and preprocessing the original energy data to obtain target energy data includes: Acquire original energy data provided by each energy data node, and perform time series analysis on the original energy data; Performing data cleaning on the original energy data based on the time series analysis result to obtain initial energy data; Extracting features from the initial energy data to obtain energy resource features; The initial energy data is subjected to dimensionality reduction processing according to the energy resource characteristics to obtain candidate energy data. The dimensionality reduction processing refers to the following formula: in, is the initial data matrix of initial energy data, is the projection matrix after dimensionality reduction, is the candidate data matrix of candidate energy data obtained after dimensionality reduction, the initial data matrix for The matrix of represents the number of samples, represents the dimension of the initial energy data, and the projection matrix is The matrix of represents the dimension after dimensionality reduction, and the candidate data matrix is Matrix of The candidate energy data are normalized according to the energy type of each candidate energy data to obtain target energy data.

[0008] Optionally, generating an asset title certificate corresponding to each energy data node based on the data contribution includes: Obtain the node identity and confirmation timestamp of each energy data node; Generate a blockchain hash value based on the data contribution, the node identity and the confirmation timestamp: in, Represents the blockchain hash value, Indicates data contribution, Indicates the node identity. The timestamp indicating when the asset title certificate was generated. represents a hash function; Generate an original certificate of ownership based on the blockchain hash value; Generate an encrypted signature for each original confirmation certificate, and asymmetrically encrypt the original confirmation certificate based on the encrypted signature to generate an asset confirmation certificate corresponding to each energy data node. The encrypted signature is generated according to the following formula: in, Indicates encrypted signature, Indicates the use of private keys to hash the blockchain Perform encryption operations.

[0009] In addition, to achieve the above-mentioned purpose, the present invention also proposes a method for confirming the ownership of energy data assets based on federated learning, the method is applied to an energy data node in an energy data asset confirmation system based on federated learning, the energy data asset confirmation system based on federated learning includes a central node and multiple energy data nodes, the central node is communicatively connected with each energy data node; The method comprises: Obtain energy data samples of original model parameters and local energy data; Based on the energy data sample, the original model parameters are gradient optimized to obtain model node parameters: in, Represents energy data node The loss function is Represents energy data node The model node parameters obtained after optimizing the original model parameters, Represents energy data node The total number of local energy data samples, Indicates The input features of energy data samples, Indicates The true labels of energy data samples, Represents the predicted value of the current model parameters for the input data, Represents the loss value of a single energy data sample; The model node parameters are encrypted to obtain encrypted model parameters, and the encrypted model parameters are sent to the central node so that the central node calculates the data contribution of each energy data node based on the encrypted model parameters, and generates an asset title certificate corresponding to each energy data node based on the data contribution.

[0010] Optionally, encrypting the model node parameters to obtain encrypted model parameters includes: Determining initial gradient parameters of the model node parameters; Add Gaussian noise to the initial gradient parameter to obtain the target gradient parameter: in, represents the initial gradient parameter, represents Gaussian noise, represents the target gradient parameter; The model node parameters are encrypted based on the target gradient parameters to obtain encrypted model parameters.

[0011] In addition, to achieve the above-mentioned purpose, the present invention also proposes an energy data asset rights confirmation system based on federated learning, wherein the energy data asset rights confirmation system based on federated learning includes a central node and a plurality of energy data nodes, wherein the central node is communicatively connected with each energy data node; The energy data node is used to obtain energy data samples of original model parameters and local energy data; The energy data node is further used to perform gradient optimization on the original model parameters based on the energy data sample to obtain model node parameters: in, Represents energy data node The loss function is Represents energy data node The model node parameters obtained after optimizing the original model parameters, Represents energy data node The total number of local energy data samples, Indicates The input features of energy data samples, Indicates The true labels of energy data samples, Represents the predicted value of the current model parameters for the input data, Represents the loss value of a single energy data sample; The energy data node is further used to encrypt the model node parameters, obtain encrypted model parameters, and send the encrypted model parameters to the central node; The central node is used to decrypt the encrypted model parameters sent by each energy data node to obtain model node parameters, where the model node parameters are obtained by each energy data node optimizing the local model based on local energy data; The central node is further used to aggregate the model node parameters to obtain global model parameters, and build a global model based on the global model parameters. The global model parameters are aggregated based on the following formula: in, is the global model parameter, Represents energy data node The model node parameters, Energy Data Node The amount of data; The central node is further used to calculate the data contribution of each energy data node according to the global model; The central node is also used to generate an asset confirmation certificate corresponding to each energy data node based on the data contribution, and the asset confirmation certificate is used to determine the data authority of the energy data node, and the data authority includes data ownership authority and data use authority.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a federated learning-based energy data asset title confirmation device, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the federated learning-based energy data asset title confirmation method as described above.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the energy data asset rights confirmation method based on federated learning as described above are implemented.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the energy data asset confirmation method based on federated learning as described above.

[0015] The present invention obtains model node parameters by decrypting the encrypted model parameters sent by each energy data node, wherein the model node parameters are obtained by optimizing the local model of each energy data node based on local energy data, aggregating the model node parameters to obtain global model parameters, and constructing a global model based on the global model parameters, calculating the data contribution of each energy data node according to the global model, and generating an asset confirmation certificate corresponding to each energy data node based on the data contribution, wherein the asset confirmation certificate is used to determine the data authority of the energy data node, and the data authority includes data ownership authority and data use authority; since the present invention obtains model node parameters by decrypting the encrypted model parameters sent by each energy data node, the risk of data leakage generated in the process of analyzing and processing multi-source heterogeneous data is effectively avoided, and the data contribution of each energy data node is calculated according to the global model, thereby accurately quantifying the data contribution of each node in a distributed energy environment, and accurately confirming the rights of energy data assets while ensuring data security and privacy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the description, are used to explain the principles of the present application.

[0017] 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, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 It is a structural diagram of an energy data asset rights confirmation device based on federated learning in a hardware operating environment involved in an embodiment of the present invention; Figure 2 It is a flow chart of the first embodiment of the energy data asset rights confirmation method based on federated learning of the present invention; Figure 3 This is a flow chart of a second embodiment of the method for confirming the ownership of energy data assets based on federated learning according to the present invention; Figure 4 This is a structural block diagram of the first embodiment of the energy data asset rights confirmation system based on federated learning of the present invention.

[0019] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0021] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of an energy data asset rights confirmation device based on federated learning in the hardware operating environment involved in an embodiment of the present invention.

[0022] like Figure 1 As shown, the energy data asset rights confirmation device based on federated learning may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage system independent of the aforementioned processor 1001.

[0023] Those skilled in the art will understand that Figure 1 The structure shown in does not constitute a limitation on the energy data asset rights confirmation device based on federated learning, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0024] like Figure 1 As shown, the memory 1005 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module, and an energy data asset rights confirmation program based on federated learning.

[0025] exist Figure 1In the energy data asset rights confirmation device based on federated learning shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the energy data asset rights confirmation device based on federated learning of the present invention can be set in the energy data asset rights confirmation device based on federated learning, and the energy data asset rights confirmation device based on federated learning calls the energy data asset rights confirmation program based on federated learning stored in the memory 1005 through the processor 1001, and executes the energy data asset rights confirmation method based on federated learning provided in the embodiment of the present invention.

[0026] The embodiment of the present invention provides a method for confirming the ownership of energy data assets based on federated learning. Figure 2 , Figure 2 This is a flow chart of the first embodiment of the energy data asset rights confirmation method based on federated learning of the present invention.

[0027] In this embodiment, the method is applied to a central node in an energy data asset rights confirmation system based on federated learning, wherein the energy data asset rights confirmation system based on federated learning includes the central node and multiple energy data nodes, and the central node is communicatively connected with each energy data node; The energy data asset rights confirmation method based on federated learning includes the following steps: Step S10: decrypt the encrypted model parameters sent by each energy data node to obtain model node parameters.

[0028] It should be understood that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a terminal electronic device capable of realizing the above functions, etc. The following takes the energy data asset rights confirmation device (rights confirmation device) based on federated learning as an example to illustrate this embodiment and the following embodiments.

[0029] It should be noted that energy data is distributed in multiple different energy data nodes (for example, energy data nodes may include power plants, user terminals, dispatch centers, etc.). In order to protect data privacy, this embodiment may use federated learning technology to jointly model the data of each energy data node. The model node parameters are obtained by optimizing the local model based on the local energy data of each energy data node.

[0030] It should be noted that the energy asset data of each energy data node is diverse. For example, the energy asset data may include electricity data, natural gas data, wind energy data, solar energy data, etc.

[0031] In some embodiments, each energy data node in the energy data asset rights confirmation system can pre-process the local energy data, and then perform local model training based on the pre-processed local energy data. Each node independently trains the local model, for example, gradient descent (SGD) optimization can be used. Each node encrypts the optimized model node parameters and sends them to the central node.

[0032] In some embodiments, an energy data node may include: Power generation companies, used to provide data such as power generation and equipment operating efficiency.

[0033] Electricity consumption units are used to provide information such as electricity consumption and time series load data.

[0034] Energy dispatch center, used to collect dynamic supply and demand data of distributed energy.

[0035] Natural resource data (such as wind power and solar power stations) is used to collect wind speed data, wind direction data, sunshine intensity data and duration data, etc.

[0036] In some embodiments, the data types of local energy data collected by the energy data node may include: Time series data, such as daily load profiles, power generation, and power consumption.

[0037] Geospatial data, such as the distribution location of energy sites or equipment.

[0038] Specific event data, such as the impact of holidays and meteorological events on energy demand.

[0039] Characteristic data, such as power load characteristics: peak-to-valley difference, load change rate.

[0040] Natural gas characteristic data, such as daily consumption and supply-demand ratio.

[0041] Wind energy characteristic data, such as wind speed distribution and wind direction distribution.

[0042] Solar energy characteristics data, such as sunshine duration and radiation intensity.

[0043] It should be noted that local energy data has diversity (such as electricity, natural gas, wind energy, solar energy, etc.) and spatiotemporal characteristics (such as seasonality and regionality). In some embodiments, each energy data node may perform data preprocessing (such as data cleaning, data standardization, feature extraction, dimension optimization, etc.) before optimizing and training the local energy data.

[0044] In some embodiments, in view of the time series characteristics of energy data, energy data nodes and central nodes can use LSTM (Long Short-Term Memory Network) to capture the time dependence and nonlinear fluctuation characteristics of the load curve. Through the LSTM gating mechanism (input gate, forget gate, output gate), the memory and forgetting of historical information are dynamically adjusted to identify the peak and trough change patterns of the daily load curve, thereby improving the accuracy of short-term predictions and providing more reliable data support for the training of the global model.

[0045] Step S20: Aggregate the model node parameters to obtain global model parameters, and construct a global model based on the global model parameters.

[0046] It should be noted that the global model parameters are obtained by aggregation based on the following formula: in, is the global model parameter, Represents energy data node The model node parameters, Energy Data Node The amount of data.

[0047] Step S30: Calculate the data contribution of each energy data node according to the global model.

[0048] It should be noted that, in this embodiment, the data contribution of each energy data node can be quantified, and the energy asset data ownership of each energy data node can be confirmed based on the data contribution.

[0049] In some embodiments, the rights confirmation device may evaluate the data contribution of each energy data node based on the degree of performance optimization of the model node parameters provided by each energy data node to the global model.

[0050] Furthermore, in order to accurately quantify the data contribution of each energy data node, the above step S30 may include: Step S301: Calculate the initial contribution of each energy data node according to the global model; Step S302: acquiring original energy data provided by each energy data node, and preprocessing the original energy data to obtain target energy data; Step S303: adjusting the initial contribution based on the target energy data corresponding to each energy data node to obtain a data contribution.

[0051] It should be noted that the confirmation of the ownership of energy data assets requires quantifying the data contribution of each node. Based on federated learning, the performance changes of the global model can reflect the contribution of each node, refer to the following formula: in, Represents energy data node The initial contribution of Represents energy data node The contribution of model node parameters to the performance improvement of the global model, Indicates the contribution of model node parameters of all energy data nodes to the performance improvement of the global model.

[0052] In some embodiments, each energy data node contributes data in multiple rounds, so for each node The formula for calculating the incremental contribution of the data accumulated over the rounds is as follows: in, Represents energy data node The initial contribution of Represents energy data node The contribution of model node parameters to the performance improvement of the global model, Indicates the contribution of the model node parameters of all energy data nodes to the performance improvement of the global model, is a smoothing factor, which is used to balance the contribution of historical rounds with the contribution of the current round. Indicates the current round. Indicates the previous round.

[0053] Furthermore, in order to deeply explore the characteristics of data and nodes, thereby improving the accuracy of contribution calculation, the above step S303 may include: Step S3031: performing time analysis on the target energy data corresponding to each energy data node to determine peak load period data and normal period data in the target energy data; Step S3032: determining the time period weight of each target energy data based on the peak load time period data and the normal time period data; Step S3033: Perform feature analysis on the target energy data corresponding to each energy data node to determine the energy type and data type corresponding to each target energy data; Step S3034: determining a characteristic weight of each target energy data according to the energy type and data type; Step S3035: performing node importance analysis on each energy data node to determine the node importance of each energy data node; Step S3036: determining the node weight of each energy data node based on the node importance; Step S3037: adjusting the initial contribution according to the time period weight, the feature weight and the node weight to obtain the data contribution.

[0054] It should be noted that the confirmation device can adjust the contribution weight according to the importance of the energy data provided by different energy data nodes, thereby adjusting the initial contribution of each energy data node. The importance of the above energy data refers to the degree of influence of different features, time periods or data sources in the model on the final prediction results or global model performance. For example, the contribution weight is adjusted according to the importance of energy data (such as peak load period data).

[0055] It should be noted that the importance used to adjust the contribution weight in this embodiment may include the importance of the data time period, the importance of the data feature, and the importance of the energy data node itself.

[0056] The importance of data time periods can be that certain time periods (such as peak load periods and holidays) are more critical to energy supply and demand forecasting. For example, the impact of electricity load data during peak hours in the model may be much greater than that of data during late night hours.

[0057] The importance of data features can be different contributions to model prediction. For example, historical data on electricity consumption may be more important than equipment status data for short-term load forecasting. Meteorological data (such as wind speed and sunshine intensity) are particularly important in wind or solar energy scenarios.

[0058] The importance of data nodes can be that the nodes where the data comes from may have different weights. For example: data from large power plants are more critical than data from small power users. Data from core nodes (such as regional dispatch centers) may have higher weights.

[0059] In some embodiments, the right confirmation device may assign weights according to the importance of the data time period, assigning a higher weight to data during peak load periods, and adjusting the time period weights according to the following formula: in, > , t represents time, is the time period weight.

[0060] In some embodiments, the right confirmation device may assign weights according to the importance of the features of different energy data. For features with greater impact, higher weights are assigned. The feature weight adjustment refers to the following formula: in, It is a feature f The weight of n is the total number of features.

[0061] In some embodiments, the right confirmation device may assign weights according to the importance of the source nodes of the energy data, assigning higher weights to the data of the core nodes, and the node weight adjustment refers to the following formula: in, Is a node i The weight of N is the total number of nodes.

[0062] It should be noted that the adjustment of contribution weight acts on the formula for contribution calculation, which is used to balance the relationship between the importance of the node and the actual contribution value. The right confirmation device can first adjust the initial contribution based on the time period weight, then adjust the initial contribution based on the feature weight, and finally adjust the initial contribution based on the node weight to obtain the final data contribution. The contribution adjustment refers to the following formula: in: Is a node The weight of is the feature weight, is the time period weight, Is a node The improvement in global model performance; is the adjusted contribution. Through this adjustment, the contribution of nodes with higher weights will increase accordingly, even if their Relatively low; nodes of low importance, even if If it is higher, its contribution will be reduced by weight adjustment.

[0063] Furthermore, in order to improve data quality and thus improve data analysis efficiency and accuracy, the above step S302: Step S3021: acquiring original energy data provided by each energy data node, and performing time series analysis on the original energy data; Step S3022: performing data cleaning on the original energy data based on the time series analysis result to obtain initial energy data; Step S3023: extracting features from the initial energy data to obtain energy resource features; Step S3024: performing dimension reduction processing on the initial energy data according to the energy resource characteristics to obtain candidate energy data; Step S3025: normalizing the candidate energy data according to the energy type of each candidate energy data to obtain target energy data.

[0064] It should be noted that local energy data has diversity (such as electricity, natural gas, wind energy, solar energy, etc.) and spatiotemporal characteristics (such as seasonality and regionality). In some embodiments, each energy data node may perform data preprocessing (such as data cleaning, data standardization, feature extraction, dimension optimization, etc.) before optimizing and training the local energy data.

[0065] It is understandable that this embodiment can perform data cleaning on the original energy data, specifically including: the right confirmation equipment detects and fills in the missing values ​​in the energy data (such as using interpolation to process the breakpoints of the power generation data). Eliminate outliers (such as eliminating abnormally high power consumption data points through box plots or Z scores). Process the impact of holidays or extreme weather in time series data.

[0066] In some embodiments, the right confirmation device can extract features and optimize dimensions of the initial energy data, including: extracting features related to the value of energy assets, including but not limited to power load characteristics: daily average load, peak-to-valley difference, load curve volatility; natural gas consumption characteristics: daily consumption, supply-demand ratio; wind and solar energy characteristics: wind speed, wind direction, sunshine duration. Use principal component analysis (PCA) to reduce the complexity of the high-dimensional feature space, and refer to the following formula for dimensionality reduction: in, is the initial data matrix of initial energy data, is the projection matrix after dimensionality reduction, is the candidate data matrix of candidate energy data obtained after dimensionality reduction, the initial data matrix for The matrix of represents the number of samples, represents the dimension of the initial energy data, and the projection matrix is The matrix of Represents the dimension after dimensionality reduction, usually k <n The column vectors of W are obtained by calculating the eigenvalues ​​and eigenvectors of the covariance matrix of X, and the eigenvectors correspond to the largest k eigenvalues. for The matrix of Each row of corresponds to a low-dimensional representation of a sample. The dimensionality reduction process is achieved by retaining the most information (i.e., the maximum variance) in the original data, thereby simplifying the model calculation and reducing the impact of noise.

[0067] In some embodiments, the right confirmation device may perform data normalization processing on the candidate energy data, and perform unified normalization processing on different types of energy data (such as electricity, natural gas, and wind energy) to eliminate the impact of dimensions and facilitate model processing, referring to the following formula: in, represents the target energy data after normalization, represents the candidate energy data that needs to be normalized, represents the minimum value among the candidate energy data, Represents the maximum value in the candidate energy data, and the normalization process is used to normalize the data range to [0,1].

[0068] Step S40: Generate an asset title certificate corresponding to each energy data node based on the data contribution.

[0069] It should be noted that the asset title certificate is used to determine the data permissions of the energy data node, and the data permissions include data ownership permissions and data usage permissions.

[0070] It should be noted that the asset confirmation certificate plays a core role in the confirmation of energy data assets. It is mainly used to clarify data ownership, protect the rights and interests of data providers, and provide a basis for data revenue distribution. By recording the unique identifier, contribution value, data type and generation timestamp of each data node, the confirmation certificate quantifies the contribution ratio of the data in the global model. At the same time, the confirmation certificate is stored in the blockchain, which is tamper-proof and traceable, ensuring the transparency and trust of the data use process. In addition, the confirmation certificate also supports data transactions and multi-party collaboration, providing technical guarantees for the sharing and utilization of energy data.

[0071] Furthermore, in order to improve the security and privacy of energy resource rights confirmation, the above step S40 may include: Step S401: Obtain the node identity and confirmation timestamp of each energy data node; Step S402: Generate a blockchain hash value based on the data contribution, the node identity and the confirmation timestamp; Step S403: Generate an original certificate of ownership according to the blockchain hash value; Step S404: Generate an encrypted signature for each original title confirmation certificate, and asymmetrically encrypt the original title confirmation certificate based on the encrypted signature to generate an asset title confirmation certificate corresponding to each energy data node.

[0072] It should be noted that the blockchain hash value calculation refers to the following formula: in, Represents the blockchain hash value, Indicates data contribution, Indicates the node identity. The timestamp indicating when the asset title certificate was generated. Represents a hash function.

[0073] It should be noted that the encrypted signature is generated according to the following formula: in, Indicates encrypted signature, Indicates the use of private keys to hash the blockchain Perform encryption operations.

[0074] In some embodiments, the asset title certificate may include the following fields: Node ID: A unique identifier of the data contributor, such as a node number or organization name.

[0075] Data contribution: records the contribution ratio of the data node to the global model, which is specifically calculated by the contribution calculation formula.

[0076] Timestamp T: records the time when the title confirmation certificate is generated to ensure the timeliness and historical traceability of the certificate.

[0077] Energy data type: identifies the type of ownership data, such as electricity data, wind energy data, natural gas data, etc.

[0078] Blockchain hash value: The unique identifier of the certificate of ownership, stored in the blockchain to prevent forgery or tampering.

[0079] Signature: A digital signature generated using asymmetric encryption technology to ensure the authenticity and integrity of the certificate.

[0080] The certificate hash value H(C) can be generated using the SHA-256 hash algorithm. The certificate of ownership is registered on the blockchain through a smart contract to ensure that data ownership cannot be tampered with.

[0081] In some embodiments, the title confirmation device may upload the asset title confirmation certificate to the blockchain storage, and the blockchain storage format may include each block containing a block hash value, a previous block hash value, and title confirmation certificate information.

[0082] In some embodiments, the authentication device may be submitted in the form of a transaction and calculate the transaction hash value: TxHash=SHA256(Certificate) In some embodiments, the rights confirmation device may use a Byzantine Fault Tolerance (PBFT) algorithm to establish a consensus mechanism to ensure data consistency.

[0083] In some embodiments, for historical energy data, the property rights confirmation device can use off-chain storage combined with on-chain indexing to improve storage efficiency.

[0084] In this embodiment, the encrypted model parameters sent by each energy data node are decrypted to obtain model node parameters, where the model node parameters are obtained by optimizing the local model based on the local energy data of each energy data node, the model node parameters are aggregated to obtain global model parameters, and a global model is constructed based on the global model parameters, and the data contribution of each energy data node is calculated according to the global model, and an asset confirmation certificate corresponding to each energy data node is generated based on the data contribution, and the asset confirmation certificate is used to determine the data authority of the energy data node, and the data authority includes data ownership authority and data use authority; since the encrypted model parameters sent by each energy data node are decrypted to obtain the model node parameters, the risk of data leakage generated in the process of analyzing and processing multi-source heterogeneous data is effectively avoided, and the data contribution of each energy data node is calculated according to the global model, so as to accurately quantify the data contribution of each node in a distributed energy environment, and accurately confirm the rights of energy data assets while ensuring data security and privacy.

[0085] refer to Figure 3 , Figure 3 This is a flow chart of the second embodiment of the energy data asset rights confirmation method based on federated learning of the present invention.

[0086] Based on the above first embodiment, in this embodiment, the method is applied to an energy data node in an energy data asset confirmation system based on federated learning, wherein the energy data asset confirmation system based on federated learning includes a central node and multiple energy data nodes, and the central node is communicatively connected with each energy data node; The method comprises: Step S1: Obtaining energy data samples of original model parameters and local energy data; Step S2: performing gradient optimization on the original model parameters based on the energy data sample to obtain model node parameters; It should be noted that the gradient optimization refers to the following formula: in, Represents energy data node The loss function is Represents energy data node The model node parameters obtained after optimizing the original model parameters, Represents energy data node The total number of local energy data samples, Indicates Energy data samples Input features, It is a multidimensional vector, which contains the characteristic information of the sample; Indicates The true labels of energy data samples, is the target value that needs to be compared when the model predicts. Represents the predicted value of the current model parameters for the input data, Represents the loss value of a single energy data sample.

[0087] In some embodiments, Based on the loss function Calculation, for example, loss functions may include mean square error loss function and cross entropy loss function, etc., refer to the following formula: The overall goal of the above formula is to Minimize the loss function , thereby optimizing the model parameters The purpose of optimization is to make the model Datasets on The specific process is as follows: All data samples Iterate; use the current model parameters Predict the output for each sample ; Calculate predicted values ​​and true labels The error between ; Finally, the errors of all samples are averaged to form the loss function , and by optimizing to minimize this loss.

[0088] Step S3: Encrypt the model node parameters to obtain encrypted model parameters, and send the encrypted model parameters to the central node, so that the central node calculates the data contribution of each energy data node based on the encrypted model parameters, and generates an asset title certificate corresponding to each energy data node based on the data contribution.

[0089] Furthermore, in order to improve the security and privacy in the process of energy asset confirmation, the above step S3 may include: Step S31: determining the initial gradient parameters of the model node parameters; Step S32: adding Gaussian noise to the initial gradient parameter to obtain the target gradient parameter; Step S33: Encrypting the model node parameters based on the target gradient parameters to obtain encrypted model parameters.

[0090] It should be noted that the right confirmation device can use differential privacy to protect gradient updates and realize encrypted transmission of model parameters: in, represents the initial gradient parameter, represents Gaussian noise, Represents the target gradient parameter. The encrypted model node parameters can be uploaded to the federated learning server through a secure communication protocol.

[0091] This embodiment obtains original model parameters and energy data samples of local energy data, performs gradient optimization on the original model parameters based on the energy data samples, obtains model node parameters, encrypts the model node parameters, obtains encrypted model parameters, and sends the encrypted model parameters to the central node, so that the central node calculates the data contribution of each energy data node based on the encrypted model parameters, and generates an asset confirmation certificate corresponding to each energy data node based on the data contribution; since the energy data nodes in this embodiment perform gradient optimization of model parameters locally, thereby effectively distributing data processing, the optimized model node parameters are encrypted, thereby ensuring privacy and security during data transmission and processing. While ensuring data security and privacy, energy data assets are accurately confirmed, effectively avoiding the risk of data leakage generated during the analysis and processing of multi-source heterogeneous data.

[0092] In addition, an embodiment of the present invention also proposes a computer-readable storage medium, on which is stored an energy data asset rights confirmation program based on federated learning. When the energy data asset rights confirmation program based on federated learning is executed by a processor, the steps of the energy data asset rights confirmation method based on federated learning as described above are implemented.

[0093] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0094] The above-mentioned computer-readable storage medium may be included in the energy data asset title confirmation device based on federated learning; or it may exist independently without being assembled into the energy data asset title confirmation device based on federated learning.

[0095] In addition, an embodiment of the present invention also proposes a computer program product, including an energy data asset title confirmation program based on federated learning, which, when executed by a processor, implements the steps of the energy data asset title confirmation method based on federated learning as described above.

[0096] The specific implementation methods of the computer program product of the present invention are basically the same as the various embodiments of the above-mentioned energy data asset rights confirmation method based on federated learning, and will not be repeated here.

[0097] Reference Figure 4 , Figure 4 This is a structural block diagram of the first embodiment of the energy data asset rights confirmation system based on federated learning of the present invention.

[0098] like Figure 4 As shown, the energy data asset rights confirmation system based on federated learning proposed in the embodiment of the present invention includes a central node and multiple energy data nodes, and the central node is communicatively connected with each energy data node; The energy data node is used to obtain energy data samples of original model parameters and local energy data; The energy data node is further used to perform gradient optimization on the original model parameters based on the energy data sample to obtain model node parameters: in, Represents energy data node The loss function is Represents energy data node The model node parameters obtained after optimizing the original model parameters, Represents energy data node The total number of local energy data samples, Indicates The input features of energy data samples, Indicates The true labels of energy data samples, Represents the predicted value of the current model parameters for the input data, Represents the loss value of a single energy data sample; The energy data node is further used to encrypt the model node parameters, obtain encrypted model parameters, and send the encrypted model parameters to the central node; The central node is used to decrypt the encrypted model parameters sent by each energy data node to obtain model node parameters, where the model node parameters are obtained by each energy data node optimizing the local model based on local energy data; The central node is further used to aggregate the model node parameters to obtain global model parameters, and build a global model based on the global model parameters. The global model parameters are aggregated based on the following formula: in, is the global model parameter, Represents energy data node The model node parameters, Energy Data Node The amount of data; The central node is further used to calculate the data contribution of each energy data node according to the global model; The central node is also used to generate an asset confirmation certificate corresponding to each energy data node based on the data contribution, and the asset confirmation certificate is used to determine the data authority of the energy data node, and the data authority includes data ownership authority and data use authority.

[0099] Furthermore, the central node is also used to calculate the initial contribution of each energy data node according to the global model: in, Represents energy data node The initial contribution of Represents energy data node The contribution of model node parameters to the performance improvement of the global model, Indicates the contribution of the model node parameters of all energy data nodes to the performance improvement of the global model, is a smoothing factor, which is used to balance the contribution of historical rounds with the contribution of the current round. Indicates the current round; Furthermore, the central node is also used to obtain the original energy data provided by each energy data node, and pre-process the original energy data to obtain target energy data; it is also used to adjust the initial contribution based on the target energy data corresponding to each energy data node to obtain data contribution.

[0100] Furthermore, the central node is also used to perform time analysis on the target energy data corresponding to each energy data node, determine the peak load period data and normal period data in the target energy data; determine the period weight of each target energy data based on the peak load period data and the normal period data; perform feature analysis on the target energy data corresponding to each energy data node, determine the energy type and data type corresponding to each target energy data; determine the feature weight of each target energy data according to the energy type and data type; perform node importance analysis on each energy data node, determine the node importance of each energy data node; determine the node weight of each energy data node based on the node importance; adjust the initial contribution according to the period weight, the feature weight and the node weight to obtain the data contribution.

[0101] Furthermore, the central node is also used to obtain the original energy data provided by each energy data node, and perform time series analysis on the original energy data; perform data cleaning on the original energy data based on the time series analysis result to obtain initial energy data; perform feature extraction on the initial energy data to obtain energy resource features; perform dimensionality reduction processing on the initial energy data according to the energy resource features to obtain candidate energy data, and the dimensionality reduction processing refers to the following formula: in, is the initial data matrix of initial energy data, is the projection matrix after dimensionality reduction, is the candidate data matrix of candidate energy data obtained after dimensionality reduction, the initial data matrix for The matrix of represents the number of samples, represents the dimension of the initial energy data, and the projection matrix is The matrix of represents the dimension after dimensionality reduction, and the candidate data matrix is Matrix of The candidate energy data are normalized according to the energy type of each candidate energy data to obtain target energy data.

[0102] Furthermore, the central node is also used to obtain the node identity and confirmation timestamp of each energy data node; and generate a blockchain hash value based on the data contribution, the node identity and the confirmation timestamp: in, Represents the blockchain hash value, Indicates data contribution, Indicates the node identity. The timestamp indicating when the asset title certificate was generated. represents a hash function; Generate an original certificate of ownership based on the blockchain hash value; Generate an encrypted signature for each original confirmation certificate, and asymmetrically encrypt the original confirmation certificate based on the encrypted signature to generate an asset confirmation certificate corresponding to each energy data node. The encrypted signature is generated according to the following formula: in, Indicates encrypted signature, Indicates the use of private keys to hash the blockchain Perform encryption operations.

[0103] Furthermore, the energy data node is also used to determine the initial gradient parameters of the model node parameters; Gaussian noise is added to the initial gradient parameters to obtain the target gradient parameters: in, represents the initial gradient parameter, represents Gaussian noise, represents the target gradient parameter; The model node parameters are encrypted based on the target gradient parameters to obtain encrypted model parameters.

[0104] In this embodiment, the encrypted model parameters sent by each energy data node are decrypted to obtain model node parameters, where the model node parameters are obtained by optimizing the local model based on the local energy data of each energy data node, the model node parameters are aggregated to obtain global model parameters, and a global model is constructed based on the global model parameters, and the data contribution of each energy data node is calculated according to the global model, and an asset confirmation certificate corresponding to each energy data node is generated based on the data contribution, and the asset confirmation certificate is used to determine the data authority of the energy data node, and the data authority includes data ownership authority and data use authority; since the encrypted model parameters sent by each energy data node are decrypted to obtain the model node parameters, the risk of data leakage generated in the process of analyzing and processing multi-source heterogeneous data is effectively avoided, and the data contribution of each energy data node is calculated according to the global model, so as to accurately quantify the data contribution of each node in a distributed energy environment, and accurately confirm the rights of energy data assets while ensuring data security and privacy.

[0105] The energy data asset confirmation system based on federated learning provided by the present application adopts the energy data asset confirmation method based on federated learning in the above-mentioned embodiment, which can solve the technical problem of energy data asset confirmation based on federated learning. Compared with the prior art, the beneficial effects of the energy data asset confirmation system based on federated learning provided by the present application are the same as the beneficial effects of the energy data asset confirmation method based on federated learning provided by the above-mentioned embodiment, and the other technical features of the energy data asset confirmation system based on federated learning are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0106] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present invention. In specific applications, technicians in this field can make settings as needed, and the present invention does not limit this.

[0107] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0108] In addition, for technical details not described in detail in this embodiment, please refer to the energy data asset rights confirmation method based on federated learning provided in any embodiment of the present invention, which will not be repeated here.

[0109] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including 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 system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0110] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0111] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0112] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for confirming the ownership of energy data assets based on federated learning, characterized in that: The method is applied to a central node in an energy data asset rights confirmation system based on federated learning, wherein the energy data asset rights confirmation system based on federated learning comprises the central node and a plurality of energy data nodes, wherein the central node is communicatively connected with each energy data node; The method comprises: Decrypting the encrypted model parameters sent by each energy data node to obtain model node parameters, where the model node parameters are obtained by optimizing the local model based on local energy data by each energy data node; The model node parameters are aggregated to obtain global model parameters, and a global model is constructed based on the global model parameters. The global model parameters are aggregated based on the following formula: in, are global model parameters, Represents energy data node The model node parameters, Energy Data Node The amount of data; Calculate the data contribution of each energy data node according to the global model; An asset confirmation certificate corresponding to each energy data node is generated based on the data contribution, and the asset confirmation certificate is used to determine the data authority of the energy data node, and the data authority includes data ownership authority and data use authority.

2. The method for confirming the ownership of energy data assets based on federated learning according to claim 1, characterized in that: Calculating the data contribution of each energy data node according to the global model includes: The initial contribution of each energy data node is calculated according to the global model: in, Represents energy data node The initial contribution of Represents energy data node The contribution of model node parameters to the performance improvement of the global model, Indicates the contribution of the model node parameters of all energy data nodes to the performance improvement of the global model, is a smoothing factor, which is used to balance the contribution of historical rounds with the contribution of the current round. Indicates the current round; Acquire raw energy data provided by each energy data node, and pre-process the raw energy data to obtain target energy data; The initial contribution is adjusted based on the target energy data corresponding to each energy data node to obtain a data contribution.

3. The method for confirming the ownership of energy data assets based on federated learning according to claim 2 is characterized in that: The adjusting the initial contribution based on the target energy data corresponding to each energy data node to obtain the data contribution includes: Performing time analysis on the target energy data corresponding to each energy data node to determine peak load period data and normal period data in the target energy data; Determine the time period weight of each target energy data based on the peak load time period data and the normal time period data; Perform feature analysis on the target energy data corresponding to each energy data node to determine the energy type and data type corresponding to each target energy data; Determine a characteristic weight of each target energy data according to the energy type and data type; Conduct node importance analysis on each energy data node to determine the node importance of each energy data node; Determine the node weight of each energy data node based on the node importance; The initial contribution is adjusted according to the time period weight, the feature weight and the node weight to obtain the data contribution.

4. The method for confirming the ownership of energy data assets based on federated learning according to claim 3 is characterized in that: The obtaining of the original energy data provided by each energy data node and preprocessing the original energy data to obtain the target energy data includes: Acquire original energy data provided by each energy data node, and perform time series analysis on the original energy data; Performing data cleaning on the original energy data based on the time series analysis result to obtain initial energy data; Extracting features from the initial energy data to obtain energy resource features; The initial energy data is subjected to dimensionality reduction processing according to the energy resource characteristics to obtain candidate energy data. The dimensionality reduction processing refers to the following formula: in, is the initial data matrix of initial energy data, is the projection matrix after dimensionality reduction, is the candidate data matrix of candidate energy data obtained after dimensionality reduction, the initial data matrix for The matrix of represents the number of samples, represents the dimension of the initial energy data, and the projection matrix is The matrix of represents the dimension after dimensionality reduction, and the candidate data matrix is Matrix of The candidate energy data are normalized according to the energy type of each candidate energy data to obtain target energy data.

5. The energy data asset rights confirmation method based on federated learning as claimed in any one of claims 1 to 4, characterized in that: The generating of the asset confirmation certificate corresponding to each energy data node based on the data contribution degree includes: Obtain the node identity and confirmation timestamp of each energy data node; Generate a blockchain hash value based on the data contribution, the node identity and the confirmation timestamp: in, Represents the blockchain hash value, Indicates data contribution, Indicates the node identity. The timestamp indicating when the asset title certificate was generated. represents a hash function; Generate an original certificate of ownership based on the blockchain hash value; Generate an encrypted signature for each original confirmation certificate, and asymmetrically encrypt the original confirmation certificate based on the encrypted signature to generate an asset confirmation certificate corresponding to each energy data node. The encrypted signature is generated according to the following formula: in, Indicates encrypted signature, Indicates the use of private keys to hash the blockchain Perform encryption operations.

6. A method for confirming the ownership of energy data assets based on federated learning, characterized in that: The method is applied to an energy data node in an energy data asset confirmation system based on federated learning, wherein the energy data asset confirmation system based on federated learning includes a central node and a plurality of energy data nodes, wherein the central node is communicatively connected with each energy data node; The method comprises: Obtain energy data samples of original model parameters and local energy data; Based on the energy data sample, the original model parameters are gradient optimized to obtain model node parameters: in, Represents energy data node The loss function is Represents energy data node The model node parameters obtained after optimizing the original model parameters, Represents energy data node The total number of local energy data samples, Indicates The input features of energy data samples, Indicates The true labels of energy data samples, Represents the predicted value of the current model parameters for the input data, Represents the loss value of a single energy data sample; The model node parameters are encrypted to obtain encrypted model parameters, and the encrypted model parameters are sent to the central node so that the central node calculates the data contribution of each energy data node based on the encrypted model parameters, and generates an asset title certificate corresponding to each energy data node based on the data contribution.

7. The method for confirming the ownership of energy data assets based on federated learning according to claim 6 is characterized in that: The step of encrypting the model node parameters to obtain encrypted model parameters includes: Determining initial gradient parameters of the model node parameters; Add Gaussian noise to the initial gradient parameter to obtain the target gradient parameter: in, represents the initial gradient parameter, represents Gaussian noise, represents the target gradient parameter; The model node parameters are encrypted based on the target gradient parameters to obtain encrypted model parameters.

8. An energy data asset rights confirmation system based on federated learning, characterized in that: The energy data asset rights confirmation system based on federated learning includes a central node and multiple energy data nodes, and the central node is communicatively connected with each energy data node; The energy data node is used to obtain energy data samples of original model parameters and local energy data; The energy data node is further used to perform gradient optimization on the original model parameters based on the energy data sample to obtain model node parameters: in, Represents energy data node The loss function is Represents energy data node The model node parameters obtained after optimizing the original model parameters, Represents energy data node The total number of local energy data samples, Indicates The input features of energy data samples, Indicates The true labels of energy data samples, Represents the predicted value of the current model parameters for the input data, Represents the loss value of a single energy data sample; The energy data node is further used to encrypt the model node parameters, obtain encrypted model parameters, and send the encrypted model parameters to the central node; The central node is used to decrypt the encrypted model parameters sent by each energy data node to obtain model node parameters, where the model node parameters are obtained by each energy data node optimizing the local model based on local energy data; The central node is further used to aggregate the model node parameters to obtain global model parameters, and build a global model based on the global model parameters. The global model parameters are aggregated based on the following formula: in, are global model parameters, Represents energy data node The model node parameters, Energy Data Node The amount of data; The central node is further used to calculate the data contribution of each energy data node according to the global model; The central node is also used to generate an asset confirmation certificate corresponding to each energy data node based on the data contribution, and the asset confirmation certificate is used to determine the data authority of the energy data node, and the data authority includes data ownership authority and data use authority.

9. An energy data asset rights confirmation device based on federated learning, characterized in that: The energy data asset title confirmation device based on federated learning includes: a memory, a processor, and an energy data asset title confirmation program based on federated learning stored on the memory and executable on the processor, wherein the energy data asset title confirmation program based on federated learning is configured to implement the energy data asset title confirmation method based on federated learning as described in any one of claims 1 to 5 or 6 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an energy data asset rights confirmation program based on federated learning, and when the energy data asset rights confirmation program based on federated learning is executed by a processor, it implements the energy data asset rights confirmation method based on federated learning as described in any one of claims 1 to 5 or 6 to 7.

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