A k-line chart visualization system and method based on power data

Abstract

The application discloses a K-line chart visualization system and method based on power data, a data acquisition module, which is used for acquiring real-time power data, historical power data and K-line chart configuration information of various devices of a power system; a data aggregation module, which is used for aggregating the real-time power data and the historical power data to generate a plurality of real-time aggregation tables and historical aggregation tables; a power data calculation module, which is used for calculating the aggregated data in the real-time aggregation tables and the historical aggregation tables according to an index type in the K-line chart configuration information through a Flink model to generate target calculation data corresponding to each device; a database, which is used for storing the target calculation data; and a configurable K-line chart module, which is used for acquiring the target calculation data associated with the K-line chart configuration information from the database to draw a power K-line chart according to the K-line chart configuration information. The technical problem that a traditional single display function in the form of a statistical chart cannot meet the demand of power analysis work is solved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a candlestick chart visualization system and method based on power data. Background Technology

[0002] With continuous economic development and the advancement of urbanization, urban electricity demand is also growing rapidly, accelerating the transformation of energy and power development methods. This has led to new changes in the characteristics of urban electricity demand, electricity structure, and load characteristics, presenting new features. Under this new situation, power grid companies need to more accurately grasp changes in electricity demand and make reasonable consumption decisions based on demand characteristics.

[0003] Currently, existing power visualization systems typically use simple statistical charts such as tables, bar charts, line charts, and pie charts to display the basic patterns and trends of data. However, with the continuous increase in the types and datasets of existing power data, the traditional single display function using statistical charts can no longer meet the needs of power analysis work. Summary of the Invention

[0004] This invention provides a candlestick chart visualization system and method based on power data, which solves the technical problem that the traditional single display function in the form of statistical charts can no longer meet the needs of power analysis.

[0005] The first aspect of the present invention provides a candlestick chart visualization system based on power data, comprising a data acquisition module, a data aggregation module, a power data calculation module, a database, and a configurable candlestick chart module that are connected in sequence via communication.

[0006] The data acquisition module is used to collect target power data and K-line chart configuration information of various devices in the power system, wherein the target power data includes real-time power data and historical power data;

[0007] The data aggregation module is used to aggregate the real-time power data and the historical power data to generate multiple real-time aggregation tables and historical aggregation tables.

[0008] The power data calculation module is used to calculate the aggregated data in each of the real-time aggregation tables and the historical aggregation tables according to the indicator types in the K-line chart configuration information, and generate target calculation data corresponding to each device and transmit it to the database.

[0009] The database is used to store the target calculation data output by the power data calculation module;

[0010] The configurable candlestick chart module is used to draw a power candlestick chart by retrieving the target calculation data associated with the candlestick chart configuration information from the database according to the candlestick chart configuration information.

[0011] Optionally, the target calculation data includes target real-time data and target historical data, and the power data calculation module includes a real-time data calculation unit and a historical data calculation unit;

[0012] The real-time data calculation unit is used to calculate the aggregated data in each of the real-time aggregation tables according to the indicator types in the K-line chart configuration information, and generate the target real-time data corresponding to each of the devices and transmit it to the database.

[0013] The historical data calculation unit is used to calculate the aggregated data in the historical aggregation table according to the indicator type in the K-line chart configuration information, through the Flink model, to generate the target historical data corresponding to each device and transmit it to the database.

[0014] Optionally, the data aggregation module includes an aggregation unit, a classification module, and a matching mapping unit;

[0015] The aggregation unit is used to aggregate the real-time power data and the historical power data based on the aggregated Cartesian product algorithm to generate multiple initial real-time aggregated data and initial historical aggregated data.

[0016] The classification module is used to classify multiple initial real-time aggregated data and initial historical aggregated data according to data type, and generate multiple target real-time aggregated data and target historical aggregated data of the same type;

[0017] The matching and mapping unit is used to map each of the target real-time aggregated data and the target historical aggregated data through a data adapter, thereby generating multiple real-time aggregated tables and historical aggregated tables.

[0018] Optionally, it also includes a supplementary data calculation module;

[0019] The supplementary data calculation module is used to detect the target power data, and when data is missing, it completes the calculation of supplementary data.

[0020] Optionally, the data supplementation calculation module includes a missing data detection unit, a complete indicator calculation unit, an indicator comparison unit, a manual data supplementation unit, a missing type judgment unit, a first automatic data supplementation unit, and a second automatic data supplementation unit;

[0021] The missing data detection unit is used to detect the collected target power data. When missing data is detected, it obtains the amount of missing data and the amount of valid data associated with the target power data.

[0022] The complete indicator calculation unit is used to calculate the corresponding complete indicator based on the preset complete indicator function and the amount of missing data.

[0023] The indicator comparison unit is used to compare the complete indicator with the preset standard indicator;

[0024] The manual supplementary recording unit is used to output an alarm and perform manual supplementary recording or retransmission if the complete indicator is less than or equal to the preset standard indicator.

[0025] The missing type determination unit is used to determine the missing type of the missing data if the completeness index is greater than the preset standard index.

[0026] The first automatic data entry unit is used to correct the data using the Markov chain Monte Carlo method if the missing data type is random and scattered.

[0027] The second automatic supplementary recording unit is used to perform automatic supplementary recording if the missing type is not random and scattered.

[0028] Optionally, the preset complete index function is:

[0029]

[0030] In the formula, X represents the complete indicator, B represents the amount of missing data, and A represents the amount of valid data.

[0031] Optionally, the configurable candlestick chart module includes a power candlestick chart drawing submodule;

[0032] The power candlestick chart drawing submodule includes a rectangular column drawing unit, an extraction unit, a power data comparison unit, a first coloring unit, a second coloring unit, a moving average drawing unit, and a power candlestick chart coupling unit;

[0033] The rectangular column drawing unit is used to obtain multiple target calculation data associated with the K-line chart configuration information from the database, with the horizontal axis representing time and the vertical axis representing values, according to the K-line chart configuration information, and to draw multiple rectangular columns corresponding to each device individually or in combination using each target calculation data;

[0034] The extraction unit is used to extract the maximum and minimum power values ​​from each of the target calculation data, and connect them vertically into a straight line.

[0035] The power data comparison unit is used to compare the power cutoff value and the power start value in each of the target calculation data respectively.

[0036] The first coloring unit is used to color the associated rectangular column with a first preset color if the power cut-off value is greater than the power start value;

[0037] The second coloring unit is used to color the associated rectangular column with a second preset color if the power cut-off value is less than or equal to the power start value;

[0038] The moving average drawing unit is used to calculate a moving average line within a specified time period based on each of the target calculation data.

[0039] The power candlestick chart coupling unit is used to couple the moving average line with the associated rectangular column to generate a power candlestick chart.

[0040] Optionally, a search function module may also be included;

[0041] The retrieval function module is used to perform retrieval based on the retrieval type corresponding to the retrieval request and determine the target retrieval information.

[0042] Optionally, a trending search module may also be included;

[0043] The trending search module is used to count the number of searches based on the indicator type and then display the results.

[0044] The second aspect of this invention provides a candlestick chart visualization method based on power data, applied to a candlestick chart visualization system based on power data. The power data-based candlestick chart visualization system includes a data acquisition module, a data aggregation module, a power data calculation module, a database, and a configurable candlestick chart module, all connected in sequence via communication. The method includes:

[0045] The data acquisition module collects target power data and K-line chart configuration information of various devices in the power system, wherein the target power data includes real-time power data and historical power data;

[0046] The data aggregation module aggregates the real-time power data and the historical power data to generate multiple real-time aggregation tables and historical aggregation tables.

[0047] The power data calculation module calculates the aggregated data in each of the real-time aggregation tables and the historical aggregation tables based on the indicator types in the K-line chart configuration information using the Flink model, generates the target calculation data corresponding to each device, and transmits it to the database.

[0048] The target calculation data output by the power data calculation module is stored in the database;

[0049] The configurable candlestick chart module retrieves the target calculation data associated with the candlestick chart configuration information from the database based on the candlestick chart configuration information to draw an electric power candlestick chart.

[0050] As can be seen from the above technical solutions, the present invention has the following advantages:

[0051] The system comprises a data acquisition module, a data aggregation module, a power data calculation module, a database, and a configurable candlestick chart module, connected sequentially via communication. The data acquisition module collects target power data and candlestick chart configuration information from various devices in the power system. The target power data includes real-time and historical power data. The data aggregation module aggregates real-time and historical power data to generate multiple real-time and historical aggregation tables. The power data calculation module calculates the aggregated data in the real-time and historical aggregation tables using a Flink model based on the indicator types within the candlestick chart configuration information, generating target calculation data for each device and transmitting it to the database. The database stores the target calculation data output by the power data calculation module. The configurable candlestick chart module retrieves the target calculation data associated with the candlestick chart configuration information from the database and draws the power candlestick chart. This addresses the technical problem that traditional single-format statistical chart display functions are insufficient to meet the needs of power analysis. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A structural block diagram of a candlestick chart visualization system based on power data provided in an embodiment of the present invention;

[0054] Figure 2 A first flowchart for processing hourly K-line data (below the hourly K-line) in a K-line chart visualization system based on power data, provided as an embodiment of the present invention;

[0055] Figure 3 A second flowchart for processing hourly K-line data of a K-line chart visualization system based on power data is provided in an embodiment of the present invention.

[0056] Figure 4This invention provides a flowchart of the power data and supplementary data processing for a candlestick chart visualization system based on power data, as provided in an embodiment of the invention.

[0057] Figure 5 The flowchart illustrates the steps of a candlestick chart visualization method based on power data, as provided in this embodiment of the invention. Detailed Implementation

[0058] This invention provides a candlestick chart visualization system and method based on power data, which addresses the technical problem that traditional single-format statistical chart display functions can no longer meet the needs of power analysis.

[0059] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0060] Please see Figure 1 , Figure 1 A structural block diagram of a candlestick chart visualization system based on power data provided in an embodiment of the present invention;

[0061] The present invention provides a candlestick chart visualization system based on power data, comprising a data acquisition module, a data aggregation module, a power data calculation module, a database, and a configurable candlestick chart module that are connected in sequence via communication.

[0062] The data acquisition module is used to collect target power data and K-line chart configuration information from various devices in the power system. The target power data includes real-time power data and historical power data.

[0063] The data aggregation module is used to aggregate various real-time and historical power data to generate multiple real-time aggregation tables and historical aggregation tables.

[0064] The power data calculation module is used to calculate the aggregated data in each real-time aggregation table and historical aggregation table according to the indicator types in the K-line chart configuration information, and generate the target calculation data corresponding to each device and transmit it to the database.

[0065] The database is used to store the target calculation data output by the power data calculation module;

[0066] The configurable candlestick chart module is used to draw power candlestick charts by retrieving target calculation data associated with the candlestick chart configuration information from the database based on the candlestick chart configuration information.

[0067] Target power data refers to the power data of each power device in the power system. Power data includes, but is not limited to, cross-section, load rate, load, active power, reactive power, current, voltage, etc.

[0068] K-line chart configuration information refers to the configuration information required to draw K-line charts, including but not limited to plant number, power equipment type, indicator type, time factor, bar chart parameters, auxiliary line parameters, whether it is a composite indicator, and chart configuration.

[0069] The substation number refers to the sequential number of a substation associated with the power system, used to identify a specific substation.

[0070] Types of electrical equipment include, but are not limited to, transformers, circuit breakers, disconnect switches, voltage transformers, current transformers, surge arresters, bushings, and insulators.

[0071] The types of indicators include, but are not limited to, cross-section, load rate, load, active power, reactive power, current, voltage, etc.

[0072] The time factor refers to the time represented on the horizontal axis of a candlestick chart, including but not limited to 15 minutes, days, weeks, months, and years.

[0073] The parameters for the bar chart include color, and the length, width, and height of the bars. Color configurations can be tailored to user and business needs, allowing for different bar colors under different data conditions. This enables quick assessment of the power grid and equipment operation through bar color. The length, width, and height parameters of the bars can be set according to actual needs. If the candlestick chart contains composite indicators, the length, width, and height of the bars need to be adjusted to display multiple data points on a single chart. This allows users to intuitively view multiple data points within a single chart, establishing time-sensitive correlations between various types of power data. This provides users with an intuitive visual experience, allowing them to directly perceive the fluctuations and impacts of power data. Viewing multiple power data points on a single chart facilitates analysis by researchers, reduces data omissions, and enables rapid generation of analysis reports.

[0074] Auxiliary line parameters, including but not limited to moving averages, month-on-month comparisons, and year-on-year comparisons, such as 10-day moving averages and 20-day moving averages, can be used in conjunction with candlestick chart trends to make accurate data analysis and predictions.

[0075] Whether a composite index is used refers to whether multiple indexes are combined, such as using the sum of "load of feeder A + load of feeder B" as a composite index.

[0076] In this embodiment of the invention, the data acquisition module is used to collect target power data and candlestick chart configuration information of various devices in the power system. The target power data includes real-time power data and historical power data. The real-time power data and historical power data are aggregated to generate multiple real-time aggregation tables and historical aggregation tables. According to the indicator types in the candlestick chart configuration information, the aggregated data in each real-time aggregation table and historical aggregation table is calculated using the Flink model to generate target calculation data corresponding to each device and transmit it to the database. The target calculation data output by the power data calculation module is stored. According to the candlestick chart configuration information, the target calculation data associated with the candlestick chart configuration information is obtained from the database to draw the power candlestick chart.

[0077] Viewing multiple data points on a single candlestick chart and establishing time-series correlations between various types of power data provides users with an intuitive visual experience, allowing them to directly perceive the fluctuations and impacts of power data. This facilitates analysis by professionals, reduces data omissions, and rapidly generates analytical reports, meeting the operational and management needs of the current system. It supports integrated control of the power system throughout the entire process; comprehensively coordinates horizontal collaboration and vertical integration of power grid operation operations and information; promotes flexible sharing of power grid operation information and flexible interaction of power grid operation operations, comprehensively improving the collaborative efficiency of power grid operation professionals; and fully utilizes the achievements of automation and intelligent technologies to enhance the intelligence level of power grid operation and intelligent auxiliary decision-making capabilities, continuously improving the safe, economical, high-quality, and environmentally friendly operation of the power grid.

[0078] This invention provides a candlestick chart visualization system based on power data. The target calculation data includes target real-time data and target historical data. The power data calculation module includes a real-time data calculation unit and a historical data calculation unit.

[0079] The real-time data calculation unit is used to calculate the aggregated data in each real-time aggregation table according to the indicator type in the K-line chart configuration information, and generate the target real-time data corresponding to each device and transmit it to the database through the Flink model.

[0080] The historical data calculation unit is used to calculate the aggregated data in the historical aggregation table based on the indicator types in the K-line chart configuration information, using the Flink model, to generate the target historical data corresponding to each device and transmit it to the database.

[0081] In this embodiment of the invention, based on the indicator type in the candlestick chart configuration information, the Flink model is used to calculate the aggregated data in each real-time aggregation table to generate target real-time data corresponding to each device and transmit it to the database. For example, if the indicator type in the candlestick chart configuration information is load, the Flink model is used to calculate the target load using the corresponding voltage and current, which is the target real-time data. Similarly, based on the indicator type in the candlestick chart configuration information, the Flink model is used to calculate the aggregated data in the historical aggregation table to generate target historical data corresponding to each device and transmit it to the database.

[0082] It is worth mentioning that you should refer to Figure 2 and Figure 3 Based on the K-line chart configuration, real-time power data is calculated and the result set is stored in the database. The minimum granularity of real-time power data calculation is 5 minutes. The real-time power data calculation is divided into two processes: calculation of current period data and calculation of non-current period data. The previous period data is cached in the relational database and continuously updated as new data is collected. After the current period data is converted into historical power data, the data in the relational database is cleared and transferred to the HBase database.

[0083] It should be noted that the historical power data includes all data from the start of telemetry data collection to the online position of the K-line chart.

[0084] It should be noted that the real-time power data calculation scheme uses the Flink model for synchronization and computation. Flink is a distributed computing framework based on streaming data processing, which can achieve efficient, reliable and scalable real-time data processing. In the construction of real-time data warehouse, using Flink for data stream processing can meet the requirements for data real-time performance, accuracy and complexity, and improve data processing efficiency and decision-making effectiveness. Data stream and state management are important components of Flink's real-time data processing.

[0085] Flink's data stream is a directed graph consisting of an infinite number of events, each containing a timestamp and event data. Flink operators can perform various transformations and computations on the data stream, such as map, filter, reduce, and join. Flink's state management enables the management of the data stream's state and historical data. For example, keyed state can be used to manage the state of each key in the stream, and fast checkpointing and restore operations are supported to ensure data reliability and consistency.

[0086] Flink also supports technologies such as windowing and time processing, connectors, and external system integration. Flink's windowing and time processing can perform time- and window-based aggregation and computation on data streams. Different time window aggregation operations can be implemented through window types such as Tumbling Windows, Sliding Windows, and Session Windows. Flink's connectors and external system integration can achieve integration with various data sources and data warehouses. Data can be read and written through connectors such as Kafka, Hadoop, and Elasticsearch.

[0087] The specific plan is as follows:

[0088] S1: Define the data stream and operators to implement the real-time data processing logic;

[0089] Data can be read through the Kafka Connector, and the map operator can be used to perform format conversion and cleaning on the data.

[0090] (1) Create the FLink execution environment;

[0091] (2) Obtain Kafka-related configurations from command-line parameters;

[0092] (3) Configure Kafka Consumer;

[0093] (4) Create FLinkKafkaConsumer;

[0094] (5) Read data from Kafka;

[0095] (6) Perform data format conversion and cleaning;

[0096] (7) Print the processed data;

[0097] (8) Performing tasks;

[0098] S2. Implement state management and fault tolerance mechanisms to ensure data reliability and consistency;

[0099] You can use Flink's built-in state backend to save the state to HDFS, RocksDB or other distributed file systems, or use external systems such as Redis or Cassandra as the state backend.

[0100] S3. Integrate the data source and data warehouse to enable data read and write operations;

[0101] Data can be read into a data stream using Flink's connectors and written to external storage HBase using Flink's output format.

[0102] Please see Figure 2 The data acquisition module collects real-time power data from the source data platform within the power system, outputting it to Kafka (a message engine system) by default, and then transmitting it to the FLink model for calculation. Based on pre-acquired candlestick chart configuration information, it performs candlestick data calculations and writes the result set to HBase, including detailed data. Simultaneously, the FLink model performs a full update of the real-time power data and caches it as historical power data. This allows the data acquisition module to collect historical power data from the MySQL cache and input it into the FLink model for calculations, while also writing the calculation results to HBase. Hive, a data warehouse tool based on Hadoop, is used for data extraction, transformation, and loading. It's a mechanism for storing, querying, and analyzing large-scale data stored in Hadoop. Here, it can be understood as a database composed of HBase and Hive, used to store the target calculation data output by the power data calculation module. Data can be transmitted to the front-end application via an interface.

[0103] For specific applications, please refer to Figure 3 The data acquisition module collects target power data from the source data platform within the power system, outputs it to Kafka (a message engine system) by default, and transmits it to the FLink model for calculation. It performs hourly time zone data calculations and writes the result set to MySQL. In MySQL, it executes data sets of daily K-level and above, and writes historical periodic data from MySQL to HBase. Data is written to MySQL at the hourly granularity. MySQL is a relational database management system; data above the current hourly K-level is aggregated in MySQL, and the final calculated target real-time data and target historical data are written to the database.

[0104] It is worth mentioning that, since the data is scattered after collection, it is not convenient for data management and application. The data aggregation module can be used to aggregate the data, making it easier to manage, apply and share the data.

[0105] This invention provides a candlestick chart visualization system based on power data, wherein the data aggregation module includes an aggregation unit, a classification module, and a matching mapping unit;

[0106] The aggregation unit is used to aggregate various real-time power data and historical power data based on the aggregated Cartesian product algorithm, and generate multiple initial real-time aggregated data and initial historical aggregated data.

[0107] It should be noted that the preferred data aggregation algorithm is the Cartesian product algorithm. The Cartesian product is a join algorithm with a special function in database queries. It generates an aggregation relationship between multiple tables, retrieving each row from each table to produce the result. The Cartesian product algorithm helps users better analyze data. This algorithm uses a SELECT statement to aggregate data from two or more tables, returning data from the first row to the last row of each table, generating a new table with the structure of the original table size multiplied by the number of records in each row. The specific steps are as follows:

[0108] S1. Generate a table from the source table by rows and columns: In order to achieve the Cartesian product, read data row by row from two or more source tables and generate a new table by rows and columns;

[0109] S2. Calculate results by cell: In order to generate the content of each table, calculate each cell in the table, that is, multiply the values ​​of each row and each column.

[0110] S3. Return the correct data: Finally, return the calculated results to the user using a SELECT statement.

[0111] The classification module is used to classify multiple initial real-time aggregated data and initial historical aggregated data according to data type, and generate multiple target real-time aggregated data and target historical aggregated data of the same type;

[0112] It should be noted that data types include, but are not limited to, integer types, floating-point types, date types, character types, and boolean types;

[0113] Integer types are the basic data types for computer storage and processing, including byte, short, int, long, etc. Integer types are used to store and process integer data and can represent any number.

[0114] Floating-point numbers are used to store and process real numbers, including float and double. Floating-point numbers are characterized by high precision and small storage space, making them suitable for scenarios that require precise calculations.

[0115] Date types include date, datetime, timestamp23015184, time, and year.

[0116] Character types are used to store and process character data, including char, etc.; character types can store character data.

[0117] Boolean types are used to store and process logical data, including boolean.

[0118] In this embodiment of the invention, multiple initial real-time aggregated data and initial historical aggregated data are classified according to data type to generate multiple target real-time aggregated data and target historical aggregated data of the same type. This step can be understood as a classification step, which classifies data of the same topic.

[0119] The matching and mapping unit is used to map the real-time aggregated data and historical aggregated data of each target respectively through the data adapter, and generate multiple real-time aggregated tables and historical aggregated tables.

[0120] In this embodiment of the invention, a data adapter maps categorized data on the same topic to the same table, generating multiple real-time aggregation tables and historical aggregation tables.

[0121] The present invention provides a candlestick chart visualization system based on power data, which also includes a data supplementation calculation module;

[0122] The data supplementation calculation module is used to detect target power data and, when data is missing, to calculate the supplementary data.

[0123] In this embodiment of the invention, a supplementary data calculation module is also provided, that is, a built-in data supplementation mechanism is included. When sampling data that has not been reported in a timely manner is detected, or data that cannot be reported temporarily due to system failure, the data supplementation mechanism will be activated to supplement the data and notify the offline calculation program to calculate this part of the data. The specific algorithm of the data supplementation mechanism is as follows:

[0124] S1. Data missing data discovered by technical verification is supplemented, and automatic or manual supplementation can be achieved through configuration.

[0125] S2. If a data transmission error is detected, the data can be retransmitted, and data for a certain period of time can be re-collected.

[0126] S3. Automatic Correction: Automatically corrects data within the specified range based on supplementary rules, supplementary scope, and supplementary time.

[0127] S4. The system will classify the missing data according to the extracted business system data and handle them differently depending on the type.

[0128] Specifically, please refer to Figure 4When supplementary data is received, the data supplementation process is initiated, and the calculation of the supplementary data is completed according to the K-line chart configuration. In the database, a program is written to detect missing data. A script notifies the pre-collection center to check for missing data, and then another script notifies the collection center to collect the missing data. The supplementary data is retrieved, parsed according to the configuration, and written to Hive. The Spark computing engine is then used to calculate historical data and supplementary data. The result set is written to HBase, detailed data is synchronized to HBase, and the Hive database is cleared.

[0129] The data supplementation calculation module includes a missing data detection unit, a complete indicator calculation unit, an indicator comparison unit, a manual supplementation unit, a missing type judgment unit, a first automatic supplementation unit, and a second automatic supplementation unit;

[0130] The missing data detection unit is used to detect the collected target power data. When missing data is detected, it obtains the amount of missing data and the amount of valid data associated with the target power data.

[0131] The complete indicator calculation unit is used to calculate the corresponding complete indicator based on the preset complete indicator function and the amount of missing data.

[0132] The default complete indicator function is:

[0133]

[0134] In the formula, X represents the complete indicator, B represents the amount of missing data, and A represents the amount of valid data.

[0135] In one example of this invention, within a certain time period, the theoretically required total amount of valid data is A, the total amount of missing data in the system is B, and x% represents the data integrity percentage. The calculation formula is as follows:

[0136]

[0137] The indicator comparison unit is used to compare complete indicators with preset standard indicators.

[0138] The manual data entry unit is used to output an alarm and perform manual data entry or retransmission if the complete index is less than or equal to the preset standard index.

[0139] The missing data type determination unit is used to determine the missing data type if the complete data index is greater than the preset standard index.

[0140] The first automatic data completion unit is used to correct the data using the Markov chain Monte Carlo method if the missing data type is random and scattered.

[0141] The second automatic data entry unit is used to perform automatic data entry if the missing data type is not random or scattered.

[0142] In this embodiment of the invention, the exact status of the data is determined based on the completeness indicator to facilitate supplementary recording according to different situations. If the completeness indicator is less than or equal to the preset standard indicator, it is considered completely random missing. This type of missing data is severe and cannot be repaired or improved, resulting in low filling efficiency. In this case, an alarm is output, and manual supplementary recording or retransmission is performed. If the completeness indicator is greater than the preset standard indicator, and the missing data is randomly scattered, the Markov Chain Monte Carlo (MCMC) method based on the assumption of multivariate normality will be used to correct the data.

[0143] In one example of the present invention, if X≤70%, it is considered completely random and an alarm is generated directly, requiring the user to manually fill in the missing data and retransmit it.

[0144] If X > 70% and the missing data are scattered randomly, the Markov Chain Monte Carlo (MCMC) method based on the assumption of multivariate normality will be used to correct the data.

[0145] This invention provides a candlestick chart visualization system based on power data, and the configurable candlestick chart module includes a power candlestick chart drawing sub-module;

[0146] The power candlestick chart drawing submodule includes a rectangular column drawing unit, an extraction unit, a power data comparison unit, a first coloring unit, a second coloring unit, a moving average drawing unit, and a power candlestick chart coupling unit;

[0147] The rectangular column drawing unit is used to retrieve multiple target calculation data associated with the K-line chart configuration information from the database, with time represented by the horizontal axis and numerical value represented by the vertical axis, and draw multiple rectangular columns corresponding to each device individually or in combination using the target calculation data.

[0148] In this embodiment of the invention, based on the K-line chart configuration information, with the horizontal axis representing time and the vertical axis representing values, multiple target calculation data associated with the K-line chart configuration information are obtained from the database, and multiple rectangular bars corresponding to each device individually or in combination are drawn using each target calculation data.

[0149] It is worth mentioning that, based on the plant number, power equipment type, indicator type, and whether it is a composite indicator in the K-line chart configuration information, multiple target calculation data associated with the K-line chart configuration information are obtained from the database.

[0150] For example, if it is not a composite indicator, the configuration path for the candlestick chart is: "Select Plant - Select Equipment - Select Indicator";

[0151] If it is a composite indicator, the configuration path for the K-line chart is: "Select plant - Select equipment - Select indicator - [Select equipment - Select indicator] - Set calculation formula"; for example, if the sum of "load of feeder A + load of feeder B" is used as the composite indicator, and the indicator data operation of multiple feeders is analyzed, the calculation formula is: Composite indicator = load of feeder A + load of feeder B.

[0152] It should be noted that, based on the obtained candlestick chart configuration information, the number of rectangular bars to be drawn depends on whether it is a composite indicator, so that they can be coupled onto the same power candlestick chart. Here, composite indicators can be understood as overlayable indicators, such as active power generation, reactive power generation, etc., and can also be configured with the relevant calculations required for auxiliary lines, such as moving averages, month-on-month comparisons, year-on-year comparisons, etc.

[0153] It should be noted that when sending data to the front-end for display, multiple candlestick charts can be configured on a single page, meeting users' needs to view multiple indicator data on one page. Establishing time-related correlations between various types of power data provides users with an intuitive visual experience, allowing them to directly perceive the fluctuations and impacts brought about by power data. Viewing multiple power data points on a single candlestick chart facilitates analysis work for researchers, reduces data omissions, and enables the rapid generation of analysis reports.

[0154] The extraction unit is used to extract the maximum and minimum power values ​​from the calculation data of each target, and connect them vertically with a straight line.

[0155] The power data comparison unit is used to compare the power cutoff value with the power start value in the calculation data of each target.

[0156] The first coloring unit is used to color the associated rectangular column with a first preset color if the power cut-off value is greater than the power start value.

[0157] The second coloring unit is used to color the associated rectangular column with a second preset color if the power cut-off value is less than or equal to the power start value.

[0158] The moving average plotting unit is used to calculate the moving average line over a specified period of time based on the calculation data of each target.

[0159] It is worth mentioning that calculating the moving average over a specified period is a standard mean calculation in this field. The target data for the mean calculation is selected according to the indicator type, which will not be elaborated here.

[0160] Moving averages are used to display the average value and trend of power data within a certain time period. Users can search for K-line data of all configured indicators for a region, various power plants, and individual equipment. The time axis allows viewing data within any time range and all historical data, enabling the stitching and display of real-time and historical data. Combined with regional data, spatiotemporal data stitching and display are also possible. Users can zoom in and out of the K-line chart and drag to view the data, making it easy to view data within a time range. Hovering the mouse over the K-line chart displays an indicator floating window, showing the indicator data at the mouse hover point, such as maximum, minimum, starting, ending, change, and rate of change. The K-line chart displays the maximum and minimum values ​​within the selected time range.

[0161] The power candlestick chart coupling unit is used to couple moving average lines with associated rectangular bars to generate power candlestick charts.

[0162] The present invention provides a candlestick chart visualization system based on power data, which also includes a retrieval function module;

[0163] The search function module is used to perform searches based on the search type corresponding to the search request and determine the target search information.

[0164] A search request refers to the request information used to perform a search.

[0165] Search types include fuzzy search, exact search, timeline search, and template search;

[0166] In this embodiment of the invention, when the search type is fuzzy search, keywords can be entered to associate the target query dimensions and indicators; when the search type is exact search, indicators under each dimension can be viewed by region, factory, equipment, etc.; when the search type is time axis search, a time range can be set and time blocks can be dragged to view historical data; when the search type is template search, multiple pre-saved query templates can be used to quickly switch to the dimensions and indicator data that are of daily interest.

[0167] The present invention provides a candlestick chart visualization system based on power data, which also includes a hot search module;

[0168] The trending search module is used to count and display the number of searches based on the type of indicator.

[0169] In this embodiment of the invention, the number of searches is counted according to the indicator type and displayed to facilitate viewing the user search popularity of various dimensions and indicators across multiple time ranges. This allows us to understand which indicators users care about, and based on the hot search data, we can perform functional mining on key indicators to meet user needs.

[0170] Please see Figure 5 , Figure 5 The flowchart illustrates the steps of a candlestick chart visualization method based on power data, as provided in this embodiment of the invention.

[0171] This invention provides a candlestick chart visualization method based on power data, applied to a candlestick chart visualization system based on power data. The system includes a data acquisition module, a data aggregation module, a power data calculation module, a database, and a configurable candlestick chart module, all connected in sequence via communication. The method includes:

[0172] Step 201: Collect target power data and K-line chart configuration information of various devices in the power system through the data acquisition module. The target power data includes real-time power data and historical power data.

[0173] Step 202: Aggregate the real-time power data and historical power data through the data aggregation module to generate multiple real-time aggregation tables and historical aggregation tables.

[0174] Step 203: Based on the indicator types in the K-line chart configuration information, the power data calculation module calculates the aggregated data in each real-time aggregation table and historical aggregation table using the Flink model, generates the target calculation data corresponding to each device, and transmits it to the database.

[0175] Step 204: Store the target calculation data output by the power data calculation module in the database.

[0176] Step 205: Using the configurable candlestick chart module, the target calculation data associated with the candlestick chart configuration information is retrieved from the database and used to draw the power candlestick chart based on the candlestick chart configuration information.

[0177] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0178] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0179] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0180] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0181] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A candlestick chart visualization system based on power data, characterized in that, It includes a data acquisition module, a data aggregation module, a power data calculation module, a database, a configurable candlestick chart module, and a supplementary data calculation module, which are connected in sequence via communication. The data acquisition module is used to collect target power data and K-line chart configuration information of various devices in the power system. The target power data includes real-time power data and historical power data, and the K-line chart configuration information includes indicator types and calculation formulas for combining multiple indicators. The data aggregation module is used to aggregate the real-time power data and the historical power data to generate multiple real-time aggregation tables and historical aggregation tables. The data aggregation module includes an aggregation unit, a classification module, and a matching mapping unit; The aggregation unit is used to aggregate the real-time power data and the historical power data based on the aggregated Cartesian product algorithm to generate multiple initial real-time aggregated data and initial historical aggregated data. The classification module is used to classify multiple initial real-time aggregated data and initial historical aggregated data according to data type, and generate multiple target real-time aggregated data and target historical aggregated data of the same type; The matching mapping unit is used to map each of the target real-time aggregated data and the target historical aggregated data respectively through a data adapter, and generate multiple real-time aggregated tables and historical aggregated tables; The power data calculation module is used to calculate the aggregated data in each of the real-time aggregation tables and the historical aggregation tables according to the indicator types and calculation formulas in the K-line chart configuration information through the Flink model. When the K-line chart configuration information is set as a composite indicator, the module generates the target calculation data corresponding to the composite indicator according to the calculation formula and transmits it to the database. The database is used to store the target calculation data output by the power data calculation module; The configurable candlestick chart module is used to draw an electric power candlestick chart by retrieving the target calculation data associated with the candlestick chart configuration information from the database according to the candlestick chart configuration information. The target calculation data includes target real-time data and target historical data, and the power data calculation module includes a real-time data calculation unit and a historical data calculation unit. The real-time data calculation unit is used to calculate the aggregated data in each of the real-time aggregation tables according to the indicator types in the K-line chart configuration information, and generate the target real-time data corresponding to each of the devices and transmit it to the database. The historical data calculation unit is used to calculate the aggregated data in the historical aggregation table according to the indicator type in the K-line chart configuration information, through the Flink model, to generate the target historical data corresponding to each device and transmit it to the database; The supplementary data calculation module is used to detect the target power data, and when data is missing, it completes the calculation of supplementary data. The missing data detection unit is used to detect the collected target power data. When missing data is detected, it obtains the amount of missing data and the amount of valid data associated with the target power data. The complete indicator calculation unit is used to calculate the corresponding complete indicator based on the missing data volume using a preset complete indicator function. The default complete indicator function is: In the formula, This refers to the complete indicator. This indicates the amount of missing data. This indicates the amount of valid data; The indicator comparison unit is used to compare the complete indicator with the preset standard indicator; The manual supplementary recording unit is used to output an alarm and perform manual supplementary recording or retransmission if the complete index is less than or equal to the preset standard index. The missing data type determination unit is used to determine the missing data type if the complete index is greater than the preset standard index. The first automatic data entry unit is used to correct the data using the Markov chain Monte Carlo method if the missing data type is random and scattered. The second automatic data entry unit is used to perform automatic data entry if the missing data type is not random.

2. The K-line chart visualization system based on power data according to claim 1, characterized in that, The configurable candlestick chart module includes a power candlestick chart drawing sub-module; The power candlestick chart drawing submodule includes a rectangular column drawing unit, an extraction unit, a power data comparison unit, a first coloring unit, a second coloring unit, a moving average drawing unit, and a power candlestick chart coupling unit; The rectangular column drawing unit is used to obtain multiple target calculation data associated with the K-line chart configuration information from the database, with the horizontal axis representing time and the vertical axis representing values, according to the K-line chart configuration information, and to draw multiple rectangular columns corresponding to each device individually or in combination using each target calculation data; The extraction unit is used to extract the maximum and minimum power values ​​from each of the target calculation data, and connect them vertically into a straight line. The power data comparison unit is used to compare the power cutoff value and the power start value in each of the target calculation data respectively. The first coloring unit is used to color the associated rectangular column with a first preset color if the power cut-off value is greater than the power start value; The second coloring unit is used to color the associated rectangular column with a second preset color if the power cut-off value is less than or equal to the power start value; The moving average drawing unit is used to calculate a moving average line within a specified time period based on each of the target calculation data. The power candlestick chart coupling unit is used to couple the moving average line with the associated rectangular column to generate a power candlestick chart.

3. The K-line chart visualization system based on power data according to claim 1, characterized in that, It also includes a search function module; The retrieval function module is used to perform retrieval based on the retrieval type corresponding to the retrieval request and determine the target retrieval information.

4. The K-line chart visualization system based on power data according to claim 3, characterized in that, It also includes a trending topics module; The trending search module is used to count the number of searches based on the indicator type and then display the results.

5. A candlestick chart visualization method based on power data, characterized in that, The method is applied to the power data-based candlestick chart visualization system as described in any one of claims 1-4, wherein the power data-based candlestick chart visualization system comprises a data acquisition module, a data aggregation module, a power data calculation module, a database, and a configurable candlestick chart module that are sequentially and communicatively connected, and the method includes: The data acquisition module collects target power data and candlestick chart configuration information from various devices in the power system, wherein the target power data includes real-time power data and historical power data. The data aggregation module aggregates the real-time power data and the historical power data to generate multiple real-time aggregation tables and historical aggregation tables. The power data calculation module calculates the aggregated data in each of the real-time aggregation tables and the historical aggregation tables based on the indicator types in the K-line chart configuration information using the Flink model, generates the target calculation data corresponding to each device, and transmits it to the database. The target calculation data output by the power data calculation module is stored in the database; The configurable candlestick chart module retrieves the target calculation data associated with the candlestick chart configuration information from the database based on the candlestick chart configuration information to draw an electric power candlestick chart.

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