Electric energy metering power monitoring system capable of monitoring and analyzing data in real time

Abstract

The invention discloses an electric energy metering electric power monitoring system capable of monitoring and analyzing data in real time, and relates to the technical field of electric power monitoring systems and electric energy metering. The system comprises a data acquisition module, a historical feature analysis module, a data storage module, a control module, a credible parameter identification module, a monitoring judgment module and a credibility evaluation module, wherein the data acquisition module encrypts electric energy data and extracts encryption features; the historical feature analysis module analyzes historical encrypted data features and calculates an error threshold; the data storage module stores data; the control module is used for coordinating execution of acquisition, analysis and monitoring tasks; the credible parameter identification module calculates a security credibility threshold; the monitoring judgment module judges whether data transmission has an error or not; according to the invention, through the data encryption function, the problem that data may be intercepted or tampered in the transmission process is solved.

Description

Technical Field

[0001] The invention relates to the technical field of electric power monitoring systems and electric energy metering, and in particular to an electric energy metering electric power monitoring system for real-time monitoring and data analysis. Background Art

[0002] With the rapid development of power systems, power monitoring systems play an increasingly important role in the operation and maintenance of power systems. Although the existing power monitoring systems can realize real-time collection and monitoring of power data, there are certain risks in data security. For example, data may be intercepted or tampered with during transmission, resulting in inaccurate data or insecure system operation. Therefore, it is necessary to develop a power monitoring system with data encryption function to improve the security of data transmission. Summary of the invention

[0003] The purpose of the present invention is to provide an electric energy metering and power monitoring system for real-time monitoring and data analysis to solve the problems raised in the prior art.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an electric energy metering and power monitoring system for real-time monitoring and analysis of data, the system comprising: a data acquisition module, a historical feature analysis module, a data storage module, a control module, a trusted parameter identification module, a monitoring and judgment module and a credibility assessment module, the data acquisition module is used to encrypt electric energy data and extract encryption features; the historical feature analysis module is used to analyze the features of historical encrypted data and calculate an error threshold; the data storage module is used to store all encrypted data in a database; the control module is used to coordinate the execution of acquisition, analysis and monitoring tasks; the trusted parameter identification module is used to extract trusted parameters and establish a security credibility threshold; the monitoring and judgment module determines whether there is a transmission error in the data based on the encrypted data and the error threshold; the credibility assessment module is used to evaluate the credibility of the error warning.

[0005] The data acquisition module includes an electric energy data acquisition unit, an encryption processing unit and a feature extraction unit. The electric energy data acquisition unit obtains the electric energy data of voltage, current and power factor of each node through the HT7036 chip. The collected data is numbered according to the nodes connected to the distribution network to form an electric energy data set P i =(V i ,C i ,PF i ), where P i represents the electric energy data set of the ith node, V i represents the voltage value of the ith node, C i represents the current value of the i-th node, PF iIndicates the power factor value of the i-th node; the encryption processing unit encrypts the collected power data through the SC1161Y chip before data transmission to generate a uniquely identified encrypted data packet D i =(P i ,T i , E.D. i ), where D i Indicates the data packet numbered i, T i Indicates the encryption timestamp of the i-th data packet, ED i Indicates the electric energy parameter P i The encrypted data is stored in the buffer and waits for transmission; the feature extraction unit is used to extract and generate the timestamp of the encrypted data packet, the length of the encrypted data and the data features of the transmission path, which are specifically defined as follows: The timestamp calculation formula is as follows: T i =T enc -T acq +T trans ; Among them, T enc Indicates the encryption processing completion time, T acq Indicates the start time of data collection, T trans Indicates the estimated time it takes for data to be transmitted. The formula for calculating the length of encrypted data is as follows: L enc =L raw +H code +2*K len Among them, L enc Indicates the total length of the encrypted data packet, L raw Indicates the byte length of the original electric energy data, H code Indicates the length of the data check code, K len Indicates the key length; the transmission path calculation formula is as follows: R j ={(N1, C1), (N2, C2),..., (N j ,C j )}; where N J represents the jth node number in the transmission path, C j Represents node N j The output end of the electric energy data acquisition unit is connected to the input end of the encryption unit, the output end of the encryption unit is connected to the input end of the feature extraction unit, and the output end of the feature extraction unit is connected to the input end of the historical feature analysis module.

[0006] The historical feature analysis module includes an abnormal analysis unit and an error threshold calculation unit. The abnormal analysis unit is used for abnormal pattern recognition. The specific process is as follows: Step 1: extract the transmission path, timestamp and encrypted data length of the data packet from the historical records, and establish a feature template library based on the normal transmission path, timestamp range and encrypted data length standard value; Step 2: perform abnormal pattern recognition including transmission path comparison, timestamp deviation evaluation and encrypted data length abnormality. The specific process is as follows: perform transmission path comparison, and convert the current transmission path R j ={(N1, C1), (N2, C2),..., (N j , C j )}Compare with the normal path template in the historical record to check the verification code C of each node j Whether it matches, if the path changes or the node check code does not match, it is marked as "intercepted feature"; evaluate the timestamp feature and calculate the current timestamp T i Deviation from the normal time range. When the delay time exceeds the set range and the path change is abnormal, it is marked as "data interception in unauthorized path"; analyze the encrypted data length and extract the current encrypted data length L enc And compare it with the normal range in the historical records. When it does not match the normal range and the length between nodes is inconsistent, it is marked as "tampering feature"; step three, when the above abnormal pattern is found, the feature deviation of path change node, delay exceeding limit, and abnormal encrypted data length is recorded in the database, and abnormal pattern labels of "interception feature", "data interception in unauthorized path" and "tampering feature" are generated. After each new abnormal pattern is identified, the feature template library is updated; the error threshold calculation unit calculates the error threshold for judging transmission security based on the historical abnormal features, the output end of the abnormal analysis unit is connected to the input end of the error threshold calculation unit, and the output end of the error threshold calculation unit is connected to the input end of the monitoring judgment module.

[0007] The error threshold calculation unit calculates the error threshold for security judgment based on the historical abnormal characteristics. The calculation process is obtained by comprehensive calculation of the encryption characteristics and the transmission path fluctuation coefficient. The encryption characteristic calculation formula is as follows:

[0008]

[0009] Among them, E enc Indicates the encrypted data characteristic parameters, hash(T i ) represents the value after hashing the timestamp, L enc represents the length of encrypted data, λ1 and λ2 represent encryption weight coefficients;

[0010] The calculation formula of transmission path fluctuation coefficient is as follows:

[0011]

[0012] Among them, D path represents the transmission path fluctuation coefficient, m represents the total number of nodes, σ i represents the standard deviation of the rate of change of the electric energy data of the ith node, ρ i represents the transmission delay rate of the i-th node;

[0013] Error threshold T error The calculation formula is as follows:

[0014]

[0015] Among them, ΔP i represents the historical characteristic deviation of the electric energy data of the i-th node, W i represents the weight of the i-th node in the data transmission path, N i represents the node stability coefficient, and θ represents the weighting coefficient.

[0016] The data storage module is used to store the collected historical and real-time electric energy data in encrypted form in the W25M512JV chip.

[0017] The trusted parameter identification module includes a trusted parameter extraction unit and a trust threshold calculation unit. The trusted parameter extraction unit extracts trusted parameters for judging security from encrypted transmission data, including encryption strength and time consistency. The specific process is as follows: the encryption strength is evaluated by analyzing the structure of the encrypted data and the key length, including the number of bits of the key and the complexity of the encryption algorithm, and the encrypted data length L is extracted. enc and the hash result of the encryption algorithm hash(T i ), evaluate the encryption strength, the formula is as follows: S enc =λ*hash(T i )+μ*L enc ; Among them, λ and μ represent weighting coefficients, which are used to adjust the influence of hash and encryption length; by comparing the timestamp of the data packet with the time reference, it is determined whether the data transmission time is within the normal range, and the timestamp T of each data packet is obtained. i and compared with the set time deviation tolerance. consistency If it is within the allowed range, it is recorded as a trusted time point, otherwise it is marked as a potential anomaly; the credibility threshold calculation unit calculates the credibility threshold of the trusted parameter according to the trusted parameter historical data, and the specific process is as follows: According to the encryption strength values ​​of all data packets obtained from the historical data, the trusted encryption strength threshold calculation formula is as follows: Ts = μ S +k*σ S ; where μS represents the mean encryption strength of historical data, k represents the sensitivity adjustment coefficient, σ S Represents the standard deviation of the encryption strength of historical data; based on the timestamp deviation T extracted from the historical data consistency ,The calculation formula of the time consistency credibility threshold is as follows: T T =μ T +h*σ T ; where μ S represents the mean deviation between the current time and the reference time, h represents the adjustment coefficient, σ S Represents the standard deviation of the deviation between the current time and the reference time; the output end of the trusted parameter extraction unit is connected to the input end of the trusted threshold calculation unit, and the output end of the trusted threshold calculation unit is connected to the input end of the monitoring judgment module.

[0018] The monitoring and judgment module includes a data monitoring unit and an error judgment unit. The data monitoring unit extracts the transmission path, timestamp and encrypted data length parameters from the encrypted data packet transmitted by the data acquisition module, and compares them with the abnormal mode in the historical feature analysis module and the encryption strength and time consistency template defined in the trusted parameter extraction unit, identifies the deviation characteristics of the encryption strength, time consistency and transmission path difference in the real-time data, and transmits the feature comparison result to the error judgment unit; the error judgment unit judges the transmission security of the data according to the error threshold provided by the error threshold calculation unit in the historical feature analysis module and the credibility threshold in the trusted parameter identification module. The specific process is as follows: Based on the real-time feature data transmitted by the data monitoring unit, the error judgment unit compares the encryption strength deviation and time consistency deviation of the current data with the preset error threshold to judge whether the transmission data meets the security requirements. When the encryption strength deviation exceeds the error threshold T error , the error judgment unit further checks the encryption strength credibility threshold T calculated in the trusted parameter identification module S ; When the timestamp deviation T consistency Exceeding the time consistency threshold T T , marked as potential abnormal data to determine the transmission security of the data packet; when the error judgment unit determines that a data packet has transmission abnormality or data tampering, the abnormal information is transmitted to the credibility assessment module, the output end of the data monitoring unit is connected to the input end of the error judgment unit, and the output end of the error judgment unit is connected to the input end of the credibility assessment module.

[0019] The credibility evaluation module includes a trusted parameter calculation unit, a credibility evaluation unit and an alarm unit. The trusted parameter calculation unit obtains the encryption strength and time consistency from the trusted parameter extraction unit of the trusted parameter identification module, and obtains the transmission path R from the feature extraction unit of the data acquisition module. j , Encrypted data length L enc and timestamp Ti, combined with the real-time deviation data of the data monitoring unit, the calculation formula of the trusted parameter value is as follows: Ven c =α*S enc +β*T consistency +γ*L enc / T i +δ*Deviation(R j ), where α, β, γ and δ represent weight coefficients, Deviation(R j ) represents the transmission path deviation; the credibility evaluation unit is based on the current credibility parameter value V enc The error threshold T output by the historical feature analysis module and the trusted parameter identification module error , encryption strength threshold T S and the time consistency threshold T T Make a comprehensive judgment, and the credibility calculation formula is as follows: C reliability =V enc / (T error +T S +T T ); when C reliability Greater than or equal to T C , then it is considered that the credibility of the current error warning reaches the credibility threshold, and the warning is judged to be valid. reliability Less than T C , then the lack of credibility is a false alarm; the alarm unit triggers an alarm when the credibility evaluation unit confirms that the warning is valid. When the credibility evaluation result reaches the credibility threshold, the warning is confirmed to be valid, and the alarm unit sends an alarm signal. The output end of the credible parameter calculation unit is connected to the input end of the credibility evaluation unit, and the output end of the credibility evaluation unit is connected to the input end of the alarm unit. Wherein, T C Represents the confidence threshold.

[0020] The control module is connected with the data acquisition module, historical feature analysis module, data storage module, monitoring judgment module and credibility assessment module through the STM32F429 chip, and is used to coordinate the execution of acquisition, analysis and monitoring tasks.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. By integrating data encryption function, the risk of electric energy data being intercepted or tampered with during transmission is prevented, thus improving data security and the reliability of the power system.

[0023] 2. Through the collaborative work of the historical feature analysis module and the trusted parameter identification module, the transmission path, encryption strength and time consistency of the encrypted data packet can be monitored in real time, which can identify potential abnormal data transmission situations, reduce the possibility of false alarms and missed alarms, and improve the monitoring reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The present invention is a schematic structural diagram of an electric energy metering and power monitoring system for real-time monitoring and data analysis. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] like Figure 1 As shown, the present invention provides a technical solution, an electric energy metering and power monitoring system for real-time monitoring and analysis of data, the system comprising: a data acquisition module, a historical feature analysis module, a data storage module, a control module, a trusted parameter identification module, a monitoring and judgment module and a credibility assessment module, the data acquisition module is used to encrypt electric energy data and extract encryption features; the historical feature analysis module is used to analyze the features of historical encrypted data and calculate the error threshold; the data storage module is used to store all encrypted data in a database; the control module is used to coordinate the execution of acquisition, analysis and monitoring tasks; the trusted parameter identification module is used to extract trusted parameters and establish a security credibility threshold; the monitoring and judgment module determines whether there is a transmission error in the data based on the encrypted data and the error threshold; the credibility assessment module is used to evaluate the credibility of the error warning.

[0027] The data acquisition module includes an electric energy data acquisition unit, an encryption processing unit and a feature extraction unit. The electric energy data acquisition unit obtains the electric energy data of voltage, current and power factor of each node through the HT7036 chip. The collected data are numbered according to the nodes connected to the distribution network to form an electric energy data set P i =(V i ,C i ,PF i ), where P i represents the electric energy data set of the ith node, Vi represents the voltage value of the ith node, C i represents the current value of the i-th node, PF i Indicates the power factor value of the i-th node; the encryption processing unit encrypts the collected power data through the SC1161Y chip before data transmission to generate a uniquely identified encrypted data packet D i =(P i ,T i , E.D. i ), where D i Indicates the data packet numbered i, T i Indicates the encryption timestamp of the i-th data packet, ED i Indicates the electric energy parameter P i The encrypted data is stored in the buffer and waits for transmission; the feature extraction unit is used to extract and generate the timestamp of the encrypted data packet, the length of the encrypted data and the data features of the transmission path, which are specifically defined as follows: The timestamp calculation formula is as follows: T i =T enc -T acq +T trans ; Among them, T enc Indicates the encryption processing completion time, T acq Indicates the start time of data collection, T trans Indicates the estimated time it takes for data to be transmitted. The formula for calculating the length of encrypted data is as follows: L enc =L raw +H code +2*K len Among them, L enc Indicates the total length of the encrypted data packet, L raw Indicates the byte length of the original electric energy data, H code Indicates the length of the data check code, K len Indicates the key length; the transmission path calculation formula is as follows: R j ={(N1, C1), (N2, C2),..., (N j ,C j )}; where N j represents the jth node number in the transmission path, C j Represents node N j The output end of the electric energy data acquisition unit is connected to the input end of the encryption unit, the output end of the encryption unit is connected to the input end of the feature extraction unit, and the output end of the feature extraction unit is connected to the input end of the historical feature analysis module.

[0028] The historical feature analysis module includes an anomaly analysis unit and an error threshold calculation unit. The anomaly analysis unit is used for abnormal pattern recognition. The specific process is as follows: Step 1: extract the transmission path, timestamp and encrypted data length of the data packet from the historical records, and establish a feature template library based on the normal transmission path, timestamp range and encrypted data length standard value; Step 2: perform abnormal pattern recognition including transmission path comparison, timestamp deviation evaluation and encrypted data length abnormality. The specific process is as follows: perform transmission path comparison, and convert the current transmission path R j ={(N1, C1), (N2, C2),..., (N j , C j )}Compare with the normal path template in the historical record to check the verification code C of each node j Whether it matches, if the path changes or the node check code does not match, it is marked as "intercepted feature"; evaluate the timestamp feature and calculate the current timestamp T i Deviation from the normal time range. When the delay time exceeds the set range and the path change is abnormal, it is marked as "data interception in unauthorized path"; analyze the encrypted data length and extract the current encrypted data length L enc And compare it with the normal range in the historical records. When it does not match the normal range and the length between nodes is inconsistent, it is marked as "tampering feature"; step three, when the above abnormal pattern is found, the feature deviation of path change node, delay exceeding limit, and abnormal encrypted data length is recorded in the database, and abnormal pattern labels of "interception feature", "data interception in unauthorized path" and "tampering feature" are generated. After each new abnormal pattern is identified, the feature template library is updated; the error threshold calculation unit calculates the error threshold for judging transmission security based on the historical abnormal features, the output end of the abnormal analysis unit is connected to the input end of the error threshold calculation unit, and the output end of the error threshold calculation unit is connected to the input end of the monitoring judgment module.

[0029] The error threshold calculation unit calculates the error threshold for security judgment based on historical abnormal characteristics. The calculation process is obtained by comprehensive calculation of encryption characteristics and transmission path fluctuation coefficient. The encryption characteristic calculation formula is as follows:

[0030]

[0031] Among them, E enc Indicates the encrypted data characteristic parameters, hash(T i ) represents the value after hashing the timestamp, L enc represents the length of encrypted data, λ1 and λ2 represent encryption weight coefficients;

[0032] The calculation formula of transmission path fluctuation coefficient is as follows:

[0033]

[0034] Among them, D path represents the transmission path fluctuation coefficient, m represents the total number of nodes, σ i represents the standard deviation of the rate of change of the electric energy data of the ith node, ρ i represents the transmission delay rate of the i-th node;

[0035] Error threshold T error The calculation formula is as follows:

[0036]

[0037] Among them, ΔP i represents the historical characteristic deviation of the electric energy data of the i-th node, W i represents the weight of the i-th node in the data transmission path, N i represents the node stability coefficient, and θ represents the weighting coefficient.

[0038] The data storage module is used to store the collected historical and real-time electric energy data in encrypted form in the W25M512JV chip.

[0039] The trusted parameter identification module includes a trusted parameter extraction unit and a trust threshold calculation unit. The trusted parameter extraction unit extracts trusted parameters for judging security from encrypted transmission data, including encryption strength and time consistency. The specific process is as follows: the encryption strength is evaluated by analyzing the structure of the encrypted data and the key length, including the number of bits of the key and the complexity of the encryption algorithm, and the encrypted data length L is extracted. enc and the hash result of the encryption algorithm hash(T i ), evaluate the encryption strength, the formula is as follows: S enc =λ*hash(T i )+μ*L enc ; Among them, λ and μ represent weighting coefficients, which are used to adjust the influence of hash and encryption length; by comparing the timestamp of the data packet with the time reference, it is determined whether the data transmission time is within the normal range, and the timestamp T of each data packet is obtained. i and compared with the set time deviation tolerance. consistency If it is within the allowed range, it is recorded as a trusted time point, otherwise it is marked as a potential anomaly; the credibility threshold calculation unit calculates the credibility threshold of the trusted parameter according to the trusted parameter historical data, and the specific process is as follows: According to the encryption strength values ​​of all data packets obtained from the historical data, the trusted encryption strength threshold calculation formula is as follows: Ts = μ S +k*σ S ; where μs represents the mean encryption strength of historical data, k represents the sensitivity adjustment coefficient, σ S Represents the standard deviation of the encryption strength of historical data; based on the timestamp deviation T extracted from the historical data consistency ,The calculation formula of the time consistency credibility threshold is as follows: T T =μ T +h*σ T ; where μ S represents the mean deviation between the current time and the reference time, h represents the adjustment coefficient, σ S Represents the standard deviation of the deviation between the current time and the reference time; the output end of the trusted parameter extraction unit is connected to the input end of the trusted threshold calculation unit, and the output end of the trusted threshold calculation unit is connected to the input end of the monitoring judgment module.

[0040] The monitoring and judgment module includes a data monitoring unit and an error judgment unit. The data monitoring unit extracts the transmission path, timestamp and encrypted data length parameters from the encrypted data packet transmitted by the data acquisition module, and compares them with the abnormal mode in the historical feature analysis module and the encryption strength and time consistency template defined in the trusted parameter extraction unit, identifies the deviation characteristics of the encryption strength, time consistency and transmission path difference in the real-time data, and transmits the feature comparison result to the error judgment unit; the error judgment unit judges the transmission security of the data according to the error threshold provided by the error threshold calculation unit in the historical feature analysis module and the credibility threshold in the trusted parameter identification module. The specific process is as follows: Based on the real-time feature data transmitted by the data monitoring unit, the error judgment unit compares the encryption strength deviation and time consistency deviation of the current data with the preset error threshold to judge whether the transmission data meets the security requirements. When the encryption strength deviation exceeds the error threshold T error , the error judgment unit further checks the encryption strength credibility threshold T calculated in the trusted parameter identification module S ; When the timestamp deviation T consistency Exceeding the time consistency threshold T T , marked as potential abnormal data to determine the transmission security of the data packet; when the error judgment unit determines that a data packet has transmission abnormality or data tampering, the abnormal information is transmitted to the credibility assessment module, the output end of the data monitoring unit is connected to the input end of the error judgment unit, and the output end of the error judgment unit is connected to the input end of the credibility assessment module.

[0041] The credibility evaluation module includes a trusted parameter calculation unit, a credibility evaluation unit and an alarm unit. The trusted parameter calculation unit obtains the encryption strength and time consistency from the trusted parameter extraction unit of the trusted parameter identification module, and obtains the transmission path R from the feature extraction unit of the data acquisition module.i , Encrypted data length L enc and timestamp Ti, combined with the real-time deviation data of the data monitoring unit, the calculation formula of the trusted parameter value is as follows: V enc =α*S enc +β*T consistency +γ*L enc / T i +δ*Deviation(R j ), where α, β, γ and δ represent weight coefficients, Deviation(R j ) represents the transmission path deviation; the credibility evaluation unit is based on the current credibility parameter value V enc The error threshold T output by the historical feature analysis module and the trusted parameter identification module error , encryption strength threshold T S and the time consistency threshold T T Make a comprehensive judgment, and the credibility calculation formula is as follows: C reliability =V enc / (T error +T S +T T ); when C reliability Greater than or equal to T C , then it is considered that the credibility of the current error warning reaches the credibility threshold, and the warning is judged to be valid. reliability Less than T c , then the lack of credibility is a false alarm; the alarm unit triggers an alarm when the credibility evaluation unit confirms that the warning is valid. When the credibility evaluation result reaches the credibility threshold, the warning is confirmed to be valid, and the alarm unit sends an alarm signal. The output end of the credible parameter calculation unit is connected to the input end of the credibility evaluation unit, and the output end of the credibility evaluation unit is connected to the input end of the alarm unit. Wherein, T C Represents the confidence threshold.

[0042] The control module is connected with the data acquisition module, the historical feature analysis module, the data storage module, the monitoring judgment module and the credibility assessment module through the STM32F429 chip, and is used for coordinating the execution of the acquisition, analysis and monitoring tasks.

[0043] In the embodiment: the HT7036 chip collects voltage, current and power factor data to form an electric energy data set P1=(220, 5, 0.95), P2=(218, 7, 0.92), the SC1161Y chip encrypts the collected data to generate uniquely identified data packets D1=(P1, T1, ED1) and D2=(P2, T2, ED2), extracts features from the encrypted data packets, and generates a uniquely identified data packet D1=(P1, T1, ED1) and D2=(P2, T2, ED2) according to the encryption completion time T. enc =10:00:02, collection start time Tacq = 10:00:00 and expected time T trans = 1 second, the time stamp T1 = 3 seconds, according to the original data length L raw =128 bytes, verification code length H code =4 bytes, key length K len = 16 bytes, the packet length L is obtained enc =164 bytes, extract the transmission path, timestamp and encrypted data length from the historical data, establish a normal transmission feature template library, compare the current data packet D1, analyze the abnormal data, and when an abnormal pattern is found, record the feature deviations of path change nodes, delay exceeding the limit, and abnormal encrypted data length into the database, generate abnormal pattern labels of "interception feature", "data interception in unauthorized path" and "tampering feature", and update the feature template library every time a new abnormal pattern is identified. According to the encryption weight coefficients λ1=0.5, λ2=0.1, the timestamp is hashed and the hash (T i )=45, calculate the encryption characteristic parameter E enc =2689.6; Based on the total number of nodes m=2, the standard deviation of the node's power data change rate σ1=0.02, σ2=0.03, the transmission delay rate p1=0.01, ρ2=0.015, calculate the transmission path fluctuation coefficient D path =0.0375; Based on the historical power data deviation ΔP i =0.02, node weight W i =1.2, node stability coefficient N i =1.5, weighting coefficient θ = 0.001, calculate the error threshold T error =72.15, from the encrypted data length L enc =164 and timestamp to extract trusted parameters, and calculate the encryption strength S according to λ=0.5 and μ=0.1 enc =2689.6, compared with the timestamp T of the data packet i = 3 seconds and the benchmark time, the time consistency threshold is set to T T = 5 seconds, the timestamp deviation of this data packet is within the allowable range. According to α = 0.04, β = 0.3, γ = 0.2, δ = 0.1, the transmission path deviation Deviation (R j )=0.05, calculate the credible parameter V enc =119.42, at this time the credible parameter V enc If the error is greater than the error threshold, it is marked as a "potential anomaly" and the encryption strength threshold T is used. S =2800, calculate the credibility C reliability =0.368, compared with C reliability <C standard=1, the warning is not credible enough and is a false alarm.

[0044] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An electric energy metering and power monitoring system for real-time monitoring and data analysis, characterized in that: The system includes: a data acquisition module, a historical feature analysis module, a data storage module, a control module, a trusted parameter identification module, a monitoring and judgment module and a credibility assessment module, wherein the data acquisition module is used to encrypt electric energy data and extract encryption features; the historical feature analysis module is used to analyze the features of historical encrypted data and calculate an error threshold; the data storage module is used to store all encrypted data in a database; the control module is used to coordinate the execution of acquisition, analysis and monitoring tasks; the trusted parameter identification module is used to extract trusted parameters and establish a security credibility threshold; the monitoring and judgment module determines whether there is a transmission error in the data based on the encrypted data and the error threshold; and the credibility assessment module is used to evaluate the credibility of the error warning.

2. The electric energy metering and power monitoring system for real-time monitoring and data analysis according to claim 1 is characterized in that: The data acquisition module includes an electric energy data acquisition unit, an encryption processing unit and a feature extraction unit. The electric energy data acquisition unit obtains the electric energy data of voltage, current and power factor of each node through the HT7036 chip. The collected data is numbered according to the nodes connected to the distribution network to form an electric energy data set P i =(V i ,C i ,PF i ), where P i represents the electric energy data set of the ith node, V i represents the voltage value of the ith node, C i represents the current value of the i-th node, PF i Indicates the power factor value of the i-th node; the encryption processing unit encrypts the collected power data through the SC1161Y chip before data transmission to generate a uniquely identified encrypted data packet D i =(P i ,T i ,ED i ), where D i Indicates the data packet numbered i, T i Indicates the encryption timestamp of the i-th data packet, ED i Indicates the electric energy parameter P i The encrypted data is stored in the buffer and waits for transmission; the feature extraction unit is used to extract and generate the timestamp of the encrypted data packet, the length of the encrypted data and the data features of the transmission path, which are specifically defined as follows: The timestamp calculation formula is as follows: T i =T enc -T acq +T trans ; Among them, T enc Indicates the encryption processing completion time, T acq Indicates the start time of data collection, T trans Indicates the estimated time it takes for data to be transmitted. The formula for calculating the length of encrypted data is as follows: L enc =L raw +H code +2*K len Among them, L enc Indicates the total length of the encrypted data packet, L raw Indicates the byte length of the original electric energy data, H code Indicates the length of the data check code, K len Indicates the key length; the transmission path calculation formula is as follows: R j ={(N1, C1), (N2, C2),..., (N j , C j )}; where N j represents the jth node number in the transmission path, C j Represents node N j The output end of the electric energy data acquisition unit is connected to the input end of the encryption unit, the output end of the encryption unit is connected to the input end of the feature extraction unit, and the output end of the feature extraction unit is connected to the input end of the historical feature analysis module.

3. The electric energy metering and power monitoring system for real-time monitoring and data analysis according to claim 1 is characterized in that: The historical feature analysis module includes an abnormal analysis unit and an error threshold calculation unit. The abnormal analysis unit is used for abnormal pattern recognition. The specific process is as follows: Step 1: extract the transmission path, timestamp and encrypted data length of the data packet from the historical records, and establish a feature template library based on the normal transmission path, timestamp range and encrypted data length standard value; Step 2: perform abnormal pattern recognition including transmission path comparison, timestamp deviation evaluation and encrypted data length abnormality. The specific process is as follows: perform transmission path comparison, and convert the current transmission path R j ={(N1, C1), (N2, C2),..., (N j , C j )}Compare with the normal path template in the historical record to check the verification code C of each node j Whether it matches or not, if the path is changed or the node check code does not match, it is marked as "intercepted feature"; evaluation Timestamp feature, calculate the current timestamp T i Deviation from the normal time range. When the delay time exceeds the set range and the path change is abnormal, it is marked as "data interception in unauthorized path"; analyze the encrypted data length and extract the current encrypted data length L enc And compare it with the normal range in the historical records. When it does not match the normal range and the length between nodes is inconsistent, it is marked as "tampering feature"; step three, when the above abnormal pattern is found, the feature deviation of path change node, delay exceeding limit, and abnormal encrypted data length is recorded in the database, and abnormal pattern labels of "interception feature", "data interception in unauthorized path" and "tampering feature" are generated. After each new abnormal pattern is identified, the feature template library is updated; the error threshold calculation unit calculates the error threshold for judging transmission security based on the historical abnormal features, the output end of the abnormal analysis unit is connected to the input end of the error threshold calculation unit, and the output end of the error threshold calculation unit is connected to the input end of the monitoring judgment module.

4. The electric energy metering and power monitoring system for real-time monitoring and data analysis according to claim 3 is characterized in that: The error threshold calculation unit calculates the error threshold for security judgment based on the historical abnormal characteristics. The calculation process is obtained by comprehensive calculation of the encryption characteristics and the transmission path fluctuation coefficient. The encryption characteristic calculation formula is as follows: Among them, E enc Indicates the encrypted data characteristic parameters, hash(T i ) represents the value after hashing the timestamp, L enc represents the length of encrypted data, λ1 and λ2 represent encryption weight coefficients; The calculation formula of transmission path fluctuation coefficient is as follows: Among them, D path represents the transmission path fluctuation coefficient, m represents the total number of nodes, σ i represents the standard deviation of the rate of change of the electric energy data of the ith node, ρ i represents the transmission delay rate of the i-th node; Error threshold T error The calculation formula is as follows: Where ΔP i represents the historical characteristic deviation of the electric energy data of the i-th node, W i represents the weight of the i-th node in the data transmission path, N i represents the node stability coefficient, and θ represents the weighting coefficient.

5. The electric energy metering and power monitoring system for real-time monitoring and data analysis according to claim 1 is characterized in that: The data storage module is used to store the collected historical and real-time electric energy data in encrypted form in the W25M512JV chip.

6. The electric energy metering and power monitoring system for real-time monitoring and data analysis according to claim 1 is characterized in that: The trusted parameter identification module includes a trusted parameter extraction unit and a trust threshold calculation unit. The trusted parameter extraction unit extracts trusted parameters for judging security from encrypted transmission data, including encryption strength and time consistency. The specific process is as follows: the encryption strength is evaluated by analyzing the structure of the encrypted data and the key length, including the number of bits of the key and the complexity of the encryption algorithm, and the encrypted data length L is extracted. enc and the hash result of the encryption algorithm hash(T i ), evaluate the encryption strength, the formula is as follows: S enc =λ*hash(T i )+μ*L enc ; Among them, λ and μ represent weighting coefficients, which are used to adjust the influence of hash and encryption length; by comparing the timestamp of the data packet with the time reference, it is determined whether the data transmission time is within the normal range, and the timestamp T of each data packet is obtained. i and compared with the set time deviation tolerance. consistency If it is within the allowed range, it is recorded as a trusted time point, otherwise it is marked as a potential anomaly; the credibility threshold calculation unit calculates the credibility threshold of the trusted parameter according to the trusted parameter historical data, and the specific process is as follows: According to the encryption strength values ​​of all data packets obtained from the historical data, the trusted encryption strength threshold calculation formula is as follows: Ts = μ S +k*σ S ; where μ S represents the mean encryption strength of historical data, k represents the sensitivity adjustment coefficient, σ S Represents the standard deviation of the encryption strength of historical data; based on the timestamp deviation T extracted from the historical data consistency ,The calculation formula of the time consistency credibility threshold is as follows: T T =μ T +h*σ T ; where μ S represents the mean deviation between the current time and the reference time, h represents the adjustment coefficient, σ S Represents the standard deviation of the deviation between the current time and the reference time; the output end of the trusted parameter extraction unit is connected to the input end of the trusted threshold calculation unit, and the output end of the trusted threshold calculation unit is connected to the input end of the monitoring judgment module.

7. The electric energy metering and power monitoring system for real-time monitoring and data analysis according to claim 1 is characterized in that: The monitoring and judgment module includes a data monitoring unit and an error judgment unit. The data monitoring unit extracts the transmission path, timestamp and encrypted data length parameters from the encrypted data packet transmitted by the data acquisition module, and compares them with the abnormal mode in the historical feature analysis module and the encryption strength and time consistency template defined in the trusted parameter extraction unit, identifies the deviation characteristics of the encryption strength, time consistency and transmission path difference in the real-time data, and transmits the feature comparison result to the error judgment unit; the error judgment unit judges the transmission security of the data according to the error threshold provided by the error threshold calculation unit in the historical feature analysis module and the credibility threshold in the trusted parameter identification module. The specific process is as follows: Based on the real-time feature data transmitted by the data monitoring unit, the error judgment unit compares the encryption strength deviation and time consistency deviation of the current data with the preset error threshold to judge whether the transmission data meets the security requirements. When the encryption strength deviation exceeds the error threshold T error , the error judgment unit further checks the encryption strength credibility threshold T calculated in the trusted parameter identification module S ; When the timestamp deviation T consistency Exceeding the time consistency threshold T T , marked as potential abnormal data to determine the transmission security of the data packet; when the error judgment unit determines that a data packet has transmission abnormality or data tampering, the abnormal information is transmitted to the credibility assessment module, the output end of the data monitoring unit is connected to the input end of the error judgment unit, and the output end of the error judgment unit is connected to the input end of the credibility assessment module.

8. The electric energy metering and power monitoring system for real-time monitoring and data analysis according to claim 1 is characterized in that: The credibility evaluation module includes a trusted parameter calculation unit, a credibility evaluation unit and an alarm unit. The trusted parameter calculation unit obtains the encryption strength and time consistency from the trusted parameter extraction unit of the trusted parameter identification module, and obtains the transmission path R from the feature extraction unit of the data acquisition module. j , Encrypted data length L enc and timestamp Ti, combined with the real-time deviation data of the data monitoring unit, the calculation formula of the trusted parameter value is as follows: V enc =α*S enc +β*T consistency +γ*L enc / T i +δ*Deviation(R j ), where α, β, γ and δ represent weight coefficients, Deviation(R j ) represents the transmission path deviation; the credibility evaluation unit is based on the current credibility parameter value V enc The error threshold T output by the historical feature analysis module and the trusted parameter identification module error , encryption strength threshold T s and the time consistency threshold T T Make a comprehensive judgment, and the credibility calculation formula is as follows: C reliability =V enc / (T error +T S +T T ); when C reliability Greater than or equal to T C , then it is considered that the credibility of the current error warning reaches the credibility threshold, and the warning is judged to be valid. reliability Less than T C , then the lack of credibility is a false alarm; the alarm unit triggers an alarm when the credibility evaluation unit confirms that the warning is valid. When the credibility evaluation result reaches the credibility threshold, the warning is confirmed to be valid, and the alarm unit sends an alarm signal. The output end of the credible parameter calculation unit is connected to the input end of the credibility evaluation unit, and the output end of the credibility evaluation unit is connected to the input end of the alarm unit. Wherein, T C Represents the confidence threshold.

9. The electric energy metering and power monitoring system for real-time monitoring and data analysis according to claim 1 is characterized in that: The control module is connected with the data acquisition module, historical feature analysis module, data storage module, monitoring judgment module and credibility assessment module through the STM32F429 chip, and is used to coordinate the execution of acquisition, analysis and monitoring tasks.

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