Data management system, method, device and equipment based on big data centralized control center

Abstract

The invention relates to the technical field of big data processing, and discloses a data management system, method, device and equipment based on a big data centralized control center, the system comprises a data acquisition module, a data processing module and a data storage module, the data acquisition module obtains multi-power station equipment operation data, branch current data and power data; the data processing module automatically generates an operation data report without manual arrangement; a fault branch is intelligently identified by analyzing current data, so that the troubleshooting time is greatly shortened, missed judgment and misjudged judgment are reduced, the fault processing period is remarkably shortened, and the power generation loss is reduced; meanwhile, format standardization is carried out on dispersed power data, data differences are eliminated, manual summarization errors are avoided, and an accurate basis is provided for formulating a power generation plan; and the data storage module safely stores various report forms and index information, so that data can be checked and available, the human input is reduced, the operation and maintenance efficiency is improved, and powerful support is provided for scientific scheduling and efficient management.

Description

Technical Field

[0001] This invention relates to the field of big data processing technology, and more specifically to a data management system, method, apparatus, and equipment based on a big data control center. Background Technology

[0002] The central control center undertakes the core responsibility of monitoring new energy power plants in multiple regions and is the "central brain" of regional new energy power production and operation. In actual operation, key operating parameters such as power, voltage, current, frequency, and temperature of each power station are generated in real time, and the amount of data is growing exponentially. Generally, manually compiling the operating parameters of each power station is difficult to meet the requirements of real-time and completeness, resulting in lag in the monitoring of operating status. Secondly, photovoltaic power stations are large in scale, with the number of photovoltaic branches in a single station generally reaching thousands, and some large power stations even exceeding tens of thousands. Relying on manual statistics for branch fault diagnosis and summary is not only time-consuming (a single diagnosis can take several hours), but also prone to missed or misdiagnosed cases, prolonging the fault handling cycle and directly causing power generation loss. Statistics show that the average processing time for faulty branches under the manual diagnosis mode exceeds 8 hours, and the power generation loss rate reaches 3%-5%. Furthermore, the power prediction system is deployed in a decentralized manner at each site, with inconsistent data formats and independent transmission links. The central control center lacks a unified data integration and display platform, and manual compilation of data from each site is required for reporting. This is not only inefficient, but also poses a risk of data errors, seriously affecting the scientific nature of power generation planning. Summary of the Invention

[0003] This invention provides a data management system, method, apparatus, and equipment based on a big data control center to solve the problems of low efficiency and low accuracy in related technologies that rely on manual troubleshooting of faulty branches and integration of data from different power plants.

[0004] In a first aspect, the present invention provides a data management system based on a big data control center. The system includes: a data acquisition module, a data processing module, and a data storage module. The data acquisition module is used to collect operating data of each device in multiple power plants, current data of each branch, and power data of each power plant. The data processing module is used to construct operating data reports based on the operating data of each device in multiple power plants, identify faulty branches in corresponding power plants based on the current data of multiple branches in each power plant, construct faulty branch investigation reports based on the faulty branches, standardize the format of the power data of multiple power plants to obtain target power data of each power plant, and construct a power report based on the target power data of multiple power plants. The data storage module is used to store the operating data reports, faulty branch investigation reports, power reports, and index information.

[0005] The data management system based on a big data control center provided by this invention includes a data acquisition module, a data processing module, and a data storage module. The data acquisition module acquires operating data, branch current data, and power data from multiple power plants, replacing manual statistics and allowing the control center to monitor equipment dynamics in real time, completely solving the problem of monitoring lag. The data processing module automatically generates operating data reports, eliminating the need for manual compilation. By analyzing current data, it intelligently identifies faulty branches, significantly shortening troubleshooting time, reducing missed and false diagnoses, significantly compressing the fault handling cycle, and reducing power generation losses. At the same time, it standardizes the format of dispersed power data, eliminating data differences and avoiding errors from manual aggregation, providing an accurate basis for power generation planning. The data storage module securely stores various reports and index information, ensuring data accessibility and usability. This not only reduces manpower input and improves operation and maintenance efficiency but also promotes the shift of new energy power plant management from experience-driven to data-driven, effectively reducing power generation losses, improving the efficiency of regional new energy power production and operation, and providing strong support for scientific scheduling and efficient management.

[0006] Secondly, the present invention provides a data management method based on a big data control center, applied to the system of the first aspect. The method includes: acquiring operating data of each device in multiple power plants, current data of each branch, and power data of each power plant, wherein the power data includes predicted power data and measured power data; constructing an operating data report based on the operating data of multiple devices; identifying target branches with faults based on the current data of each branch, and constructing a fault branch investigation report based on the target branches; performing format standardization processing on the predicted power data and measured power data of multiple power plants to obtain target power prediction data and target measured power data of each power plant; constructing a power report based on the target power prediction data and target measured power data of each power plant; and storing the operating data report, fault branch investigation report, and power report in a data storage module.

[0007] This invention provides a data management method based on a big data control center, which comprehensively collects equipment operation data, branch current data, and power data including predictions and measurements. This replaces fragmented manual collection, ensuring comprehensive and timely data acquisition, laying a complete data foundation for subsequent analysis, and avoiding monitoring blind spots caused by missing or delayed data in traditional methods. When constructing operation data reports, manual processing and calculation are unnecessary; data is automatically generated directly from multi-device operation data, significantly improving data presentation efficiency and allowing the control center to quickly grasp the operating status of each power station's equipment, eliminating the tediousness and delays of manual reports. By analyzing current data, target faulty branches are accurately identified and troubleshooting reports are generated, eliminating the need for manual branch-by-branch checks. This not only shortens fault location time and reduces missed or incorrect diagnoses but also accelerates fault handling, effectively reducing power generation losses due to faults. In power data processing, the predicted and measured power data are standardized in format, eliminating data format differences between stations and avoiding the error risks of manual aggregation. This provides unified and accurate data support for subsequent power report construction, ensuring reliable data for power generation planning and improving the scientific nature of scheduling.

[0008] In one optional implementation, the step of constructing an operation data report based on the operation data of multiple devices includes: identifying device identifiers, operation parameters, and time information in the operation data; and constructing the operation data report based on the device identifiers, operation parameters, and time information.

[0009] In one optional implementation, the steps of identifying a faulty target branch based on the current data of each branch and constructing a faulty branch investigation report based on the target branch include: determining the target current threshold of each branch based on a preset dynamic threshold mechanism; if the current value of each branch is less than the target current threshold of the corresponding branch, the corresponding branch is identified as a faulty target branch, and the time information in the current data of the target branch is used as the fault time; and constructing a faulty branch investigation report based on the identification information, location information and fault time of the target branch.

[0010] In one optional implementation, the step of constructing a power report based on the target power prediction data and the target measured power data of each power station includes: determining the power deviation information of the corresponding power station based on the target power prediction data and the target measured power data of each power station; and constructing a power report based on the target power prediction data, the target measured power data and the power deviation information of each power station.

[0011] In an optional implementation, the method further includes: when a report viewing request is received, retrieving the target report corresponding to the request from the data storage module; and sending the target report to the display terminal for display.

[0012] Thirdly, the present invention provides a data management device based on a big data control center, applied to the system of the first aspect. The device includes: an acquisition module for acquiring operating data of each device in multiple power plants, current data of each branch, and power data of each power plant, wherein the power data includes predicted power data and measured power data; a first construction module for constructing an operating data report based on the operating data of multiple devices; a second construction module for identifying target branches with faults based on the current data of each branch, and constructing a fault branch investigation report based on the target branches; a processing module for standardizing the format of the predicted power data and measured power data of multiple power plants to obtain target power prediction data and target measured power data of each power plant; a third construction module for constructing a power report based on the target power prediction data and target measured power data of each power plant; and a storage module for storing the operating data report, the fault branch investigation report, and the power report in a data storage module.

[0013] Fourthly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the data management method based on a big data control center as described in the second aspect above or any corresponding embodiment thereof.

[0014] Fifthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the data management method based on a big data control center according to the first aspect or any corresponding embodiment described above.

[0015] In a sixth aspect, the present invention provides a computer program product, including computer instructions for causing a computer to execute the data management method based on a big data control center as described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of a data management system based on a big data control center according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first type of data management method based on a big data control center according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a second process for a data management method based on a big data control center according to an embodiment of the present invention; Figure 4 This is a structural block diagram of a data management method and apparatus based on a big data control center according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0020] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0021] In related technologies, key operating parameters such as power, voltage, current, frequency, and temperature of each power station are generated in real time, resulting in a massive increase in data volume. Manually compiling these parameters is insufficient to meet the real-time and completeness requirements, leading to lags in operational status monitoring. Secondly, photovoltaic power stations are large-scale, with thousands of photovoltaic branches per station, and some large stations exceeding ten thousand. Relying on manual statistics for branch fault diagnosis and summarization is not only time-consuming (each check takes several hours) but also prone to omissions and misjudgments, extending the fault handling cycle and directly causing power generation losses. Statistics show that the average processing time for faulty branches under manual diagnosis exceeds 8 hours, with a power generation loss rate of 3%-5%. Furthermore, power prediction systems are distributed across various stations, resulting in inconsistent data formats and independent transmission links. The central control center lacks a unified data integration and display platform, requiring manual summarization of data from each station before reporting. This is not only inefficient but also carries the risk of data errors, seriously affecting the scientific nature of power generation planning.

[0022] In view of this, the present invention provides a data management system based on a big data control center, including a data acquisition module, a data processing module, and a data storage module. The data acquisition module acquires operating data, branch current data, and power data of multiple power station equipment, replacing manual statistics and allowing the control center to monitor equipment dynamics in real time, completely solving the problem of monitoring lag. The data processing module automatically generates operating data reports without manual compilation. By analyzing current data, it intelligently identifies faulty branches, significantly shortening troubleshooting time, reducing missed and false diagnoses, significantly compressing the fault handling cycle, and reducing power generation losses. At the same time, it standardizes the format of dispersed power data, eliminating data differences and avoiding errors in manual aggregation, providing an accurate basis for power generation planning. The data storage module securely stores various reports and index information, ensuring data accessibility and usability. This not only reduces manpower input and improves operation and maintenance efficiency, but also promotes the shift of new energy power station management from experience-driven to data-driven, effectively reducing power generation losses, improving the efficiency of regional new energy power production and operation, and providing strong support for scientific scheduling and efficient management.

[0023] This embodiment provides a data management system based on a big data control center, such as... Figure 1 As shown, the system includes: a data acquisition module 101, a data processing module 102, and a data storage module 103.

[0024] The data acquisition module 101 is used to collect operating data of each device in multiple power stations, current data of each branch, and power data of each power station.

[0025] For example, the data acquisition module 101 includes data acquisition sensors and data acquisition terminals configured for different monitoring objects. The data acquisition sensors may include, but are not limited to, voltage sensors, current sensors, and power sensors, which are directly installed on equipment such as combiner boxes, inverters, and distribution cabinets in the power station to collect core electrical parameters such as branch current, equipment output voltage, and instantaneous power in real time; the environmental and equipment status sensors include temperature sensors, humidity sensors, irradiance sensors, wind speed and direction sensors, and vibration sensors. Data acquisition terminals act as "data relay stations," connecting sensors to upper-level systems. These terminals can include, but are not limited to, data transmission units (DTUs) and remote terminal units (RTUs). DTUs often employ 4G, 5G, or LoRa wireless communication, suitable for remote power plants with difficult wiring. They package scattered data collected by sensors and transmit it wirelessly or via wired links to the data acquisition terminal at the site, then via dedicated power lines to the central control center. For example, branch current data from photovoltaic power plants in mountainous areas is often uploaded via DTUs. RTUs, on the other hand, combine data acquisition and local control functions. They can not only aggregate sensor data but also receive commands from the central control center to control local equipment (such as adjusting switch status). They are often deployed in larger power plants with concentrated equipment, such as the booster stations of large wind farms, where RTUs integrate data from multiple devices within the station. In this embodiment, a multi-source heterogeneous data integration and acquisition architecture is constructed. Relying on the central control center's monitoring backend (SCADA system), real-time acquisition of operational data from all core equipment at each substation's booster station is achieved through standardized data interfaces (such as IEC 104, 61850, and Modbus protocols). Taking photovoltaic power plants as an example, a minute-level on-time data acquisition mechanism is adopted (the acquisition interval can be set to 1-5 minutes as needed). For key equipment such as the Yilan line (transmission line), main transformer high and low voltage, 35kV station service transformer, and 319-323 switch, multiple core parameters such as three-phase current (I), active power (P), reactive power (Q), power factor (COSφ), three-phase voltage (U), and transformer temperature are accurately collected.

[0026] The data processing module 102 is used to construct operation data reports based on the operation data of each device in multiple power plants, identify the faulty branch in the corresponding power plant based on the current data of multiple branches in each power plant, construct a faulty branch investigation report based on the faulty branch, perform format standardization processing on the power data of multiple power plants to obtain the target power data of each power plant, and construct a power report based on the target power data of multiple power plants.

[0027] For example, in this embodiment, the operating data of each device in multiple power plants is automatically written into the corresponding fields of the operating data report, forming a three-dimensional data matrix of "equipment-parameter-time" covering all stations, providing on-duty personnel with a high-precision, full-dimensional view of operating data. Current data from multiple branches in each power plant identifies the corresponding faulty branch in the power plant, generating a visual report containing the specific location of the faulty branch and the fault trigger time, providing maintenance personnel with accurate fault location information and shortening fault handling time. Addressing the issues of inconsistent power prediction data formats and fragmented management across stations, the power data from multiple power plants is standardized in format, and a power report is generated based on the processed power data. The power data may include, but is not limited to, predicted power data and measured power data.

[0028] The data storage module 103 is used to store operation data reports, fault branch troubleshooting reports, power reports, and index information.

[0029] For example, the data storage module 103 can adopt a combined architecture of "centralized file storage system and relational database" to precisely match the storage needs of the two types of data. The centralized file storage system (such as MinIO, HDFS) is the core, specifically storing three types of report files. This type of system is easy to manage and control, suitable for scenarios with small data volumes and centralized management, can easily handle reports in Excel and CSV formats, and has redundant backup capabilities to prevent report file loss; it also supports establishing directory structures by power plant and time dimension, facilitating subsequent retrieval according to scenarios. For example, a regional control center generates hundreds of power plant reports daily, which can be quickly received and stored by the centralized file system, allowing maintenance personnel to download and view them at any time through access control. The relational database (such as MySQL, PostgreSQL) is used to store index information. Index information usually records key related data of the report, such as report name, power plant ID, generation time, storage path, etc. The structure is fixed and requires high-frequency querying, such as quickly locating the power report of the power plant for the day by "power plant ID and date". The SQL query capability and transaction consistency of the relational database can ensure the accuracy of index matching and avoid reports that cannot be retrieved due to index errors. In addition, the database can store report version information, making it easy to trace historical modification records and further ensuring the standardization of data management.

[0030] According to an embodiment of the present invention, a data management method based on a big data control center is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] This embodiment provides a data management method based on a big data control center, which can be used in the data management system based on a big data control center described in the above embodiments. Figure 2 This is a flowchart of a data management method based on a big data control center according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the operating data of each device in multiple power plants, the current data of each branch, and the power data of each power plant. The power data includes predicted power data and measured power data.

[0032] For example, in this embodiment, the specific methods for obtaining the operating data, current data, and power data are described in the relevant content of the above embodiments, and will not be repeated here.

[0033] Step S202: Build an operation data report based on the operation data of multiple devices.

[0034] For example, the embodiments of this application do not limit the specific construction method of the running data report, as long as it is reasonable.

[0035] Step S203: Identify the target branch with a fault based on the current data of each branch, and build a fault branch investigation report based on the target branch.

[0036] For example, in this embodiment of the application, the current of each branch can be compared with a preset fault current threshold, and the target branch with a fault can be determined based on the comparison result. The fault branch investigation report may include, but is not limited to, the unique identifier of the fault branch, the fault time, and other information. This embodiment of the application does not limit the specific construction method of the fault branch investigation report, as long as it is reasonable.

[0037] Step S204: Standardize the format of the predicted power data and measured power data of multiple power plants to obtain the target power prediction data and target measured power data of each power plant.

[0038] For example, in this embodiment of the application, prediction files (supporting multiple formats such as XML, CSV, and TXT) sent by power prediction systems at various power plants are received periodically via the SFTP protocol or API interface. The system has a built-in "format parsing engine" that can automatically extract key information such as prediction time, prediction power, and prediction accuracy from the files according to preset parsing rules (pre-configured for different power plant file formats), and convert non-standardized data into a unified JSON format. After the parsed standardized data undergoes quality verification (removing outliers that exceed reasonable ranges, such as negative power or power values ​​exceeding the power plant's installed capacity), it is stored in a relational database (such as MySQL), and an "power plant-prediction type-time" index is established to ensure data query efficiency (single data query response time ≤ 1 second).

[0039] Step S205: A power report is constructed based on the target power prediction data and the target measured power data of each power station.

[0040] For example, the embodiments of this application do not limit the specific construction method of the power report, as long as it is reasonable.

[0041] Step S206: Store the running data report, fault branch troubleshooting report, and power report into the data storage module.

[0042] For example, please refer to the description of the relevant content in the above system embodiments, which will not be repeated here.

[0043] This embodiment provides a data management method based on a big data control center, which comprehensively collects equipment operation data, branch current data, and power data including predictions and measurements. This replaces fragmented manual collection, ensuring comprehensive and timely data acquisition, laying a complete data foundation for subsequent analysis, and avoiding monitoring blind spots caused by missing or delayed data in traditional methods. When constructing operation data reports, manual processing and calculation are unnecessary; data is automatically generated directly from multi-device operation data, significantly improving data presentation efficiency. This allows the control center to quickly grasp the operating status of equipment in each power station, eliminating the tediousness and delays of manual reports. By analyzing current data, target faulty branches are accurately identified and troubleshooting reports are generated, eliminating the need for manual branch-by-branch checks. This not only shortens fault location time and reduces missed or incorrect diagnoses but also accelerates the fault handling process, effectively reducing power generation losses due to faults. In power data processing, the predicted and measured power data are standardized in format, eliminating data format differences between stations and avoiding the error risks of manual aggregation. This provides unified and accurate data support for subsequent power report construction, ensuring reliable data for power generation planning and improving the scientific nature of scheduling.

[0044] This embodiment provides a data management method based on a big data control center, which can be used in the data management system based on a big data control center described in the above embodiments.Figure 3 This is a flowchart of a data management method based on a big data control center according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: Step S301: Obtain operating data for each device in multiple power plants, current data for each branch, and power data for each power plant. The power data includes predicted power data and measured power data. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0045] Step S302: Build an operation data report based on the operation data of multiple devices.

[0046] Specifically, step S302 includes: Step S3021: Identify the device identifier, operating parameters, and time information in the operating data.

[0047] For example, the device identifier may be the device number. This application embodiment does not limit the specific content represented by the device, but those skilled in the art can determine it according to their needs.

[0048] Step S3022: Construct an operation data report based on the device identifier, operating parameters, and time information.

[0049] For example, the specific content of the operation data report can be found in the description of the relevant content in the above system embodiment, and will not be repeated here. Furthermore, at the data application level, the operation data report supports advanced multi-dimensional electrical quantity analysis functions: on the one hand, it can realize data comparison of equipment within a single station, such as calculating the difference between the total power and current of the collection line and the power and current of the outgoing line (difference calculation refers to the deviation of current, power, power generation, etc., between different levels of equipment or data sources, which originates from equipment conversion or transmission losses, such as the data differences between the transformer substation and its subordinate inverters, the collection line and its subordinate equipment, and the step-up substation and the collection line). By comparing these data, abnormal losses and abnormal data can be identified, thereby determining the fault type: abnormal loss rate points to a primary fault. In the event of equipment failure (or abnormal data source pointing to secondary equipment failure), if the difference exceeds a preset threshold (set based on equipment parameters and operating experience), it is determined that the main transformer loss is abnormal or the measurement data is incorrect, and the abnormal data is automatically marked. On the other hand, for scenarios where multiple stations such as the Luowugou photovoltaic power station share transmission lines, the system can integrate the outgoing data of each power station across stations. By comparing the total power and total current, the system can check the rationality of the data (such as a sudden increase or decrease in data from a single station causing an abnormality in the total data). The abnormal data will be pushed to the operation early warning module simultaneously as the core basis for equipment failure early warning, realizing the integration of "data collection-analysis-early warning".

[0050] Step S303: Identify the target branch with a fault based on the current data of each branch, and build a fault branch investigation report based on the target branch.

[0051] Specifically, step S303 includes: Step S3031: Determine the target current threshold for each branch based on a preset dynamic threshold mechanism.

[0052] For example, in this embodiment, the operating current data of all photovoltaic branches are collected in real time (with a 1-minute interval), and the raw data is preprocessed (filtering out null values ​​and spike data caused by communication interruptions) to ensure data validity. Based on the power plant's operating characteristics (daytime power generation is the core monitoring period), 9:00-15:00 is set as the core diagnostic time window (which can be flexibly adjusted according to season and regional sunshine conditions). Simultaneously, a dynamic threshold mechanism is introduced. The fault current threshold is not a fixed value, but is set based on 30% of the average current value of the branch during the same time period over the previous n days (e.g., seven days) (after removing abnormal data). (For example, if the average current of a branch during 9:00-15:00 for the previous 7 days was 10A, then the fault threshold is set to 3A). This determines the target current threshold and avoids misjudgments caused by changes in sunlight intensity.

[0053] Step S3032: If the current value of each branch is less than the target current threshold of the corresponding branch, the corresponding branch is identified as the target branch with a fault, and the time information in the current data of the target branch is used as the fault time.

[0054] For example, in this embodiment of the application, within the core diagnostic time window, if the maximum current value of a branch is less than the fault threshold for three consecutive sampling cycles, it is initially determined to be a faulty branch; subsequently, the system calls the historical database (stores branch operation data for the past 30 days). If the branch's operation data for the previous day was normal (no fault record), it is determined to be a "newly added faulty branch"; if a fault record already existed for the previous day and the processing status has not been updated, it is marked as an "unprocessed faulty branch"; if the previous day was a faulty state and the current data has returned to normal, it is marked as a "processed faulty branch".

[0055] Step S3033: Based on the identification information, location information and fault time of the target branch, a fault branch investigation report is constructed.

[0056] For example, in this embodiment of the application, seven key indicators are automatically counted: "number of newly added faulty branches, number of processed faulty branches, number of unprocessed faulty branches, number of branches affected by inverter communication failure (branch data missing due to inverter communication interruption), total number of branches involved, number of branches with communication failure excluded, and number of branches with communication failure counted." A visual report is generated, and the specific location of the faulty branch (site-inverter-branch number) and the fault trigger time are marked, providing maintenance personnel with accurate fault location information and shortening fault handling time.

[0057] Step S304: Standardize the format of the predicted power data and measured power data of multiple power plants to obtain the target power prediction data and target measured power data of each power plant.

[0058] Step S305: A power report is constructed based on the target power prediction data and the target measured power data of each power station.

[0059] Specifically, step S305 includes: Step S3051: Determine the power deviation information of the corresponding power station based on the target power prediction data and the target measured power data of each power station. For example, the power deviation information may include, but is not limited to, the power deviation rate.

[0060] Step S3052: A power report is constructed based on the target power prediction data, target measured power data and power deviation information of each power station.

[0061] For example, in this embodiment of the application, the report covers two types of data: "short-term power forecast" and "ultra-short-term power forecast". The short-term forecast data includes hourly power forecast values ​​for the next 10 days, and the ultra-short-term forecast data includes power forecast values ​​at 15-minute intervals for the next 4 hours. This solution does not improve the power forecast process itself, but mainly guides the work of the central control center and optimizes the power plant assessment indicators through secondary analysis of the power forecast results and actual power generation. The system automatically generates power forecast reports on a daily basis. The reports not only present the forecast data, but also simultaneously display the "deviation rate between the forecast value and the actual power generation" (calculated by comparing with the historical actual power generation), providing a basis for the on-duty personnel to evaluate the forecast accuracy. The reports support filtering by station, forecast type, and time interval (such as querying "ultra-short-term forecast data for the next 24 hours of Yimen photovoltaic power station"), and can generate trend curves (the trend of forecast power change over time), providing accurate data support for power plant scheduling and power generation plan formulation.

[0062] Step S306: Store the running data report, fault branch troubleshooting report, and power report into the data storage module.

[0063] Step S307: When a report viewing request is received, the target report corresponding to the request is extracted from the data storage module.

[0064] Step S308: Send the target report to the display terminal for display.

[0065] For example, the display terminal may include, but is not limited to, a display screen. The specific content of the display terminal is not limited in the embodiments of this application, and those skilled in the art can determine it according to their needs.

[0066] The method provided in this application, through a multi-source data integration and acquisition architecture, achieves real-time and comprehensive acquisition of operational data from all monitored substations and booster stations, with a data acquisition accuracy rate of 99.9%, a significant improvement compared to traditional manual acquisition methods (accuracy rate of approximately 85%). Simultaneously, the unified data integration platform eliminates "data silos," eliminating the need for on-duty personnel to query data across systems, thus improving data acquisition efficiency by over 70%. Based on a fault diagnosis algorithm using time windows and dynamic thresholds, combined with a historical data comparison mechanism, the efficiency of photovoltaic branch fault diagnosis is improved by 90% (single diagnosis time reduced from 4-6 hours traditionally done manually to 15-30 minutes), with a fault misjudgment rate controlled below 5% and a missed diagnosis rate below 2%. Furthermore, it reduces the frequency of manual inspections (from once a day to once a week on-site verification), saving approximately 100,000 yuan in labor costs per station annually. A unified processing platform for cross-site power forecast data integrates scattered forecast data into standardized reports. Power generation plans based on these reports can increase power plant load factor by 3%-5%, resulting in an annual increase of approximately 200,000 kWh of power generation per site, significantly improving power generation efficiency. A standardized process of "format parsing engine + quality verification + unified storage" is established, supporting automatic parsing of multiple forecast file formats (XML / CSV / TXT), enabling unified management of power forecast data from different sites and of different types (short-term and ultra-short-term). This transforms the inefficient traditional manual data aggregation model, shifting power forecast data management from "dispersed management" to "standardized and refined management," providing more scientific decision support for the central control center's operation and scheduling.

[0067] This embodiment also provides a data management device based on a big data control center, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0068] This embodiment provides a data management device based on a big data control center, such as... Figure 4 As shown, it includes: The acquisition module 401 is used to acquire the operating data of each device in multiple power plants, the current data of each branch, and the power data of each power plant. The power data includes predicted power data and measured power data. The first construction module 402 is used to construct an operation data report based on the operation data of multiple devices; The second construction module 403 is used to identify the target branch with a fault based on the current data of each branch, and to build a fault branch investigation report based on the target branch. The processing module 404 is used to perform format standardization processing on the predicted power data and measured power data of multiple power plants to obtain the target power prediction data and target measured power data of each power plant. The third construction module 405 is used to construct a power report based on the target power prediction data and target measured power data of each power station. Storage module 406 is used to store operating data reports, fault branch troubleshooting reports, and power reports into the data storage module.

[0069] In some alternative implementations, the first building module 402 includes: The identification submodule is used to identify device identifiers, operating parameters, and time information in the operating data; The first construction submodule is used to build operation data reports based on device identification, operating parameters, and time information.

[0070] In some alternative implementations, the second building module 403 includes: The first determining submodule is used to determine the target current threshold of each branch based on a preset dynamic threshold mechanism. The second determination submodule is used to determine the corresponding branch as the target branch with a fault if the current value of each branch is less than the target current threshold of the corresponding branch, and to use the time information in the current data of the target branch as the fault time. The second construction submodule is used to construct a fault branch investigation report based on the target branch's identification information, location information, and fault time.

[0071] In some alternative implementations, the third building module 405 includes: The third determination submodule is used to determine the power deviation information of the corresponding power station based on the target power prediction data and the target measured power data of each power station. The third submodule is used to construct power reports based on the target power prediction data, target measured power data, and power deviation information of each power station.

[0072] In some alternative embodiments, the above-described apparatus further includes: The extraction module is used to extract the target report corresponding to the request from the data storage module when a report viewing request is received. The sending module is used to send the target report to the display terminal for display.

[0073] The data management device based on a big data control center provided in this embodiment of the invention can execute the data management method based on a big data control center provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0074] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0075] The following is a detailed reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from memory 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device. The processor 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0076] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0077] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a memory 508, or installed from a ROM 502. When the computer program is executed by the processor 501, it performs the functions defined in the data management method based on a big data control center according to embodiments of the present invention.

[0078] Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0079] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the data management method based on a big data control center shown in the above embodiments is implemented.

[0080] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0081] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A big data set control center based data management system, characterized by, The system comprises a data acquisition module, a data processing module and a data storage module; The data acquisition module is configured to acquire operation data of each device in multiple power stations, current data of each branch, and power data of each power station; The data processing module is configured to construct an operation data report according to the operation data of each device in the multiple power stations, identify a faulty branch in a corresponding power station according to the current data of the multiple branches in each power station, construct a fault branch troubleshooting report based on the faulty branch, perform format standardization processing on the power data of the multiple power stations to obtain target power data of each power station, and construct a power report based on the target power data of the multiple power stations; The data storage module is configured to store the operation data report, the fault branch troubleshooting report, the power report and index information.

2. A data management method based on a large data set control center, characterized by, The method applied to the system of claim 1 comprises: acquiring operation data of each device in multiple power stations, current data of each branch, and power data of each power station, wherein the power data comprises predicted power data and measured power data; constructing an operation data report based on the operation data of the multiple devices; identifying a target branch with a fault based on the current data of the branches, and constructing a fault branch troubleshooting report based on the target branch; performing format standardization processing on the predicted power data and the measured power data of the multiple power stations to obtain target predicted power data and target measured power data of each power station; constructing a power report based on the target predicted power data and the target measured power data of each power station; and storing the operation data report, the fault branch troubleshooting report and the power report in a data storage module.

3. The method of claim 2, wherein, The step of constructing the operation data report based on the operation data of the multiple devices comprises: identifying device identifiers, operation parameters and time information in the operation data; constructing an operation data report based on the device identifiers, the operation parameters and the time information.

4. The method according to claim 2 or 3, characterized in that, The step of identifying a target branch with a fault based on the current data of the branches, and constructing a fault branch troubleshooting report based on the target branch comprises: determining target current thresholds of the branches based on a preset dynamic threshold mechanism; if a current value of each branch is less than the target current threshold of the corresponding branch, determining the corresponding branch as the target branch with a fault, and taking time information in the current data of the target branch as a fault time; constructing a fault branch troubleshooting report based on identifier information, location information and the fault time of the target branch.

5. The method according to claim 2 or 3, characterized in that, The step of constructing a power report based on the target predicted power data and the target measured power data of each power station comprises: determining power deviation information of a corresponding power station based on the target predicted power data and the target measured power data of each power station; constructing a power report based on the target predicted power data, the target measured power data and the power deviation information of each power station.

6. The method of claim 2, wherein, The method comprises: when receiving a report viewing request, extracting a target report corresponding to the request from the data storage module; sending the target report to a display terminal for display.

7. A big data set control center-based data management apparatus, characterized by comprising: The device applied to the system of claim 1 comprises: An acquisition module is configured to acquire operation data of devices in multiple power stations, current data of branches, and power data of the multiple power stations, the power data including predicted power data and measured power data; A first construction module is configured to construct an operation data report based on the operation data of the multiple devices; A second construction module is configured to identify a target branch with a fault based on the current data of the branches, and to construct a fault branch troubleshooting report based on the target branch; A processing module is configured to perform format standardization processing on the predicted power data and the measured power data of the multiple power stations to obtain target power prediction data and target measured power data of the multiple power stations; A third construction module is configured to construct a power report based on the target power prediction data and the target measured power data of the multiple power stations; A storage module is configured to store the operation data report, the fault branch troubleshooting report, and the power report in a data storage module.

8. An electronic device, comprising: comprising: a memory and a processor, which are in communication connection with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the data management method of the centralized control center based on big data in any one of claims 2 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 make the computer execute the data management method of the centralized control center based on big data in any one of claims 2 to 6.

10. A computer program product, characterised in that, comprising computer instructions, which are used to make the computer execute the data management method of the centralized control center based on big data in any one of claims 2 to 6.

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