CN117633881A - Power data optimization processing method - Google Patents
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Abstract
The invention discloses an electric power data optimization processing method, which relates to the field of electric power data processing, and the technical scheme main points of the method comprise the following steps: the user performs identity verification through a Web browsing page of the client, judges whether to permit logging in the system, acquires corresponding access and management authority according to a role given by the user identity, and unlocks electric power data corresponding to the security level through carrying a corresponding key given by the role; the file package of each acquisition point is encrypted through a public key issued by the central processing unit and is uploaded to the cloud end through the wireless network, and the cloud end decrypts the file package through a private key and stores the decrypted file package; the improved K-means clustering algorithm takes the median of the electric power data in each file package as a clustering centroid, carries out iterative aggregation on the electric power data in the file package, carries out abnormal display on the electric power data which is not contained in the aggregation value set, generates warning information and a new command, and realizes the encryption transmission and access of the electric power data and the abnormal data highlighting processing.
Description
Technical Field
The invention relates to the field of power data processing, in particular to a power data optimization processing method.
Background
In recent years, with the continuous development of computer related technologies and the continuous expansion of the total amount of power data, in the analysis of various types of power grid data, the application of a machine learning method to analyze the power grid data is a conventional option
The electric data has the characteristics of real time, reality, large volume, fine granularity and the like, the data sharing technology can effectively solve the problem of electric data island, plays an important supporting role in the aspects of enterprise production and processing monitoring analysis and business scenic spot index analysis, most of modern equipment and personnel activities are not separated from electric power supply, so that hidden information of an area or region can be known in detail through electric data analysis, however, for some secret units or secret data, leakage of the electric data can also cause project or hidden information leakage, so that electric data security events occur, how to ensure the electric data security has become the focus of attention of various industries, the safety of the electric data is mainly concentrated in the data access and data transmission process, the risk of attack and theft number tampering is easily generated in the electric data access process of users, and each acquisition equipment is a weak protection link in the electric data transmission process, and is extremely easy to attack and causes leakage, so that attention is required.
The invention of patent number CN105787597B discloses a data optimization processing system, which comprises: the data preprocessing module is used for selecting a data subset for optimization processing from the service data to be processed; the communication module is used for performing MPS coding on the data subset selected by the data preprocessing module to obtain an MPS data packet, analyzing the MPS data packet according to a preset data structure to obtain structured data conforming to optimization processing, and transmitting the obtained structured data to the calculation module; and the calculation module is used for optimizing the structured data by utilizing an intelligent optimization algorithm. The invention is only aimed at optimizing the data according to the requirements of users, but the invention is not related to the aspects of encrypted transmission of the data and the highlighting of abnormal values, so the invention is innovatively designed aiming at the problems.
Disclosure of Invention
Aiming at the problems that information leakage and power abnormal values are easy to cause and difficult to be highlighted in the power data transmission process in the prior art, the invention aims to provide a power data optimization processing method for realizing efficiency and confidentiality in the power data optimization processing process.
In order to achieve the above purpose, the present invention provides the following technical solutions:
The power data optimization processing method is applied to a power data optimization processing system and comprises a data layer, a transmission layer, a processing layer and an output layer, and the technical scheme of the power data optimization processing method mainly comprises the following steps:
step S1: the user performs identity verification through a Web browsing page of the client, judges whether to permit logging in the system, acquires corresponding access and management authority according to a role endowed by the user identity, unlocks electric power data corresponding to the security level through a corresponding key endowed by the carrying role, and checks information from the client;
step S2: the file package of each acquisition point is encrypted through a public key issued by the central processing unit and is uploaded to the cloud end through the wireless network, and the cloud end decrypts the file package through a private key and stores the decrypted file package;
step S3: the improved K-means clustering algorithm takes the median of the electric power data in each file package as a clustering centroid, carries out iterative aggregation on the electric power data in the file package to form a value set, carries out abnormal display on the electric power data which is not contained in the value set, and generates warning information and a new command.
Preferably, the data layer comprises a database, wherein the database comprises power data management information and user management information, and performs distributed storage and call management;
The power data management information comprises file packages acquired by all acquisition points, and each file package comprises a daily power use value in the corresponding acquisition point;
the user management information comprises user identity authentication and authority management, wherein the user identity authentication is applied to user login, and the calling group checks the identity information input during the user login with the identity information stored in the database and judges whether the user has login authority; the authority management carries out design roles through an authority model, corresponding roles are assigned according to authentication identities of users, corresponding authorities are acquired according to the roles, user information is stored in a database by a server, when the users carry out authority authentication, information of the current users and the roles are taken out from the database by a calling group, corresponding authorities associated with the roles are inquired, whether the users have authorities for operating certain resources or not is judged, and authority authentication is completed.
Preferably, the data layer and the transmission layer are designed based on NET open source architecture, the transmission layer is used for encrypting the transmission process of the data packet of each acquisition point to the database and encrypting and accessing the electric data in the database by the user through roles,
the transmission layer comprises a user encryption module, wherein the user encryption module is used for giving corresponding roles to unlock keys corresponding to the security level power data in the process of accessing the power data with different security levels by a user, namely constructing keys corresponding to the roles, and the specific process is as follows:
Step S31: when a user inputs an identity into the system, the database gives the user a unique identity, the identity comprises a corresponding role given by the user, the role is given according to authority determination of the user, n levels of electric security data sets are set in the database, the number of the roles is the same as the number of the levels of the electric security data sets, namely, the number of the roles is n, the electric security data sets of each level can generate a secret lock, and a private key generated by the user can unlock the data sets in the corresponding level;
each power data set corresponds to one role, and the role of the higher level can access the power data sets corresponding to all secondary roles;
step S32: the users corresponding to each role can generate different secret keys through an encryption algorithm, the attribute set of all roles is set as V, and the attribute corresponding to each role is set as V i The key of each user is constructed according to an encryption algorithm, and the encryption formula of the encryption algorithm is specifically as follows:
(1)V＝{v 1 ,……，v i ,……}
(2)
(3)
the formula (1) is used for defining an attribute set V;
equation (2) is used to obtain the key generated by each user;
the formula (3) is used for acquiring any two values of the non-empty digital set A;
in the formula (1), i represents the label of a character, i is more than or equal to 1 and less than or equal to n, and A represents a non-empty number set; And->Representing the numerical values represented by the reference numerals g and f in the non-empty digital set A, and n < g < f;
in the formula (2), k represents the number of the user, p k Representing the ranking number, ZH, of the user when entering the system k (v i ) A key representing that user k generates under the authority of role i;
step S33: and the user verifies the login system through the identity mark, unlocks the secret lock of the corresponding grade power data set according to the secret key generated by the encryption algorithm, and then performs related access through the calling set.
By carrying out distributed design on the database, enough sufficient space is provided for the real-time update of the power data and the mass data in each acquisition point, and the running performance of the whole system is improved; the user information is managed and designed by establishing roles and rights, the security of identity verification when the user logs in is improved, and different rights are acquired by giving different roles to the user, so that the tightness of the user in the system management process and the management efficiency of the data information are realized; the user encryption module endows the corresponding roles with the ability of unlocking the corresponding security level power data keys, so that dynamic construction of the keys corresponding to the roles is realized in the process of accessing the power data of different security levels by the user.
Preferably, the transmission layer includes a file encryption module, the file encryption module encrypts the data packet of each acquisition point to the database based on the Paillier algorithm, and generates a public key and a private key, wherein the specific process of generating the public key and the private key is as follows:
(4)r＝pq
(5)λ＝lcm(p-1,q-1)
(6)
obtaining a public key (r, q) and a private key (lambda, mu);
equation (4) is used for obtaining the factor value r of the public key;
equation (5) is used for obtaining the factor value lambda of the private key;
equation (6) is used for obtaining the factor value mu of the private key;
where p and q represent randomly selected prime numbers, respectively, and p and q are equal in length, and pq, p-1 and q-1 are prime with each other, lcm () represents a least common multiple function.
Preferably, the file encryption module respectively endows a private key and a public key for the database and the file package, distributes the private key to the database to decrypt the capability of each collection point file package public key, constructs the encryption transmission of the file package from the collection point encryption to the cloud database, and the specific working process comprises the following steps:
step S31: setting the number of the electric power data acquisition points as w, respectively generating a public key (r, q) and a private key (lambda, mu) for each acquisition point, distributing the generated public key (r, q) to the corresponding acquisition point, and distributing the generated private key (lambda, mu) to a database:
Step S31: the process of encrypting the file packet by the public key (r, q) is specifically as follows, according to the file packet encryption formula:
(7)M x ＝((m x r+1mod(r 2 ))s r )mod(r 2 )
(8)acquiring encrypted file package M x ，
Wherein M represents an encrypted package, x represents a label of the package, and M x The method comprises the steps of representing an unencrypted file packet generated by an acquisition point x, wherein the labels of the file packet are equal to and correspond to the labels of the acquisition points one by one, and s represents a random value;
step S31: assigning a private key (lambda, mu) corresponding to the public key of each package to the database, the private key (lambda, mu) pair encrypting the package M x The decryption process of (a) specifically comprises:
(9)
obtaining decrypted file packet m x ，
Where d () represents a decryption function;
step S31: storing the decrypted file packet into a database.
Preferably, when the transmission layer is specifically applied to the file encryption module, each acquisition point carries out field encapsulation on the file package acquired in the period, after the encapsulation is completed, an uploading signal is sent to the central processing unit, the central processing unit starts the file encryption module after receiving the uploading signal, the file encryption module generates a public key and a private key in the central processing unit, the central processing unit sends the public key to the file package through the wireless network and encrypts the file package, the central processing unit sends the private key to the data layer, the encrypted file package is uploaded to the data layer through the wireless network, the data layer carries out decryption according to the distributed private key, if the decryption is completed, the decrypted file package is stored in the database, if the decryption is unsuccessful, a risk warning is sent out, or the acquisition point carries out repeated data acquisition and public key encryption according to the acquisition point corresponding to the file package which fails in decryption, and the public key used in the re-acquisition is regenerated by the file encryption module.
The file encryption module encrypts the uploading and network transmission processes of the file package based on the Paillier algorithm, constructs encrypted transmission of the file package from the collection points to the cloud database, enables each collection point to have a public key by respectively endowing the database and the file package with the private key and the public key, reduces the exposure of data, decrypts and stores the data after the data is safely uploaded, generates and stores and alerts two different application instructions according to the decrypted state, and passes through the security of the data.
Preferably, the processing layer performs statistical analysis on the data in each acquisition point data packet based on a K-means clustering algorithm, and improves the K-means algorithm to meet the processing of the data, including selection of cluster centroids and optimization of distance calculation, wherein the specific process of cluster centroids selection includes the following steps:
step S41: setting a file packet of each acquisition point to contain j pieces of electric power data, sequencing the numerical values through excel, and selecting a median from the j data values as a clustering centroid of each data packet, wherein the specific working process of the clustering centroid is as follows according to a selection formula;
(10)the cluster centroid z is obtained and,
where j represents the number of power data in each package, c represents the power data in the package, And->Reference numerals each representing power data;
step S43: each file package contains a cluster centroid, and the obtained w cluster centroids form a cluster centroid set.
Preferably, the specific process of optimizing the distance calculation in the K-means clustering algorithm comprises the following steps:
step S51: acquiring a cluster centroid set, extracting a cluster centroid corresponding to each file package, and acquiring each electric power data in the file package;
step S52: for any one file package, carrying out clustering calculation according to the extracted clustering centroid and the power data, and according to a formula
(11)
(12)Acquiring any two electric power data c in file package m ε And c β The distance to the cluster centroid z is extracted, the smaller of the two values is extracted to form a cluster value set, the iteration number is set as y, y is more than or equal to 1 and less than j,
wherein c ε (t ε ,h ε ) Representing power data c ε The recording time t contained in the recording medium ε And a metering value h ε ，c β (t β ,h β ) Representing power data c β The recording time t contained in the recording medium β And a metering value h β And epsilon and beta both represent the position marks of the power data in the file package, t 0 Represents the recording time, h, represented by the median power data 0 Representing the measured value represented by the median power data.
The median of the electric power data in each file package is used as a clustering centroid, so that the aggregation degree of each electric power data is improved, the aggregation value set expression of the electric power data is more accurate, the iteration times are set through a distance optimization formula, the aggregation value set is formed, the electric power data with abnormal characteristics or predicted abnormal conditions is isolated from normal data, the abnormal characteristic electric power data value is highlighted, direct observation is facilitated, and the processing efficiency and accuracy in the electric power data aggregation processing process are realized based on an improved clustering algorithm.
Preferably, the database stores each file package in a distributed manner, the processing layer extracts abnormal data of the data packages which are meshed in the database based on a K-means clustering algorithm, the median of j sequence power data in each file package is determined as a clustering centroid through a clustering algorithm, the aggregation degree of j power data in each file package is determined by calculating the distance value between each clustering centroid and each power data in the corresponding file package, the aggregation value sets are sequentially filled with the power data with smaller distance values according to the iteration times, the power data which are not filled with the aggregation value sets are displayed and positioned, the corresponding acquisition points, the acquisition time and the abnormal values are obtained, and page display is performed through display equipment of the output layer for users with corresponding role rights to access.
Preferably, the power data optimizing processing system is applied to a cloud processing platform, the data layer is applied to a cloud, hadoopHDFS distributed storage is carried out, the central processing unit is arranged on the cloud processing platform and adopts an integrated information processing system, the processing layer and the transmission layer are processed based on the central processing unit, the central processing unit is connected with each acquisition point and client through a wireless network, the output layer carries out information display through a Web page program of output equipment, the processing result of the central processing unit is output and is provided for management personnel to carry out information receiving, the output layer adopts an information system structure of multiple clients, and a user accesses the system through the clients and carries out authority access optimization on information according to the given roles.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the database is designed in a distributed manner, so that the rapid response during data reading is facilitated, the elastic expansion of the storage space is realized, sufficient space is provided for the real-time updating of the electric power data and the mass data in each acquisition point, and the running performance of the whole system is improved; the user information is managed and designed by establishing roles and rights, so that the security of identity verification when the user logs in is improved, different rights are acquired by giving different roles to the user, file disclosure caused by user rights fluctuation is reduced, and the tightness of the user in the system management process and the management efficiency of data information are realized; the user encryption module endows the corresponding roles with the ability of unlocking the corresponding security level power data keys, so that dynamic construction of the keys corresponding to the roles is realized in the process of accessing the power data of different security levels by the user.
2. According to the invention, the file encryption module encrypts the uploading and network transmission processes of the file package based on the Paillier algorithm, constructs the encrypted transmission of the file package from the encrypted uploading of the acquisition points to the cloud database, and endows the database and the file package with a private key and a public key respectively to enable each acquisition point to have the public key respectively, so that the exposure of data is reduced, the data is decrypted and stored after being safely uploaded, and two different application instructions of storage and warning are generated according to the decrypted state, and the security of the data is passed.
3. According to the invention, the median of the electric power data in each file package is used as the clustering centroid, so that the aggregation degree of each electric power data is improved, the aggregation value set expression of the electric power data is more accurate, the iteration times are set through the distance optimization formula, the aggregation value set is formed, the electric power data with abnormal characteristics or predicted abnormal conditions is isolated from the normal data, the abnormal characteristic electric power data value is highlighted, the direct observation is facilitated, and the processing efficiency and accuracy in the electric power data aggregation processing process are realized based on the improved clustering algorithm.
Drawings
Fig. 1 is a schematic structural diagram of a power data optimization method according to the present invention;
FIG. 2 is a schematic diagram of the method steps in the present invention;
fig. 3 is a flow chart of the file encrypting module in the present invention.
Detailed Description
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Example 1
Referring to fig. 1 and fig. 2, a power data optimization processing method according to the present invention is further described in an embodiment.
The power data optimizing processing method is applied to a power data optimizing processing system and comprises a data layer, a transmission layer, a processing layer and an output layer,
the data layer comprises a database, wherein the database comprises power data management information and user management information, and performs distributed storage and call management; the power data management information comprises file packages acquired by the acquisition points, and each file package comprises daily power use values in the corresponding acquisition point.
The user management information comprises user identity authentication and authority management, wherein the user identity authentication is applied to user login, and the calling group checks the identity information input during the user login with the identity information stored in the database and judges whether the user has login authority; the authority management carries out design roles through an authority model, corresponding roles are assigned according to authentication identities of users, corresponding authorities are acquired according to the roles, user information is stored in a database by a server, when the users carry out authority authentication, information of the current users and the roles are taken out from the database by a calling group, corresponding authorities associated with the roles are inquired, whether the users have authorities for operating certain resources or not is judged, and authority authentication is completed.
The user management information comprises user identity authentication and authority management, wherein the user identity authentication is applied to user login, identity information input during user login is checked with identity information stored in a database, and whether login authority exists or not is judged; the authority management carries out design roles through an authority model, corresponding roles are assigned according to authentication identities of users, corresponding authorities are acquired according to the roles, user information is stored in a database by a server, when the users carry out authority authentication, information of the current users and the roles are taken out from the database, corresponding authorities associated with the roles are inquired, whether the users have authorities for operating the corresponding-level electric security data sets is judged, and authority authentication is completed.
The invention adopts the role access control authority model to design, the authority model introduces the concept of the role, interprets the relationship between the authority and the user, distributes the authority to the role instead of the user, distributes the authority to a certain role according to the responsibility of the user, acquires the corresponding authority according to the role, and distributes the authority of the user to the role through the association of the user and the role, wherein the role is associated with the resource or the operation.
After a user logs in a system, a server stores user information in a database, when user authority authentication is required, the information of the current user and the roles of the current user are taken out from the database, corresponding authorities associated with the roles are inquired, whether the user has authorities for operating the corresponding-level electric power security data set is judged, authority authentication is completed, the authority authentication is completed through a filter in use, the authority filter intercepts requests sent by each client, when the requests are intercepted, operation authorities of the current logged-in user are searched from a domain, whether the requests can operate resources is judged, and if the authorities do not exist, error information is returned.
The database stores information related to users, including:
user table: the table is mainly used for storing basic information of a system user, and comprises a user name and a corresponding password, wherein the user is a specific operator of the system, can possess own authority information and can belong to a plurality of roles, the authority of the user comprises own authority and the authority of the role to which the user belongs, and the user comprises management personnel, operation and maintenance personnel and responsible personnel of each acquisition point;
color chart: the method mainly stores roles of an operating system, wherein different roles have different rights and are used for distinguishing the rights of users, one role can be owned by a plurality of users and can have a plurality of rights, and a many-to-many association relationship is formed between the roles and the users and the rights;
Rights table: all authority information of the system is mainly described, including addition, deletion, modification and checking of data; association table: and storing the many-to-many association relation of the user and the role.
The electric power data has the characteristics of real time, reality, large volume, fine granularity and the like, the data sharing technology can effectively solve the problem of electric power data island, plays an important supporting role in the aspects of enterprise production and processing monitoring analysis and business scenic spot index analysis, and most of modern equipment and personnel activities are not separated from power supply, so that hidden information of a region or area can be known in detail through electric power data analysis, however, for some secret units or secret data, leakage of the electric power data can also cause project or hidden information leakage, so that electric power data security events occur, and how to ensure electric power data security has become the focus of attention of various industries. The security of the power data is mainly concentrated in the data access and data transmission process, and in order to enable a user to be vulnerable to attack and theft number tampering in the process of accessing the power data, the identity authentication and the authority are stripped, and the user is subjected to role entry through a management program after authentication. The authority of the user is independently endowed through the roles, and the direct acquisition of the authority attribute after the user account is tampered is avoided.
The data layer and the transmission layer are designed based on the NET open source architecture, the transmission layer is used for encrypting the transmission process of the data packet of each acquisition point to the database and encrypting and accessing the electric data in the database by the user through the roles, the transmission layer comprises a user encryption module, and the user encryption module is used for endowing the corresponding roles with keys for unlocking the electric data corresponding to the security levels in the electric data access process of the user to the different security levels, namely constructing keys corresponding to the roles, and the specific process is as follows:
step S31: when a user inputs an identity into the system, the database gives the user a unique identity, the identity comprises a corresponding role given by the user, the role is given according to authority determination of the user, n levels of electric security data sets are set in the database, the number of the roles is the same as the number of the levels of the electric security data sets, namely, the number of the roles is n, the electric security data sets of each level can generate a secret lock, and a private key generated by the user can unlock the data sets in the corresponding level;
each power data set corresponds to one role, and the role of the higher level can access the power data sets corresponding to all secondary roles;
Step S32: the users corresponding to each role can generate different secret keys through an encryption algorithm, the attribute set of all roles is set as V, and the attribute corresponding to each role is set as V i Constructing each user according to encryption algorithmThe encryption formula of the encryption algorithm is specifically as follows:
(1)V＝{v 1 ,……，v i ,……}
(2)
(3)
the formula (1) is used for defining an attribute set V;
equation (2) is used to obtain the key generated by each user;
the formula (3) is used for acquiring any two values of the non-empty digital set A;
in the formula (1), i represents the label of a character, i is more than or equal to 1 and less than or equal to n, and A represents a non-empty number set;and->Representing the numerical values represented by the reference numerals g and f in the non-empty digital set A, and n < g < f;
in the formula (2), k represents the number of the user, p k Representing the ranking number, ZH, of the user when entering the system k (v i ) A key representing that user k generates under the authority of role i;
step S33: and the user verifies the login system through the identity mark, unlocks the secret lock of the corresponding grade power data set according to the secret key generated by the encryption algorithm, and then performs related access through the calling set.
Wherein the trusted authority uses the secure channel to handle ZH k (v i ) Transmitting to the user making the access request, and giving each key access to the respective level of electric security data set according to the attribute of each role, due to And->Two random values in the non-null digital set A are adopted, so that keys given to users with the same roles are different, the safety of each user in access is improved, dynamic keys generated in each access are different, and information leakage of a database is prevented.
In the embodiment, the database is designed in a distributed manner, so that the rapid response during data reading is facilitated, the elastic expansion of the storage space is realized, sufficient space is provided for the real-time updating of the electric power data and the mass data in each acquisition point, and the running performance of the whole system is improved; the user information is managed and designed by establishing roles and rights, so that the security of identity verification when the user logs in is improved, different rights are acquired by giving different roles to the user, file disclosure caused by user rights fluctuation is reduced, and the tightness of the user in the system management process and the management efficiency of data information are realized; the user encryption module endows the corresponding roles with the ability of unlocking the corresponding security level power data keys, so that dynamic construction of the keys corresponding to the roles is realized in the process of accessing the power data of different security levels by the user.
Example two
Referring to fig. 1, fig. 2 and fig. 3, a second embodiment of the present invention provides a power data optimization method.
In the process of uploading data to the cloud by the acquisition point, the file is easy to leak due to the fact that the file is extremely easy to attack, so that the exposure in the data transmission process is reduced by encrypting the file, and the Paillier meets the standard security definition of an encryption scheme: semantic security, i.e., indistinguishability of ciphertext under selective plaintext attack (IND-CPA). Intuitively, the ciphertext does not reveal any information in the plaintext. The scheme security can be reduced to the Deterministic Complex Remainder Assumption (DCRA), so the security of the Paillier encryption scheme is considered quite reliable.
The transmission layer comprises a file encryption module, the file encryption module encrypts data packets of all acquisition points to a database based on a Paillier algorithm, and a public key and a private key are generated, wherein the specific process of generation is as follows. According to the formula:
(4)r＝pq
(5)λ＝lcm(p-1,q-1)
(6)
obtaining a public key (r, q) and a private key (lambda, mu);
equation (4) is used for obtaining the factor value r of the public key;
equation (5) is used for obtaining the factor value lambda of the private key;
equation (6) is used for obtaining the factor value mu of the private key;
Where p and q represent randomly selected prime numbers, respectively, and p and q are equal in length, and pq, p-1 and q-1 are prime with each other, lcm () represents a least common multiple function.
The Paillier algorithm is used for generating a matched public key and private key, and optimizing the solution of the factor value mu, and on the premise of not affecting the accuracy of the algorithm, the algorithm can take g=r+1 in the key generation stage for simplifying operation, and the encryption process is accelerated for 1-time modular multiplication through modular exponentiation simplification, so that the solution of the factor value mu is faster.
The file encryption module respectively endows a private key and a public key for the database and the file package, distributes the private key to the database to decrypt the capability of the public key of the file package of each acquisition point, constructs the encryption transmission of the file package which is encrypted and uploaded from the acquisition point to the cloud database, and comprises the following steps:
step S31: setting the number of the electric power data acquisition points as w, respectively generating a public key (r, q) and a private key (lambda, mu) for each acquisition point, distributing the generated public key (r, q) to the corresponding acquisition point, and distributing the generated private key (lambda, mu) to a database:
step S31: the process of encrypting the file packet by the public key (r, q) is specifically as follows, according to the file packet encryption formula:
(7)M x ＝((m x r+1mod(r 2 ))s r )mod(r 2 )
(8)
Acquiring encrypted file package M x ，
Wherein M represents an encrypted package, x represents a label of the package, and M x The method comprises the steps of representing an unencrypted file packet generated by an acquisition point x, wherein the labels of the file packet are equal to and correspond to the labels of the acquisition points one by one, and s represents a random value;
step S31: assigning a private key (lambda, mu) corresponding to the public key of each package to the database, the private key (lambda, mu) pair encrypting the package M x The decryption process of (a) specifically comprises:
(9)
obtaining decrypted file packet m x ，
Where d () represents a decryption function;
step S31: storing the decrypted file packet into a database.
When the transmission layer is specifically applied to the file encryption module, each acquisition point carries out field encapsulation on the file package acquired in the period, after the encapsulation is completed, an uploading signal is sent to the central processing unit, the central processing unit starts the file encryption module after receiving the uploading signal, the file encryption module generates a public key and a private key in the central processing unit, the central processing unit sends the public key to the file package through a wireless network and encrypts the file package, the central processing unit sends the private key to the data layer, the encrypted file package is uploaded to the data layer through the wireless network, the data layer decrypts according to the distributed private key, if decryption is completed, the decrypted file package is stored in the database, if decryption is unsuccessful, a risk warning is sent out, or a re-acquired signal is sent down according to the acquisition point corresponding to the file package with failure decryption, so that the acquisition point carries out repeated data acquisition and public key encryption, and the public key used for re-acquisition is regenerated by the file encryption module.
After the acquisition point acquires the power data, the power data is ordered according to the homomorphism property of the Paillier algorithm and the date of each power data, the bucket ordering can be used for most data, the rest of the data can be completed by adopting algorithms such as quick ordering, merging ordering and the like,
the private key is issued to the data layer, the public key is issued to the file package, the encrypted file package is uploaded to the data layer through the wireless network, the data layer can call the corresponding private key under the action of the central processing unit to decrypt, if decryption is completed, the decrypted file package is stored in the database through gridding distribution, if decryption is unsuccessful, a risk warning is issued, a risk acquisition point is positioned, and meanwhile, the secret key of a logging user and the secret lock generated by the data layer are updated, and the security system is updated.
In addition, the Paillier algorithm also has homomorphic calculation, and can carry out addition and multiplication calculation on each electric power data in the file package, so that the calculation power occupation of a central processing unit is reduced, and the data processing efficiency is improved.
In this embodiment, the file encryption module encrypts the uploading and network transmission processes of the file package based on the Paillier algorithm, constructs encrypted transmission of the file package from the collection point to the cloud database, and endows the database and the file package with a private key and a public key respectively to enable each collection point to have the public key respectively, so that the exposure of data is reduced, decryption and storage are performed after the data is safely uploaded, and two different application instructions of storage and warning are generated according to the decrypted state, so that the security of the data is passed.
Example III
Referring to fig. 1 and fig. 2, a second embodiment of the present invention provides a power data optimization method.
The power data optimization processing system is applied to a cloud processing platform, a data layer is applied to a cloud, hadoopHDFS distributed storage is carried out, a central processing unit is arranged on the cloud processing platform and adopts an integrated information processing system, a processing layer and a transmission layer are used for carrying out cloud processing based on the central processing unit, the central processing unit is connected with each acquisition point and a client through a wireless network, an output layer carries out information display through a Web page program of output equipment, a processing result of the central processing unit is output and is used for a manager to carry out information receiving, the output layer adopts an information system structure of multiple clients, and a user accesses the system through the clients and carries out authority access on information according to given roles.
The technical scheme of the power data optimization processing method mainly comprises the following steps:
step S1: the user performs identity verification through a Web browsing page of the client, judges whether to permit logging in the system, acquires corresponding access and management authority according to a role endowed by the user identity, unlocks electric power data corresponding to the security level through a corresponding key endowed by the carrying role, and checks information from the client;
Step S2: the file package of each acquisition point is encrypted through a public key issued by the central processing unit and is uploaded to the cloud end through the wireless network, and the cloud end decrypts the file package through a private key and stores the decrypted file package;
step S3: the improved K-means clustering algorithm takes the median of the electric power data in each file package as a clustering centroid, carries out iterative aggregation on the electric power data in the file package to form a value set, carries out abnormal display on the electric power data which is not contained in the value set, and generates warning information and a new command.
The processing layer carries out statistical analysis on the data in each acquisition point data packet based on a K-means clustering algorithm, improves the K-means algorithm for meeting the processing of the data, and comprises the steps of selecting a cluster centroid and optimizing distance calculation, wherein the specific process of selecting the cluster centroid comprises the following steps:
step S41: setting a file packet of each acquisition point to contain j pieces of electric power data, sequencing the numerical values through excel, and selecting a median from the j data values as a clustering centroid of each data packet, wherein the specific working process of the clustering centroid is as follows according to a selection formula;
(10)
the cluster centroid z is obtained and,
where j represents the number of power data in each package, c represents the power data in the package, And->Reference numerals each representing power data;
step S43: each file package contains a cluster centroid, and the obtained w cluster centroids form a cluster centroid set.
The average, mode and median are all called statistics, which have wide application in statistics. The average, median and mode are all "feature numbers" describing the central tendency of the data, which provide us with the appearance of the same set of data from different sides, both have units (the mode also has units if represented as a number); the units of the data are the same as the unit of the data of the group, and all three can be used as the representative of the group of data.
However, in a set of data, both the average and median have uniqueness, but the mode sometimes does not, in a set of data, there may be more than one mode, or there may be no mode, the average has uniqueness, calculated, that is not the original data in the data. The average size of a group of data is reflected, the average size is commonly used for representing the overall average level of the data, and if the extreme value deviation of the data is large, the meaning of the average as representing the average level is weakened;
The median has uniqueness, when a group of data has odd numbers, the data in the middle after the group of data is ordered is the data in the group of data which really exists, but in the case that the number of the data is even, the median is the average number of the two data in the middle, which is not necessarily equal to the certain data in the group of data, the median at this time is a virtual number, like a dividing line, the data is divided into a first half part and a second half part, and the power data represents the "middle level" of the group of data, and is very representative as the clustering centroid of each file packet.
The specific process of distance calculation optimization in the K-means clustering algorithm comprises the following steps:
step S51: acquiring a cluster centroid set, extracting a cluster centroid corresponding to each file package, and acquiring each electric power data in the file package;
step S52: for any one file package, carrying out clustering calculation according to the extracted clustering centroid and the power data, and according to a formula
(11)
(12)
Acquiring any two electric power data c in file package m ε And c β The distance to the cluster centroid z is extracted, the smaller of the two values is extracted to form a cluster value set, the iteration number is set as y, y is more than or equal to 1 and less than j,
Wherein c ε (t ε ,h ε ) Representing power data c ε The recording time t contained in the recording medium ε And a metering value h ε ，c β (t β ,h β ) Representing power data c β The recording time t contained in the recording medium β And a metering value h β And epsilon and beta both represent the position marks of the power data in the file package, t 0 Represents the recording time, h, represented by the median power data 0 Representing the measured value represented by the median power data.
The median of the electric power data in each file package is selected as a clustering centroid, so that the gathering degree of the electric power data to the centroid can be better reflected, average errors caused by abnormal data are reduced, because the numerical value of the abnormal electric power data is prominent and different from that of normal data, the electric power data in each file package are gathered around the centroid through a distance calculation optimization formula, and the electric power data in the file package are gathered in a stacking way, and the gathering iteration times are generally smaller than the number of the electric power data in each file package, so that the abnormal data are highlighted.
The method comprises the steps that a database performs distributed storage on each file package, a processing layer extracts abnormal data of a meshed data package in the database based on a K-means clustering algorithm, the median of j sequence power data in each file package is determined as a clustering centroid through the clustering algorithm, the aggregation degree of j power data in each file package is judged by calculating the distance value between each clustering centroid and each power data in the corresponding file package, the aggregation value sets are sequentially filled with the power data which are smaller in the distance value according to iteration times, the power data which are not filled with the aggregation value sets are displayed and positioned, corresponding acquisition points, acquisition time and abnormal values are obtained, page display is performed through display equipment of an output layer, and users with corresponding role rights are provided with access.
In this embodiment, the median of the electric power data in each file packet is used as the cluster centroid, so that the aggregation degree of each electric power data is improved, the aggregation value set of the electric power data is expressed more accurately, the iteration times are set through the distance optimization formula, the aggregation value set is formed, the electric power data with abnormal characteristics or predicted abnormal conditions is isolated from the normal data, the abnormal characteristic electric power data value is highlighted, direct observation is facilitated, and the processing efficiency and accuracy in the electric power data aggregation processing process are realized based on an improved clustering algorithm.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to those skilled in the art without departing from the principles of the present invention are intended to be comprehended within the scope of the present invention.
Claims (10)
1. The power data optimization processing method is characterized by being applied to a power data optimization processing system and comprising a data layer, a transmission layer, a processing layer and an output layer, wherein the technical scheme of the power data optimization processing method mainly comprises the following steps of:
Step S1: the user performs identity verification through a Web browsing page of the client, judges whether to permit logging in the system, acquires corresponding access and management authority according to a role endowed by the user identity, unlocks electric power data corresponding to the security level through a corresponding key endowed by the carrying role, and checks information from the client;
step S2: the file package of each acquisition point is encrypted through a public key issued by the central processing unit and is uploaded to the cloud end through the wireless network, and the cloud end decrypts the file package through a private key and stores the decrypted file package;
step S3: the improved K-means clustering algorithm takes the median of the electric power data in each file package as a clustering centroid, carries out iterative aggregation on the electric power data in the file package to form a value set, carries out abnormal display on the electric power data which is not contained in the value set, and generates warning information and a new command.
2. The power data optimization processing method according to claim 1, wherein the data layer comprises a database, the database comprises power data management information and user management information, and distributed storage and call management are performed;
the power data management information comprises file packages acquired by all acquisition points, and each file package comprises a daily power use value in the corresponding acquisition point;
The user management information comprises user identity authentication and authority management, wherein the user identity authentication is applied to user login, and the calling group checks the identity information input during the user login with the identity information stored in the database and judges whether the user has login authority; the authority management carries out design roles through an authority model, corresponding roles are assigned according to authentication identities of users, corresponding authorities are acquired according to the roles, user information is stored in a database by a server, when the users carry out authority authentication, information of the current users and the roles are taken out from the database by a calling group, corresponding authorities associated with the roles are inquired, whether the users have authorities for operating certain resources or not is judged, and authority authentication is completed.
3. The method of claim 2, wherein the data layer and the transmission layer are designed based on NET open source architecture, the transmission layer is used for encrypting the transmission process of the data packet of each acquisition point to the database and encrypting access of the user to the power data in the database by roles,
the transmission layer comprises a user encryption module, wherein the user encryption module is used for giving corresponding roles to unlock keys corresponding to the security level power data in the process of accessing the power data with different security levels by a user, namely constructing keys corresponding to the roles, and the specific process is as follows:
Step S31: when a user inputs an identity into the system, the database gives the user a unique identity, the identity comprises a corresponding role given by the user, the role is given according to authority determination of the user, n levels of electric security data sets are set in the database, the number of the roles is the same as the number of the levels of the electric security data sets, namely, the number of the roles is n, the electric security data sets of each level can generate a secret lock, and a private key generated by the user can unlock the data sets in the corresponding level;
each power data set corresponds to one role, and the role of the higher level can access the power data sets corresponding to all secondary roles;
step S32: the users corresponding to each role can generate different secret keys through an encryption algorithm, the attribute set of all roles is set as V, and the attribute corresponding to each role is set as V i The key of each user is constructed according to an encryption algorithm, and the encryption formula of the encryption algorithm is specifically as follows:
(1)V＝{v 1 ,......，v i ,......}
(2)
(3)
the formula (1) is used for defining an attribute set V;
equation (2) is used to obtain the key generated by each user;
the formula (3) is used for acquiring any two values of the non-empty digital set A;
in the formula (1), i represents the label of a character, i is more than or equal to 1 and less than or equal to n, and A represents a non-empty number set; And->Representing the numerical values represented by the reference numerals g and f in the non-empty digital set A, and n < g < f;
in the formula (2), k represents the number of the user, p k Representing the ranking number, ZH, of the user when entering the system k (v i ) A key representing that user k generates under the authority of role i;
step S33: and the user verifies the login system through the identity mark, unlocks the secret lock of the corresponding grade power data set according to the secret key generated by the encryption algorithm, and then performs related access through the calling set.
4. The method for optimizing power data according to claim 1, wherein the transmission layer includes a file encryption module, the file encryption module encrypts data packets of each acquisition point to a database based on a Paillier algorithm to generate a public key and a private key, and the specific process of generating the public key and the private key is as follows:
(4)r＝pq
(5)λ＝lcm(p-1,q-1)
(6)
obtaining a public key (r, q) and a private key (lambda, mu);
equation (4) is used for obtaining the factor value r of the public key;
equation (5) is used for obtaining the factor value lambda of the private key;
equation (6) is used for obtaining the factor value mu of the private key;
where p and q represent randomly selected prime numbers, respectively, and p and q are equal in length, and pq, p-1 and q-1 are prime with each other, lcm () represents a least common multiple function.
5. The method for optimizing power data according to claim 4, wherein the file encryption module assigns a private key and a public key to the database and the file package, respectively, and assigns the private key to the database to decrypt the public key of the file package at each collection point, and constructs the encrypted transmission of the file package from the encryption at the collection point to the cloud database, and the specific working process comprises the following steps:
Step S31: setting the number of the electric power data acquisition points as w, respectively generating a public key (r, q) and a private key (lambda, mu) for each acquisition point, distributing the generated public key (r, q) to the corresponding acquisition point, and distributing the generated private key (lambda, mu) to a database:
step S31: the process of encrypting the file packet by the public key (r, q) is specifically as follows, according to the file packet encryption formula:
(7)M x ＝((m x r+1mod(r 2 ))s r )mod(r 2 )
(8)
acquiring encrypted file package M x ，
Wherein M represents an encrypted package, x represents a label of the package, and M x The method comprises the steps of representing an unencrypted file packet generated by an acquisition point x, wherein the labels of the file packet are equal to and correspond to the labels of the acquisition points one by one, and s represents a random value;
step S31: assigning a private key (lambda, mu) corresponding to the public key of each package to the database, the private key (lambda, mu) pair encrypting the package M x The decryption process of (a) specifically comprises:
(9)
obtaining decrypted file packet m x ，
Where d () represents a decryption function;
step S31: storing the decrypted file packet into a database.
6. The method for optimizing power data according to claim 4, wherein when the transmission layer is applied to the file encryption module, each acquisition point carries out field encapsulation on the file packet acquired in the period, after the encapsulation is completed, an uploading signal is sent to the central processing unit, the central processing unit starts the file encryption module after receiving the uploading signal, the file encryption module generates a public key and a private key in the central processing unit, the central processing unit sends the public key to the file packet through a wireless network and encrypts the file packet, the central processing unit sends the private key to the data layer, the encrypted file packet is uploaded to the data layer through the wireless network, the data layer carries out decryption according to the distributed private key, if decryption is completed, the decrypted file packet is stored in the database, if decryption is unsuccessful, a risk warning is sent out, or a re-acquired signal is sent according to an acquisition point corresponding to the file packet with decryption failure, so that the acquisition point carries out repeated data acquisition and public key encryption, and the public key used by re-acquisition is regenerated by the file encryption module.
7. The power data optimization processing method according to claim 1, wherein the processing layer performs statistical analysis on data in each acquisition point data packet based on a K-means clustering algorithm, improves the K-means algorithm to meet the processing of the data, and includes selecting a cluster centroid and optimizing distance calculation, wherein the specific process of selecting the cluster centroid includes the following steps:
step S41: setting a file packet of each acquisition point to contain j pieces of electric power data, sequencing the numerical values through excel, and selecting a median from the j data values as a clustering centroid of each data packet, wherein the specific working process of the clustering centroid is as follows according to a selection formula;
(10)
the cluster centroid z is obtained and,
where j represents the number of power data in each package, c represents the power data in the package,andreference numerals each representing power data;
step S43: each file package contains a cluster centroid, and the obtained w cluster centroids form a cluster centroid set.
8. The power data optimization processing method according to claim 7, wherein the specific process of distance calculation optimization in the K-means clustering algorithm comprises the following steps:
Step S51: acquiring a cluster centroid set, extracting a cluster centroid corresponding to each file package, and acquiring each electric power data in the file package;
step S52: for any one file package, carrying out clustering calculation according to the extracted clustering centroid and the power data, and according to a formula
(11)
(12)
Acquiring any two electric power data c in file package m ε And c β Reach the polyThe distance of the centroid z is similar, the smaller of the two values is extracted to form a convergence value set, the iteration number is set as y, y is more than or equal to 1 and less than j,
wherein c ε (t ε ,h ε ) Representing power data c ε The recording time t contained in the recording medium ε And a metering value h ε ，c β (t β ,h β ) Representing power data c β The recording time t contained in the recording medium β And a metering value h β And epsilon and beta both represent the position marks of the power data in the file package, t 0 Represents the recording time, h, represented by the median power data 0 Representing the measured value represented by the median power data.
9. The power data optimization processing method according to claim 5, wherein the database stores each file packet in a distributed manner, the processing layer extracts abnormal data of the data packets which are meshed in the database based on a K-means clustering algorithm, the median of j series of power data in each file packet is determined as a clustering centroid through the clustering algorithm, the aggregation degree of j power data in each file packet is determined by calculating the distance value between each clustering centroid and each power data in the corresponding file packet, the aggregation degree of j power data in each file packet is sequentially filled in the aggregation value sets according to the iteration times, the power data which are not filled in the aggregation value sets are displayed and positioned, corresponding acquisition points, acquisition time and abnormal values of the power data are obtained, page display is performed through display equipment of an output layer, and users with corresponding roles are given access.
10. The power data optimization processing method according to claim 1, wherein the power data optimization processing system is applied to a cloud processing platform, a data layer is applied to a cloud, hadoopHDFS distributed storage is performed, a central processing unit is arranged on the cloud processing platform and adopts an integrated information processing system, a processing layer and a transmission layer are used for cloud processing based on the central processing unit, the central processing unit is connected with each acquisition point and a client through a wireless network, an output layer is used for displaying information through a Web page program of an output device, the processing result of the central processing unit is output and is used for receiving information by management staff, the output layer adopts a multi-client information system structure, and a user accesses the system through clients and accesses the information according to given roles.
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