阅读说明：本技术 一种电网数据的应用方法、装置及系统 (Power grid data application method, device and system ) 是由 留毅 汪李忠 姚海燕 高俊青 张静 李题印 张旭峰 胡翔 郭强 于 2021-08-24 设计创作，主要内容包括：本发明公开了一种电网数据的应用方法,通过先将多个客户端中保存的电网数据上传至区块链进行保存,无论是上传还是获取,其行为是受到相关机制保护的,具有绝对的安全性,即便遭遇网络攻击或者信号影响,都不会影响区块链对数据进行传递和分享。而通过建立在区块链节点,基于上传至区块链的电网数据进行联邦学习来训练任务模型,可以在各个客户端上传加密数据的情景下,保证客户端中电网数据的安全应用；而通过基于联邦学习所计算的贡献参数来确定各个客户端对应的查看范围,可以保证各个客户端对数据的安全共享。本发明还提供了一种电网数据的应用装置以及一种电网数据的应用系统,同样具有上述有益效果。(The invention discloses an application method of power grid data, which is characterized in that the power grid data stored in a plurality of clients are uploaded to a block chain for storage, the behavior of the power grid data is protected by a relevant mechanism no matter the power grid data is uploaded or acquired, the absolute safety is realized, and the block chain cannot be influenced to transmit and share the data even if the power grid data is subjected to network attack or signal influence. The task model is trained by establishing the node of the block chain and performing federal learning based on the power grid data uploaded to the block chain, so that the safe application of the power grid data in the client can be ensured under the situation that each client uploads encrypted data; and the view range corresponding to each client is determined based on the contribution parameters calculated by the federal study, so that the safe sharing of data by each client can be ensured. The invention also provides an application device and an application system of the power grid data, and the application device and the application system of the power grid data also have the beneficial effects.)

1. A method for applying power grid data is characterized by comprising the following steps:

uploading the power grid data stored in the plurality of clients to a block chain; the power grid data is encrypted data;

calling a federal learning model established in the block chain, training a pre-established task model according to the power grid data, and generating contribution parameters corresponding to the clients;

calling the task model to execute a corresponding task according to the power grid data to generate task information; the task information is stored in the block chain;

and determining the viewing range of the data stored in the block chain corresponding to the client according to the contribution parameters.

2. The method of claim 1, wherein the grid data comprises internal viewing data and external shared data;

the calling of the federal learning model established in the block chain, training of a pre-established task model according to the power grid data, and generation of contribution parameters corresponding to the clients comprise:

and calling a federal learning model established in the block chain, training a pre-established task model according to the external shared data, and generating contribution parameters corresponding to the clients.

3. The method of claim 2, wherein the grid data comprises any one or any combination of the following:

power supply data, power consumption data, line equipment loss data, line construction data, data of sending points, charging data and configuration information.

4. The method of claim 3, wherein the data formats of the external shared data are unified with each other.

5. The method of claim 1, further comprising, prior to the uploading the grid data stored in the plurality of clients to the blockchain:

verifying the authentication certificate of the client sending the uploading request; the verification certificate is a digital certificate issued when the client passes qualification verification in advance;

the uploading of the power grid data stored in the plurality of clients to the block chain comprises:

and uploading the verified power grid data stored in the plurality of clients to the block chain.

6. The method according to claim 1, wherein the encryption model invoked when encrypting the grid data comprises any one of;

a differential privacy model, a secure multiparty computation model, a homomorphic encryption model, and a functional encryption model.

7. An apparatus for applying grid data, comprising:

the uploading module is used for uploading the power grid data stored in the plurality of clients to the block chain; the power grid data is encrypted data;

the training module is used for calling a federal learning model established in the block chain, training a pre-established task model according to the power grid data and generating contribution parameters corresponding to the clients;

the execution module is used for calling the task model to execute a corresponding task according to the power grid data and generating task information; the task information is stored in the block chain;

and the range determining module is used for determining the viewing range of the data stored in the block chain corresponding to the client according to the contribution parameters.

8. The apparatus of claim 7, wherein the grid data comprises internal viewing data and external shared data;

the training module is specifically configured to:

and calling a federal learning model established in the block chain, training a pre-established task model according to the external shared data, and generating contribution parameters corresponding to the clients.

9. The utility model provides an application system of electric wire netting data which characterized in that, includes a plurality of customer ends and block chain:

the client is used for:

uploading the power grid data stored in the plurality of clients to a block chain; the power grid data is encrypted data;

the blockchain is to:

calling a federal learning model established in the block chain, training a pre-established task model according to the power grid data, and generating contribution parameters corresponding to the clients;

calling the task model to execute a corresponding task according to the power grid data to generate task information; the task information is stored in the block chain;

and determining the viewing range of the data stored in the block chain corresponding to the client according to the contribution parameters.

10. The system of claim 9, wherein the grid data includes internal viewing data and external shared data;

the blockchain is specifically configured to:

and calling a federal learning model established in the block chain, training a pre-established task model according to the external shared data, and generating contribution parameters corresponding to the clients.

Technical Field

The invention relates to the technical field of power grids, in particular to a power grid data application method, a power grid data application device and a power grid data application system.

Background

With the increasing demand for electricity of the public, power enterprises are continuously developed to promote the rapid growth of power grid service data, and how to safely store and rapidly apply the power data becomes a problem to be solved by the current power enterprises. However, the power system has a complicated structure, many related devices are provided, and related managers and departments are also distributed in different areas, which brings difficulty to the management of power data. Secondly, if a malicious person may tamper with the data in the server, once the central platform fails or crashes completely, the security and reliability of the power data will be affected, which may bring a serious security problem to the whole system. Finally, sharing power data among multiple units also greatly increases the security threat of the data.

How to secure the application and sharing of grid data among multiple clients is an urgent problem to be solved by those skilled in the art.

Disclosure of Invention

The invention aims to provide an application method of power grid data, which can realize the safe application and sharing of the power grid data in a plurality of clients; the invention also provides an application device and an application system of the power grid data, which can realize the safe application and sharing of the power grid data in a plurality of clients.

In order to solve the technical problem, the invention provides an application method of power grid data, which comprises the following steps:

uploading the power grid data stored in the plurality of clients to a block chain; the power grid data is encrypted data;

calling a federal learning model established in the block chain, training a pre-established task model according to the power grid data, and generating contribution parameters corresponding to the clients;

calling the task model to execute a corresponding task according to the power grid data to generate task information; the task information is stored in the block chain;

and determining the viewing range of the data stored in the block chain corresponding to the client according to the contribution parameters.

Optionally, the power grid data includes internal viewing data and external shared data;

the calling of the federal learning model established in the block chain, training of a pre-established task model according to the power grid data, and generation of contribution parameters corresponding to the clients comprise:

and calling a federal learning model established in the block chain, training a pre-established task model according to the external shared data, and generating contribution parameters corresponding to the clients.

Optionally, the grid data includes any one or any combination of the following:

power supply data, power consumption data, line equipment loss data, line construction data, data of sending points, charging data and configuration information.

Optionally, the data formats of the external shared data are unified with each other.

Optionally, before uploading the power grid data stored in the plurality of clients to the block chain, the method further includes:

verifying the authentication certificate of the client sending the uploading request; the verification certificate is a digital certificate issued when the client passes qualification verification in advance;

the uploading of the power grid data stored in the plurality of clients to the block chain comprises:

and uploading the verified power grid data stored in the plurality of clients to the block chain.

Optionally, the encryption model invoked when the power grid data is encrypted includes any one of the following items;

a differential privacy model, a secure multiparty computation model, a homomorphic encryption model, and a functional encryption model.

The invention also provides an application device of the power grid data, which comprises the following components:

the uploading module is used for uploading the power grid data stored in the plurality of clients to the block chain; the power grid data is encrypted data;

the training module is used for calling a federal learning model established in the block chain, training a pre-established task model according to the power grid data and generating contribution parameters corresponding to the clients;

the execution module is used for calling the task model to execute a corresponding task according to the power grid data and generating task information; the task information is stored in the block chain;

and the range determining module is used for determining the viewing range of the data stored in the block chain corresponding to the client according to the contribution parameters.

Optionally, the power grid data includes internal viewing data and external shared data;

the training module is specifically configured to:

and calling a federal learning model established in the block chain, training a pre-established task model according to the external shared data, and generating contribution parameters corresponding to the clients.

The invention also provides an application system of the power grid data, which comprises a plurality of clients and a block chain:

the client is used for:

uploading the power grid data stored in the plurality of clients to a block chain; the power grid data is encrypted data;

the blockchain is to:

calling a federal learning model established in the block chain, training a pre-established task model according to the power grid data, and generating contribution parameters corresponding to the clients;

calling the task model to execute a corresponding task according to the power grid data to generate task information; the task information is stored in the block chain;

and determining the viewing range of the data stored in the block chain corresponding to the client according to the contribution parameters.

Optionally, the power grid data includes internal viewing data and external shared data;

the blockchain is specifically configured to:

and calling a federal learning model established in the block chain, training a pre-established task model according to the external shared data, and generating contribution parameters corresponding to the clients.

The invention provides an application method of power grid data, which comprises the steps of uploading the power grid data stored in a plurality of clients to a block chain; the power grid data is encrypted data; calling a federal learning model established in a block chain, training a pre-established task model according to power grid data, and generating contribution parameters corresponding to each client; calling a task model to execute a corresponding task according to the power grid data to generate task information; storing the task information in a block chain; and determining the viewing range of the stored data in the block chain corresponding to the client according to the contribution parameters.

The power grid data stored in the plurality of clients are uploaded to the block chain for storage, the behavior of the distributed data structure based on the block chain is protected by a relevant mechanism no matter the data structure is uploaded or acquired in the data transmission process, absolute safety is achieved, and even if the distributed data structure is subjected to network attack or signal influence, the block chain cannot be influenced to transmit and share the data. The task model is trained by establishing the node of the block chain and performing federal learning based on the power grid data uploaded to the block chain, so that the training effect of the task model can be ensured and the safe application of the power grid data in the client can be ensured under the situation that each client uploads encrypted data; and the view range corresponding to each client is determined based on the contribution parameters calculated by the federal study, so that the safe sharing of data by each client can be ensured.

The invention also provides an application device and an application system of the power grid data, which also have the beneficial effects and are not repeated herein.

Drawings

In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.

Fig. 1 is a flowchart of an application method of power grid data according to an embodiment of the present invention;

fig. 2 is a flowchart of a specific method for applying grid data according to an embodiment of the present invention;

fig. 3 is a block diagram of an application apparatus of grid data according to an embodiment of the present invention;

fig. 4 is a block diagram of an application system of power grid data according to an embodiment of the present invention.

Detailed Description

The core of the invention is to provide an application method of power grid data. In the prior art, the structure of a power system is complicated, a plurality of devices are involved, and related managers and departments are also distributed in different areas, so that difficulty is brought to management of power data. Secondly, if a malicious person may tamper with the data in the server, once the central platform fails or crashes completely, the security and reliability of the power data will be affected, which may bring a serious security problem to the whole system.

The application method of the power grid data provided by the invention comprises the steps of uploading the power grid data stored in a plurality of clients to a block chain; the power grid data is encrypted data; calling a federal learning model established in a block chain, training a pre-established task model according to power grid data, and generating contribution parameters corresponding to each client; calling a task model to execute a corresponding task according to the power grid data to generate task information; storing the task information in a block chain; and determining the viewing range of the stored data in the block chain corresponding to the client according to the contribution parameters.

The power grid data stored in the plurality of clients are uploaded to the block chain for storage, the behavior of the distributed data structure based on the block chain is protected by a relevant mechanism no matter the data structure is uploaded or acquired in the data transmission process, absolute safety is achieved, and even if the distributed data structure is subjected to network attack or signal influence, the block chain cannot be influenced to transmit and share the data. The task model is trained by establishing the node of the block chain and performing federal learning based on the power grid data uploaded to the block chain, so that the training effect of the task model can be ensured and the safe application of the power grid data in the client can be ensured under the situation that each client uploads encrypted data; and the view range corresponding to each client is determined based on the contribution parameters calculated by the federal study, so that the safe sharing of data by each client can be ensured.

In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1, fig. 1 is a flowchart illustrating a method for applying grid data according to an embodiment of the present invention.

Referring to fig. 1, in an embodiment of the present invention, an application method of power grid data includes:

s101: and uploading the power grid data stored in the plurality of clients to the block chain.

In an embodiment of the present invention, the power grid data is encrypted data. Namely, when each client uploads power grid data to a block chain, namely, a block chain link point where the block chain is built, the power grid data needs to be encrypted. Specifically, in the embodiment of the present invention, the encryption model invoked when encrypting the power grid data includes any one of the following items; a differential privacy model, a secure multiparty computation model, a homomorphic encryption model, and a functional encryption model. The differential privacy model is a model constructed based on a differential privacy algorithm, the secure multi-party computing model is a model constructed based on a secure multi-party computing algorithm, the homomorphic encryption model is a model constructed based on a homomorphic encryption algorithm, and the function encryption model is a model constructed based on a function encryption algorithm. It should be noted that, in this step, the power grid data may be encrypted by the client itself, or may be encrypted by the blockchain according to the uploaded power grid data, which is determined according to the specific situation and is not limited herein. When the power grid data is encrypted by the blockchain, the blockchain needs to issue a key to the corresponding client after receiving the power grid data, where the key may be generated by the blockchain according to the hash value of the power grid data uploaded by each client.

It should be noted that, since the blockchain is specifically based on a data structure established by distributed blockchain nodes, when uploading power grid data, each client needs to select one blockchain node from a plurality of blockchain nodes to upload power grid data, so as to implement uploading of power grid data. It should be noted that, since the plurality of clients specifically upload the grid data to the block chain in this step, each grid data is usually stored in the form of a block and stored in the block chain.

S102: and calling a federal learning model established in the block chain, training a pre-established task model according to the power grid data, and generating contribution parameters corresponding to each client.

The federal learning model is specifically a model constructed based on a federal learning algorithm, and specific contents related to the federal learning algorithm can refer to the prior art and are not described herein again. The federated learning model is established in the block chain, specifically in each block chain node, and can call data stored in each block in the block chain. In this step, the federal learning model specifically calls the block in which the power grid data are stored, and trains a pre-established task model according to the power grid data. For a specific process of training a model according to data by using a federal learning algorithm, reference may be made to the prior art, and details thereof are not described herein.

The task model is a model of a corresponding task executed by combining the power grid data uploaded by each client according to actual needs, and the model is usually a neural network model, tasks executed by the task model include but are not limited to predicting power consumption of each region, predicting power consumption of each time period, and the like, and the tasks are determined according to specific situations and are not particularly limited herein. Accordingly, the task model is also determined according to the actual task, and is not limited herein.

In this step, when the federal learning model trains the task model, the contribution degree of the power grid data uploaded by each client to the task model during training can be calculated, that is, according to the incentive mechanism of the federal learning model, the contribution parameter, that is, the contribution degree of the power grid data uploaded by each client can be calculated, so that the viewing range of each client can be determined according to the contribution parameter in the subsequent steps.

S103: and calling the task model to execute the corresponding task according to the power grid data to generate task information.

In the embodiment of the present invention, the task information is stored in the block chain. In this step, the task trained in S102 may be specifically called to execute the corresponding task, and generate corresponding task information. The task information needs to correspond to the task model and the task executed by the task model, and is not specifically limited herein. Accordingly, the task information needs to be stored in the block chain.

S104: and determining the viewing range of the stored data in the block chain corresponding to the client according to the contribution parameters.

In this step, it is necessary to determine, according to the calculated contribution parameters, data stored in the block chain corresponding to each client, including a view range of the task data, so that each client can query the data stored in the block chain in the view range corresponding to each client. The corresponding relation between the contribution parameters and the viewing range needs to be set according to actual conditions, and is not particularly limited. In general, the blockchain generates a corresponding key according to a viewing range corresponding to each client, and sends the key to the corresponding client, so that the client can view corresponding data in the blockchain according to the received key.

It should be noted that this step may be executed in parallel with S103 described above, but the viewing range generated in this step generally needs to involve the task information described above.

The power grid data application method provided by the embodiment of the invention comprises the steps of uploading power grid data stored in a plurality of clients to a block chain; the power grid data is encrypted data; calling a federal learning model established in a block chain, training a pre-established task model according to power grid data, and generating contribution parameters corresponding to each client; calling a task model to execute a corresponding task according to the power grid data to generate task information; storing the task information in a block chain; and determining the viewing range of the stored data in the block chain corresponding to the client according to the contribution parameters.

The power grid data stored in the plurality of clients are uploaded to the block chain for storage, the behavior of the distributed data structure based on the block chain is protected by a relevant mechanism no matter the data structure is uploaded or acquired in the data transmission process, absolute safety is achieved, and even if the distributed data structure is subjected to network attack or signal influence, the block chain cannot be influenced to transmit and share the data. The task model is trained by establishing the node of the block chain and performing federal learning based on the power grid data uploaded to the block chain, so that the training effect of the task model can be ensured and the safe application of the power grid data in the client can be ensured under the situation that each client uploads encrypted data; and the view range corresponding to each client is determined based on the contribution parameters calculated by the federal study, so that the safe sharing of data by each client can be ensured.

The specific content of the method for applying the grid data provided by the present invention will be described in detail in the following embodiments of the invention.

Referring to fig. 2, fig. 2 is a flowchart illustrating a specific method for applying grid data according to an embodiment of the present invention.

Referring to fig. 2, in the embodiment of the present invention, an application method of power grid data includes:

s201: and verifying the authentication certificate of the client sending the uploading request.

In the embodiment of the present invention, the certificate of authentication is a digital certificate issued when the client passes qualification authentication in advance. In the embodiment of the present invention, the blockchain may review the qualification of the client in advance, and the review process may specifically review information of the power equipment and the like owned by the client, and the review process may be set by itself according to actual needs, and is not described herein again.

In this step, when uploading the grid data, the client needs to send an upload request first. When the blockchain or other relay devices receive the upload request, the corresponding client certificate, which is usually an electronic certificate, is verified according to the upload request, the timeliness, correctness and the like of the certificate are verified in the verification process, and only after the verification is passed, the blockchain receives the power grid data uploaded by the client.

S202: and uploading the verified power grid data stored in the plurality of clients to the block chain.

The present step is substantially the same as S101, except that the power grid data in the client that is verified in S201 is specifically obtained in the present step. The rest of the contents are already described in detail in the above embodiments of the present invention, and are not described herein again.

Specifically, in the embodiment of the present invention, the power grid data includes internal viewing data and external shared data. The internal viewing data is similar to the internal data, and the external sharing data is data shared to the outside. In this step, the client typically uploads both the internal view data, which is typically encrypted by the client itself, and the external share data, which is typically encrypted by the blockchain, to the blockchain. The internal viewing data and the external sharing data can be the same or different, as the case may be. Typically, the data formats of the external shared data need to be unified with each other in order to facilitate the use of other clients or the federated learning model described below.

In the embodiment of the present invention, the grid data may include any one or any combination of the following items: power supply data, power consumption data, line equipment loss data, line construction data, data of sending points, charging data and configuration information. Of course, when the federal learning model described below calls data, corresponding data can be called for different tasks instead of all data, so that the training speed of the task model is increased. It should be noted that the internal viewing data and the external shared data may include any one or any combination of the above data, and are not limited specifically herein.

S203: and calling a federal learning model established in the block chain, training a pre-established task model according to external shared data, and generating contribution parameters corresponding to each client.

In this step, the federal learning model may specifically invoke the external shared data to train the task model, and the rest of the contents are described in detail in S102 in the foregoing embodiment of the present invention, and are not described herein again.

S204: and calling the task model to execute the corresponding task according to the power grid data to generate task information.

S205: and determining the viewing range of the stored data in the block chain corresponding to the client according to the contribution parameters.

S204 to S205 are substantially the same as S103 to S104 in the above embodiment of the invention, and for details, reference is made to the above embodiment of the invention, which is not repeated herein.

According to the power grid data application method provided by the embodiment of the invention, the power grid data stored in the plurality of clients are uploaded to the block chain for storage, and the behavior of the block chain is protected by a relevant mechanism no matter the block chain is uploaded or acquired in the process of data transmission based on the distributed data structure of the block chain, so that the absolute safety is realized, and the block chain cannot be influenced to transmit and share the data even if the block chain is attacked by a network or influenced by signals. The task model is trained by establishing the node of the block chain and performing federal learning based on the power grid data uploaded to the block chain, so that the training effect of the task model can be ensured and the safe application of the power grid data in the client can be ensured under the situation that each client uploads encrypted data; and the view range corresponding to each client is determined based on the contribution parameters calculated by the federal study, so that the safe sharing of data by each client can be ensured.

In the following, the application device of the grid data provided by the embodiment of the present invention is introduced, and the application device of the grid data described below and the application method of the grid data described above may be referred to correspondingly.

Referring to fig. 3, fig. 3 is a block diagram of an application apparatus of grid data according to an embodiment of the present invention.

Referring to fig. 3, in an embodiment of the present invention, the device for applying grid data may include:

the uploading module 100 is configured to upload power grid data stored in a plurality of clients to a block chain; the power grid data is encrypted data.

And the training module 200 is configured to invoke a federal learning model established in the block chain, train a pre-established task model according to the power grid data, and generate contribution parameters corresponding to the clients.

The execution module 300 is configured to invoke the task model to execute a corresponding task according to the power grid data, and generate task information; the task information is stored in the block chain.

A range determining module 400, configured to determine, according to the contribution parameter, a viewing range of the data stored in the block chain corresponding to the client.

Preferably, in the embodiment of the present invention, the power grid data includes internal viewing data and external shared data.

The training module 200 is specifically configured to:

and calling a federal learning model established in the block chain, training a pre-established task model according to the external shared data, and generating contribution parameters corresponding to the clients.

Preferably, in the embodiment of the present invention, the grid data includes any one or any combination of the following items:

power supply data, power consumption data, line equipment loss data, line construction data, data of sending points, charging data and configuration information.

Preferably, in the embodiment of the present invention, the data formats of the external shared data are unified with each other.

Preferably, in the embodiment of the present invention, the method further includes:

the certificate verification module is used for verifying the authentication certificate of the client side which sends the uploading request; the verification certificate is a digital certificate issued when the client passes qualification verification in advance.

The upload module 100 is specifically configured to:

and uploading the verified power grid data stored in the plurality of clients to the block chain.

Preferably, in the embodiment of the present invention, the encryption model invoked when the power grid data is encrypted includes any one of the following items;

a differential privacy model, a secure multiparty computation model, a homomorphic encryption model, and a functional encryption model.

The power grid data application apparatus of this embodiment is used to implement the foregoing power grid data application method, and therefore specific implementations of the power grid data application apparatus may refer to the foregoing power grid data application method, for example, the uploading module 100, the training module 200, the executing module 300, and the range determining module 400, which are respectively used to implement steps S101 to S104 in the power grid data application method, so that the specific implementations may refer to descriptions of corresponding partial embodiments, and are not described herein again.

The application system of the grid data, the application method of the grid data and the application device of the grid data, which are described below, may be referred to in correspondence with each other.

Referring to fig. 4, fig. 4 is a block diagram of an application system of grid data according to an embodiment of the present invention.

Referring to fig. 4, the application system device of the grid data includes a plurality of clients 11 and a blockchain 12:

the client 11 is configured to:

uploading the power grid data stored in the plurality of clients 11 to a block chain; the power grid data is encrypted data;

the block chain 12 is used for:

calling a federal learning model established in the block chain 12, training a pre-established task model according to the power grid data, and generating contribution parameters corresponding to the clients;

calling the task model to execute a corresponding task according to the power grid data to generate task information; the task information is stored in the block chain 12;

and determining the viewing range of the data stored in the block chain corresponding to the client according to the contribution parameters.

Preferably, in the embodiment of the present invention, the power grid data includes internal viewing data and external shared data.

The block chain 12 is specifically configured to:

and calling a federal learning model established in the block chain 12, training a pre-established task model according to the external shared data, and generating contribution parameters corresponding to the clients.

Preferably, in the embodiment of the present invention, the grid data includes any one or any combination of the following items:

power supply data, power consumption data, line equipment loss data, line construction data, data of sending points, charging data and configuration information.

Preferably, in the embodiment of the present invention, the data formats of the external shared data are unified with each other.

Preferably, in the embodiment of the present invention, the block chain 12 is further configured to:

verifying the authentication certificate of the client sending the uploading request; the verification certificate is a digital certificate issued when the client passes qualification verification in advance.

The client 11 is specifically configured to:

and uploading the power grid data stored in the plurality of verified clients 11 to the block chain.

Preferably, in the embodiment of the present invention, the encryption model invoked when the power grid data is encrypted includes any one of the following items;

a differential privacy model, a secure multiparty computation model, a homomorphic encryption model, and a functional encryption model.

The application system of the power grid data in this embodiment is used to implement the foregoing application method of the power grid data, and therefore, the specific implementation manner in the application system of the power grid data may be found in the foregoing embodiment section of the application method of the power grid data, and therefore, the specific implementation manner thereof may refer to the description of each corresponding section embodiment, and is not described herein again.

The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.

Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The application method of the power grid data, the application device of the power grid data and the application system of the power grid data provided by the invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.