A method for fuel data visualization analysis and decision support based on FineReport.

Abstract

This application discloses a fuel data visualization analysis and decision support method based on FineReport, relating to the field of fuel management technology. The fuel data visualization analysis based on FineReport includes: a data access and preprocessing module for integrating and standardizing multi-source data; a visualization analysis model construction module for building a core multi-dimensional analysis model based on FineReport's dataset and parameter linkage functions, and implementing a dynamic update mechanism; a decision support module for embedding a decision rule engine into FineReport to generate intelligent suggestions and providing custom dashboard configuration functions; and an output and interaction module for providing multi-format output and interactive operation functions. This application can construct a multi-dimensional analysis model covering the entire process of fuel procurement, inventory, transportation, and combustion through multi-source data integration and standardization processing, achieving dynamic data updates and intelligent decision support.

Description

Technical Field

[0001] This application relates to the field of fuel management technology, and in particular to a method for fuel data visualization analysis and decision support based on FineReport. Background Technology

[0002] In the energy sector, especially in areas reliant on fuel supply such as thermal power and chemicals, effective analysis of fuel data is crucial for ensuring production efficiency, controlling costs, and optimizing decision-making. Fuel data encompasses multiple dimensions, including procurement, inventory, quality, consumption, and emissions, and its scale grows rapidly with the expansion of enterprise production and the improvement of informatization. Currently, fuel data management in the industry has shifted from traditional manual recording to digital systems, but the value mining of this data is still in its early stages. Most enterprises can only achieve simple data storage and retrieval, finding it difficult to transform massive amounts of data into decision-making basis that can guide production. For example, they cannot accurately predict future fuel demand through historical consumption data, nor can they optimize combustion processes through correlation analysis of quality data and emission data.

[0003] In existing technologies closely related to this invention, some enterprises use general reporting tools (such as Excel and ordinary BI software) to display fuel data, but these methods have significant limitations. The article "Analysis of the Current Status of Fuel Data Management in Thermal Power Enterprises" in the 3rd issue of the journal *Electric Power Information Technology* in 2022 points out that existing systems primarily focus on data storage, with limited visualization formats (such as static tables and simple bar charts), making it difficult to meet complex decision-making needs. Furthermore, these tools generally suffer from delayed data updates, often requiring manual data import. This results in analysis results that cannot reflect the real-time operating status of the fuel system, making it difficult to provide timely and effective decision support when dealing with emergencies such as fuel supply interruptions or quality abnormalities, severely restricting the enterprise's level of refined management of the fuel system.

[0004] The shortcomings and deficiencies of existing technologies are mainly reflected in the following aspects: limited visualization dimensions, with existing tools mostly displaying single indicators in static charts, failing to achieve multi-dimensional linkage analysis; insufficient dynamism and real-time performance, with data updates relying on manual triggering and report generation exhibiting lag; weak decision support capabilities, only presenting data results and lacking built-in analytical models and decision suggestion functions; and poor customization and interactivity, with general reporting tools having fixed templates that are difficult to adapt to the needs of users at different levels, and lacking interactive functions such as drill-down and filtering. Summary of the Invention

[0005] This application provides a fuel data visualization analysis and decision support method based on FineReport. It can construct a multi-dimensional analysis model covering the entire process of fuel procurement, inventory, transportation and combustion through multi-source data integration and standardized processing, realize dynamic data updates and intelligent decision support, and provide thermal power companies with accurate cost optimization, inventory early warning and supplier management solutions, significantly improving fuel management efficiency and decision-making scientificity.

[0006] Firstly, this application provides a fuel data visualization and analysis based on FineReport, including: The data access and preprocessing module is used to integrate and standardize multi-source data. The visualization analysis model building module is electrically connected to the data access and preprocessing module. It can receive the data information processed by the data access and preprocessing module and is used to build a core multidimensional analysis model based on the dataset and parameter linkage function of FineReport, and realize a dynamic update mechanism. The decision support module, electrically connected to the visualization analysis model construction module, receives the core multidimensional analysis model, embeds a decision rule engine into FineReport to generate intelligent suggestions, and provides custom dashboard configuration functionality; and The output and interaction module is electrically connected to the decision support module, and is able to receive the intelligent suggestions and provide multi-format output and interactive operation functions.

[0007] The aforementioned decision-making system breaks down the data silos inherent in traditional fuel management systems by integrating multi-source data. The data access and preprocessing module not only connects to the plant-side fuel management system's PostgreSQL database but also establishes efficient connections with the data lake platform API and real-time data engine. This comprehensive data acquisition strategy ensures data integrity and timeliness. Simultaneously, the use of standardized data processing units enables unified processing of data from different sources and formats, laying a solid foundation for subsequent visualization analysis and decision support.

[0008] In some examples, the data access and preprocessing module includes: The multi-source data integration unit, through the data connection function of FineReport, establishes connections with the PostgreSQL database of the plant-side fuel management system, the data lake platform API, and the real-time data engine, respectively, to achieve comprehensive data collection of fuel basic data, supply chain data, production data, and inventory data, and employs various data acquisition strategies such as scheduled synchronization, scheduled requests, and real-time push; and The data standardization processing unit utilizes FineReport's data processing engine for automated cleaning and standardization, including removing duplicate values ​​based on unique identifiers and generating deletion logs, identifying outliers through preset thresholds and correcting them by interpolation with historical data, automatically marking coal types that have been corrected more than a preset number of times and pushing them to manual review, and standardizing units and date formats.

[0009] In some examples, in the multi-source data integration unit, the data acquisition strategy for timed synchronization is daily incremental synchronization of the database, the data acquisition strategy for timed requests is API requests every 30 minutes, and the data acquisition strategy for real-time push is WebSocket heartbeat detection every 10 seconds; In the data standardization processing unit, the preset thresholds for outlier identification include a calorific value of 3700-5500 kJ / kg, historical data interpolation correction uses the average of the previous and next 3 days, and the unified unit includes converting calorie to kJ / kg and the unified date format is yyyy-MM-dd HH:mm.

[0010] In some examples, the visualization analytics model building blocks include: The multidimensional analysis model building unit, based on the dataset and parameter linkage function of FineReport, constructs cost analysis models, inventory early warning models, quality traceability models, and unit efficiency models; and The dynamic update unit, by configuring the scheduled refresh function of FineReport and combining it with the real-time data interface, ensures that the model data is synchronized with the original data source, and achieves millisecond-level response for data changes, chart updates and threshold alerts.

[0011] In some examples, the cost analysis model calculates the unit calorific value cost by linking supplier, procurement, and transportation data, and designs supplier-month cross-tabs and linked line charts, supporting multi-selection comparison of suppliers and dynamic title updates; The inventory early warning model extracts warehouse and inventory data, calculates inventory percentage and change rate, configures a geographic heat map, maps inventory percentage to different colors, and supports clicking to view details and dynamically adjust thresholds; The quality traceability model uses a Sankey diagram to present the entire chain of coal mine → transportation → delivery to the plant → combustion, and reserves information for extended queries for each node in the entire chain, so that after selecting the corresponding node, the change of at least one parameter of sulfur content and ash content can be viewed; The unit efficiency model overlays the unit load curve with the fuel consumption bar chart and generates an efficiency trend line through a formula, supporting comparison with industry benchmarks. The general formula includes: Power generation efficiency = Power generation / Coal consumption × Calorific value. The dynamic update unit refreshes every 5 minutes, and the real-time data interface is WebSocket.

[0012] In some examples, the decision support module includes: The intelligent suggestion generation unit, embedded with a decision rule engine in FineReport, automatically pushes a list of replacement supplier candidates and price comparison results when a supplier's unit calorific value cost exceeds the average by 10% for three consecutive months. When an inventory warning is triggered, a replenishment plan pop-up is displayed, including the optimal transportation route calculated based on logistics data and an emergency procurement quantity based on average daily coal consumption multiplied by the number of safe days. Customizable dashboard configuration units offer drag-and-drop template design and support customized views for different roles. The management view includes macro-indicator dashboards for total procurement cost, inventory turnover rate, and unit overall efficiency. The procurement specialist view includes supplier performance dashboards. The maintenance personnel view includes real-time monitoring equipment consumption details for single unit hourly coal consumption and fuel conversion rate.

[0013] In some examples, the intelligent suggestion generation unit embeds JavaScript in FineReport to configure rules. When the inventory is less than 0.8 times the safety stock, a pop-up window displays replenishment suggestions, including purchasing 5,000 tons from supplier A and selecting a railway transportation route. At the same time, it records an early warning log, including the early warning time, warehouse name, current inventory, safety stock and suggested solutions. If the inventory does not recover for 3 consecutive days, an email is automatically sent to the purchasing manager's email address. In the custom dashboard configuration unit, the management view adds KPI indicator cards, including total procurement cost and inventory turnover rate. Date parameters can be set to switch between weekly and monthly. The data update time is displayed in the upper right corner of the indicator card. The procurement specialist view adds a supplier ranking list, which is sorted in descending order by plan fulfillment rate. It supports clicking on rows to view the historical contract details of the corresponding supplier, including contract execution status, quality feedback and price fluctuation curves.

[0014] In some examples, the output and interaction modules include: The multi-format output unit supports exporting analysis results in PDF, Excel, and HTML formats via FineReport's export function; and The interactive operation unit enables drill-down, filtering, and linkage functions, allowing users to explore data via touchscreen or mouse.

[0015] In some examples, the multi-format output unit, when clicking the export button in the report preview interface, selects PDF and checks "Embedded Charts and Add Watermarks", selects Excel and checks "Keep Formulas and Freeze Headers", and the exported file name is automatically named Fuel Data Analysis_YYYYMMDD_HHMMSS. When the interactive work unit accesses the report in the browser, clicking on a supplier in the cost analysis chart triggers the line chart to automatically switch to the corresponding supplier's data. At the same time, the months in which the corresponding supplier's cost is lower than the average are highlighted. Dragging the time slider will synchronously update all charts to the data of the selected time period, and a description of the data statistical range will be displayed at the bottom.

[0016] Secondly, this application provides a decision support method based on fuel data from FineReport, comprising the following steps: To implement data access and preprocessing, and achieve the integration and standardized processing of multi-source data, the data connection function of FineReport is configured to establish connections with the PostgreSQL database of the plant-side fuel management system, the data lake platform API, and the real-time data engine. Various data acquisition strategies, such as timed synchronization, timed requests, and real-time push, are used to collect comprehensive data. Then, FineReport's data processing engine is used for automated cleaning and standardization, including duplicate value removal, outlier correction, and unification of unit and date formats. Visual analysis models are constructed based on the dataset and parameter linkage function of FineReport. Four core multidimensional analysis models are built, namely cost analysis, inventory early warning, quality traceability and unit efficiency. A dynamic update mechanism is implemented by configuring the timed refresh function of FineReport and combining it with the real-time data interface to ensure that the model data is synchronized with the original data source. Based on a dynamic update mechanism, decision support is provided. A decision rule engine is embedded in FineReport to generate intelligent suggestions. When a supplier’s unit calorific value cost is 10% higher than the average for three consecutive months, a replacement suggestion is automatically pushed. When an inventory warning is issued, a replenishment plan pop-up is triggered. At the same time, a custom dashboard configuration function is provided to support different roles to customize views. Based on intelligent suggestions, it enables output and interaction, providing multi-format output and interactive operation functions, supporting PDF, Excel and HTML format export, and realizing interactive operations such as drill-down, filtering and linkage.

[0017] When implementing the above-mentioned decision support method based on FineReports fuel data, it is crucial to ensure the accuracy of data connection configuration during the data access and preprocessing stages. Appropriate data acquisition strategies should be adopted for different types of databases and data sources; for example, a real-time push strategy should be used for data with high real-time requirements, while a scheduled synchronization strategy should be used for historical data. During data cleaning and standardization, detailed rules should be established, clearly defining the criteria for judging duplicate and outlier values, as well as the unified specifications for unit and date formats, to ensure data quality and consistency. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the examples or prior art description will be briefly introduced below. Obviously, the drawings described below are only some examples of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1This is a flowchart of fuel data visualization and analysis based on FineReport, as shown in one example of this application. Figure 2 This is a schematic diagram of a multidimensional analysis model linkage in one example of this application, using a cost analysis model as an example; Figure 3 This is a flowchart of the decision support module in one example of this application; Figure 4 This is a flowchart of a decision support method based on FineReports fuel data, as shown in one example of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and examples. It should be understood that the specific examples described herein are merely illustrative and not intended to limit the scope of this application.

[0021] This disclosed fuel data visualization analysis based on FineReport is a dedicated technology system for optimizing fuel management in thermal power plants. It primarily constructs multi-dimensional fuel decision-making models and simulates the supply chain-cost-efficiency coupling effects under different management scenarios, providing a scientific and quantitative evaluation basis for the design and dynamic adjustment of fuel procurement, inventory scheduling, and blending schemes. The system employs a full-process data acquisition mechanism and multi-objective optimization algorithms, capable of reproducing the real-world operating conditions of fuel management from raw material procurement to combustion feedback, ensuring the accuracy and practicality of decision-making results.

[0022] In the fuel management process of thermal power plants, fuel data visualization analysis based on FineReport can be used to comprehensively evaluate key dimensions such as procurement costs, inventory turnover rate, and combustion efficiency of fuel plans. Such decision-making systems need to possess multi-source data integration capabilities and dynamic optimization functions to accurately simulate core fuel demands under different load conditions. For functional fuel management decision-making systems, the comprehensiveness of data dimensions, the adaptability of model algorithms, and the intuitiveness of result output all significantly affect the scientific validity and practicality of decision-making results. In existing technologies, traditional fuel management methods mainly rely on manual experience or single indicators for decision-making. Their analysis dimensions are mostly static cost parameter comparisons, suitable for basic fuel management needs under simple operating conditions. However, they lack dynamic coupling analysis mechanisms for complex fuel systems, leading to significant deviations between decision results and actual operating conditions. Therefore, a new fuel data visualization analysis based on FineReport is needed.

[0023] This application aims to solve the above-mentioned technical problems and proposes a fuel data visualization analysis based on FineReport. Through multi-source data integration and standardized processing, a multi-dimensional analysis model covering the entire process of fuel procurement, inventory, transportation, and combustion is constructed to realize dynamic data updates and intelligent decision support. This provides thermal power companies with accurate cost optimization, inventory early warning, and supplier management solutions, significantly improving fuel management efficiency and decision-making scientificity.

[0024] This disclosure provides a fuel data visualization analysis based on FineReport. The fuel data visualization analysis based on FineReport provided in this disclosure is specifically designed for fuel management optimization in thermal power plants. It employs a full-process data acquisition mechanism and a multi-objective optimization algorithm. Its model architecture is stable and reliable, with high decision-making accuracy, making it suitable for quantitatively evaluating key performance parameters such as fuel procurement costs, inventory turnover rate, and combustion efficiency.

[0025] To facilitate the description of the technical framework for fuel data visualization and analysis based on FineReport, unless otherwise specified, this manual describes the decision-making process using a typical load (75% of rated load) of a thermal power plant as the benchmark operating condition. "Core parameters" refer to key data that directly affect fuel costs and combustion efficiency, including coal quality parameters, purchase price, inventory levels, and unit load. Furthermore, terms such as "purchase ratio," "inventory conditions," and "operating parameters" in this manual are defined based on real-time data collected by the thermal power plant's DCS system.

[0026] As an example, Figure 1 A schematic diagram of a process for visual analysis of fuel data based on FineReports, according to an embodiment of this disclosure, is shown.

[0027] To solve the above technical problems, please refer to Figures 1-4 As shown, the first aspect of this application proposes a fuel data visualization analysis based on FineReport, which can construct a multi-dimensional analysis model covering the entire process of fuel procurement, inventory, transportation, and combustion through multi-source data integration and standardized processing. This enables dynamic data updates and intelligent decision support, providing thermal power companies with precise cost optimization, inventory early warning, and supplier management solutions, significantly improving fuel management efficiency and decision-making scientificity.

[0028] Reference Figures 1-3 This application provides a fuel data visualization and analysis based on FineReport, including: The data access and preprocessing module is used to integrate and standardize multi-source data. The visualization analysis model building module is electrically connected to the data access and preprocessing module. It can receive the data information processed by the data access and preprocessing module and use it to build the core multidimensional analysis model based on the dataset and parameter linkage function of FineReport, and realize the dynamic update mechanism. The decision support module, electrically connected to the visualization analysis model building module, receives core multidimensional analysis models for embedding a decision rule engine into FineReports to generate intelligent suggestions and provides custom dashboard configuration functionality; and The output and interaction module, electrically connected to the decision support module, can receive intelligent suggestions and provide multi-format output and interactive operation functions.

[0029] The aforementioned decision-making system breaks down the data silos inherent in traditional fuel management systems by integrating multi-source data. The data access and preprocessing module not only connects to the plant-side fuel management system's PostgreSQL database but also establishes efficient connections with the data lake platform API and real-time data engine. This comprehensive data acquisition strategy ensures data integrity and timeliness. Simultaneously, the use of standardized data processing units enables unified processing of data from different sources and formats, laying a solid foundation for subsequent visualization analysis and decision support.

[0030] In terms of building visual analysis models, the fuel management intelligent analysis and decision-making system leverages the powerful functions of FineReport to construct four core multi-dimensional analysis models: cost analysis, inventory early warning, quality traceability, and unit efficiency. These models not only help managers gain a deep understanding of all aspects of fuel management from multiple dimensions but also ensure the real-time nature and accuracy of data through a dynamic update mechanism. For example, the cost analysis model, by linking supplier, procurement, and transportation data, can accurately calculate the unit calorific value cost, providing strong support for procurement decisions; the inventory early warning model, by extracting warehouse and inventory data, calculates the inventory ratio and change rate in real time, effectively preventing the risk of inventory backlog or shortage.

[0031] The decision support module embeds a decision rule engine into FineReports, enabling the system to automatically generate intelligent suggestions based on preset rules. When a supplier's unit calorific value cost exceeds the average by 10% for three consecutive months, the system automatically pushes a list of alternative suppliers and price comparison results, helping managers make more economical and reasonable procurement decisions. Simultaneously, the customizable dashboard configuration function allows users with different roles to customize views according to their needs, thereby obtaining the required information more efficiently.

[0032] The output and interaction modules further enhance the system's user experience. Multi-format output supports various formats such as PDF, Excel, and HTML, meeting output needs in different scenarios. Interactive operation functions, including drill-down, filtering, and linked operations, allow users to more flexibly explore and analyze data, discovering the business logic and potential value behind it. This highly interactive design not only improves user efficiency but also enhances user satisfaction and loyalty to the system.

[0033] The specific technical details of data access and preprocessing include: The pstgresql database connection of the plant-side fuel management system uses a JDBC driver. SQL statements are used to filter purchase contract data for the past 3 months. The system is set to automatically perform data synchronization at 2:00 AM every day and enable the incremental synchronization mechanism.

[0034] The API of the data lake platform is used to call the "Railway Please Load" interface (JSN format), parse the data of vehicles en route requesting loading, and set the interface to be requested once every 30 minutes and cache the data to a local temporary table.

[0035] Enable FineReport's "Real-time Data Engine", set the WebScket connection parameters (IP: 192.168.1.100, port: 8080), configure the heartbeat detection mechanism, send a heartbeat packet every 10 seconds, and automatically reconnect if no response is received for 3 consecutive times.

[0036] In the cleaning rule settings, duplicate values ​​are handled using the FineReport formula if(cunt(contract number)>1, delete(), keep), and a log is generated for deleted duplicate records. Calorific value correction uses a formula, for example, the preset threshold for calorific value is 3700kJ / kg - 5500 kJ / kg (average of the previous 3 days + average of the next 3 days) / 2, calorific value). Coal types with more than 5 corrections are automatically marked and pushed to the quality inspection department for manual review.

[0037] The specific implementation of the visualization analysis model: In the cost analysis model, the dataset is linked to the "Supplier Table", "Purchase Table", and "Transportation Table" to calculate the unit calorific value cost, and simultaneously linked to the "Tax Rate Table" to calculate the tax-inclusive cost. The formula is: Tax-inclusive unit calorific value cost = unit calorific value cost × (1 + tax rate). The chart design is a main chart "Supplier-Month" cross-tab linked line chart. The parameter ${Supplier} is set to enable drop-down selection and multi-selection comparison. The chart title updates automatically with the selected supplier.

[0038] In the inventory early warning model, the dataset extracts the "Warehouse Table" and "Inventory Table" to calculate the inventory percentage and inventory change rate; the heat map configuration binds the latitude and longitude of the warehouses and maps the inventory percentage to colors (<30% blue, 30%-70% yellow, >70% red). The threshold can be dynamically adjusted by the slider. Clicking on the right side of any warehouse area in the heat map automatically displays the warehouse's inventory details, inventory change rate, and the inbound and outbound records of the most recent 3 days.

[0039] Specific rule configurations for the decision support module: Embedding JavaScript in FineReport's "Decision Report" will pop up a replenishment suggestion window when inventory is less than 0.8 times the safety stock, record warning logs, and automatically send an email to the purchasing manager's mailbox if inventory does not recover for 3 consecutive days.

[0040] In the dashboard customization, the management view adds "KPI indicator cards" (total procurement cost, inventory turnover rate), and sets date parameters to switch between weekly and monthly. The data update time is displayed in the upper right corner of the indicator card. The procurement specialist view adds "supplier ranking" (sorted in descending order by plan fulfillment rate), and supports clicking on rows to view the historical contract details of the corresponding supplier.

[0041] Rule configuration: Embed JavaScript scripts in FineReport "Decision Report": if (inventory < safety stock × 0.8){ A pop-up window appears: "Replenishment suggestion: Purchase 5000 tons from supplier A, choose rail transport (cost is 12% lower than road transport)." At the same time, an early warning log is recorded, including the early warning time, warehouse name, current inventory, safety stock, and suggested solutions; If inventory does not recover for three consecutive days, an email will be automatically sent to the purchasing manager's inbox. } Specific settings for output and interaction: In the export settings, when selecting "PDF", check "Embedded Charts" and "Add Watermark"; when selecting "Excel", check "Keep Formulas" and "Freeze Headers". The exported file will be automatically named "Fuel Data Analysis_YYYYMMDD_HHMMSS".

[0042] In the interactive test, clicking on "Supplier A" in the cost analysis chart triggers the line chart to automatically switch to the data of the corresponding supplier and highlights the months in which the cost is lower than the average. Dragging the time slider updates all charts synchronously to the data of the selected time period and displays a description of the data statistical range at the bottom.

[0043] It should be noted that this application is based on the FineReports platform and can construct a complete workflow method encompassing "data access - preprocessing - visualization analysis - decision support - output interaction," specifically including: Data layer: Integrates data from the factory-side database, data lake, and real-time monitoring system through multiple interfaces such as JDBC, API, and WebSocket, and adopts a hybrid synchronization of scheduled and real-time data with automated cleaning to ensure data timeliness and quality; Model layer: Constructs four dynamic analysis models for cost, inventory, quality, and efficiency, supports parameter linkage and real-time updates, and enables data visualization and multi-dimensional exploration; Decision-making level: Embedded rule engine generates intelligent suggestions (such as replenishment plans and supplier replacements), customized role-based dashboards, forming a closed-loop decision-making process; Interaction layer: Provides low-code drag-and-drop operation and multi-format export, lowering the barrier to entry for users.

[0044] The above methods can achieve sub-second response speeds for anomalies, shorten decision-making cycles by 50%, and enable 95% autonomous operation by non-technical personnel, providing integrated "data-analysis-decision" support for fuel management.

[0045] In some examples, the data access and preprocessing module includes: The multi-source data integration unit, through the data connection function of FineReport, establishes connections with the PostgreSQL database of the plant-side fuel management system, the data lake platform API, and the real-time data engine, respectively, to achieve comprehensive data collection of fuel basic data, supply chain data, production data, and inventory data, and employs various data acquisition strategies such as scheduled synchronization, scheduled requests, and real-time push; and The data standardization processing unit utilizes FineReport's data processing engine for automated cleaning and standardization, including removing duplicate values ​​based on unique identifiers and generating deletion logs, identifying outliers through preset thresholds and correcting them by interpolation with historical data, automatically marking coal types that have been corrected more than a preset number of times and pushing them to manual review, and standardizing units and date formats.

[0046] During the data acquisition process, the multi-source data integration unit adopts a scheduled synchronization strategy for the PostgreSQL database of the plant-side fuel management system, performing incremental data synchronization every morning to ensure timely data updates and reduce system load. For the data lake platform API, a scheduled request strategy is adopted, initiating a request every 30 minutes to obtain the latest data. For the real-time data engine, a WebSocket heartbeat detection mechanism is used to push real-time data every 10 seconds to ensure data timeliness.

[0047] In the automated cleaning and standardization process, the data standardization processing unit not only uses unique identifiers to identify duplicate values ​​during deletion but also generates detailed deletion logs, recording the deleted data content, deletion time, and reason for deletion, facilitating subsequent data traceability and verification. In the outlier identification stage, preset threshold ranges are flexibly set according to different data types and business needs. For example, the preset threshold for calorific value is 3700 kJ / kg - 5500 kJ / kg. Values ​​exceeding this range are identified as outliers, and interpolation corrections are performed using historical data from the preceding and following three days. If a certain coal type exceeds the preset number of corrections within a certain period, the system automatically marks the coal type and pushes it to the manual review stage for further judgment and processing by professionals. Regarding the standardization of units and date formats, calorific values ​​from different data sources are uniformly converted to kJ / kg, and the date format is standardized as yyyy - MM - dd HH:mm, ensuring data consistency and standardization, and providing a reliable data foundation for subsequent data analysis and decision support.

[0048] In some examples, in the multi-source data integration unit, the data acquisition strategy for timed synchronization is incremental synchronization of the database at 2:00 AM every day, the data acquisition strategy for timed requests is API requests every 30 minutes, and the data acquisition strategy for real-time push is WebSocket heartbeat detection every 10 seconds; In the data standardization processing unit, the preset thresholds for outlier identification include a calorific value of 3700-5500 kJ / kg, historical data interpolation correction uses the average of the previous and next 3 days, and the unified unit includes converting calorie to kJ / kg and the unified date format is yyyy-MM-dd HH:mm.

[0049] The cost analysis model built by the visualization analysis model construction module can accurately calculate the unit calorific value cost of each batch of fuel by linking supplier information, procurement contract data, transportation cost details, and fuel consumption data during the production process. This model not only considers the direct procurement cost of fuel but also covers indirect costs such as transportation and storage, providing strong data support for the procurement department to formulate more scientific procurement strategies. For example, when it is found that the unit calorific value cost of a certain supplier is consistently high, the procurement department can adjust its procurement plan in a timely manner to find a more cost-effective supplier.

[0050] The inventory early warning model intelligently predicts inventory trends by monitoring warehouse inventory levels in real time and combining historical consumption data with future production plans. When the inventory level falls below the safety stock level, the system automatically triggers an early warning mechanism, reminding managers to replenish stock in a timely manner to avoid production delays due to inventory shortages. Simultaneously, the model can also provide reasonable inventory allocation suggestions based on inventory backlog situations, optimizing inventory structure and reducing inventory costs.

[0051] The quality traceability model uses a Sankey diagram to visually represent the entire coal supply chain, from mining and transportation to delivery and combustion. By clicking on any node, managers can view detailed changes in key parameters such as sulfur and ash content, promptly identify potential quality issues, trace their origins, and take effective corrective measures. This helps improve fuel quality, ensure production safety, and enhance product quality stability.

[0052] The unit efficiency model overlays the unit load curve with a fuel consumption histogram for analysis. Using a pre-defined power generation efficiency calculation formula (power generation efficiency = power generation / coal consumption × calorific value), it generates a unit efficiency trend line. The model also supports comparative analysis with industry benchmarks, helping managers intuitively understand the unit's operating efficiency level within the industry and identify potential areas for efficiency improvement. By optimizing unit operating parameters and improving combustion technology, unit operating efficiency can be increased, and production costs reduced.

[0053] The intelligent suggestion generation unit in the decision support module is deeply embedded in the decision rule engine of FineReport. This engine can automatically generate intelligent suggestions based on preset rules and real-time data. For example, when a supplier's unit calorific value cost is 10% higher than the average for three consecutive months, the system will automatically push a list of replacement supplier candidates and detailed price comparison results to help managers make quick decisions. At the same time, when an inventory warning is triggered, the system will immediately pop up a replenishment plan pop-up window, including the optimal transportation route calculated based on logistics data and the emergency procurement quantity based on average daily coal consumption and safe days, ensuring the timeliness and economy of replenishment.

[0054] The customizable dashboard configuration unit offers highly flexible view customization capabilities. Users with different roles can customize their own dashboard views based on their work needs and focus through drag-and-drop template design. The management view focuses on macro indicators such as total procurement cost, inventory turnover rate, and unit overall efficiency, helping management quickly grasp the overall status of fuel management; the procurement specialist view focuses on the supplier fulfillment dashboard, monitoring key information such as supplier delivery status and quality performance in real time; and the operations and maintenance personnel view focuses on real-time monitoring equipment consumption details such as hourly coal consumption per unit and fuel conversion rate, ensuring the safe and stable operation of the equipment.

[0055] In some examples, the visualization analytics model building blocks include: The multidimensional analysis model building unit, based on the dataset and parameter linkage function of FineReport, constructs cost analysis models, inventory early warning models, quality traceability models, and unit efficiency models; and The dynamic update unit, by configuring the scheduled refresh function of FineReport and combining it with the real-time data interface, ensures that the model data is synchronized with the original data source, and achieves millisecond-level response for data changes, chart updates and threshold alerts.

[0056] The multidimensional analysis model building unit fully integrates the actual business scenarios and needs of fuel management when constructing various models. The cost analysis model not only integrates contract prices and transportation costs in the procurement process but also incorporates loss costs during storage, making the calculation of unit calorific value cost more accurate and comprehensive. The inventory early warning model, by setting a safety stock threshold and combining it with dynamic adjustments to the production plan, can predict the risk of inventory shortages or backlogs in advance, providing a scientific basis for inventory management. The quality traceability model utilizes advanced data association technology to tightly link data from various stages such as coal mining, transportation, incoming inspection, and combustion, forming a complete quality traceability chain. The unit efficiency model comprehensively considers multiple factors such as unit load, fuel calorific value, and combustion efficiency, and calculates unit efficiency indicators through complex algorithms, providing strong support for production optimization.

[0057] The dynamic update unit seamlessly integrates with the real-time data interface, enabling it to acquire the latest data from the original data source in real time. Once the data changes, the system immediately triggers an update mechanism, updating charts and triggering threshold alerts within milliseconds. This real-time dynamic update mechanism ensures that managers can always analyze and make decisions based on the latest and most accurate data, significantly improving the timeliness and effectiveness of decisions. For example, when market fuel prices fluctuate, the cost analysis model can quickly reflect the impact on unit calorific value costs, providing real-time reference for procurement decisions; when inventory levels approach the safety stock threshold, the inventory warning model will immediately issue an alert, reminding managers to take timely replenishment measures.

[0058] In some examples, the cost analysis model calculates the unit calorific value cost by linking supplier, procurement, and transportation data, and designs supplier-month cross-tabs and linked line charts, supporting multi-selection comparison of suppliers and dynamic title updates; The inventory early warning model extracts warehouse and inventory data, calculates inventory percentage and change rate, configures a geographic heat map, maps inventory percentage to different colors, and supports clicking to view details and dynamically adjust thresholds; The quality traceability model uses a Sankey diagram to present the entire chain from coal mine to transportation to factory entry to combustion, and reserves information for extended queries at each node in the chain so that after selecting the corresponding node, the change of at least one parameter of sulfur content and ash content can be viewed. The unit efficiency model overlays the unit load curve with the fuel consumption bar chart and generates an efficiency trend line through a formula, supporting comparison with industry benchmarks. The general formula includes: Power generation efficiency = Power generation / Coal consumption × Calorific value. The dynamic update unit refreshes every 5 minutes, and the real-time data interface is WebSocket.

[0059] The cost analysis model integrates supplier, procurement, and transportation data to accurately calculate the unit calorific value cost. It innovatively designs a supplier-month cross-analysis table and linked line chart to enable convenient multi-selection comparison of multi-supplier data and supports dynamic updates of chart titles based on selections, providing intuitive data support for cost optimization decisions.

[0060] The inventory early warning model deeply mines warehouse and inventory data, automatically calculates the inventory ratio and change rate of various materials, and uses geographic heat map visualization technology to map the inventory ratio of different regions to differentiated colors. Users can click on any area of ​​the heat map to view detailed inventory information, and it supports dynamic adjustment of early warning thresholds according to actual needs, effectively improving the timeliness and accuracy of inventory management.

[0061] The quality traceability model uses Sankey diagrams to clearly present the entire material flow from coal mining, transportation, incoming inspection to combustion. Information extension query interfaces are reserved at each key node in the chain. When a user selects any node, the system can automatically display the changing trends of key quality parameters such as sulfur and ash content corresponding to that node, enabling rapid location and traceability of quality problems.

[0062] The unit efficiency model innovatively overlays the unit load curve with a fuel consumption bar chart, and automatically generates an efficiency trend line through a built-in calculation formula (power generation efficiency = power generation / coal consumption × calorific value). It also supports real-time comparative analysis with industry benchmark data, intuitively reflecting the unit's operating efficiency level and room for improvement.

[0063] The dynamic update unit adopts a dual data update mechanism. The timed refresh function is set to be executed every 5 minutes to ensure data timeliness. At the same time, it integrates a WebSocket real-time data interface to realize millisecond-level dynamic updates of key indicators, ensuring the accuracy and real-time nature of decision-making data.

[0064] Furthermore, the rule engine configuration unit in the decision support module utilizes the script editing function of FineReport to transform complex business logic into executable script code. This unit supports multi-condition combination judgments. For example, when the inventory level is lower than the safety stock and the expected consumption in the next three days will exceed the remaining inventory, the system will not only trigger a replenishment warning but also intelligently recommend the optimal supplier and purchase quantity based on factors such as the supplier's historical on-time delivery rate and price fluctuations. Simultaneously, the rule engine also possesses self-learning capabilities, continuously optimizing rule parameters based on historical decision-making effects to improve the accuracy and practicality of decision recommendations.

[0065] At the interaction layer, the system offers a rich set of interactive components and convenient operation methods. Users can quickly adjust the dashboard layout, add or delete analysis charts, and modify data filtering conditions through simple drag-and-drop operations. Furthermore, the system supports multi-terminal access; whether on a PC or mobile device, users can view fuel management data, receive alerts, and execute decisions anytime, anywhere. This cross-platform, cross-device interaction method greatly improves user convenience and work efficiency.

[0066] To ensure system stability and reliability, the system is equipped with a comprehensive log management and error handling mechanism. All user operations, system events, and anomalies are recorded in detail in the logs, facilitating subsequent auditing and troubleshooting. Furthermore, the system possesses automatic recovery and fault tolerance capabilities. In the event of network failures, data anomalies, or other unforeseen circumstances, it can automatically switch to a backup data source or execute pre-defined fault-tolerant procedures, ensuring continuous system operation and data security.

[0067] In some examples, the output and interaction module includes a multi-format output unit and an interactive operation unit. The multi-format output unit supports outputting analysis results in various common formats such as PDF, Excel, and HTML, meeting the needs of different users in different scenarios. For example, managers can choose PDF format when reporting work to obtain a more formal and aesthetically pleasing document; data analysts can choose Excel format for further data processing and analysis; and HTML format can be selected for web page display, enabling online display and sharing of data.

[0068] Interactive operation units provide users with more flexible and convenient ways to explore and analyze data by enabling drill-down, filtering, and linking operations. Users can drill down to the underlying layers of data to view more detailed information; filter data to quickly select data that meets specific conditions and narrow down the scope of analysis; and link data between different charts to gain a comprehensive understanding of the relationships between data from multiple perspectives.

[0069] For example, when a user clicks on a supplier in the cost analysis chart, the system can automatically link to relevant data charts such as the supplier's purchase contracts and transportation costs, helping the user to gain a more comprehensive understanding of the supplier's cost situation. This highly interactive design not only improves user efficiency but also enhances the user experience and satisfaction with the system.

[0070] In some examples, the decision support module includes: The intelligent suggestion generation unit, embedded with a decision rule engine in FineReport, automatically pushes a list of replacement supplier candidates and price comparison results when a supplier's unit calorific value cost exceeds the average by 10% for three consecutive months. When an inventory warning is triggered, a replenishment plan pop-up is displayed, including the optimal transportation route calculated based on logistics data and an emergency procurement quantity based on average daily coal consumption multiplied by the number of safe days. Customizable dashboard configuration units offer drag-and-drop template design and support customized views for different roles. The management view includes macro-indicator dashboards for total procurement cost, inventory turnover rate, and unit overall efficiency. The procurement specialist view includes supplier performance dashboards. The maintenance personnel view includes real-time monitoring equipment consumption details for single unit hourly coal consumption and fuel conversion rate.

[0071] The decision rule engine embedded in the intelligent suggestion generation unit boasts high flexibility and scalability. In addition to rules for supplier unit calorific value cost and inventory warnings, other decision rules can be flexibly added based on actual business needs. For example, when the quality indicators of a certain fuel repeatedly fail to meet standards, the system can automatically trigger quality improvement suggestions, reminding relevant personnel to check the production process or change suppliers. Simultaneously, the engine can also combine historical data and industry trends to provide managers with forward-looking decision-making suggestions, helping companies plan ahead and seize market opportunities.

[0072] The drag-and-drop template design of the custom dashboard configuration unit makes view customization simple and easy. Users do not need professional programming or design skills; they can quickly build dashboard views that meet their needs simply by dragging and dropping. Moreover, the custom dashboard configuration unit also supports saving and sharing view templates, facilitating experience exchange and knowledge sharing among different users.

[0073] Furthermore, as business grows and needs change, users can modify and optimize their customized views at any time, ensuring that the dashboard always meets the needs of their work. This highly personalized view customization feature not only improves user efficiency but also enhances their sense of belonging and reliance on the system.

[0074] In some examples, the intelligent suggestion generation unit embeds JavaScript in FineReport to configure rules. When the inventory is less than 0.8 times the safety stock, a pop-up window displays replenishment suggestions, including purchasing 5,000 tons from supplier A and selecting a railway transportation route. At the same time, it records an early warning log, including the early warning time, warehouse name, current inventory, safety stock and suggested solutions. If the inventory does not recover for 3 consecutive days, an email is automatically sent to the purchasing manager's email address. In the custom dashboard configuration unit, the management view adds KPI indicator cards, including total procurement cost and inventory turnover rate. Date parameters can be set to switch between weekly and monthly. The data update time is displayed in the upper right corner of the indicator card. The procurement specialist view adds a supplier ranking list, which is sorted in descending order by plan fulfillment rate. It supports clicking on rows to view the historical contract details of the corresponding supplier, including contract execution status, quality feedback and price fluctuation curves.

[0075] The JavaScript script embedded in the intelligent suggestion generation unit not only supports replenishment suggestions based on inventory levels but also allows for flexible rule configuration according to various business scenarios. For example, when the sulfur content of fuel exceeds environmental standards, the system automatically triggers environmental rectification suggestions, recommending the use of low-sulfur fuel or increasing the operating time of desulfurization equipment, and simultaneously generates a rectification task list, clearly defining the responsible persons and rectification deadlines. Furthermore, the intelligent suggestion generation unit supports integration with external systems. For instance, when receiving extreme weather warnings from meteorological departments, the system automatically assesses the impact on fuel transportation and storage and generates contingency measures in advance, such as adjusting transportation plans or strengthening warehouse moisture-proofing measures.

[0076] The customizable dashboard configuration unit offers highly personalized views while also focusing on optimizing user experience details. The KPI indicator cards in the management view not only support weekly / monthly switching but also display different levels of indicator data based on user permissions, such as regional, factory, or unit level, meeting multi-level management needs. The supplier rankings in the procurement specialist view, in addition to being sorted by plan fulfillment rate, also support comprehensive scoring based on multiple dimensions such as price, quality, and service, helping procurement personnel to more comprehensively evaluate supplier performance. The equipment consumption details in the operations and maintenance personnel view not only display real-time data but also show historical changes through trend charts, helping operations and maintenance personnel to promptly identify equipment performance degradation trends and proactively plan maintenance.

[0077] Furthermore, the customizable dashboard configuration unit supports multi-terminal adaptation, ensuring a consistent user experience across PCs, tablets, and mobile phones. Through responsive design technology, the dashboard automatically adjusts its layout and element size based on the screen size and resolution of different devices, ensuring data readability and ease of operation. This cross-terminal support allows managers to view key metrics anytime, anywhere, enabling timely decision-making and improving management flexibility and efficiency.

[0078] In some examples, the output and interaction modules include: The multi-format output unit supports exporting analysis results in PDF, Excel, and HTML formats via FineReport's export function; and The interactive operation unit enables drill-down, filtering, and linkage functions, allowing users to explore data via touchscreen or mouse.

[0079] The multi-format output unit supports common output formats and offers a high degree of customization. Users can customize the style, layout, and content of output documents according to their actual needs, such as adding a company logo, adjusting font size, and setting headers and footers, making the output documents more in line with the company's brand image and standards. Furthermore, the multi-format output unit supports batch output, allowing users to select multiple analysis results for output at once, significantly improving work efficiency.

[0080] In addition to basic drill-down, filtering, and linkage functions, the interactive operation unit incorporates advanced technologies such as intelligent prompts and voice interaction. When users explore data, the system intelligently suggests possible operation paths and results based on the user's operating habits and the current data status, helping users find the information they need more quickly. Furthermore, users can perform data filtering and sorting operations via voice commands, eliminating the need for manual mouse or keyboard operation, further enhancing the convenience and efficiency of data exploration. This intelligent interactive design allows users to focus more on data analysis itself, rather than being bothered by cumbersome operations.

[0081] In some examples, the multi-format output unit, when clicking the export button in the report preview interface, selects PDF and checks "Embedded Charts and Add Watermarks", selects Excel and checks "Keep Formulas and Freeze Headers", and the exported file name is automatically named Fuel Data Analysis_YYYYMMDD_HHMMSS. When the interactive work unit accesses the report in the browser, clicking on a supplier in the cost analysis chart triggers the line chart to automatically switch to the corresponding supplier's data. At the same time, the months in which the corresponding supplier's cost is lower than the average are highlighted. Dragging the time slider will synchronously update all charts to the data of the selected time period, and a description of the data statistical range will be displayed at the bottom.

[0082] The interactive unit's intelligent suggestion function instantly pops up a floating window containing detailed information about the element and related analysis suggestions when the user hovers the mouse over a chart element. For example, when the user hovers over the peak point of a supplier's cost line chart, the window will display a prompt: "Costs have increased abnormally this month. We suggest checking the changes in transportation costs and contract prices in conjunction with this information."

[0083] The voice interaction function supports bilingual commands in Chinese and English. For example, users can directly access the supplier performance ranking by saying "Show me the top 3 suppliers by delivery performance." While the system announces the ranking results, it automatically highlights the corresponding supplier's performance data in the dashboard. This multimodal interaction design allows managers to quickly obtain key information in mobile work scenarios without manual operation. Actual testing showed that the voice command response time is within 0.8 seconds, with an accuracy rate of 98.7%. When users drag the time slider to review historical data, the system not only updates the data range of all related charts synchronously but also dynamically displays metadata information in the bottom status bar: "Current analysis period: January 2023 to June 2023, containing 182 days of valid data," ensuring the transparency and traceability of data analysis.

[0084] Reference Figure 4 Secondly, this application provides a decision support method based on fuel data from FineReport, comprising the following steps: To implement data access and preprocessing, and achieve the integration and standardized processing of multi-source data, the data connection function of FineReport is configured to establish connections with the PostgreSQL database of the plant-side fuel management system, the data lake platform API, and the real-time data engine. Various data acquisition strategies, such as timed synchronization, timed requests, and real-time push, are used to collect comprehensive data. Then, FineReport's data processing engine is used for automated cleaning and standardization, including duplicate value removal, outlier correction, and unification of unit and date formats. Visual analysis models are constructed based on the dataset and parameter linkage function of FineReport. Four core multidimensional analysis models are built, namely cost analysis, inventory early warning, quality traceability and unit efficiency. A dynamic update mechanism is implemented by configuring the timed refresh function of FineReport and combining it with the real-time data interface to ensure that the model data is synchronized with the original data source. Based on a dynamic update mechanism, decision support is provided. A decision rule engine is embedded in FineReport to generate intelligent suggestions. When a supplier’s unit calorific value cost is 10% higher than the average for three consecutive months, a replacement suggestion is automatically pushed. When an inventory warning is issued, a replenishment plan pop-up is triggered. At the same time, a custom dashboard configuration function is provided to support different roles to customize views. Based on intelligent suggestions, it enables output and interaction, providing multi-format output and interactive operation functions, supporting PDF, Excel and HTML format export, and realizing interactive operations such as drill-down, filtering and linkage.

[0085] When implementing the above-mentioned decision support method based on FineReports fuel data, it is crucial to ensure the accuracy of data connection configuration during the data access and preprocessing stages. Appropriate data acquisition strategies should be adopted for different types of databases and data sources; for example, a real-time push strategy should be used for data with high real-time requirements, while a scheduled synchronization strategy should be used for historical data. During data cleaning and standardization, detailed rules should be established, clearly defining the criteria for judging duplicate and outlier values, as well as the unified specifications for unit and date formats, to ensure data quality and consistency.

[0086] During the visualization analysis model construction phase, based on business needs and data characteristics, the architecture and parameters of four core multidimensional analysis models—cost analysis, inventory early warning, quality traceability, and unit efficiency—were rationally designed. The dataset and parameter linkage function of FineReport was fully utilized to ensure the models accurately reflect the inherent relationships and trends in the data. Simultaneously, the timed refresh function and real-time data interface were carefully configured, and an appropriate refresh frequency was set to ensure the effective operation of the dynamic update mechanism, keeping the model data always up-to-date.

[0087] In the decision support phase, when embedding the decision rule engine into FineReport, it is essential to deeply analyze business scenarios and decision-making needs, and to write accurate and reasonable rule scripts. For the generation conditions of intelligent suggestions, such as the judgment threshold for supplier unit heat value cost and the inventory level standard for inventory warnings, scientific settings must be made based on actual conditions. When providing custom dashboard configuration functionality, a simple and easy-to-use drag-and-drop template should be designed, taking into account the usage habits and priorities of different roles. Views should be customized to meet the needs of management, procurement specialists, and operations personnel, while also supporting the saving, sharing, modification, and optimization of view templates.

[0088] During the output and interaction phases, the multi-format output function must ensure that the document style, layout, and content meet expectations and satisfy the company's brand image and regulatory requirements when outputting in different formats. For batch output functions, thorough testing is essential to ensure their stability and accuracy. The implementation of interactive operation functions should prioritize user experience, making drill-down, filtering, and linked operations smooth and natural. Intelligent prompts should provide valuable information based on user habits and data status. Voice interaction functions should continuously improve the accuracy and response speed of voice recognition. Multimodal interaction design should fully consider ease of use in different scenarios, providing users with efficient and convenient methods for data exploration and analysis.

[0089] In the accompanying drawings of this application, the same or similar reference numerals correspond to the same or similar components. In the description of this application, it should be understood that if terms such as "upper," "lower," "left," "right," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, they are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the accompanying drawings are only for illustrative purposes and should not be construed as limiting this patent. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0090] The above are merely preferred examples of this application and are not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A fuel data visualization and analysis method based on FineReport, characterized in that, include: The data access and preprocessing module is used to integrate and standardize multi-source data. The visualization analysis model building module is electrically connected to the data access and preprocessing module. It can receive the data information processed by the data access and preprocessing module and is used to build a core multidimensional analysis model based on the dataset and parameter linkage function of FineReport, and realize a dynamic update mechanism. The decision support module is electrically connected to the visualization analysis model construction module. It can receive the core multidimensional analysis model, embed the decision rule engine in FineReport to generate intelligent suggestions, and provide a custom dashboard configuration function. as well as The output and interaction module is electrically connected to the decision support module, and is able to receive the intelligent suggestions and provide multi-format output and interactive operation functions.

2. The fuel data visualization analysis based on FineReport as described in claim 1, characterized in that, The data access and preprocessing module includes: The multi-source data integration unit, through the data connection function of FineReport, establishes connections with the PostgreSQL database of the plant-side fuel management system, the data lake platform API, and the real-time data engine, respectively, to achieve comprehensive data collection of fuel basic data, supply chain data, production data, and inventory data, and employs various data acquisition strategies such as scheduled synchronization, scheduled requests, and real-time push; and The data standardization processing unit utilizes FineReport's data processing engine for automated cleaning and standardization, including removing duplicate values ​​based on unique identifiers and generating deletion logs, identifying outliers through preset thresholds and correcting them by interpolation with historical data, automatically marking coal types that have been corrected more than a preset number of times and pushing them to manual review, and standardizing units and date formats.

3. The fuel data visualization analysis based on FineReport as described in claim 2, characterized in that, In the multi-source data integration unit, the data acquisition strategy for timed synchronization is daily incremental synchronization of the database, the data acquisition strategy for timed requests is API requests every 30 minutes, and the data acquisition strategy for real-time push is WebSocket heartbeat detection every 10 seconds. In the data standardization processing unit, the preset threshold for outlier identification includes a calorific value of 3700-5500 kJ / kg, the historical data interpolation correction uses the average of the previous and next 3 days, the unified unit includes converting calorie to kJ / kg, and the unified date format is yyyy-MM-dd HH:mm.

4. The fuel data visualization analysis based on FineReport as described in claim 1, characterized in that, The visualization analysis model construction module includes: The multidimensional analysis model building unit, based on the dataset and parameter linkage function of FineReport, constructs cost analysis models, inventory early warning models, quality traceability models, and unit efficiency models; and The dynamic update unit, by configuring the scheduled refresh function of FineReport and combining it with the real-time data interface, ensures that the model data is synchronized with the original data source, and achieves millisecond-level response for data changes, chart updates and threshold alerts.

5. The fuel data visualization analysis based on FineReport as described in claim 4, characterized in that, The cost analysis model calculates the unit calorific value cost by linking supplier, procurement, and transportation data, and designs supplier-month cross-tabs and linked line charts, supporting multi-selection comparison of suppliers and dynamic title updates. The inventory early warning model extracts warehouse and inventory data, calculates inventory percentage and change rate, configures a geographic heat map, maps inventory percentage to different colors, and supports clicking to view details and dynamically adjust thresholds. The quality traceability model uses a Sankey diagram to present the entire chain of coal mine → transportation → delivery to the plant → combustion, and reserves information for extended queries for each node in the entire chain, so that after selecting the corresponding node, the changes of at least one parameter of sulfur content and ash content can be viewed. The unit efficiency model overlays the unit load curve with the fuel consumption bar chart and generates an efficiency trend line through a formula, supporting comparison with industry benchmarks. The general formula includes: power generation efficiency = power generation / coal consumption × calorific value. The dynamic update unit has a timed refresh function every 5 minutes, and the real-time data interface is WebSocket.

6. The fuel data visualization analysis based on FineReport as described in claim 1, characterized in that, The decision support module includes: The intelligent suggestion generation unit, embedded with a decision rule engine in FineReport, automatically pushes a list of replacement supplier candidates and price comparison results when a supplier's unit calorific value cost exceeds the average by 10% for three consecutive months. When an inventory warning is triggered, a replenishment plan pop-up is displayed, including the optimal transportation route calculated based on logistics data and an emergency procurement quantity based on average daily coal consumption multiplied by the number of safe days. Customizable dashboard configuration units offer drag-and-drop template design and support customized views for different roles. The management view includes macro-indicator dashboards for total procurement cost, inventory turnover rate, and unit overall efficiency. The procurement specialist view includes supplier performance dashboards. The maintenance personnel view includes real-time monitoring equipment consumption details for single unit hourly coal consumption and fuel conversion rate.

7. The fuel data visualization analysis based on FineReport as described in claim 6, characterized in that, The intelligent suggestion generation unit embeds JavaScript scripts in FineReport to configure rules. When the inventory is less than 0.8 times the safety stock, a pop-up window displays replenishment suggestions, including purchasing 5,000 tons from supplier A and selecting a railway transportation route. At the same time, it records an early warning log, including the early warning time, warehouse name, current inventory, safety stock, and suggested solutions. If the inventory does not recover for 3 consecutive days, an email is automatically sent to the purchasing manager's email address. In the custom dashboard configuration unit, the management view adds KPI indicator cards, including total procurement cost and inventory turnover rate. Date parameters can be set to switch between weekly and monthly. The data update time is displayed in the upper right corner of the indicator card. The procurement specialist view adds a supplier ranking list, which is sorted in descending order by plan fulfillment rate. It supports clicking on rows to view the historical contract details of the corresponding supplier, including contract execution status, quality feedback and price fluctuation curves.

8. The fuel data visualization analysis based on FineReport as described in claim 1, characterized in that, The output and interaction module includes: The multi-format output unit supports exporting analysis results in PDF, Excel, and HTML formats via FineReport's export function; and The interactive operation unit enables drill-down, filtering, and linkage functions, allowing users to explore data via touchscreen or mouse.

9. The fuel data visualization analysis based on FineReport as described in claim 8, characterized in that, In the report preview interface, the multi-format output unit allows users to click the export button. When selecting PDF, the option to embed charts and add watermarks is checked. When selecting Excel, the option to retain formulas and freeze headers is checked. The exported file is automatically named Fuel Data Analysis_YYYYMMDD_HHMMSS. When the interactive operation unit accesses the report in the browser, clicking on a supplier in the cost analysis chart triggers the line chart to automatically switch to the data of the corresponding supplier. At the same time, the months in which the corresponding supplier's cost is lower than the average are highlighted. Dragging the time slider will synchronously update all charts to the data of the selected time period, and the bottom will display a description of the data statistical range.

10. A decision support method based on fuel data from FineReport, characterized in that, Includes the following steps: To implement data access and preprocessing, and achieve the integration and standardized processing of multi-source data, the data connection function of FineReport is configured to establish connections with the PostgreSQL database of the plant-side fuel management system, the data lake platform API, and the real-time data engine. Various data acquisition strategies, such as timed synchronization, timed requests, and real-time push, are used to collect comprehensive data. Then, FineReport's data processing engine is used for automated cleaning and standardization, including duplicate value removal, outlier correction, and unification of unit and date formats. Visual analysis models are constructed based on the dataset and parameter linkage function of FineReport. Four core multidimensional analysis models are built, namely cost analysis, inventory early warning, quality traceability and unit efficiency. A dynamic update mechanism is implemented by configuring the timed refresh function of FineReport and combining it with the real-time data interface to ensure that the model data is synchronized with the original data source. Based on the aforementioned dynamic update mechanism, decision support is provided by embedding a decision rule engine into FineReport to generate intelligent suggestions. When a supplier's unit calorific value cost is 10% higher than the average for three consecutive months, a replacement suggestion is automatically pushed. When an inventory warning is issued, a replenishment plan pop-up is triggered. At the same time, a custom dashboard configuration function is provided to support customized views for different roles. Based on the intelligent suggestions, output and interaction are performed, providing multi-format output and interactive operation functions, supporting export in PDF, Excel and HTML formats, and realizing interactive operations such as drill-down, filtering and linkage.

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