CN116599766B - Smart electric meter detection method, device, equipment and storage medium - Google Patents
Smart electric meter detection method, device, equipment and storage medium Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
- H04L63/045—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload wherein the sending and receiving network entities apply hybrid encryption, i.e. combination of symmetric and asymmetric encryption
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/12—Applying verification of the received information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/14—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using a plurality of keys or algorithms
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/40—Network security protocols
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security
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Abstract
The invention relates to the technical field of ammeter detection, and discloses a detection method, device and equipment of an intelligent ammeter and a storage medium. The detection method of the intelligent ammeter comprises the following steps: acquiring data to be transmitted, and classifying the data to be transmitted to obtain sensitive data and non-sensitive data; the sensitive data at least comprises user privacy information and energy consumption data, and the non-sensitive data at least comprises equipment state data and firmware update information; encrypting the sensitive data through a preset first encryption model to obtain first encrypted data, and encrypting the non-sensitive data through a preset second encryption model to obtain second encrypted data; the method can meet the basic requirements of information security protection under the constraint of limited computing capacity and communication bandwidth of the intelligent ammeter.
Description
Technical Field
The present invention relates to the field of electric meter detection technologies, and in particular, to a method, an apparatus, a device, and a storage medium for detecting an intelligent electric meter.
Background
Along with the rapid development of science and technology, the intelligent ammeter is widely applied to an electric power system, and the efficiency of electricity consumption information acquisition, monitoring and management is effectively improved. However, information security and reliability problems of smart meters are increasingly highlighted. In the process of information transmission, the problems of hacking, illegal power stealing by tampering data and protecting information privacy become a core challenge of the intelligent ammeter.
The current technical scheme mainly focuses on enhancing the security of data transmission, hardware protection strategies and software protection mechanisms through encryption algorithms to improve the reliability of information security. However, with the increase of data volume and the related sensitive information, how to ensure the secure transmission of data and the normal operation of the smart meter is an urgent problem to be solved.
Disclosure of Invention
The invention provides a detection method, a detection device, detection equipment and a storage medium of a smart meter, which are used for solving the technical problems.
The first aspect of the present invention provides a method for detecting a smart meter, where the method for detecting a smart meter includes:
acquiring data to be transmitted, and classifying the data to be transmitted to obtain sensitive data and non-sensitive data; the sensitive data at least comprises user privacy information and energy consumption data, and the non-sensitive data at least comprises equipment state data and firmware update information;
encrypting the sensitive data through a preset first encryption model to obtain first encrypted data, and encrypting the non-sensitive data through a preset second encryption model to obtain second encrypted data; the preset first encryption model is obtained by training an elliptic curve encryption algorithm in advance, and the preset second encryption model is obtained by training an AES encryption algorithm in advance;
combining the common attributes of the first encryption model and the second encryption model to obtain a hybrid encryption transmission protocol, and fusing the first encryption data and the second encryption data according to a transmission proportion based on the hybrid encryption transmission protocol to obtain transmission data;
based on a preset self-adaptive fault detection model, importing transmission data transmitted by the intelligent electric meter into the self-adaptive fault detection model to obtain detection data, carrying out fault analysis on the intelligent electric meter through the detection data to obtain a fault analysis evaluation report, and carrying out information safety and reliability detection on the intelligent electric meter through the fault analysis evaluation report.
Optionally, in a first implementation manner of the first aspect of the present invention, the step of encrypting the sensitive data through a preset first encryption model to obtain first encrypted data, and encrypting the non-sensitive data through a preset second encryption model to obtain second encrypted data includes:
multiple combinations are carried out on elliptic curve encryption algorithms through selecting different types of elliptic curves to obtain an elliptic curve algorithm with a multiple encryption structure, and the sensitive data are encrypted based on the elliptic curve algorithm with the multiple encryption structure to obtain first encrypted data;
performing secondary encryption on an initial key generated based on an AES encryption algorithm through a preset dynamic key generation algorithm to generate a secondary encryption key, and encrypting the non-sensitive data through the secondary encryption key to obtain second encrypted data.
Optionally, in a second implementation manner of the first aspect of the present invention, the step of combining common attributes of the first encryption model and the second encryption model to obtain a hybrid encryption transport protocol, and fusing the first encrypted data and the second encrypted data according to the transmission proportion based on the hybrid encryption transport protocol to obtain transmission data includes:
constructing a hybrid encryption transmission protocol of a first encryption model combined with an elliptic curve encryption algorithm and a second encryption model of an AES encryption algorithm;
dynamically adjusting the transmission proportion of sensitive data and non-sensitive data according to the mixed encryption transmission protocol and real-time requirements so as to adapt to different network conditions, delays and priorities of data types;
and fusing the sensitive data encrypted by the first encryption model and the non-sensitive data encrypted by the second encryption model by adopting a specified fusion algorithm according to the transmission proportion to obtain transmission data.
Optionally, in a third implementation manner of the first aspect of the present invention, the step of importing the transmission data transmitted by the smart meter into the adaptive fault detection model based on a preset adaptive fault detection model to obtain detection data, and performing fault analysis on the smart meter through the detection data to obtain a fault analysis evaluation report includes:
constructing and configuring a self-adaptive fault detection model based on a machine learning algorithm; wherein the machine learning algorithm comprises a supervised learning algorithm and an unsupervised learning algorithm;
preprocessing transmission data transmitted by the intelligent ammeter; the preprocessing comprises data cleaning, data standardization and feature extraction, and a data fusion and association analysis algorithm is adopted to integrate support information of multiple sources;
according to the specific scene, equipment type and real-time operation parameters transmitted by the intelligent ammeter, the super-parameters and the prediction properties of the self-adaptive fault detection model are adjusted in real time;
importing the processed transmission data into a self-adaptive fault detection model, outputting detection data, performing noise reduction processing on the detection data, and generating a fault analysis evaluation report; wherein the fault analysis evaluation report comprises equipment health diagnosis, preventive maintenance advice and equipment life prediction;
the fault analysis assessment report is presented to the operation and maintenance personnel through a visualization tool and the report is sent to a plurality of operation and maintenance intervention teams.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the step of importing the processed transmission data into an adaptive fault detection model, outputting detection data, performing noise reduction processing on the detection data, and generating a fault analysis evaluation report includes:
acquiring detection data, and performing feature extraction on the detection data to obtain a first detection feature vector; wherein the detection data comprises corresponding tags;
inputting the first detection feature vector into a preset coding model for feature extraction to obtain a first coding vector;
inputting the first coding vector into a preset decoding model for decoding processing to obtain a corresponding second detection feature vector;
inputting the second detection feature vector into a preset coding model for feature extraction to obtain a second coding vector;
inputting the first coding vector, the second coding vector and the corresponding labels into a classification layer for training, and iteratively adjusting model parameters of the preset coding model and decoding model until the loss function of the classification layer converges, and completing model training; and taking the coding model and the decoding model as noise reduction models, and performing noise reduction processing on the detection data.
The second aspect of the present invention provides a detection device for a smart meter, where the detection device for a smart meter includes:
the acquisition module is used for acquiring data to be transmitted and classifying the data to be transmitted to obtain sensitive data and non-sensitive data; the sensitive data at least comprises user privacy information and energy consumption data, and the non-sensitive data at least comprises equipment state data and firmware update information;
the encryption module is used for encrypting the sensitive data through a preset first encryption model to obtain first encrypted data, and encrypting the non-sensitive data through a preset second encryption model to obtain second encrypted data; the preset first encryption model is obtained by training an elliptic curve encryption algorithm in advance, and the preset second encryption model is obtained by training an AES encryption algorithm in advance;
the fusion module is used for combining the common attributes of the first encryption model and the second encryption model to obtain a hybrid encryption transmission protocol, and based on the hybrid encryption transmission protocol, the first encryption data and the second encryption data are fused according to the transmission proportion to obtain transmission data;
the detection module is used for importing transmission data transmitted by the intelligent electric meter into the self-adaptive fault detection model based on a preset self-adaptive fault detection model to obtain detection data, carrying out fault analysis on the intelligent electric meter through the detection data to obtain a fault analysis evaluation report, and carrying out information safety and reliability detection on the intelligent electric meter through the fault analysis evaluation report.
A third aspect of the present invention provides a detection apparatus for a smart meter, including: a memory and at least one processor, the memory having instructions stored therein; and the at least one processor calls the instruction in the memory so that the detection equipment of the intelligent ammeter executes the detection method of the intelligent ammeter.
A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein, which when run on a computer, cause the computer to perform the method of detecting a smart meter as described above.
In the technical scheme provided by the invention, the beneficial effects are as follows: the invention provides a detection method, a device, equipment and a storage medium of an intelligent ammeter, which can adopt different encryption strategies according to the sensitivity degree of data by classifying sensitive data and non-sensitive data, thereby effectively protecting the safety of user privacy information and equipment. And encrypting the data under two preset encryption models, and transmitting based on a hybrid encryption transmission protocol, so that the data transmission safety is greatly improved. The data is encrypted and fused, so that the risk of being cracked is reduced, and the information safety and reliability are improved. And the transmission data of the intelligent electric meter is subjected to fault analysis and information safety and reliability detection by using a preset self-adaptive fault detection model, so that faults and potential safety hazards existing in the electric meter can be found out rapidly, and more stable and safe power consumption monitoring can be realized.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a method for detecting a smart meter according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an embodiment of a detection device for a smart meter according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a detection method, device and equipment of an intelligent ammeter and a storage medium. The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For easy understanding, the following describes a specific flow of an embodiment of the present invention, referring to fig. 1, and an embodiment of a method for detecting a smart meter according to the embodiment of the present invention includes:
step 101, acquiring data to be transmitted, and classifying the data to be transmitted to obtain sensitive data and non-sensitive data; the sensitive data at least comprises user privacy information and energy consumption data, and the non-sensitive data at least comprises equipment state data and firmware update information;
it can be understood that the execution body of the present invention may be a detection device of a smart meter, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a detection device of a smart meter as an execution main body.
Specifically, in this embodiment, the data transmission module is responsible for acquiring data to be transmitted, and classifying the data according to the attribute thereof to obtain sensitive data and non-sensitive data. This helps to preserve user privacy while optimizing allocation of network resources at the time of data transmission.
The data transmission module firstly acquires data to be transmitted. This may include data collected from various sub-modules (e.g., a charging module and a monitoring module), as well as control instructions received from a user.
The acquired data to be transmitted are classified, and the data transmission module divides the data into sensitive data and non-sensitive data according to the sensitivity of the data. The sensitive data includes at least user privacy information (e.g., personal identification information, charging history, etc.) and energy consumption data (e.g., energy consumption analysis, electricity usage statistics, etc.). The non-sensitive data at least comprises equipment state data (such as the residual quantity of a battery, the charging speed and the like) and firmware update information (such as a software upgrade prompt, a Bug repair prompt and the like).
For sensitive data, the data transmission module takes necessary encryption measures, such as using SSL/TLS encryption techniques to protect the security and privacy of the data during transmission. Meanwhile, when sensitive data are stored, strict encryption measures are adopted to protect privacy rights of safe users.
For non-sensitive data, the data transmission module can transmit the non-sensitive data through network channels with different priorities, so that the real-time performance and the reliability are ensured. For example, when device status data and firmware update information are transmitted simultaneously, the device status data may be transmitted preferentially to ensure real-time feedback to the user; the firmware update information may be transmitted later, avoiding congestion of network resources.
To ensure the security and high efficiency of data transmission, the data transmission module may employ a blockchain technique as a bottom layer transmission and storage means. The non-trust and encryption characteristics based on the blockchain technology enable both sensitive data and non-sensitive data to be transmitted and stored in a secure, decentralized network.
In summary, in the embodiment of the invention, the beneficial effects are as follows: the embodiment of the invention improves the data safety and the transmission efficiency through the classification of the data to be transmitted and corresponding processing measures. At the same time, in an innovative aspect, the embodiment attempts to incorporate blockchain techniques into data transmission and storage, further enhancing data security and system robustness.
102, encrypting the sensitive data through a preset first encryption model to obtain first encrypted data, and encrypting the non-sensitive data through a preset second encryption model to obtain second encrypted data; the preset first encryption model is obtained by training an elliptic curve encryption algorithm in advance, and the preset second encryption model is obtained by training an AES encryption algorithm in advance;
specifically, in this embodiment, the sensitive data and the non-sensitive data are encrypted by a preset first encryption model and second encryption model, respectively. Such classified encryption policies aim to ensure a higher degree of security protection for sensitive data while optimizing the processing speed for non-sensitive data.
The preset first encryption model is obtained by encrypting sensitive data and training an elliptic curve encryption algorithm. The sensitive data includes at least privacy information and energy consumption data of the user. And encrypting the sensitive data by using an elliptic curve encryption algorithm to obtain first encrypted data. The elliptic curve encryption algorithm has higher security and shorter key length, and is suitable for protecting the transmission of private information.
And encrypting the non-sensitive data by a preset second encryption model, and training through an AES encryption algorithm. The non-sensitive data includes at least device status data and firmware update information. And encrypting the non-sensitive data by using an AES encryption algorithm to obtain second encrypted data. The AES encryption algorithm has higher encryption and decryption speeds while ensuring certain security, and is more suitable for processing non-sensitive data.
When the data transmission module needs to send the encrypted data to other devices or systems, the first encrypted data or the second encrypted data is selected to be used according to the attribute of the data. The adoption of the first encrypted data for the sensitive data can ensure that the privacy information is more ensured in the data transmission. Whereas non-sensitive data can achieve higher processing speeds with the second encrypted data.
To further optimize the encryption process, the encryption model in this embodiment may be implemented using a Hardware Security Module (HSM). The hardware security module is a hardware device capable of providing encryption and decryption functions, and can realize secure storage and management of encryption keys. By using the HSM, the speed of encryption and decryption operations and the security of encryption keys can be greatly improved.
In summary, in the embodiment of the invention, the beneficial effects are as follows: the embodiment of the invention encrypts the sensitive data and the non-sensitive data through the first encryption model and the second encryption model respectively, thereby ensuring the safety of the sensitive data and the processing speed of the non-sensitive data. By introducing a Hardware Security Module (HSM), the encryption and decryption efficiency and the key security are further improved.
Step 103, combining the common attributes of the first encryption model and the second encryption model to obtain a hybrid encryption transmission protocol, and fusing the first encryption data and the second encryption data according to a transmission proportion based on the hybrid encryption transmission protocol to obtain transmission data;
specifically, in the present embodiment, the hybrid encryption transport protocol is obtained by combining the common attribute of the first encryption model (elliptic curve encryption algorithm for sensitive data encryption) and the second encryption model (AES encryption algorithm for non-sensitive data encryption). By using the hybrid encryption transmission protocol, the safety and the efficiency of data transmission are improved.
Design of a hybrid encryption transmission protocol: by combining the common attributes of elliptic curve encryption algorithm and AES encryption algorithm, a hybrid encryption transmission protocol capable of adapting to different encrypted data types is designed. The protocol should compromise the high security provided by elliptic curve cryptography and the high efficiency provided by AES cryptography. The protocol needs to automatically judge the type of the encrypted data and adopts proper encryption and decryption strategies.
Fusing encrypted data: based on the hybrid encryption transmission protocol, the first encrypted data (sensitive data) and the second encrypted data (non-sensitive data) are fused according to the transmission proportion on the premise of ensuring the security. The transmission ratio is adjusted according to the scene, the network condition, the urgency of the data and other factors. The fused data can realize the simultaneous transmission of sensitive data and non-sensitive data, and save transmission time and network resources.
Transmission data reception and decryption: after receiving the transmission data based on the hybrid encryption transmission protocol fusion, the receiving end needs to acquire the transmission proportion of the sensitive data and the non-sensitive data according to the protocol rule, and then decrypts the first encryption data and the second encryption data respectively. After decryption, the receiving end can correctly acquire the original sensitive data and the non-sensitive data.
To further optimize the hybrid encryption transport protocol, a mechanism to dynamically adjust the encryption strength may be introduced. And dynamically adjusting the encryption strength of the first encryption model and the second encryption model according to the sensitivity, the transmission environment and the real-time network condition of the data, so that the transmission process is safer and more efficient. For example, in a case where security requirements are high, the key length of the elliptic curve encryption algorithm or the AES encryption algorithm may be increased to increase encryption strength.
In summary, in the embodiment of the invention, the beneficial effects are as follows: according to the embodiment of the invention, the hybrid encryption transmission protocol is obtained by combining the first encryption model and the second encryption model, so that the safety and the efficiency of data transmission are improved. Meanwhile, a mechanism for dynamically adjusting encryption strength is introduced in the embodiment, so that the safety and efficiency of a data transmission process are further enhanced.
Step 104, based on a preset self-adaptive fault detection model, importing the transmission data transmitted by the intelligent electric meter into the self-adaptive fault detection model to obtain detection data, carrying out fault analysis on the intelligent electric meter through the detection data to obtain a fault analysis evaluation report, and carrying out information safety and reliability detection on the intelligent electric meter through the fault analysis evaluation report.
Specifically, in this embodiment, based on a preset adaptive fault detection model, transmission data transmitted by the smart meter is imported to perform fault analysis, and the analysis result is used to determine information security reliability of the smart meter.
Adaptive fault detection model: and designing and realizing a preset self-adaptive fault detection model for carrying out fault detection on the data transmitted by the intelligent ammeter. The model may be based on machine learning algorithms (e.g., decision trees, support vector machines, etc.) to improve the accuracy and sensitivity of fault diagnosis.
Importing transmission data: and importing transmission data (comprising the first encrypted data and the second encrypted data which are fused by the mixed encryption transmission protocol) transmitted by the intelligent ammeter into the self-adaptive fault detection model. Before importing, the transmitted data needs to be decrypted so that the model analyzes the true sensitive data and the non-sensitive data.
And (3) fault analysis: and the self-adaptive fault detection model automatically performs fault analysis according to the input transmission data. The model detects potential faults according to the characteristic data, and provides possible reasons for each fault and influences the degree of information safety and reliability.
Fault analysis evaluation report: and generating a fault analysis evaluation report according to the fault analysis result. The report includes the discovered faults, the corresponding possible causes, and the impact on the security reliability of the smart meter information. This report can be used for smart meter information security reliability detection and helps to take targeted resolution measures.
To further improve reliability of fault detection, deep learning algorithms (e.g., convolutional neural networks, long and short term memory networks, etc.) may be introduced to enable more complex and accurate analysis of different faults. In addition, the information sharing among the intelligent electric meters is realized through the cooperative detection among the intelligent electric meters, and the detection effect is improved.
In summary, in the embodiment of the invention, the beneficial effects are as follows: the embodiment of the invention detects the information safety reliability of the intelligent ammeter based on a preset self-adaptive fault detection model. The fault analysis result is helpful to take corresponding measures to ensure the safe operation of the intelligent ammeter. Meanwhile, the embodiment introduces a deep learning algorithm and multi-intelligent ammeter cooperative detection, and further improves the accuracy and efficiency of fault detection.
Another embodiment of the method for detecting a smart meter according to the embodiment of the present invention includes:
the step of encrypting the sensitive data through a preset first encryption model to obtain first encrypted data, and encrypting the non-sensitive data through a preset second encryption model to obtain second encrypted data comprises the following steps:
multiple combinations are carried out on elliptic curve encryption algorithms through selecting different types of elliptic curves to obtain an elliptic curve algorithm with a multiple encryption structure, and the sensitive data are encrypted based on the elliptic curve algorithm with the multiple encryption structure to obtain first encrypted data;
performing secondary encryption on an initial key generated based on an AES encryption algorithm through a preset dynamic key generation algorithm to generate a secondary encryption key, and encrypting the non-sensitive data through the secondary encryption key to obtain second encrypted data.
Another embodiment of the method for detecting a smart meter according to the embodiment of the present invention includes:
the step of combining the common attributes of the first encryption model and the second encryption model to obtain a hybrid encryption transmission protocol, and fusing the first encryption data and the second encryption data according to the transmission proportion based on the hybrid encryption transmission protocol to obtain transmission data comprises the following steps:
constructing a hybrid encryption transmission protocol of a first encryption model combined with an elliptic curve encryption algorithm and a second encryption model of an AES encryption algorithm;
dynamically adjusting the transmission proportion of sensitive data and non-sensitive data according to the mixed encryption transmission protocol and real-time requirements so as to adapt to different network conditions, delays and priorities of data types;
and fusing the sensitive data encrypted by the first encryption model and the non-sensitive data encrypted by the second encryption model by adopting a specified fusion algorithm according to the transmission proportion to obtain transmission data.
Specifically, in this embodiment, a hybrid encryption transmission protocol is constructed, and the protocol combines a first encryption model of an elliptic curve encryption algorithm and a second encryption model of an AES encryption algorithm, and can dynamically adjust the transmission ratio of sensitive data and non-sensitive data according to real-time requirements.
Hybrid encryption transport protocol: a hybrid encryption transmission protocol combining an elliptic curve encryption algorithm and an AES encryption algorithm is designed, and the protocol combines the high security of a first encryption model and the high efficiency of a second encryption model. By the protocol, the security of sensitive data and non-sensitive data in the data transmission process can be ensured.
Dynamically adjusting the transmission ratio: and dynamically adjusting the transmission proportion of the sensitive data and the non-sensitive data in real time according to factors such as network conditions, delay, priority of data types and the like. For example, in the case of poor network conditions or high delay, the transmission ratio of the sensitive data may be appropriately reduced to improve the transmission efficiency.
Fusing encrypted data: and adopting a specified fusion algorithm to fuse the sensitive data encrypted by the first encryption model and the non-sensitive data encrypted by the second encryption model according to the adjusted transmission proportion. The fusion process should ensure data integrity and reliability and restore the original data when decrypting.
To further optimize the process of dynamically adjusting the transmission ratio, artificial intelligence based algorithms, such as reinforcement learning, are introduced to help the system automatically learn and determine the optimal transmission ratio. In addition, the safety of the fusion encryption data in the transmission process can be ensured by using a blockchain technology, and the data is prevented from being tampered and leaked.
In summary, in the embodiment of the invention, the beneficial effects are as follows: the technical scheme of the embodiment of the invention combines a first encryption model of an elliptic curve encryption algorithm and a second encryption model of an AES encryption algorithm to construct a hybrid encryption transmission protocol. Meanwhile, the transmission proportion of the sensitive data and the non-sensitive data is dynamically adjusted according to real-time requirements so as to adapt to different scenes. And an artificial intelligence algorithm and a blockchain technology are introduced, so that the safety and the efficiency of data transmission are further enhanced.
Another embodiment of the method for detecting a smart meter according to the embodiment of the present invention includes:
the step of importing the transmission data transmitted by the intelligent ammeter into the self-adaptive fault detection model based on a preset self-adaptive fault detection model to obtain detection data, and carrying out fault analysis on the intelligent ammeter through the detection data to obtain a fault analysis evaluation report comprises the following steps:
constructing and configuring a self-adaptive fault detection model based on a machine learning algorithm; wherein the machine learning algorithm comprises a supervised learning algorithm and an unsupervised learning algorithm;
preprocessing transmission data transmitted by the intelligent ammeter; the preprocessing comprises data cleaning, data standardization and feature extraction, and a data fusion and association analysis algorithm is adopted to integrate support information of multiple sources;
according to the specific scene, equipment type and real-time operation parameters transmitted by the intelligent ammeter, the super-parameters and the prediction properties of the self-adaptive fault detection model are adjusted in real time;
importing the processed transmission data into a self-adaptive fault detection model, outputting detection data, performing noise reduction processing on the detection data, and generating a fault analysis evaluation report; wherein the fault analysis evaluation report comprises equipment health diagnosis, preventive maintenance advice and equipment life prediction;
the fault analysis assessment report is presented to the operation and maintenance personnel through a visualization tool and the report is sent to a plurality of operation and maintenance intervention teams.
Another embodiment of the method for detecting a smart meter according to the embodiment of the present invention includes:
the step of importing the processed transmission data into the self-adaptive fault detection model, outputting detection data, performing noise reduction processing on the detection data, and generating a fault analysis evaluation report comprises the following steps:
acquiring detection data, and performing feature extraction on the detection data to obtain a first detection feature vector; wherein the detection data comprises corresponding tags;
inputting the first detection feature vector into a preset coding model for feature extraction to obtain a first coding vector;
inputting the first coding vector into a preset decoding model for decoding processing to obtain a corresponding second detection feature vector;
inputting the second detection feature vector into a preset coding model for feature extraction to obtain a second coding vector;
inputting the first coding vector, the second coding vector and the corresponding labels into a classification layer for training, and iteratively adjusting model parameters of the preset coding model and decoding model until the loss function of the classification layer converges, and completing model training; and taking the coding model and the decoding model as noise reduction models, and performing noise reduction processing on the detection data.
Specifically, in this embodiment, the processed transmission data is imported into the adaptive fault detection model, the detection data is output, and the detection data is subjected to noise reduction processing, so as to generate a fault analysis evaluation report.
Obtaining detection data: the transmission data is first imported into an adaptive fault detection model. The transmission data should contain detection data and corresponding tags. The unlabeled data may be considered to be pre-processed using an unsupervised learning method.
Feature extraction and processing: and extracting the characteristics of the detection data to obtain a first detection characteristic vector. The stability and accuracy of the feature representation is improved using suitable feature extraction methods (e.g., principal component analysis, fourier transform, etc.).
Encoding and decoding: and designing and realizing a preset coding model and decoding model. And inputting the first detection feature vector into a coding model to perform feature extraction to obtain a first coding vector. Then, the vector is input into a decoding model to obtain a corresponding second detection feature vector.
Feature denoising and classification: and inputting the second detection feature vector into a preset coding model to perform feature extraction to obtain a second coding vector. Then, the first code vector, the second code vector and the corresponding label are input into a classification layer for training. And iteratively adjusting parameters of the coding model and the decoding model until the loss function of the classification layer converges, so as to complete model training. This process realizes the noise reduction processing of the detection data.
To enhance feature extraction performance, deep learning methods (e.g., convolutional neural networks, long and short term memory networks, etc.) may be introduced. Meanwhile, in order to improve the noise reduction effect, a plurality of noise reduction methods can be tried to be combined, and multi-level noise reduction processing is realized.
In summary, in the embodiment of the invention, the beneficial effects are as follows: according to the embodiment of the invention, the detection data input into the self-adaptive fault detection model is subjected to noise reduction treatment, so that the accuracy and reliability of fault analysis can be improved. In addition, the embodiment adopts a deep learning and multi-level noise reduction method, so as to further improve the fault detection process.
The method for detecting the smart meter according to the embodiment of the present invention is described above, and the following describes a device for detecting the smart meter according to the embodiment of the present invention, referring to fig. 2, an embodiment of a device 1 for detecting the smart meter according to the embodiment of the present invention includes:
the acquisition module 11 is configured to acquire data to be transmitted, and classify the data to be transmitted to obtain sensitive data and non-sensitive data; the sensitive data at least comprises user privacy information and energy consumption data, and the non-sensitive data at least comprises equipment state data and firmware update information;
the encryption module 12 is configured to encrypt the sensitive data through a preset first encryption model to obtain first encrypted data, and encrypt the non-sensitive data through a preset second encryption model to obtain second encrypted data; the preset first encryption model is obtained by training an elliptic curve encryption algorithm in advance, and the preset second encryption model is obtained by training an AES encryption algorithm in advance;
the fusion module 13 is configured to combine common attributes of the first encryption model and the second encryption model to obtain a hybrid encryption transmission protocol, and fuse the first encrypted data and the second encrypted data according to a transmission ratio based on the hybrid encryption transmission protocol to obtain transmission data;
the detection module 14 is configured to introduce the transmission data transmitted by the smart meter into the adaptive fault detection model based on a preset adaptive fault detection model, obtain detection data, perform fault analysis on the smart meter according to the detection data, obtain a fault analysis evaluation report, and perform information safety and reliability detection on the smart meter according to the fault analysis evaluation report.
The invention also provides a detection device of the intelligent ammeter, which comprises a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the detection method of the intelligent ammeter in the above embodiments.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, or may be a volatile computer readable storage medium, where instructions are stored in the computer readable storage medium, where the instructions, when executed on a computer, cause the computer to perform the steps of the method for detecting a smart meter.
The beneficial effects are that: the invention provides a detection method, a device, equipment and a storage medium of an intelligent ammeter, which can adopt different encryption strategies according to the sensitivity degree of data by classifying sensitive data and non-sensitive data, thereby effectively protecting the safety of user privacy information and equipment. And encrypting the data under two preset encryption models, and transmitting based on a hybrid encryption transmission protocol, so that the data transmission safety is greatly improved. The data is encrypted and fused, so that the risk of being cracked is reduced, and the information safety and reliability are improved. And the transmission data of the intelligent electric meter is subjected to fault analysis and information safety and reliability detection by using a preset self-adaptive fault detection model, so that faults and potential safety hazards existing in the electric meter can be found out rapidly, and more stable and safe power consumption monitoring can be realized.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (randomaccess memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. The detection method of the intelligent ammeter is characterized by comprising the following steps of:
acquiring data to be transmitted, and classifying the data to be transmitted to obtain sensitive data and non-sensitive data; the sensitive data at least comprises user privacy information and energy consumption data, and the non-sensitive data at least comprises equipment state data and firmware update information;
encrypting the sensitive data through a preset first encryption model to obtain first encrypted data, and encrypting the non-sensitive data through a preset second encryption model to obtain second encrypted data; the preset first encryption model is obtained by training an elliptic curve encryption algorithm in advance, and the preset second encryption model is obtained by training an AES encryption algorithm in advance;
combining the common attributes of the first encryption model and the second encryption model to obtain a hybrid encryption transmission protocol, and fusing the first encryption data and the second encryption data according to a transmission proportion based on the hybrid encryption transmission protocol to obtain transmission data;
based on a preset self-adaptive fault detection model, importing transmission data transmitted by the intelligent electric meter into the self-adaptive fault detection model to obtain detection data, carrying out fault analysis on the intelligent electric meter through the detection data to obtain a fault analysis evaluation report, and carrying out information safety and reliability detection on the intelligent electric meter through the fault analysis evaluation report.
2. The method according to claim 1, wherein the step of encrypting the sensitive data by a preset first encryption model to obtain first encrypted data, and encrypting the non-sensitive data by a preset second encryption model to obtain second encrypted data comprises:
multiple combinations are carried out on elliptic curve encryption algorithms through selecting different types of elliptic curves to obtain an elliptic curve algorithm with a multiple encryption structure, and the sensitive data are encrypted based on the elliptic curve algorithm with the multiple encryption structure to obtain first encrypted data;
performing secondary encryption on an initial key generated based on an AES encryption algorithm through a preset dynamic key generation algorithm to generate a secondary encryption key, and encrypting the non-sensitive data through the secondary encryption key to obtain second encrypted data.
3. The method according to claim 1, wherein the step of combining the common attributes of the first encryption model and the second encryption model to obtain a hybrid encryption transport protocol, and fusing the first encryption data and the second encryption data according to the transmission ratio based on the hybrid encryption transport protocol to obtain transmission data includes:
constructing a hybrid encryption transmission protocol of a first encryption model combined with an elliptic curve encryption algorithm and a second encryption model of an AES encryption algorithm;
dynamically adjusting the transmission proportion of sensitive data and non-sensitive data according to the mixed encryption transmission protocol and real-time requirements so as to adapt to different network conditions, delays and priorities of data types;
and fusing the sensitive data encrypted by the first encryption model and the non-sensitive data encrypted by the second encryption model by adopting a specified fusion algorithm according to the transmission proportion to obtain transmission data.
4. The method according to claim 1, wherein the step of importing the transmission data transmitted by the smart meter into the adaptive fault detection model based on a preset adaptive fault detection model to obtain detection data, and performing fault analysis on the smart meter through the detection data to obtain a fault analysis evaluation report includes:
constructing and configuring a self-adaptive fault detection model based on a machine learning algorithm; wherein the machine learning algorithm comprises a supervised learning algorithm and an unsupervised learning algorithm;
preprocessing transmission data transmitted by the intelligent ammeter; the preprocessing comprises data cleaning, data standardization and feature extraction, and a data fusion and association analysis algorithm is adopted to integrate support information of multiple sources;
according to the specific scene, equipment type and real-time operation parameters transmitted by the intelligent ammeter, the super-parameters and the prediction properties of the self-adaptive fault detection model are adjusted in real time;
importing the processed transmission data into a self-adaptive fault detection model, outputting detection data, performing noise reduction processing on the detection data, and generating a fault analysis evaluation report; wherein the fault analysis evaluation report comprises equipment health diagnosis, preventive maintenance advice and equipment life prediction;
the fault analysis assessment report is presented to the operation and maintenance personnel through a visualization tool and the report is sent to a plurality of operation and maintenance intervention teams.
5. The method of claim 4, wherein the step of importing the processed transmission data into an adaptive fault detection model, outputting detection data, performing noise reduction processing on the detection data, and generating a fault analysis evaluation report comprises:
acquiring detection data, and performing feature extraction on the detection data to obtain a first detection feature vector; wherein the detection data comprises corresponding tags;
inputting the first detection feature vector into a preset coding model for feature extraction to obtain a first coding vector;
inputting the first coding vector into a preset decoding model for decoding processing to obtain a corresponding second detection feature vector;
inputting the second detection feature vector into a preset coding model for feature extraction to obtain a second coding vector;
inputting the first coding vector, the second coding vector and the corresponding labels into a classification layer for training, and iteratively adjusting model parameters of the preset coding model and decoding model until the loss function of the classification layer converges, and completing model training; and taking the coding model and the decoding model as noise reduction models, and performing noise reduction processing on the detection data.
6. The utility model provides a detection device of smart electric meter, its characterized in that, smart electric meter's detection device includes:
the acquisition module is used for acquiring data to be transmitted and classifying the data to be transmitted to obtain sensitive data and non-sensitive data; the sensitive data at least comprises user privacy information and energy consumption data, and the non-sensitive data at least comprises equipment state data and firmware update information;
the encryption module is used for encrypting the sensitive data through a preset first encryption model to obtain first encrypted data, and encrypting the non-sensitive data through a preset second encryption model to obtain second encrypted data; the preset first encryption model is obtained by training an elliptic curve encryption algorithm in advance, and the preset second encryption model is obtained by training an AES encryption algorithm in advance;
the fusion module is used for combining the common attributes of the first encryption model and the second encryption model to obtain a hybrid encryption transmission protocol, and based on the hybrid encryption transmission protocol, the first encryption data and the second encryption data are fused according to the transmission proportion to obtain transmission data;
the detection module is used for importing transmission data transmitted by the intelligent electric meter into the self-adaptive fault detection model based on a preset self-adaptive fault detection model to obtain detection data, carrying out fault analysis on the intelligent electric meter through the detection data to obtain a fault analysis evaluation report, and carrying out information safety and reliability detection on the intelligent electric meter through the fault analysis evaluation report.
7. The utility model provides a smart electric meter's check out test set, its characterized in that, smart electric meter's check out test set includes: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invokes the instructions in the memory to cause the detection device of the smart meter to perform the method of detecting a smart meter according to any one of claims 1-5.
8. A computer readable storage medium having instructions stored thereon, wherein the instructions when executed by a processor implement the method of detecting a smart meter according to any one of claims 1-5.
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