Report visualization method and device, equipment, medium and product

Abstract

The invention discloses a report visualization method and device, equipment, a medium and a product. The method comprises the following steps: in response to a configuration operation of a target user, determining a visual component, a data field and an interaction rule selected by the target user; monitoring data and index data corresponding to the data fields in a preset time period are determined, abnormal data are determined from the monitoring data based on a preset exception screening strategy, and whether report generation conditions are met or not is determined according to the monitoring data and / or a preset period in combination with report generation rules in the interaction rules; and if yes, performing report visualization on the monitoring data, the index data and the abnormal data based on a visualization component according to an abnormal annotation rule in the interaction rule. According to the technical scheme, the intelligent report generation system can interact with the user, the report is automatically generated and visualized, the report generation efficiency and accuracy are improved, and the user can make a decision conveniently.

Description

Technical Field

[0001] The present invention relates to the field of big data, and in particular to a report visualization method, device, equipment, medium and product. Background Art

[0002] With the continuous development of big data technology, many business systems involve data analysis and visualization, especially in industries such as new energy and manufacturing. Business personnel often need to regularly review the resulting visualization reports to make better production decisions. However, traditional report production suffers from high technical barriers, lack of real-time performance, and frequent manual intervention.

[0003] Therefore, how to use the intelligent report production system to interact with users, automatically produce and visualize reports, improve report generation efficiency and accuracy, and facilitate user decision-making is a problem that needs to be solved urgently. Summary of the Invention

[0004] The present invention provides a report visualization method, device, equipment, medium and product, which utilize an intelligent report production system to interact with users, automatically generate and visualize reports, improve report generation efficiency and accuracy, and facilitate user decision-making.

[0005] According to one aspect of the present invention, a report visualization method is provided, comprising:

[0006] In response to a configuration operation by a target user, determining a visualization component, data field, and interaction rule selected by the target user;

[0007] Determine the monitoring data and indicator data corresponding to the data field within a preset time period, and based on the preset abnormality screening strategy, determine abnormal data from the monitoring data, and determine whether the report generation conditions are met based on the monitoring data and / or the preset period, combined with the report generation rules in the interaction rules;

[0008] If so, the monitoring data, indicator data and abnormal data are visualized in reports based on the visualization component according to the abnormality annotation rules in the interaction rules.

[0009] According to another aspect of the present invention, there is provided a report visualization device, comprising:

[0010] a determination module, configured to determine the visualization component, data field, and interaction rule selected by the target user in response to a configuration operation by the target user;

[0011] A judgment module is used to determine the monitoring data and indicator data corresponding to the data field within a preset time period, and based on a preset abnormality screening strategy, determine abnormal data from the monitoring data, and determine whether the report generation conditions are met based on the monitoring data and / or the preset period, combined with the report generation rules in the interaction rules;

[0012] The visualization module is used to visualize the monitoring data, indicator data and abnormal data in reports based on the visualization components according to the abnormality annotation rules in the interaction rules.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the report visualization method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the report visualization method according to any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the report visualization method according to any embodiment of the present invention is implemented.

[0019] The technical solution of the embodiment of the present invention determines the visualization components, data fields and interaction rules selected by the target user in response to the configuration operation of the target user; determines the monitoring data and indicator data corresponding to the data fields within a preset time period, and based on the preset abnormality screening strategy, determines the abnormal data from the monitoring data, and determines whether the report generation conditions are met based on the monitoring data and / or the preset period, combined with the report generation rules in the interaction rules; if so, the monitoring data, indicator data and abnormal data are visualized in reports based on the visualization components according to the abnormality annotation rules in the interaction rules. By utilizing the intelligent report production system to interact with the user, the monitoring data can be analyzed and reports can be automatically generated for visualization, thereby improving the efficiency and accuracy of report generation, helping users to intuitively understand the data situation, and facilitating user decision-making.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is a flow chart of a report visualization method provided by the first embodiment of the present invention;

[0023] Figure 2 This is a flow chart of a report visualization method provided by the second embodiment of the present invention;

[0024] Figure 3 This is a structural block diagram of a report visualization device provided by the third embodiment of the present invention;

[0025] Figure 4 It is a structural diagram of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", "target", "candidate", "alternative", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices. The acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of laws and regulations.

[0028] It should be noted that in the report production system in the related art, most of its reports are offline batch processing, which is not real-time enough, and data collection, generation, and distribution rely on manual operations, with weak automation capabilities. At the same time, there is a problem of a single output format, resulting in poor scenario adaptability. To address the above problems, the present invention proposes a solution that uses an intelligent report production system to interact with users to automatically generate and visualize reports, which can improve the efficiency and accuracy of report generation and facilitate user decision-making. The specific implementation method will be introduced in detail in subsequent embodiments.

[0029] Example 1

[0030] Figure 1 This is a flow chart of a report visualization method provided by the first embodiment of the present invention; this embodiment is applicable to situations where an intelligent report production system interacts with a user to automatically generate and visualize reports. The method can be executed by a report visualization device, which can be implemented in the form of hardware and / or software. The report visualization device can be configured in an electronic device, such as an electronic device configured with an intelligent report production system, and executed by the intelligent report production system, such as Figure 1 As shown, the report visualization method includes:

[0031] S101 : In response to a configuration operation of a target user, determining a visualization component, data field, and interaction rule selected by the target user.

[0032] Among them, target users refer to users who interact with the intelligent report production system. Target users can specifically be users with report query needs or business personnel who manage report distribution. Target users can correspond to different user roles, such as managers, analysts, and operation and maintenance personnel. Configuration operations refer to operations performed by target users to configure the display of reports in the intelligent report production system. Configuration operations can include, for example, operations such as dragging components, selecting fields, and entering rules by target users in the intelligent report production system. Visualization components refer to components used to display reports in a corresponding display method. Visualization components can include, for example, line charts, tables, dashboards, pie charts, and bar charts.

[0033] A data field refers to the name of the data that the target user requests for visualization. Data fields can include at least one of the following: equipment operation data, meteorological data, and user role information. Equipment operation data can include at least one of the following: wind turbine power, wind turbine power generation, photovoltaic inverter efficiency, downtime, and equipment operating time. Meteorological data can include data such as wind speed, temperature, and humidity. User role information can include, for example, manager, analyst, or operation and maintenance personnel. Interaction rules refer to the rules configured by the target user for setting the method for report generation and the method for anomaly data annotation. Interaction rules can include report generation rules and anomaly annotation rules.

[0034] Optionally, the visualization component selected by the target user through the graphical drag operation can be obtained, that is, the visualization component selected by the target user is determined, and the data fields and interaction rules input by the target user through clicking or typing are obtained.

[0035] For example, the target user (such as an operator) can drag the "table component" and "line chart component" from the component library to the canvas, and bind the "device ID (Identity document, unique code)", "downtime", "utilization" fields and "time-power generation" data respectively; set "generate weekly report at 0:00 every Friday" and configure it to be automatically sent to the preset mailbox. The intelligent report production system can respond to the configuration operation of the target user and execute the report visualization solution described in the embodiment of the present invention to perform report visualization.

[0036] For example, a target user (such as an analyst) can create a "photovoltaic module health template", drag a "scatter plot component", associate the "light intensity" and "corrected power" fields, set the abnormal marking rule of "mark red when the deviation is >15%", and select HTML (Hyper Text Markup Language) format output, and configure "automatic refresh every 5 minutes". In response to the configuration operation of the target user, the intelligent report production system can execute the report visualization solution described in the embodiment of the present invention to perform report visualization.

[0037] S102. Determine the monitoring data and indicator data corresponding to the data field within a preset time period, and based on a preset abnormality screening strategy, determine abnormal data from the monitoring data, and determine whether the report generation conditions are met based on the monitoring data and / or the preset period, combined with the report generation rules in the interaction rules.

[0038] Among them, the target user can choose to visualize the data of the preset historical time period in the report, or set the data of the future preset time period to be collected and visualized. Accordingly, the preset time period refers to the data visualization time period specified by the target user, for example, it can be a preset historical time period or a preset future time period. Report generation rules refer to rules that characterize report generation conditions. For example, report generation rules can be to generate a report when the equipment utilization rate is less than a preset utilization rate threshold (such as <80%). The report generation condition can be a timed generation condition based on a preset period, a conditional generation condition based on a preset judgment process, or a temporary generation condition triggered in response to the instruction operation of relevant personnel. The present invention does not limit this.

[0039] A preset anomaly screening strategy is a strategy for filtering abnormal data from monitoring data. This strategy can be based on an anomaly threshold, a preset anomaly filtering algorithm, or both. Equipment operating data can include fan bearing temperature.

[0040] Optionally, the intelligent report production system can periodically acquire monitoring data and store it using corresponding storage strategies. If the preset time period is a preset historical time period, the monitoring data within the corresponding historical time period can be acquired from the corresponding database or storage system based on the content of the data field. If the preset time period is a preset future time period, the corresponding data collection strategy can be used in the corresponding time period to collect data to obtain monitoring data.

[0041] Optionally, the intelligent report production system can periodically collect data and store the user configuration information, equipment operation data, report files and real-time data in the collected data in a relational database, a time series database, a file storage system and a cache system respectively. When the preset time period is a preset historical time period, monitoring data can be obtained from the corresponding database or storage system according to the field content of the data field. For example, if the field content of the data field is equipment operation data, the data of the preset historical time period can be obtained from the time series database to determine the monitoring data.

[0042] Optionally, when the preset time period is a preset future time period, the monitoring data and indicator data corresponding to the data field within the preset time period are determined, including: according to the field content of the data field, corresponding data collection strategies are used to collect data within the preset time period to obtain collected data, and missing filling processing is performed on the collected data to obtain monitoring data; based on the preset indicator calculation rules, indicator calculation is performed on the monitoring data to obtain indicator data.

[0043] The indicator data may include at least one of the following: equipment utilization indicator data, power generation efficiency indicator data, and related indicator data. The data collection strategy can be based on an edge computing gateway, a preset API (Application Programming Interface), or a strategy that uses a multi-source connector to collect data from a preset time series database and relational data.

[0044] Optionally, if the field content of the data field is device operation data, the corresponding data collection strategy can be a collection strategy based on the edge computing gateway. Specifically, the protocol data can be obtained through the preset edge computing gateway and converted into MQTT (Message Queuing Telemetry Transport) messages to obtain device operation data.

[0045] Optionally, a missing value filling process may be performed on the collected data after outlier filtering based on a linear interpolation method to obtain monitoring data.

[0046] Optionally, based on preset indicator calculation rules, indicator calculation is performed on the monitoring data to obtain indicator data, including: determining the effective operating time of the target equipment based on the equipment operation data in the monitoring data, and performing indicator calculation based on the equipment utilization indicator rules to determine the equipment utilization indicator data corresponding to the target equipment; determining the AC side power and DC side power of the target equipment based on the equipment operation data in the monitoring data, and obtaining power generation efficiency indicator data based on the AC side power and DC side power calculated based on the preset calculation rules; determining the correlation indicator data between the equipment operation data and the meteorological data in the monitoring data based on the preset causal detection strategy.

[0047] The target device refers to the operating device corresponding to the device operating data, and may refer to industrial equipment involved in the new energy or manufacturing industries. For example, the target device may be a wind turbine, a photovoltaic inverter, or an electrical device. The preset causal detection strategy may be, for example, a Granger causality test.

[0048] For example, the influence relationship between wind turbine power and wind speed can be identified based on the Granger causality test, that is, correlation index data between equipment operation data and meteorological data in the monitoring data can be obtained.

[0049] Optionally, according to the effective operating time of each target device recorded in the device operation data, the device utilization corresponding to the target device can be calculated based on the following formula: device utilization = effective operating time / 24×100% to determine the device utilization indicator data corresponding to the target device.

[0050] Optionally, based on the AC side power and DC side power in the equipment operation data, the power generation efficiency corresponding to the target equipment can be calculated based on the following formula: power generation efficiency = AC side power / DC side power × 100% to determine the power generation efficiency index data.

[0051] Optionally, when the preset anomaly screening strategy is a screening strategy based on an anomaly threshold and a preset anomaly value filtering algorithm, abnormal data is determined from the monitoring data based on the preset anomaly screening strategy, including: based on the preset anomaly threshold, invalid data in the monitoring data is determined, and the invalid data is discarded to update the monitoring data; based on the preset anomaly value filtering algorithm, the mean and standard deviation of the monitoring data in the preset sliding window are determined according to the updated monitoring data, and based on the mean and standard deviation, the abnormal data is determined from the monitoring data based on the preset filtering rules.

[0052] The abnormality threshold may be a threshold value pre-set for each data field to indicate whether the data is abnormal. For example, when the filter power value in the device operation data exceeds the rated value of 120, the corresponding device operation data may be considered invalid. The preset outlier filtering algorithm may be, for example, a Z-score method (also known as a standard score method) based on a sliding window. The preset sliding window refers to a preset time period for data analysis. The preset sliding window may be, for example, a 30-minute sliding window.

[0053] Optionally, based on the mean and standard deviation, abnormal data in the monitoring data can be determined based on the following preset filtering rule |X-μ|>3σ, where μ represents the mean, σ represents the standard deviation, and X represents the abnormal data determined in the monitoring data.

[0054] Optionally, based on a preset abnormality screening strategy, data with obvious abnormalities in the collected data can be filtered. For example, if the device status code in the device operation data is "999", the data can be directly determined to be invalid data. It should be noted that the present invention performs abnormal annotation and visualization on data within the normal abnormal fluctuation range, and directly filters abnormal data that is obviously invalid for data analysis, thereby improving the efficiency of subsequent data analysis.

[0055] Optionally, it is possible to determine whether the time interval between the current moment and the last report generation reaches a preset period. If so, it is determined whether the report generation conditions are met based on the monitoring data and the report generation rules in the interaction rules. That is, it is determined whether the report generation conditions are met based on the monitoring data and the preset period and the report generation rules in the interaction rules.

[0056] Optionally, when it is determined that the time interval between the current moment and the last report generation reaches a preset period, the report visualization operation described in S103 of the embodiment of the present invention may be directly executed in combination with the report generation rule in the interaction rule to perform report visualization.

[0057] Optionally, based on the monitoring data and in combination with the report generation rules in the interaction rules, determine whether the report generation conditions are met, including: analyzing the equipment operation data in the monitoring data based on the preset health model and fault prediction model to determine the health score and predicted failure rate corresponding to the target equipment; determining the preset score threshold and the preset fault threshold according to the report generation rules, and determining whether the report generation conditions are met based on the correlation between the health score and the preset score threshold, and the correlation between the predicted failure rate and the preset fault threshold.

[0058] The preset health model can be a model that analyzes the failure frequency of target equipment to assess its health. For example, the preset health model can be based on the Analytic Hierarchy Process (AHP) to dynamically adjust indicator weights (e.g., power generation accounts for 40% and the number of failures accounts for 30%) to generate a device health score. The fault prediction model can be a model used to predict the failure rate of the target equipment in a preset time period in the future. For example, the fault prediction model can be an LSTM (Long Short-Term Memory) network.

[0059] Optionally, a fault prediction model can be trained based on the historical operating data (such as power, temperature, vibration frequency, etc.) and actual failure rate corresponding to the target device, and the device operating data in the monitoring data can be input into the fault prediction model to obtain the predicted failure rate.

[0060] Optionally, the report generation condition may be determined to be met when the predicted failure rate is greater than a preset failure threshold, or when the health score is less than a preset score threshold. The report generation condition may also be determined to be met when the predicted failure rate is greater than the preset failure threshold and the health score is less than the preset score threshold. The present invention does not impose any restrictions on this.

[0061] S103: If yes, then perform report visualization on the monitoring data, indicator data and abnormal data based on the visualization component according to the abnormality marking rule in the interaction rule.

[0062] Among them, the anomaly labeling rule refers to the rule for labeling abnormal data in the monitoring data so that the target user can intuitively understand the data anomaly.

[0063] Optionally, according to the exception labeling rules in the interaction rules, the monitoring data, indicator data and exception data are visualized in reports based on the visualization component, including: determining the default labeling method corresponding to the visualization component, and determining the exception labeling method corresponding to the exception data based on the exception labeling rules in the interaction rules; based on the visualization component, according to the default labeling method, other monitoring data and indicator data in the monitoring data except the exception data are visualized in reports, and the exception data is visualized in reports based on the exception labeling method.

[0064] The default annotation method refers to the default data display method corresponding to the visualization component. For example, if the visualization component is a line chart, the default annotation method may be a method of displaying data by connecting each monitoring data with a black line segment.

[0065] Optionally, based on the visualization component, a report visualization is performed for all monitoring data and indicator data other than abnormal data in the monitoring data according to the default annotation method, and after the abnormal data is visualized according to the abnormal annotation method, the corresponding report output format can be determined based on the role type of the target user, and the report visualization can be further performed according to the default annotation method, the report output format, and the abnormal annotation method. The report output format can be PDF (Portable Document Format), Excel (spreadsheet), or HTML format.

[0066] Optionally, the monitoring data, abnormal data and indicator data can be filtered according to the role type of the target user, the visualization data within the target user's authority range can be determined, and the report output format corresponding to the role type of the target user can be determined, and the report visualization can be further performed according to the default annotation method, report output format and abnormal annotation method.

[0067] For example, if the target user's role type is a manager, the report output format can be determined to be PDF format, and the main visualization is for summary charts. Decision recommendations can also be generated and displayed in combination with indicator data; if the target user's role type is an analyst, the report output format can be determined to be EXCEL format, so that the target user can reuse formulas and visualize the full amount of data; if the target user's role category is an operator, the report output format can be determined to be HTML format, so that the target user can conduct real-time interactive dashboards.

[0068] The technical solution of the embodiment of the present invention determines the visualization components, data fields and interaction rules selected by the target user in response to the configuration operation of the target user; determines the monitoring data and indicator data corresponding to the data fields within a preset time period, and based on the preset abnormality screening strategy, determines the abnormal data from the monitoring data, and determines whether the report generation conditions are met based on the monitoring data and / or the preset period, combined with the report generation rules in the interaction rules; if so, the monitoring data, indicator data and abnormal data are visualized in reports based on the visualization components according to the abnormality annotation rules in the interaction rules. By utilizing the intelligent report production system to interact with the user, the monitoring data can be analyzed and reports can be automatically generated for visualization, thereby improving the efficiency and accuracy of report generation, helping users to intuitively understand the data situation, and facilitating user decision-making.

[0069] Example 2

[0070] Figure 2 This is a flow chart of a report visualization method provided by the second embodiment of the present invention; based on the above embodiment, this embodiment provides a preferred example of report visualization, specifically, Figure 2 As shown, the method includes the following processes:

[0071] S201 : In response to a configuration operation of a target user, determining a visualization component, data field, and interaction rule selected by the target user.

[0072] S202: According to the field content of the data field, corresponding data collection strategies are respectively adopted to collect data within a preset time period to obtain collected data, and missing filling processing is performed on the collected data to obtain monitoring data.

[0073] S203: Determine the effective operating time of the target device according to the device operating data in the monitoring data, and perform an indicator calculation based on the device utilization indicator rule to determine the device utilization indicator data corresponding to the target device.

[0074] S204. Determine the AC side power and DC side power of the target device according to the device operation data in the monitoring data, and calculate the power generation efficiency index data based on the AC side power and the DC side power according to the preset calculation rules.

[0075] S205: Determine correlation index data between equipment operation data and meteorological data in the monitoring data based on a preset causal detection strategy.

[0076] S206: Based on a preset abnormality threshold, invalid data in the monitoring data is determined, and the invalid data is discarded to update the monitoring data.

[0077] S207: Based on a preset outlier filtering algorithm, determine the mean and standard deviation of the monitoring data in a preset sliding window according to the updated monitoring data, and determine abnormal data from the monitoring data based on the mean and standard deviation and preset filtering rules.

[0078] S208: Analyze the device operation data in the monitoring data based on the preset health model and fault prediction model to determine the health score and predicted failure rate corresponding to the target device.

[0079] S209: Determine a preset score threshold and a preset fault threshold according to the report generation rule, and determine whether the report generation condition is met based on the correlation between the health score and the preset score threshold, and / or the correlation between the predicted failure rate and the preset fault threshold.

[0080] S210: If yes, then perform report visualization on the monitoring data, indicator data and abnormal data based on the visualization component according to the abnormality annotation rule in the interaction rule.

[0081] Example 3

[0082] Figure 3This is a structural block diagram of a report visualization device provided by the third embodiment of the present invention; this embodiment is applicable to situations where an intelligent report production system interacts with a user to automatically generate and visualize reports. The report visualization device provided by the embodiment of the present invention can execute the report visualization method provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method; the report visualization device can be implemented in the form of hardware and / or software, and configured in an electronic device with a report visualization function, such as an electronic device configured with an intelligent report production system, and executed by the intelligent report production system, such as Figure 3 As shown, the report visualization device may include:

[0083] Determination module 301, for determining the visualization component, data field, and interaction rule selected by the target user in response to the configuration operation of the target user;

[0084] The judgment module 302 is used to determine the monitoring data and indicator data corresponding to the data field within a preset time period, and based on the preset abnormality screening strategy, determine abnormal data from the monitoring data, and determine whether the report generation conditions are met based on the monitoring data and / or the preset period, combined with the report generation rules in the interaction rules;

[0085] The visualization module 303 is configured to, if yes, perform report visualization on the monitoring data, indicator data and abnormal data based on the visualization component according to the abnormality marking rule in the interaction rule.

[0086] The technical solution of the embodiment of the present invention determines the visualization components, data fields and interaction rules selected by the target user in response to the configuration operation of the target user; determines the monitoring data and indicator data corresponding to the data fields within a preset time period, and based on the preset abnormality screening strategy, determines the abnormal data from the monitoring data, and determines whether the report generation conditions are met based on the monitoring data and / or the preset period, combined with the report generation rules in the interaction rules; if so, the monitoring data, indicator data and abnormal data are visualized in reports based on the visualization components according to the abnormality annotation rules in the interaction rules. By utilizing the intelligent report production system to interact with the user, the monitoring data can be analyzed and reports can be automatically generated for visualization, thereby improving the efficiency and accuracy of report generation, helping users to intuitively understand the data situation, and facilitating user decision-making.

[0087] Furthermore, the visualization module 303 is specifically configured to:

[0088] Determine the default annotation method corresponding to the visualization component, and determine the exception annotation method corresponding to the abnormal data based on the exception annotation rules in the interaction rules;

[0089] Based on the visualization component, the monitoring data and indicator data other than the abnormal data in the monitoring data are visualized in reports according to the default annotation method, and the abnormal data are visualized in reports based on the abnormal annotation method.

[0090] Furthermore, the determination module 302 may include:

[0091] The obtaining unit is used to collect data within a preset time period using a corresponding data collection strategy according to the field content of the data field to obtain collected data, and perform missing filling processing on the collected data to obtain monitoring data;

[0092] The calculation unit is used to perform indicator calculation on the monitoring data based on preset indicator calculation rules to obtain indicator data.

[0093] Furthermore, the computing unit is specifically used for:

[0094] Determine the effective operating time of the target device based on the device operating data in the monitoring data, and perform indicator calculation based on the device utilization indicator rules to determine the device utilization indicator data corresponding to the target device;

[0095] Determine the AC side power and DC side power of the target device based on the device operation data in the monitoring data, and calculate the power generation efficiency index data based on the AC side power and DC side power according to the preset calculation rules;

[0096] Based on the preset causal detection strategy, the correlation indicator data between the equipment operation data and meteorological data in the monitoring data is determined.

[0097] Furthermore, the judgment module 302 is further configured to:

[0098] Based on the preset abnormality threshold, invalid data in the monitoring data is determined and discarded to update the monitoring data;

[0099] Based on the preset outlier filtering algorithm, the mean and standard deviation of the monitoring data in the preset sliding window are determined according to the updated monitoring data, and based on the mean and standard deviation, the abnormal data is determined from the monitoring data based on the preset filtering rules.

[0100] Furthermore, the judgment module 302 is further configured to:

[0101] Based on the preset health model and fault prediction model, the equipment operation data in the monitoring data is analyzed to determine the health score and predicted failure rate corresponding to the target equipment;

[0102] The preset scoring threshold and the preset fault threshold are determined according to the report generation rule, and whether the report generation condition is met is determined according to the correlation between the health score and the preset scoring threshold, and / or the correlation between the predicted failure rate and the preset fault threshold.

[0103] Example 4

[0104] Figure 4 It is a structural diagram of an electronic device provided in Embodiment 4 of the present invention. Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0105] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0106] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0107] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the report visualization method.

[0108] In some embodiments, the report visualization method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the report visualization method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the report visualization method in any other suitable manner (e.g., via firmware).

[0109] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0110] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0111] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be a machine-readable signal medium. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0112] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0113] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0114] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within a cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0115] In one embodiment, the present invention further includes a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the report visualization method of any embodiment of the present invention.

[0116] The computer program product may be implemented in a computer program code for performing the operations of the present invention written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​and conventional procedural programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0117] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0118] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A report visualization method, characterized in that: include: In response to a configuration operation by a target user, determining a visualization component, data field, and interaction rule selected by the target user; Determine the monitoring data and indicator data corresponding to the data field within a preset time period, and based on the preset abnormality screening strategy, determine abnormal data from the monitoring data, and determine whether the report generation conditions are met based on the monitoring data and / or the preset period, combined with the report generation rules in the interaction rules; If so, the monitoring data, indicator data and abnormal data are visualized in reports based on the visualization component according to the abnormality annotation rules in the interaction rules.

2. The method according to claim 1, characterized in that Based on the anomaly annotation rules in the interaction rules, monitoring data, indicator data, and anomaly data are visualized in reports using visualization components, including: Determine the default annotation method corresponding to the visualization component, and determine the exception annotation method corresponding to the abnormal data based on the exception annotation rules in the interaction rules; Based on the visualization component, the monitoring data and indicator data other than the abnormal data in the monitoring data are visualized in reports according to the default annotation method, and the abnormal data are visualized in reports based on the abnormal annotation method.

3. The method according to claim 1, characterized in that Determine the monitoring data and indicator data corresponding to the data field within the preset time period, including: According to the field content of the data field, the corresponding data collection strategy is used to collect data within the preset time period to obtain the collected data, and the collected data is filled with missing information to obtain the monitoring data; Based on the preset indicator calculation rules, the monitoring data is calculated to obtain indicator data.

4. The method according to claim 3, characterized in that Based on the preset indicator calculation rules, the monitoring data is calculated to obtain indicator data, including: Determine the effective operating time of the target device based on the device operating data in the monitoring data, and perform indicator calculation based on the device utilization indicator rules to determine the device utilization indicator data corresponding to the target device; Determine the AC side power and DC side power of the target device based on the device operation data in the monitoring data, and calculate the power generation efficiency index data based on the AC side power and DC side power according to the preset calculation rules; Based on the preset causal detection strategy, the correlation indicator data between the equipment operation data and meteorological data in the monitoring data is determined.

5. The method according to claim 1, wherein Based on the preset anomaly screening strategy, abnormal data is identified from the monitoring data, including: Based on the preset abnormality threshold, invalid data in the monitoring data is determined and discarded to update the monitoring data; Based on the preset outlier filtering algorithm, the mean and standard deviation of the monitoring data in the preset sliding window are determined according to the updated monitoring data, and based on the mean and standard deviation, the abnormal data is determined from the monitoring data based on the preset filtering rules.

6. The method according to claim 1, characterized in that Based on the monitoring data and the report generation rules in the interaction rules, determine whether the report generation conditions are met, including: Based on the preset health model and fault prediction model, the equipment operation data in the monitoring data is analyzed to determine the health score and predicted failure rate corresponding to the target equipment; The preset scoring threshold and the preset fault threshold are determined according to the report generation rule, and whether the report generation condition is met is determined according to the correlation between the health score and the preset scoring threshold, and / or the correlation between the predicted failure rate and the preset fault threshold.

7. A report visualization device, characterized in that: include: a determination module, configured to determine the visualization component, data field, and interaction rule selected by the target user in response to a configuration operation by the target user; A judgment module is used to determine the monitoring data and indicator data corresponding to the data field within a preset time period, and based on a preset abnormality screening strategy, determine abnormal data from the monitoring data, and determine whether the report generation conditions are met based on the monitoring data and / or the preset period, combined with the report generation rules in the interaction rules; The visualization module is used to visualize the monitoring data, indicator data and abnormal data in reports based on the visualization components according to the abnormality annotation rules in the interaction rules.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executed by the at least one processor, and the computer program is executed by the at least one processor so as to enable the at least one processor to execute the report visualization method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the report visualization method according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the computer program implements the report visualization method according to any one of claims 1 to 6.

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