Measurement acquisition equipment information flow monitoring system based on virtualization technology

Abstract

The invention discloses a measurement acquisition equipment information flow monitoring system based on a virtualization technology, which relates to the technical field of measurement information monitoring and comprises an acquisition terminal, an operation sensing module, a communication uploading module, a data preprocessing module, a data flow analysis module and a log management module. And the acquisition terminal is used for monitoring and acquiring the flow information of the equipment information, and the acquired data of the acquisition terminal is divided into a first type of data, a second type of data and a third type of data according to time attributes. According to the invention, the online process of the uploading module is intelligently analyzed, and is concisely displayed on a terminal liquid crystal, so that operation and maintenance personnel can conveniently check and process on site, the pertinence of terminal operation and maintenance and the efficiency of on-site troubleshooting are improved, and information such as terminal version information, software and hardware information, batch information, manufacturer information and the like is collected in combination. Big data analysis is carried out on information sensed by the acquisition terminal, and field terminal operation quality evaluation is given from multiple dimensions, so that development of work such as acquisition terminal replacement and quality evaluation is supported.

Description

Technical Field

[0001] The present invention relates to the technical field of metering information monitoring, and in particular to a metering collection equipment information flow monitoring system based on virtualization technology. Background Art

[0002] With the high proportion of new energy access, the control mode of the power system has changed from large-capacity centralized control of large units to large-scale decentralized control of small units, and the difficulty of operation monitoring and control has increased significantly. It is very necessary to conduct research on distributed new energy monitoring technology; according to the characteristics and needs of photovoltaic grid-connected power generation systems, in order to improve power generation efficiency, improve power quality, and improve monitoring functions, an integrated group control strategy is proposed to uniformly monitor and coordinate the control of large-capacity photovoltaic arrays and multi-inverter groups.

[0003] At the same time, it is necessary to develop primary and secondary fusion terminal equipment based on domestic platforms that integrates new communications, edge computing, information perception and modularization to realize intelligent collection, analysis and control of distributed photovoltaic systems.

[0004] At present, distributed photovoltaics generally only connect to one system at a time; use plant terminals and other equipment to connect to the metering system; use distribution transformer terminals and other equipment to connect to the distribution system to measure the electricity consumption data obtained by the distribution system; generally use concentrators or dedicated transformers to connect to this system; and currently the monitoring and control system for information flow is relatively simple, and abnormal data still mainly relies on human judgment, and real-time information flow data collection operations cannot be achieved. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a metering and collection equipment information flow monitoring system based on virtualization technology, which solves the problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a metering collection equipment information flow monitoring system based on virtualization technology, including a collection terminal, an operation perception module, a communication upload module, a data preprocessing module, a data flow analysis module, and a log management module;

[0007] The collection terminal is used to monitor and collect equipment information flow information. The collected data is divided into Class 1 data, Class 2 data and Class 3 data according to time attributes;

[0008] in,

[0009] Category 1 data includes real-time data and current data; Category 1 data is used to reflect the current real-time power consumption information of power users;

[0010] The second type of data includes historical single-day data and historical single-month data;

[0011] The three types of data include event data and abnormal data, among which the abnormal data include current loop abnormality, voltage loop abnormality, phase sequence abnormality, active total electric energy differential over-limit event record, voltage over-limit record, current over-limit record, electric energy indication degree drop, and detailed information of electric energy over-difference;

[0012] The running perception unit is used to provide data support for the information flow monitoring system, so as to make decisions based on the information provided by the perception module. The Docker system is used in the running perception unit;

[0013] The communication upload module is used to collect terminal uplink communication data and record at least 3 days of signal strength curve data with a density of 1 minute; obtain information on network standard, cell number, signal strength, bit error rate, receiving power, receiving quality, signal-to-noise ratio and current frequency to form curve data, and analyze the signal operation quality from different dimensions. The above information can be queried through the local terminal;

[0014] The data preprocessing module is used to clean the data, remove noise from the data, repair missing values, handle outliers, establish accurate models, and provide standardization and normalization operations to convert the data to a uniform specification or a specific distribution. At the same time, it processes the missing data in the collection process of the telecommunications system; among them, the missing attributes are used as dependent variables, and other attributes are used as independent variables, and the n-order polynomial approximation method is used to interpolate missing values;

[0015] The data flow analysis module is used to collect data from the route request to the route reporting data, classify the meter collection situation in the substation, monitor the collection status of the electric energy meter, and give a comprehensive evaluation report on the meter reading situation in the substation; according to the monitoring customer's electric parameter information and the power consumption abnormality monitoring model, compare the electric parameter information collected by different methods at the same metering point, or compare the real-time information and historical electric parameter information of the same metering point, and judge whether the customer's power consumption is stable according to the comparison curve value difference and trend difference. If an abnormality is found, start the abnormal handling process, carry out continuous monitoring of other parameters of the customer, and according to the suspected power theft prediction model, make a unified analysis and judgment of the monitored information to set the "power theft probability value";

[0016] The log management module is a component used to record, store, analyze and report log information generated during system operation.

[0017] Optionally, the data collected in the collection terminal is divided into the following types according to physical properties:

[0018] (1) Electric energy data, specifically total electric energy, electric energy at each rate, and maximum demand;

[0019] (2) AC analog quantities, specifically voltage, current, active power, reactive power, and power factor;

[0020] (3) Operating condition data, specifically switch status, terminal and metering equipment operating condition information;

[0021] (4) Statistical data on power quality exceeding limits, specifically voltage, power factor, and harmonics of metering equipment;

[0022] (5) Event record data, specifically terminal record data and event record data of electric energy meters.

[0023] Optionally, the terminal records data as follows:

[0024] 1. Terminal power-off information, terminal power-on time, terminal power-off time, and continuous working time information;

[0025] 2. Reasons for the terminal restart: power failure, upgrade, CPU abnormality, program abnormality, reset command;

[0026] 3. Number and time of terminal restarts;

[0027] 4. Resource usage of the terminal: CPU, RAM and Flash utilization, storage density is set to 1min, and the minimum storage value is set to 72h according to the time limit;

[0028] 5. Working status of the terminal module: whether the upstream and downstream modules, backup batteries are valid, whether the clock battery is undervoltage, metering module, encryption chip; used to record the time nodes of the last three normal and abnormal occurrences.

[0029] Optionally, the Docker system includes three monitoring commands, namely, ps, top, and stats;

[0030] (1) ps, used to understand the status of the currently running container;

[0031] Run the command as follows: [root@host1~]#Docker_ps;

[0032] (2) top, used to locate the status of processes running in the container;

[0033] Run the command as follows: [root@host1~]#Dockertop266910b9b;

[0034] (3) Stats, used to display the usage of various resources of each container, using dynamic refresh;

[0035] Run the following command: [root@host1~]#Dockerstats266910b9b;

[0036] In this way, the above information is recorded periodically in the host machine, and a line chart of the data is dynamically generated. When an abnormality occurs in the container, the abnormality is recorded and the cause of the abnormality is analyzed;

[0037] In addition, it is also used to record and store power-on information to obtain complete terminal power-off information, terminal power-on time, terminal power-off time, and continuous working time information.

[0038] Optionally, the local terminal uses a liquid crystal display device, and the displayed content includes:

[0039] (1) The power-on success signal is displayed;

[0040] (2) Obtain communication module version information;

[0041] (3) Obtain the SIM card serial number;

[0042] (4) Obtaining signal strength data;

[0043] (5) Obtain current network registration status information;

[0044] (6) Obtaining remote communication module type data;

[0045] (7) Set network connection initialization parameters, including APN, user name, and registration password;

[0046] (8) Status information of activated network connection;

[0047] (9) Set the client service parameters, including network type, master station IP and port.

[0048] Optionally, the steps of interpolating missing attributes of active power parameters in the data preprocessing module are as follows:

[0049] Step 1: Select the 3rd (or 5th) order fitting equation; suppose the function fitting equation between the active power parameter x and the attribute time 1 is:

[0050] x(t i )=a0+a1t i +a2t i 2 +a3t i 3 +……+a5t i 5

[0051] Step 2: Select N data before and after the missing active power data [xn, tn]:

[0052]

[0053] Substitute [xi, ti] into the equation of the first step of fitting, and solve a0, a1, a2, a3 (taking the third-order fitting equation as an example) as the unknown coefficients of the fitting equation;

[0054] Step 3: According to the principle of least squares method, the mean square error of the interpolated [xi, ti] values ​​is minimized, and the coefficients a0, a1, a2, and a3 are solved, that is, the following mean square error equation:

[0055]

[0056] For coefficients a0, a1, a2, a3, find the minimum derivative and establish the matrix:

[0057]

[0058] The fourth step is to optimize the function structure in step one, input the missing attribute parameter value tn, and calculate the active power missing attribute data [xn, tn].

[0059] Optionally, the log management module specifically includes:

[0060] Record logs to record key information during system operation, including user operations and system events;

[0061] Store logs and save log information to different target domains, including log files and databases;

[0062] Analyze logs and provide log analysis functions to help operation and maintenance personnel find potential problems and trends;

[0063] Report logs, generate log reports, and provide statistical information on system operation status and security events.

[0064] The present invention provides a metering collection equipment information flow monitoring system based on virtualization technology, which has the following beneficial effects:

[0065] The information flow monitoring system for metering and collection equipment based on virtualization technology combines the version information, software and hardware information, batch information, manufacturer information and other information of the collection terminal with the self-information perceived by the collection terminal to conduct big data analysis, and gives the on-site terminal operation quality evaluation from multiple dimensions to support the replacement of the collection terminal, quality evaluation and other work;

[0066] The use of virtualization technology can break the barrier of traditional hardware that cannot be divided into blocks. It can be combined at will according to the usage scenario, and the hardware resources can be used more efficiently and reasonably. The virtualized computing resources are not restricted by the hardware region or physical configuration mode, and can be configured more flexibly. The introduction of virtualization technology avoids the stacking of a large number of server hardware resources, simplifies the complexity and management difficulty of networking, and reduces procurement and operation and maintenance costs; therefore, the new power information interaction equipment has irreplaceable advantages over traditional terminals in terms of its own state perception and judgment. By utilizing the self-perception of the new power information interaction equipment, the terminal can record its own data, such as: the number of terminal restarts, the reason for the terminal restart, the terminal resource usage, whether the peripheral equipment is working normally, whether the APP is running normally, and the analysis and prediction of possible bugs in the APP. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0068] The present invention provides a technical solution: a metering collection equipment information flow monitoring system based on virtualization technology, including a collection terminal, an operation perception module, a communication upload module, a data preprocessing module, a data flow analysis module, and a log management module;

[0069] The collection terminal is used to monitor and collect equipment information flow information. The collected data is divided into Class 1 data, Class 2 data and Class 3 data according to time attributes;

[0070] in,

[0071] Category 1 data includes real-time data and current data; Category 1 data is used to reflect the current real-time power consumption information of power users;

[0072] The second type of data includes historical single-day data and historical single-month data;

[0073] The three types of data include event data and abnormal data, among which the abnormal data include current loop abnormality, voltage loop abnormality, phase sequence abnormality, active total electric energy differential over-limit event record, voltage over-limit record, current over-limit record, electric energy indication degree drop, and detailed information of electric energy over-difference;

[0074] The data collected in the acquisition terminal are divided into the following categories according to their physical properties:

[0075] (1) Electric energy data, specifically total electric energy, electric energy at each rate, and maximum demand;

[0076] (2) AC analog quantities, specifically voltage, current, active power, reactive power, and power factor;

[0077] (3) Operating condition data, specifically switch status, terminal and metering equipment operating condition information;

[0078] (4) Statistical data on power quality exceeding limits, specifically voltage, power factor, and harmonics of metering equipment;

[0079] (5) Event record data, specifically terminal record data and event record data of electric energy meters.

[0080] The terminal records the data as follows:

[0081] 1. Terminal power-off information, terminal power-on time, terminal power-off time, and continuous working time information;

[0082] 2. Reasons for the terminal restart: power failure, upgrade, CPU abnormality, program abnormality, reset command;

[0083] 3. Number and time of terminal restarts;

[0084] 4. Resource usage of the terminal: CPU, RAM and Flash utilization, storage density is set to 1min, and the minimum storage value is set to 72h according to the time limit;

[0085] 5. Working status of the terminal module: whether the upstream and downstream modules, backup batteries are valid, whether the clock battery is undervoltage, metering module, encryption chip; used to record the time nodes of the last three normal and abnormal occurrences;

[0086] The running perception unit is used to provide data support for the information flow monitoring system, so as to make decisions based on the information provided by the perception module. The Docker system is used in the running perception unit;

[0087] The Docker system includes three monitoring commands, namely ps, top, and stats;

[0088] (1) ps, used to understand the status of the currently running container;

[0089] Run the command as follows: [root@host1~]#Docker_ps;

[0090]

[0091]

[0092] (2) top, used to locate the status of processes running in the container;

[0093] Run the command as follows: [root@host1~]#Dockertop266910b9b;

[0094] UID PID PPID C STIME TTY TIME CMD ROOT 7162 7144 0 14：55 pts / 0 00：00：00 sh

[0095] (3) Stats, used to display the usage of various resources of each container, using dynamic refresh;

[0096] Run the following command: [root@host1~]#Dockerstats266910b9b;

[0097] CONTAINERID 266910b9bede NAME b3 CPU% 0.00％ MEMUSAGE / LIMIT 52KiB / 487.5MiB MEM% 0.01％ NETI / O 978B / 280B BLOCKI / O 1.97MB / 8.19kB PIDS 1

[0098] In this way, the above information is recorded periodically in the host machine, and a line chart of the data is dynamically generated. When an abnormality occurs in the container, the abnormality is recorded and the cause of the abnormality is analyzed;

[0099] In addition, it is used to record and store power-on information, obtain complete terminal power-off information, terminal power-on time, terminal power-off time, and continuous working time information;

[0100] The communication upload module is used to collect terminal uplink communication data and record at least 3 days of signal strength curve data with a density of 1 minute; obtain information on network standard, cell number, signal strength, bit error rate, receiving power, receiving quality, signal-to-noise ratio and current frequency to form curve data, and analyze the signal operation quality from different dimensions. The above information can be queried through the local terminal;

[0101] The local terminal uses a liquid crystal display device, and the display content includes:

[0102] (1) The power-on success signal is displayed;

[0103] (2) Obtain communication module version information;

[0104] (3) Obtain the SIM card serial number;

[0105] (4) Obtaining signal strength data;

[0106] (5) Obtain current network registration status information;

[0107] (6) Obtaining remote communication module type data;

[0108] (7) Set network connection initialization parameters, including APN, user name, and registration password;

[0109] (8) Status information of activated network connection;

[0110] (9) Set the client service parameters, including network type, master station IP and port;

[0111] The data preprocessing module is used to clean the data, remove noise from the data, repair missing values, handle outliers, establish accurate models, and provide standardization and normalization operations to convert the data to a uniform specification or a specific distribution. At the same time, it processes the missing data in the collection process of the telecommunications system; among them, the missing attributes are used as dependent variables, and other attributes are used as independent variables, and the n-order polynomial approximation method is used to interpolate missing values;

[0112] The steps for interpolating missing attributes of active power parameters in the data preprocessing module are as follows:

[0113] Step 1: Select the 3rd (or 5th) order fitting equation; suppose the function fitting equation between the active power parameter x and the attribute time 1 is:

[0114] x(t i )=a0+a1t i +a2t i 2 +a3t i 3 +……+a5t i 5

[0115] Step 2: Select N data before and after the missing active power data [xn, tn]:

[0116]

[0117] Substitute [xi, ti] into the equation of the first step of fitting, and solve a0, a1, a2, a3 (taking the third-order fitting equation as an example) as the unknown coefficients of the fitting equation;

[0118] Step 3: According to the principle of least squares method, the mean square error of the interpolated [xi, ti] values ​​is minimized, and the coefficients a0, a1, a2, and a3 are solved, that is, the following mean square error equation:

[0119]

[0120] For coefficients a0, a1, a2, a3, find the minimum derivative and establish the matrix:

[0121]

[0122] The fourth step is to optimize the function structure in step 1, input the missing attribute parameter value tn, and calculate the active power missing attribute data [xn, tn];

[0123] The data flow analysis module is used to collect data from the route request to the route reporting data, classify the meter collection situation in the substation, monitor the collection status of the electric energy meter, and give a comprehensive evaluation report on the meter reading situation in the substation; according to the monitoring customer's electric parameter information and the power consumption abnormality monitoring model, compare the electric parameter information collected by different methods at the same metering point, or compare the real-time information and historical electric parameter information of the same metering point, and judge whether the customer's power consumption is stable according to the comparison curve value difference and trend difference. If an abnormality is found, start the abnormal handling process, carry out continuous monitoring of other parameters of the customer, and according to the suspected power theft prediction model, make a unified analysis and judgment of the monitored information to set the "power theft probability value";

[0124] Log management module, a component used to record, store, analyze and report log information generated during system operation;

[0125] The log management module specifically includes:

[0126] Record logs to record key information during system operation, including user operations, system events, etc.

[0127] Store logs and save log information to different target domains, such as log files, databases, etc.

[0128] Analyze logs and provide log analysis functions to help operation and maintenance personnel discover potential problems and trends.

[0129] Report logs, generate log reports, and provide statistical information on system operation status and security events.

[0130] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. The information flow monitoring system of metering and collection equipment based on virtualization technology is characterized by: It includes collection terminal, operation perception module, communication upload module, data preprocessing module, data flow analysis module and log management module; The collection terminal is used to monitor and collect equipment information flow information. The collected data is divided into Class 1 data, Class 2 data and Class 3 data according to time attributes; in, Category 1 data includes real-time data and current data; Category 1 data is used to reflect the current real-time power consumption information of power users; The second type of data includes historical single-day data and historical single-month data; The three types of data include event data and abnormal data, among which the abnormal data include current loop abnormality, voltage loop abnormality, phase sequence abnormality, active total electric energy differential over-limit event record, voltage over-limit record, current over-limit record, electric energy indication degree drop, and detailed information of electric energy over-difference; The running perception unit is used to provide data support for the information flow monitoring system, so as to make decisions based on the information provided by the perception module. The Docker system is used in the running perception unit; The communication upload module is used to collect terminal uplink communication data and record at least 3 days of signal strength curve data with a density of 1 minute; obtain information on network standard, cell number, signal strength, bit error rate, receiving power, receiving quality, signal-to-noise ratio and current frequency to form curve data, and analyze the signal operation quality from different dimensions. The above information can be queried through the local terminal; The data preprocessing module is used to clean the data, remove noise from the data, repair missing values, handle outliers, establish accurate models, and provide standardization and normalization operations to convert the data to a uniform specification or a specific distribution. At the same time, it processes the missing data in the collection process of the telecommunications system; among them, the missing attributes are used as dependent variables, and other attributes are used as independent variables, and the n-order polynomial approximation method is used to interpolate missing values; The data flow analysis module is used to collect data from the route request to the route reporting data, classify the meter collection situation in the substation, monitor the collection status of the electric energy meter, and give a comprehensive evaluation report on the meter reading situation in the substation; according to the monitoring customer's electric parameter information and the power consumption abnormality monitoring model, compare the electric parameter information collected by different methods at the same metering point, or compare the real-time information and historical electric parameter information of the same metering point, and judge whether the customer's power consumption is stable according to the comparison curve value difference and trend difference. If an abnormality is found, start the abnormality handling process, carry out continuous monitoring of other parameters of the customer, and according to the suspected power theft prediction model, make a unified analysis and judgment of the monitored information to set the "power theft probability value"; The log management module is a component used to record, store, analyze and report log information generated during system operation.

2. The information flow monitoring system for metering and collection equipment based on virtualization technology according to claim 1 is characterized in that: The data collected in the collection terminal are divided into the following categories according to physical properties: (1) Electric energy data, specifically total electric energy, electric energy at each rate, and maximum demand; (2) AC analog quantities, specifically voltage, current, active power, reactive power, and power factor; (3) Operating condition data, specifically switch status, terminal and metering equipment operating condition information; (4) Statistical data on power quality exceeding limits, specifically voltage, power factor, and harmonics of metering equipment; (5) Event record data, specifically terminal record data and event record data of electric energy meters.

3. The information flow monitoring system for metering and collection equipment based on virtualization technology according to claim 2 is characterized in that: The terminal records data as follows:

1. Terminal power-off information, terminal power-on time, terminal power-off time, and continuous working time information; 2. Reasons for the terminal restart: power failure, upgrade, CPU abnormality, program abnormality, reset command; 3. Number and time of terminal restarts; 4. Resource usage of the terminal: CPU, RAM and Flash utilization, storage density is set to 1min, and the minimum storage value is set to 72h according to the time limit; 5. Working status of the terminal module: whether the upstream and downstream modules, backup batteries are valid, whether the clock battery is undervoltage, metering module, encryption chip; used to record the time nodes of the last three normal and abnormal occurrences.

4. The information flow monitoring system for metering and collection equipment based on virtualization technology according to claim 1 is characterized in that: The Docker system includes three monitoring commands, namely ps, top, and stats; (1) ps, used to understand the status of the currently running container; Run the command as follows: [root@host1~]#Docker_ps; (2) top, used to locate the status of processes running in the container; Run the command as follows: [root@host1~]#Dockertop266910b9b; (3) Stats, used to display the usage of various resources of each container, using dynamic refresh; Run the following command: [root@host1~]#Dockerstats266910b9b; In this way, the above information is recorded periodically in the host machine, and a line chart of the data is dynamically generated. When an abnormality occurs in the container, the abnormality is recorded and the cause of the abnormality is analyzed; In addition, it is also used to record and store power-on information to obtain complete terminal power-off information, terminal power-on time, terminal power-off time, and continuous working time information.

5. The information flow monitoring system for metering and collection equipment based on virtualization technology according to claim 1 is characterized in that: The local terminal uses a liquid crystal display device, and the displayed content includes: (1) The power-on success signal is displayed; (2) Obtain communication module version information; (3) Obtain the SIM card serial number; (4) Obtaining signal strength data; (5) Obtain current network registration status information; (6) Obtaining remote communication module type data; (7) Set network connection initialization parameters, including APN, user name, and registration password; (8) Status information of activated network connection; (9) Set the client service parameters, including network type, master station IP and port.

6. The information flow monitoring system for metering and collection equipment based on virtualization technology according to claim 1 is characterized in that: The steps for interpolating missing attributes of active power parameters in the data preprocessing module are as follows: Step 1: Select the 3rd (or 5th) order fitting equation; suppose the function fitting equation between the active power parameter x and the attribute time 1 is: x(t i )=a0+a1t i +a2t i 2 +a3t i 3 +……+a5t i 5 Step 2: Select N data before and after the missing active power data [xn, tn]: Substitute [xi, ti] into the equation of the first step of fitting, and solve a0, a1, a2, a3 (taking the third-order fitting equation as an example) as the unknown coefficients of the fitting equation; Step 3: According to the principle of least squares method, the mean square error of the interpolated [xi, ti] values ​​is minimized, and the coefficients a0, a1, a2, and a3 are solved, that is, the following mean square error equation: For coefficients a0, a1, a2, a3, find the minimum derivative and establish the matrix: The fourth step is to optimize the function structure in step one, input the missing attribute parameter value tn, and calculate the active power missing attribute data [xn, tn].

7. The information flow monitoring system for metering and collection equipment based on virtualization technology according to claim 1 is characterized in that: The log management module specifically includes: Record logs to record key information during system operation, including user operations and system events; Store logs and save log information to different target domains, including log files and databases; Analyze logs and provide log analysis functions to help operation and maintenance personnel find potential problems and trends; Report logs, generate log reports, and provide statistical information on system operation status and security events.

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