Analysis method and data analysis system for telemetering data of energy storage cabin

Abstract

The invention discloses an analysis method for telemetry data of an energy storage cabin, which comprises the following steps: a user logs in a data analysis system and selects telemetry data needing to be analyzed; the data analysis system is used for collecting telemetry data from a Mysql database based on an Apache Flume technical framework; performing big data processing on the telemetry data; drawing a broken line graph of the telemetry data after big data processing according to the telemetry data in the Mysql database based on a front-end technical framework; and on the basis of a time sequence analysis method, according to the broken line graph, predicting the telemetry data so as to predict the future operation health condition of the equipment in the energy storage cabin. According to the method, abnormal telemetry data is removed and displayed in a chart form, the health trend of each device in the whole energy storage cabin is clearly displayed, the trend of the data is more visually represented, and the data is analyzed; and the telemetry data is predicted through a time sequence analysis method.

Description

Technical Field

[0001] The present invention relates to a data analysis method and a data analysis system, and in particular to an analysis method and a data analysis system for telemetry data of an energy storage cabin. Background Art

[0002] The energy storage system in the energy storage cabin helps to use and manage energy and reduce electricity expenses. It is used in scenarios where the demand for energy regulation is large and concentrated, such as the power generation side, the grid side, and large industrial users. In order to ensure the healthy operation of various equipment inside the energy storage cabin, such as the BMS battery management system, EMS energy management system, PCS energy storage inverter and liquid cooling equipment, and reduce their operating risks, it is necessary to analyze, summarize and monitor the operating data of each device.

[0003] There are multiple sensors in the energy storage cabin, and each sensor reports a lot of telemetry data every day, but most of the telemetry data is stored in the database of the cloud server. At present, the data analysis of the equipment in the energy storage cabin mainly relies on the telemetry data uploaded by each sensor. Most of the data is not analyzed in a targeted manner, and the analysis results of some existing data are only based on the fault information reported by the sensor telemetry data.

[0004] Judging whether a device is working properly based on telemetering data can only display specific device conditions, such as normal, fault, and alarm. And based on these data, there is no effective analysis of the effective information in the telemetering data to predict and monitor the operating status of the entire energy storage cabin equipment. Summary of the invention

[0005] In order to solve the deficiencies of the above-mentioned technology, the present invention provides an analysis method and a data analysis system for telemetry data of an energy storage cabin.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for analyzing telemetry data of an energy storage cabin, which specifically includes the following steps:

[0007] Step S1: The user logs in to the data analysis system and selects the telemetry data to be analyzed; the data analysis system saves the task record and invokes the data analysis task;

[0008] Step S2: The data analysis system collects telemetry data from the MySQL database based on the Apache Flume technology framework;

[0009] Step S3: performing big data processing on the telemetry data in step S2;

[0010] Big data processing includes the following steps:

[0011] Step S31: Clean the telemetry data based on MapReduce technology to remove obvious abnormal values ​​and store them in the Hbase data table;

[0012] Step S32: storing the cleaned telemetry data into the Hbase data table;

[0013] Step S33: Process the telemetry data in the Hbase data table and store it in the Hive table;

[0014] Step S34: import the telemetry data back to the MySQL database based on Sqoop;

[0015] Step S4: The telemetry data after big data processing is used to draw a line graph of the telemetry data based on the telemetry data in the Mysql database based on the front-end technical framework;

[0016] Step S5: Based on the time series analysis method, according to the line graph in step S4, the telemetry data is predicted to estimate the future operating health of the equipment in the energy storage compartment.

[0017] Furthermore, the outlier judgment standard in step S31 is based on the Grubbs test method when the standard deviation is unknown, and the average value and standard deviation of the five groups of telemetry data before and after each value in the telemetry data are calculated; if the deviation of the current value from its average value is greater than twice its standard deviation, the current value is an outlier.

[0018] Furthermore, the data processing in step S33 includes sorting, statistics and analysis.

[0019] Furthermore, in step S33, the telemetry data is sorted in order according to its reporting time.

[0020] Furthermore, in step S33, the telemetry data is partitioned according to the same standard, and the maximum value, minimum value, average value, standard deviation and partition data volume of the partition are counted.

[0021] Furthermore, in step S33, each partition is analyzed, that is, the data of each partition is attributed;

[0022] If the standard deviation of the partition is within a reasonable range and the average value is not greater than the set threshold, the partition is marked as a stable partition;

[0023] If the standard deviation of the partition is not within a reasonable range and the maximum value is greater than the threshold, the partition is marked as an abnormally increased partition;

[0024] If the standard deviation of the partition is not within a reasonable range and the minimum value is greater than the threshold, the partition is marked as an abnormally declining partition.

[0025] Furthermore, the set threshold is input into the front end made based on the open source Vue technology framework, and the front-end and back-end interaction is realized based on the open source SpringBoot technology framework.

[0026] Furthermore, in step S4, based on the ECharts open source technology in the Vue front-end technology, a line chart is drawn according to the telemetry data sorted in chronological order provided by the back-end server.

[0027] Furthermore, in step S5, the specific method for predicting the telemetry data is to predict the telemetry data based on the exponential smoothing method in the time series analysis method. The calculation formula of the exponential smoothing method is:

[0028]

[0029] Among them, α is the smoothing coefficient, α∈(0,1); is the exponential smoothing value at time t; y t is the actual value at time t.

[0030] A data analysis system for an analysis method of energy storage tank data, comprising a user login module for a user to log in to an account, facilitating user operation and viewing of data analysis results;

[0031] The data source module is used to read all data tables and related attribute fields stored in the Mysql database; each data table represents a type of telemetry data;

[0032] The data processing module is used to perform big data processing on the data specified by the user;

[0033] The chart display module is used to display each data analysis result in the form of a chart.

[0034] The present invention discloses an analysis method and a data analysis system for telemetry data of an energy storage cabin. By eliminating abnormal telemetry data, and performing statistical analysis on effective telemetry data and drawing it into a line graph, the system displays it in the form of a chart. This solves the problem that when judging whether a device is working normally based on telemetry data, only the status of a specific device can be displayed. The health trend of each device in the entire energy storage cabin is clearly displayed, and the trend of the data is more intuitively expressed for data analysis. In addition, the telemetry data is predicted through a time series analysis method, which is convenient for monitoring equipment failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of the present invention.

[0036] Figure 2 This is a big data processing flow chart of the present invention.

[0037] Figure 3 This is a data analysis flow chart in Embodiment 2 of the present invention.

[0038] Figure 4 This is a flowchart of the graphical interface configuration in the second embodiment of the present invention.

[0039] Figure 5 This is a graphical interface diagram in the second embodiment of the present invention.

[0040] Figure 6 This is a line graph of result analysis in the second embodiment of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0042] Embodiment 1:

[0043] like Figure 1 As shown, a method for analyzing telemetry data of an energy storage cabin specifically includes the following steps:

[0044] Step S1: The user logs in to the data analysis system and selects the telemetry data to be analyzed; the data analysis system saves the task record and invokes the data analysis task;

[0045] Step S2: The data analysis system collects telemetry data from the MySQL database based on the Apache Flume technology framework;

[0046] Step S3: Perform big data processing on the telemetry data in step S2; in step S3, the big data processing is performed, such as Figure 2 As shown, the following steps are included:

[0047] Step S31: Clean the telemetry data based on MapReduce technology to remove obvious abnormal values ​​and store them in the Hbase data table;

[0048] Step S32: storing the cleaned telemetry data into the Hbase data table;

[0049] Step S33: Process the telemetry data in the Hbase data table and store it in the Hive table;

[0050] Data processing includes sorting, statistics and analysis;

[0051] In step S33, the telemetry data is sorted according to the reporting time. That is, since the MapReduce technology performs data cleaning in parallel, the data summarized after cleaning is in a disordered state. In order to facilitate drawing the line graph and calculating the predicted value below, the telemetry data is sorted in order according to the reporting time;

[0052] In step S33, the telemetry data is partitioned according to the same standard, and the maximum value, minimum value, average value, standard deviation and partition data volume of the partition are counted; in this embodiment, the telemetry data is partitioned according to the time of each whole hour, and the maximum value, minimum value, average value, standard deviation and partition data volume of the partition are counted;

[0053] Afterwards, in step S33, each partition is analyzed and the data of each partition is marked with attributes; if the standard deviation of the partition is within a reasonable range and the average value is not greater than the set threshold, the partition is marked as a stable partition; if the standard deviation of the partition is not within a reasonable range and the maximum value is greater than the threshold, the partition is marked as an abnormally rising partition; if the standard deviation of the partition is not within a reasonable range and the minimum value is greater than the threshold, the partition is marked as an abnormally falling partition.

[0054] The set threshold is input into the front-end made based on the open source Vue technology framework, and the front-end and back-end interaction is realized based on the open source SpringBoot technology framework.

[0055] Step S34: import the telemetry data back to the MySQL database based on Sqoop;

[0056] Step S4: Draw a line graph of the telemetry data based on the telemetry data in the Mysql database based on the front-end technology framework; draw a line graph based on the telemetry data sorted in chronological order provided by the back-end server based on the ECharts open source technology in the Vue front-end technology.

[0057] Step S5: Based on the time series analysis method, according to the line graph in step S4, the telemetry data is predicted to estimate the future operating health of the equipment in the energy storage compartment; the specific method is to predict the telemetry data based on the exponential smoothing method in the time series analysis method, and the calculation formula of the exponential smoothing method is:

[0058]

[0059] Among them, α is the smoothing coefficient, α∈(0,1); is the exponential smoothing value at time t; y t is the actual value at time t.

[0060] The selection criteria of the smoothing coefficient α are:

[0061] 1. If the time series does not fluctuate much and is relatively stable, the smoothing coefficient is smaller to reduce the correction amplitude so that the forecast model can include information from a longer time series;

[0062] 2. If the time series has a tendency to change rapidly and obviously, the smoothing coefficient should be larger to improve the sensitivity of the prediction model.

[0063] Initial Value The selection criteria are:

[0064] 1. When the time series data n is less than 20, take the average of the actual values ​​of the first three periods of the time series data as the initial prediction value

[0065] 2. When the time series data n is not less than 20, take the actual value of the first period in the time series data as the initial prediction value.

[0066] A data analysis system for an analysis method of energy storage cabin telemetry data, comprising a user login module for a user to log in to an account, facilitating user operation and viewing of data analysis results;

[0067] The data source module is used to read all data tables and related attribute fields stored in the Mysql database; each data table represents a type of telemetry data;

[0068] The data processing module is used to perform big data processing on the data specified by the user;

[0069] The chart display module is used to display each data analysis result in the form of a chart.

[0070] By eliminating abnormal telemetry data, performing statistical analysis on valid telemetry data and drawing a line graph, and displaying it in the form of a chart, the problem of judging whether a device is working normally based on telemetry data and only being able to display the status of specific equipment is solved. The health trend of each device in the entire energy storage compartment is clearly displayed, and the trend of the data is more intuitively expressed for data analysis; and the telemetry data is predicted through time series analysis methods to facilitate the monitoring of equipment failures.

[0071] Embodiment 2:

[0072] like Figure 3 and Figure 4 As shown, the front-end graphical interface is a table, and the user operates on the front end and analyzes the results:

[0073] Step A1: The user selects the data source to be analyzed through the graphical interface. In this embodiment, the data source selected to be analyzed is the table field of telemetry data; the graphical interface is developed based on Vue technology, and the front-end and back-end interaction is realized based on SpringBoot technology; the drop-down box selects the data source to be added for analysis and monitoring this time, and different related fields and meanings are displayed according to the selected data source. Click to select the data field to be added for analysis and monitoring this time;

[0074] like Figure 5As shown, the data source selected in this embodiment is the liquid cooling telemetry data transmitted by the current system sensor. The data fields of the data source include liquid cooling environment temperature, liquid cooling water outlet temperature, liquid cooling water return temperature and other fields. The data field selected for analysis and monitoring this time is the liquid cooling environment temperature;

[0075] The back-end cleans the liquid cooling environment temperature data to remove obvious outliers; the standards for outliers in telemetry data in the liquid cooling module include key parameter temperature being empty and key parameter temperature meeting the outlier standards defined in national statistical standards;

[0076] After cleaning, the telemetry data of the liquid cooling module is sorted, counted and analyzed; the telemetry data of the liquid cooling module is sorted according to the reported time, and the telemetry data is partitioned according to the entire hour, and the maximum value, minimum value, average value, standard deviation and partition data volume in each partition are counted; the data of each partition is attributed. In this embodiment, if the partition standard deviation is in the range of -5 to 5 degrees Celsius and the average temperature is not higher than the threshold temperature, i.e., 60 degrees Celsius, the current partition is a stable partition and does not require special attention from the user; if the partition standard deviation is greater than 5 degrees Celsius and the maximum value is greater than the threshold, the current partition is an abnormally pulled-up partition, and the user needs to check whether the liquid cooler is damaged; if the partition standard deviation is less than 5 degrees Celsius and the minimum value is greater than the threshold, the current partition is a descending partition, and the user needs to check whether the liquid cooler is damaged.

[0077] Step A2: The user edits the reasonable threshold range of the data field through the graphical interface, that is, configures the reasonable threshold range of the data field, and inputs the threshold into the graphical interface of the front end; in this embodiment, the threshold is set to 60 degrees Celsius, and the comparison symbol is less than, that is, the liquid cooling environment temperature is less than 60 degrees Celsius, which is a reasonable range. The threshold is displayed in the chart through the chart display module to facilitate users to share data;

[0078] Step A3: The user performs data analysis through a graphical interface; that is, a button for performing data analysis is set on the front end for easy operation by the user, and the analysis result is displayed in a chart through a chart display module after clicking on data analysis;

[0079] Step A4: Analyze according to the analysis results;

[0080] like Figure 6As shown, this embodiment performs analysis based on the liquid cooling environment temperature curve; the red line segment is the set threshold, that is, 60 degrees Celsius; the blue broken line is drawn by the liquid cooling environment temperature at each time point; the horizontal axis in the figure is the time point with a period of 5 minutes between 10 and 11 o'clock, which is determined by the period of the sensor's reported data, and the energy storage cabin system's reported telemetry data period is 5 minutes; the vertical axis in the figure is the value of the field to be analyzed, that is, the liquid cooling environment temperature, and the setting interval is 0 degrees Celsius to 80 degrees Celsius. This chart shows that the temperature exceeds the threshold at 10:50;

[0081] Displaying the data in the form of a chart can more smoothly and clearly show the changing trend of the data, as well as whether the data at each time node is within a reasonable threshold, so as to intuitively show whether the energy storage compartment is in a normal state, a fault state or an alarm state at each time node.

[0082] The data is predicted by the exponential smoothing method. In this embodiment, the liquid cooling environment temperature continues to rise in an abnormal rising curve from 10:25, indicating that the liquid cooling system may be overloaded or fail. The smoothing coefficient of the exponential smoothing method in this embodiment is 0.8, and the average value of the first 20 values ​​in the prediction time period is taken as the initial prediction value for prediction.

[0083] The intersection of the red line segment and the blue broken line is the time when the liquid cooling module failed, that is, 10:48, which is used for early warning and prompts the role of troubleshooting.

[0084] Users can analyze data through the front-end graphical interface without having to master the development and use of data analysis tools, which reduces the technical threshold of data analysis services. The change curve of telemetry data is displayed in the form of a chart, allowing users to display the operating status of the equipment more smoothly and make a certain estimate of the operating health status of the equipment.

[0085] The above implementation modes are not limitations of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the scope of the technical solution of the present invention also belong to the protection scope of the present invention.

Claims

1. A method for analyzing telemetry data of an energy storage cabin, characterized in that: The following steps are involved: Step S1: The user logs into the data analysis system and selects the telemetry data to be analyzed; The data analysis system saves task records and invokes data analysis tasks; Step S2: The data analysis system collects telemetry data from the MySQL database based on the Apache Flume technology framework; Step S3: performing big data processing on the telemetry data in step S2; Big data processing includes the following steps: Step S31: Clean the telemetry data based on MapReduce technology to remove obvious abnormal values ​​and store them in the Hbase data table; Step S32: storing the cleaned telemetry data into the Hbase data table; Step S33: Process the telemetry data in the Hbase data table and store it in the Hive table; Step S34: import the telemetry data back to the MySQL database based on Sqoop; Step S4: The telemetry data after big data processing is used to draw a line graph of the telemetry data based on the telemetry data in the Mysql database based on the front-end technical framework; Step S5: Based on the time series analysis method, according to the line graph in step S4, the telemetry data is predicted to estimate the future operating health of the equipment in the energy storage compartment.

2. The method for analyzing telemetry data of an energy storage cabin according to claim 1, characterized in that: The abnormal value judgment standard in step S31 is based on the Grubbs test method when the standard deviation is unknown, and the average value and standard deviation of the five groups of telemetry data before and after each value in the telemetry data are calculated; if the deviation of the current value from its average value is greater than twice its standard deviation, the current value is an abnormal value.

3. The method for analyzing telemetry data of an energy storage cabin according to claim 1, characterized in that: The data processing in step S33 includes sorting, statistics and analysis.

4. The method for analyzing telemetry data of an energy storage cabin according to claim 3 is characterized in that: In step S33, the telemetry data are sorted in order according to their reporting time.

5. The method for analyzing telemetry data of an energy storage cabin according to claim 4 is characterized in that: In step S33, the telemetry data is partitioned according to the same standard, and the maximum value, minimum value, average value, standard deviation and partition data volume of the partition are counted.

6. The method for analyzing telemetry data of an energy storage cabin according to claim 5, characterized in that: In step S33, each partition is analyzed, that is, the data of each partition is attributed; If the standard deviation of the partition is within a reasonable range and the average value is not greater than the set threshold, the partition is marked as a stable partition; If the standard deviation of the partition is not within a reasonable range and the maximum value is greater than the threshold, the partition is marked as an abnormally pulled partition; If the standard deviation of the partition is not within a reasonable range and the minimum value is greater than the threshold, the partition is marked as an abnormally declining partition.

7. The method for analyzing telemetry data of an energy storage cabin according to claim 6, characterized in that: The set threshold is input into the front-end made based on the open source Vue technology framework, and the front-end and back-end interaction is realized based on the open source SpringBoot technology framework.

8. The method for analyzing telemetry data of an energy storage cabin according to claim 1, characterized in that: In the step S4, based on the ECharts open source technology in the Vue front-end technology, a line chart is drawn according to the telemetry data sorted in chronological order provided by the back-end server.

9. The method for analyzing telemetry data of an energy storage cabin according to claim 1, characterized in that: The specific method for predicting the telemetry data in step S5 is to predict the telemetry data based on the exponential smoothing method in the time series analysis method. The calculation formula of the exponential smoothing method is: Among them, α is the smoothing coefficient, α∈(0,1); is the exponential smoothing value at time t; y t is the actual value at time t.

10. A data analysis system for the method for analyzing telemetry data of an energy storage cabin as claimed in claim 1, characterized in that: It includes a user login module, which is used for users to log in to their accounts, making it easier for users to operate and view data analysis results; The data source module is used to read all data tables and related attribute fields stored in the Mysql database; each data table represents a type of telemetry data; The data processing module is used to perform big data processing on the data specified by the user; The chart display module is used to display each data analysis result in the form of a chart.

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