Air energy storage power station information platform data management method and device, equipment and medium

Abstract

This application relates to a data management method, apparatus, equipment, and medium for an air-based energy storage power station information platform. The method includes: acquiring a newly added dataset and current data filtering rules corresponding to the current dataset; partitioning the newly added dataset based on the current data filtering rules to obtain multiple partition data; processing each partition data independently based on a distributed computing framework to obtain classification evaluation results corresponding to each partition data; if any of the multiple classification evaluation results contain unqualified results, then modifying the current data filtering rules based on the unqualified classification evaluation results and the multiple partition data to obtain modified data filtering rules, thereby providing the user with first-target data based on the modified data filtering rules. This application dynamically modifies the current data filtering rules according to the classification evaluation results, enabling the data filtering rules to adaptively adjust and ensure the convenience, accuracy, and relevance of the filtering results.

Description

Technical Field

[0001] This application relates to the technical field of air energy storage power station information platforms, and in particular to a data management method, apparatus, equipment and medium for air energy storage power station information platforms. Background Technology

[0002] With the continuous advancement of energy technology and the increasing demand for electricity, air energy storage power stations, as a highly efficient and environmentally friendly energy storage method, are playing an increasingly important role in the power system. However, the operation of air energy storage power stations involves a large number of devices, complex processes, and stringent safety requirements, which poses extremely high challenges to their operation monitoring and management.

[0003] Traditional power plant operation monitoring methods mainly rely on manual inspections and periodic testing. This method is not only inefficient, but also makes it difficult to achieve real-time monitoring and perception of the main operating and health status of the power plant.

[0004] Currently, several power plant operation monitoring systems based on Internet of Things (IoT) technology have emerged in the market. These systems collect real-time operational data from power plant equipment by installing sensors and other sensing devices, and then upload the data to a cloud platform for processing via a signal transmission system. However, due to the massive amount of data processed by the air-source energy storage power station information platform, data filtering presents numerous inconveniences, further increasing the difficulty of operation and management. Summary of the Invention

[0005] To improve the convenience of data filtering and reduce the difficulty of operation and management, this application provides a data management method, device, equipment and medium for an air energy storage power station information platform.

[0006] Firstly, this application provides a data management method for an air-based energy storage power station information platform, including:

[0007] Obtain the current data filtering rules corresponding to the newly added dataset and the current dataset, wherein the newly added dataset is preprocessed newly added device operation data;

[0008] Based on the current data filtering rules, the newly added dataset is partitioned to obtain multiple partitioned data.

[0009] Based on a distributed computing framework, each partition data is processed independently to obtain a classification evaluation result corresponding to each partition data, wherein the classification evaluation result is either qualified or unqualified.

[0010] If any of the multiple classification evaluation results are unqualified, the current data filtering rule is modified based on the unqualified classification evaluation results and the multiple partition data to obtain a modified data filtering rule. The newly added dataset is then stored in the database corresponding to the current dataset, so as to filter the first target data for the user from the database based on the modified data filtering rule.

[0011] The beneficial effects of this application are as follows: It dynamically modifies the current data filtering rules based on the classification and evaluation results, allowing the data filtering rules to be adaptively adjusted to ensure the convenience, accuracy, and relevance of the filtering results. Based on the modified data filtering rules, the system queries the first target data corresponding to the user's data filtering operation. Through partitioning, the newly added dataset can be split into multiple smaller datasets for separate processing, thereby improving processing efficiency.

[0012] Furthermore, before partitioning the newly added dataset based on the current data filtering rules to obtain multiple partitioned data, the method further includes:

[0013] Extract the first data feature corresponding to the current dataset and the second data feature corresponding to the newly added dataset;

[0014] Based on the first data feature and the second data feature, a similarity assessment result between the current dataset and the newly added dataset is obtained, wherein the similarity assessment result is a parameter characterizing the degree of similarity between the two datasets;

[0015] If the similarity assessment result is lower than the similarity threshold, then the step of partitioning the newly added dataset based on the current data filtering rules to obtain multiple partitioned data is executed.

[0016] The beneficial effects of adopting the above-mentioned further scheme are: through similarity evaluation, when the new dataset is sufficiently similar to the current dataset, it is not necessary to use the current data filtering rules for partitioning, which reduces the amount of data processing computation when the dataset differences are too small, reduces the workload of subsequent partitioning and classification evaluation, and improves the efficiency of data processing.

[0017] Furthermore, before determining whether the similarity assessment result is below the similarity threshold, the method further includes:

[0018] If the first platform application scenario corresponding to the current dataset is different from the second platform application scenario corresponding to the newly added dataset, then the similarity threshold is re-determined based on the second platform application scenario.

[0019] The beneficial effects of adopting the above-mentioned further solution are: dynamically adjusting the similarity threshold according to changes in the platform application scenario, thereby enhancing the flexibility of data management. When the platform application scenario of a new dataset differs from that of the current dataset, re-determining the similarity threshold ensures that subsequent data processing procedures are more in line with the needs of the current second platform application scenario, which helps improve the efficiency and accuracy of data processing.

[0020] Furthermore, the method also includes:

[0021] Based on the user's identity information, obtain the data access permissions corresponding to the user, and adjust the data access interface corresponding to the air energy storage power station information platform based on the data access permissions.

[0022] Based on the historical data access records and preference settings corresponding to the identity information, a first target dataset that meets the data access permissions is predicted, and the first target dataset is displayed based on the data access interface.

[0023] The beneficial effects of adopting the above-mentioned further solutions are as follows: By customizing the data access interface and displayed content based on the user's identity information, historical data access records, and preference settings, a more personalized and convenient data access experience can be provided to the user. Adjusting the data access interface based on the user's data access permissions ensures that users can only access the data they are authorized to access, thereby protecting sensitive data from unauthorized access. By predicting and displaying datasets that users may be interested in, users can find the data they need more quickly, thereby improving data utilization efficiency.

[0024] Furthermore, after obtaining the user's identity information, the method also includes:

[0025] Based on the current dataset and the newly added dataset, the operating trend of the target device is obtained. The air energy storage power station information platform monitors multiple monitoring devices, and the target device is any one of the preset multiple monitoring devices.

[0026] Based on the operating trend, the historical data access records, and the preference settings, the user's data filtering operation needs are predicted, and the second target data corresponding to the data filtering operation needs is obtained.

[0027] The beneficial effects of adopting the above-mentioned further solutions are: by predicting users' data filtering needs, the information platform can provide users with more accurate and personalized data displays, reducing the time and effort users spend manually filtering data. It can provide data support based on users' actual needs, facilitating a better understanding of equipment status, prediction of potential problems, and timely decision-making and adjustments, thereby improving the overall operational efficiency of the power plant.

[0028] Furthermore, after obtaining the user's identity information, the method also includes:

[0029] If, based on the identity information, the user is determined to be an unregular user, a simplified access interface is generated, and based on the user's operation behavior, prompt information is generated for the simplified access interface. The prompt information includes operation instructions displayed when the mouse hovers over the user, error messages and solutions, and interactive prompts.

[0030] The beneficial effects of adopting the above-mentioned further solutions are: providing users with necessary prompts in the simplified access interface to help them quickly understand how to operate the system. These prompts include operation instructions displayed on mouse hover, error messages and solutions, and interactive instructions. The interface may only include basic power plant status monitoring, real-time data viewing of key equipment, and simple alarm notifications. The interface layout should be concise and clear, avoiding excessive text and icons.

[0031] Furthermore, after obtaining the current data filtering rules corresponding to the newly added dataset and the current dataset, the method further includes:

[0032] Based on the current dataset, obtain the historical operation information corresponding to each monitoring device;

[0033] For each of the monitoring devices, based on the historical operation information, the display level information corresponding to the monitoring device is obtained. The display level information is a parameter that characterizes the importance of the device operation data of the monitoring device. The display level information is a first level, a second level, or a third level.

[0034] The beneficial effects of adopting the above-mentioned further solutions are: through hierarchical display, operators can identify key information more quickly, thereby making faster and more accurate decisions. Organizing the interface layout according to the importance of the data makes the interface cleaner and more orderly. By providing different display methods for different levels of data, operators can more easily find the information they need, thereby improving the overall user experience.

[0035] Secondly, this application provides a data management device for an air-based energy storage power station information platform, comprising:

[0036] The first acquisition module is used to acquire the current data filtering rules corresponding to the newly added dataset and the current dataset, wherein the newly added dataset is preprocessed newly added device operation data;

[0037] The partitioning module is used to partition the newly added dataset based on the current data filtering rules to obtain multiple partitioned data.

[0038] The evaluation module is used to independently process the data of each partition based on a distributed computing framework to obtain the classification evaluation result corresponding to each partition data, wherein the classification evaluation result is either qualified or unqualified.

[0039] The correction module is used to correct the current data filtering rule based on the unqualified classification evaluation results and the multiple partition data when there are unqualified classification evaluation results among the multiple classification evaluation results, to obtain the corrected data filtering rule, and to store the new dataset in the database corresponding to the current dataset, so as to filter the first target data for the user from the database based on the corrected data filtering rule.

[0040] Thirdly, this application provides an electronic device, including a processor and a memory, wherein the processor is coupled to the memory;

[0041] The processor is configured to execute a computer program stored in the memory, causing the electronic device to perform the method as described in any of the first aspects.

[0042] Fourthly, this application provides a computer-readable storage medium including a computer program or instructions that, when executed on a computer, cause the computer to perform the method as described in any of the first aspects. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the data management method for the air-source energy storage power station information platform according to an embodiment of this application.

[0044] Figure 2 This is a structural block diagram of the data management device for the air energy storage power station information platform, as described in an embodiment of this application.

[0045] Figure 3 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0046] The present application will be further described in detail below with reference to the accompanying drawings.

[0047] This application provides a data management method for an air-source energy storage power station information platform. This method can be executed by a device, which can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, desktop computer, etc., but is not limited to these.

[0048] like Figure 1As shown, an air-based energy storage power station information platform data management method, with electronic devices as the execution subject, is described in its main process flow as follows (steps S101 to S104):

[0049] Step S101: Obtain the current data filtering rules corresponding to the newly added dataset and the current dataset. The newly added dataset is the preprocessed newly added device operation data.

[0050] Electronic devices can collect operational data from various data sources, including log files and monitoring sensors. The current dataset is stored in a database, containing data collected over historical monitoring steps.

[0051] As the number of power plants increases and equipment operation data grows, new datasets can be collected. Preprocessing includes data cleaning, which involves handling missing values, removing duplicate data, correcting erroneous values, and handling outliers in the collected data to obtain the new datasets. By preprocessing the new datasets, it is ensured that they have consistent data quality and format with the current datasets.

[0052] Step S102: Based on the current data filtering rules, the newly added dataset is partitioned to obtain multiple partitioned data.

[0053] Data filtering rules refer to the criteria or conditions used to select or exclude specific data from a dataset. In an air-source energy storage power station information platform, current data filtering rules can be formulated based on multiple dimensions such as data type, time range, and equipment status. For example, the current data filtering rules might be based on time range and / or data type.

[0054] Step S103: Based on the distributed computing framework, each partition data is processed independently to obtain the classification evaluation result corresponding to each partition data, wherein the classification evaluation result is qualified or unqualified.

[0055] By leveraging the parallel processing capabilities of distributed computing frameworks (such as Apache Spark) to process multiple partitions simultaneously, data processing efficiency can be significantly improved.

[0056] Based on business needs and data characteristics, define reasonable evaluation metrics to quantify the quality of the partitioning results. For example, one could examine whether the data distribution after partitioning is balanced, whether processing time is reduced, and whether memory usage is decreased. Based on the distributed computing framework, evaluate the partitioning results of each partition according to the defined evaluation metrics to obtain the classification evaluation results for each partition. For instance, if the data distribution after partitioning is unbalanced and / or processing time is not reduced and / or memory usage is not decreased, the classification evaluation result is considered unqualified.

[0057] Step S104: If there is a classification failure among the multiple classification evaluation results, the current data filtering rule is modified based on the failure classification evaluation result and the multiple partition data to obtain a modified data filtering rule, and the new dataset is stored in the database corresponding to the current dataset, so as to filter the first target data for the user from the database based on the modified data filtering rule.

[0058] Based on the evaluation results of each classification, analyze the rationality and shortcomings of the classification results, and identify the problems and deficiencies in the current data partitioning rules. For example, if the data distribution is uneven or the processing time is too long, the current partitioning rules can be adjusted. If the classification effect of the current dataset is good, but the classification effect of the newly added dataset is poor, it indicates that the current data partitioning rules are not suitable for the new data.

[0059] For the identified problems with the current partitioning rule, specific correction schemes are formulated. The electronic device stores the correspondence between the identified problems with the current partitioning rule and the correction schemes; for example, the correction schemes may include adjusting the parameters of the current partitioning rule.

[0060] In this embodiment, after obtaining the revised data filtering rules, the newly added dataset is merged into the database corresponding to the current dataset. According to the revised data filtering rules, the first target data corresponding to the user's data filtering operation is queried, and the revised data filtering rules are used as the new current data filtering rules.

[0061] Air-based energy storage power station information platforms involve a large amount of equipment operation data. Directly processing newly added datasets leads to low computational efficiency. By partitioning the datasets, new datasets can be split into multiple smaller datasets and processed separately, thereby improving processing efficiency.

[0062] As new data arrives and changes, the current data filtering rules are dynamically revised based on the classification and evaluation results. This allows the data filtering rules to adapt adaptively, ensuring the accuracy and relevance of the filtering results. As the amount of data in the database increases, more information can be obtained to optimize the data filtering rules, helping to improve their accuracy and generalization ability.

[0063] In this embodiment, before step S102, the method further includes:

[0064] Extract the first data feature corresponding to the current dataset and the second data feature corresponding to the newly added dataset; based on the first data feature and the second data feature, obtain the similarity evaluation result between the current dataset and the newly added dataset, wherein the similarity evaluation result is a parameter characterizing the degree of similarity between the two datasets; if the similarity evaluation result is higher than the similarity threshold, then perform the step of partitioning the newly added dataset based on the current data filtering rules to obtain multiple partitioned data.

[0065] Data features refer to specific indicators or attributes extracted from equipment operation data that reflect the equipment's operating status, performance, and relationships with other devices or datasets. Electronic devices can extract key data features from both the current dataset and newly added datasets, resulting in first and second data features, respectively.

[0066] The first data feature is extracted from the existing current dataset and represents the current operating status and performance of the power plant equipment. The first data feature may include parameters such as the operating time, power output, temperature, pressure, and vibration of the monitoring equipment, as well as the statistical values ​​of these parameters (such as average, standard deviation, maximum, and minimum values).

[0067] The second data feature is extracted from the newly collected or generated dataset. Similar to the first data feature, it represents the characteristics and state of the new dataset. By comparing the first and second data features, the similarity and differences between the new dataset and the current dataset can be assessed.

[0068] In this embodiment, the feature extraction method can be selected according to the type and structure of the data, such as using statistical methods, machine learning algorithms, or deep learning models.

[0069] After features are extracted, the similarity between the first and second data features can be evaluated by calculating correlation coefficients, distance metrics (such as Euclidean distance, Manhattan distance, etc.), or similarity metrics (such as cosine similarity, Jaccard similarity, etc.). The similarity evaluation result can be a numerical value representing the degree of similarity between the two datasets.

[0070] In this embodiment, in addition to evaluating feature similarity, it is also necessary to check whether the category labels or feature values ​​in the newly added dataset exist in the current dataset, thereby analyzing whether the newly added dataset has introduced new categories or features of device operation data. If the newly added dataset contains new categories or features, the current data filtering rules also need to be updated to adapt to the new data distribution, so step S102 also needs to be performed.

[0071] For example, a similarity threshold can be set. If the similarity between the new dataset and the current dataset is higher than the similarity threshold, and the new dataset does not introduce new categories or features, then it can be decided not to process the new dataset immediately.

[0072] By using similarity assessment, when a new dataset is sufficiently similar to the current dataset, it is not necessary to use the current data filtering rules for partitioning. This reduces the computational workload of data processing when the datasets are too different, decreases the workload of subsequent partitioning and classification assessment, and improves the efficiency of data processing.

[0073] In this embodiment, before determining whether the similarity assessment result is lower than the similarity threshold, the method further includes: if the first platform application scenario corresponding to the current dataset is different from the second platform application scenario corresponding to the newly added dataset, then the similarity threshold is re-determined based on the second platform application scenario.

[0074] Platform application scenarios refer to the specific environments in which the air-based energy storage power station information platform is actually used. Different platform application scenarios have different similarity requirements. For example, in air quality monitoring, a high sensitivity to changes in pollutant concentrations is required, thus necessitating a lower threshold; while in energy management, a higher stability of the power station's operating status is required, thus necessitating a higher threshold. If the application scenario changes, the similarity threshold needs to be adjusted to adapt to new business requirements and data characteristics.

[0075] For example, the first platform application scenario is air quality monitoring. Air quality monitoring primarily focuses on changes in the concentration of air pollutants, such as PM2.5, PM10, and sulfur dioxide. These pollutant concentration changes have a direct impact on public health and environmental quality, thus requiring real-time monitoring and rapid response. Air quality monitoring data is typically characterized by high frequency, high accuracy, and real-time performance. Because pollutant concentrations can be affected by various factors (such as meteorological conditions and traffic conditions), the data fluctuates significantly. To capture minute changes in pollutant concentrations, a low similarity threshold needs to be set. For example, the similarity threshold between two datasets could be 0.8 (assuming the similarity assessment result ranges from 0 to 1, where 1 indicates complete similarity).

[0076] The second platform application scenario is energy management. With business development, the air-based energy storage power station information platform needs to support energy management functions, primarily focusing on the power station's operating status, energy efficiency, and stability. Compared to air quality monitoring, energy management places greater emphasis on data stability and long-term trends. Energy management data is typically characterized by low frequency and low accuracy, but it is continuous over a long period. While the power station's operating status is usually relatively stable with minimal data fluctuations, the accumulated data over time can reveal changes in energy efficiency and potential faults.

[0077] To adapt to new business needs and data characteristics, the similarity threshold needs to be adjusted. In energy management, where there is a greater focus on data stability and long-term trends, a higher similarity threshold can be set. For example, a similarity threshold of 0.95 could be used.

[0078] For air-based energy storage power station information platforms, different platform application scenarios require different data management and analysis strategies. Dynamically adjusting the similarity threshold according to changes in the platform application scenario enhances the flexibility of data management. When a new dataset has a different platform application scenario than the current dataset, re-determining the similarity threshold ensures that subsequent data processing procedures (such as partitioning and classification evaluation) better meet the needs of the current second platform application scenario, thus improving the efficiency and accuracy of data processing.

[0079] In this embodiment, the method further includes the following processing: obtaining the data access permissions corresponding to the user based on the user's identity information, and adjusting the data access interface corresponding to the air energy storage power station information platform based on the data access permissions; predicting a first target dataset that conforms to the data access permissions based on the historical data access records and preference settings corresponding to the identity information, and displaying the first target dataset based on the data access interface.

[0080] In this embodiment, the user's identity information (such as username, role, etc.) can be obtained through the user login system. Then, based on a preset permission rule base, the user's data access permissions can be determined. The access permissions may include the data that is allowed to be accessed. Through the user login system, the user's login status and activities can be tracked, thereby automatically detecting and handling user changes. When events such as user logout or session timeout are detected, the system can automatically trigger an identity verification process, requiring the new user to authenticate.

[0081] Based on user data access permissions, the layout, menu items, and buttons of the data access interface are dynamically adjusted to ensure that users can only see and operate the data they are authorized to access. By analyzing users' historical data access records and preference settings (such as frequently accessed data types, time periods, and filtering conditions), machine learning algorithms are used to predict the dataset that the user might be interested in in the current context—the primary target dataset. In the adjusted data access interface, the predicted primary target dataset is displayed first, so that users can quickly find the data they are interested in. Simultaneously, search and filtering functions can also be provided, allowing users to further refine their data needs.

[0082] By customizing the data access interface and displayed content based on user identity information, historical data access records, and preferences, a more personalized and convenient data access experience can be provided. Adjusting the data access interface based on user data access permissions ensures that users can only access data they are authorized to access, thus protecting sensitive data from unauthorized access. Predicting and displaying datasets that users may be interested in helps them find the data they need more quickly, thereby improving data utilization efficiency.

[0083] In this embodiment, after obtaining the user's identity information, the following processing is also included: based on the current dataset and the newly added dataset, the operating trend of the target device is obtained. The air energy storage power station information platform monitors multiple monitoring devices, and the target device is any one of the preset multiple monitoring devices; based on the operating trend, the historical data access records, and the preference settings, the user's data filtering operation needs are predicted, and the second target data corresponding to the data filtering operation needs is obtained.

[0084] By integrating the current dataset and the newly added dataset, a complete equipment operation dataset can be formed. Then, the data of the target equipment (such as key equipment like generators and compressors) is analyzed, and techniques such as time series analysis and machine learning are used to extract the equipment's operating trends, such as performance degradation and fault warnings.

[0085] By combining users' historical data access records (such as frequently accessed data types, time periods, and filtering criteria) and preference settings (such as preferred data views and alarm thresholds), machine learning algorithms (such as collaborative filtering and decision trees) are used to analyze this information and predict the data filtering needs that users may be interested in in the current context.

[0086] Based on the predicted user data filtering needs, the system automatically filters and extracts relevant data to form a second target dataset. This second target dataset can include real-time operating data, historical performance data, and fault warning information from the equipment, to meet specific user requirements.

[0087] By anticipating users' data filtering needs, the information platform can provide more accurate and personalized data displays, reducing the time and effort users spend manually filtering data. It can provide data support based on users' actual needs, facilitating a better understanding of equipment status, predicting potential problems, and making timely decisions and adjustments, thereby improving the overall operational efficiency of the power plant.

[0088] In this embodiment, after obtaining the user's identity information, the following processing is also included: if the user is determined to be an unregular user based on the identity information, a simplified access interface is generated, and based on the user's operation behavior, prompt information of the simplified access interface is generated. The prompt information includes operation instructions displayed when the mouse hovers over the screen, error prompts and error solutions, and interactive prompts.

[0089] In the management and application scenarios of air-source energy storage power station information platforms, users can be categorized into different types based on factors such as their professional background, job responsibilities, and frequency of use. Among these, "non-routine users" refer to those who are not professional operators or lack in-depth professional knowledge. Non-routine users can include the following groups: temporary visitors, maintenance personnel, and non-professional management personnel.

[0090] Based on the user type field or user permission list in the user's identity information, determine whether the user is an irregular user. If the user is identified as an irregular user, proceed with the next steps.

[0091] In this embodiment, a simplified data access interface is designed for non-standard users. This interface can include the most frequently used functions and data views. The simplified interface corresponds to a simplified operation process, easy navigation and operation, and avoids too many options and complex settings.

[0092] Streamlining the workflow includes: providing fixed answer choices, using checkboxes and switches, automation and pre-filling, simplifying form design and guided operations.

[0093] Providing fixed answer options: For situations where user input is required but the answer is relatively fixed, providing drop-down menus or radio buttons for users to select can reduce the possibility of user input errors and speed up the operation.

[0094] For multiple-choice or on / off states, checkboxes and toggle controls simplify user interaction and are generally more intuitive and easier to use than text input boxes. Automate the user input process as much as possible, such as by automatically filling in user information via API calls. For information requiring repeated input, provide pre-fill functionality to reduce the amount of user input.

[0095] Simplifying form design means optimizing form design to make it more concise and clear, using labels, placeholders, and grouping to guide user input, and reducing unnecessary fields.

[0096] Guided operation refers to providing a guided operation interface, which guides users to complete complex tasks through step indicators or progress bars, and can help users understand the current progress and remaining steps of the operation.

[0097] The simplified access interface should provide users with necessary prompts to help them quickly understand how to operate the system. These prompts include operation instructions displayed on mouse hover, error messages and solutions, and interactive explanations. It can include only basic power plant status monitoring, real-time data viewing of key equipment, and simple alarm notifications. The interface layout should be concise and clear, avoiding excessive text and icons.

[0098] Mouse hover instructions display instructions for a button or icon when the user hovers the mouse over it. Error messages and solutions display error messages and provide simple solutions or guidance to the user when the user enters incorrect information or performs an invalid operation. Interactive prompts add interactive prompts to the user interface, such as guiding the user to click a button to view more information or perform a specific action.

[0099] By providing simplified access interfaces and prompts for non-standard users, the difficulty and complexity of operation are reduced, enabling users to obtain the information they need more quickly and accurately. Providing error messages and solutions helps users avoid operational errors, improving the accuracy and reliability of data access. The introduction of simplified access interfaces and prompts reduces the time users spend learning and using the information platform, improving work efficiency.

[0100] In this embodiment, after step S101, the following processing is also included: based on the current dataset, obtaining the historical operation information corresponding to each monitoring device; for each monitoring device, based on the historical operation information, obtaining the display level information corresponding to the monitoring device, wherein the display level information is a parameter characterizing the importance of the device operation data of the monitoring device, and the display level information is a first level, a second level, or a third level.

[0101] For each monitoring device, historical operational information is extracted from its current dataset, including the device's operating parameters, fault records, and maintenance history. Based on this historical operational information, a display level is determined for each monitoring device.

[0102] The displayed information is divided into three levels: Level 1, Level 2, and Level 3. Level 1 represents the most important or urgent data that requires immediate attention from operators; Level 2 represents data that is relatively important or requires regular monitoring; and Level 3 represents general or less important data.

[0103] Based on the determined display level information and the designed display scheme, these changes are implemented in the operating parameter display interface of the information platform to ensure that operators can see and understand the operating data of each monitoring device and its importance.

[0104] For each of the monitoring devices, if the display level information corresponding to the monitoring device is the first level, then the device operation data is controlled to be displayed in the form of a chart or icon in the first target area. The first target area is an independent sub-area in the operation parameter display interface of the air energy storage power station information platform.

[0105] For each of the monitoring devices, if the display level information corresponding to that monitoring device is Level 2, then the device operation data is displayed in text form in the second target area. The second target area may contain the device operation data of multiple monitoring devices, but the device operation data of each monitoring device is displayed in an easily distinguishable way, such as using different colors. In addition, an alert function can be set for the Level 2 device operation data, such as issuing a warning sound or flashing an icon when the device operation data exceeds the normal range.

[0106] For each of the aforementioned monitoring devices, if the display level information corresponding to that device is Level 3, then the device's operating data can be displayed in a broader data browsing interface or report. This interface or report may include operating data from all monitoring devices in the air energy storage power station, but Level 3 operating data will not be displayed in a particularly prominent manner; instead, it will be provided as background information. However, this does not mean that Level 3 operating data is unimportant or requires no attention. On the contrary, Level 3 operating data can be used for more in-depth analysis or long-term trend monitoring. Therefore, when displaying Level 3 operating data, it should be ensured that it is easily accessible and queried so that operators can find it quickly.

[0107] By using hierarchical displays, operators can more quickly identify key information, enabling them to make faster and more accurate decisions. Organizing the interface layout according to the importance of the data makes the interface cleaner and more organized. By providing different display methods for different levels of data, operators can more easily find the information they need, thus improving the overall user experience.

[0108] Based on the same technical concept, this application also provides a data management device for an air energy storage power station information platform, such as... Figure 2 As shown, the data management device 200 of the air energy storage power station information platform mainly includes:

[0109] The first acquisition module 201 is used to acquire the current data filtering rules corresponding to the newly added dataset and the current dataset, wherein the newly added dataset is preprocessed newly added device operation data;

[0110] The partitioning module 202 is used to partition the newly added dataset based on the current data filtering rules to obtain multiple partitioned data.

[0111] Evaluation module 203 is used to process each partition data independently based on a distributed computing framework to obtain the classification evaluation result corresponding to each partition data, wherein the classification evaluation result is qualified or unqualified.

[0112] The correction module 204 is used to correct the current data filtering rule based on the unqualified classification evaluation result and the multiple partition data when there is an unqualified classification evaluation result among the multiple classification evaluation results, to obtain a corrected data filtering rule, and to store the new dataset in the database corresponding to the current dataset, so as to filter the first target data for the user from the database based on the corrected data filtering rule.

[0113] Optionally, prior to partition module 202, the following may also be included:

[0114] The feature extraction module is used to extract the first data feature corresponding to the current dataset and the second data feature corresponding to the newly added dataset;

[0115] A similarity acquisition module is used to acquire a similarity assessment result between the current dataset and the newly added dataset based on the first data feature and the second data feature, wherein the similarity assessment result is a parameter characterizing the degree of similarity between the two datasets;

[0116] The similarity comparison module is used to perform the partitioning module 202 processing when the similarity evaluation result is lower than the similarity threshold.

[0117] Optionally, before the similarity comparison module, the following may also be included:

[0118] The threshold redetermining module is used to redetermine the similarity threshold based on the second platform application scenario when the first platform application scenario corresponding to the current dataset is different from the second platform application scenario corresponding to the newly added dataset.

[0119] Optionally, the device further includes:

[0120] The second acquisition module is used to acquire the data access permissions corresponding to the user based on the user's identity information, and adjust the data access interface corresponding to the air energy storage power station information platform based on the data access permissions.

[0121] The prediction and display module is used to predict a first target dataset that meets the data access permissions based on the historical data access records and preference settings corresponding to the identity information, and to display the first target dataset based on the data access interface.

[0122] Optionally, following the second acquisition module, the following may also be included:

[0123] The third acquisition module is used to acquire the operating trend of the target device based on the current dataset and the newly added dataset. The air energy storage power station information platform monitors multiple monitoring devices, and the target device is any one of the preset multiple monitoring devices.

[0124] The demand prediction module is used to predict the user's data filtering operation demand based on the operating trend, the historical data access records, and the preference settings, and to obtain the second target data corresponding to the data filtering operation demand.

[0125] Optionally, following the second acquisition module, the following may also be included:

[0126] A simplified interface module is used to generate a simplified access interface when the user is determined to be an irregular user based on the identity information, and to generate prompt information for the simplified access interface based on the user's operation behavior. The prompt information includes operation instructions displayed when the mouse hovers over the user, error prompts and error solutions, and interaction prompts.

[0127] Optionally, after the first acquisition module 201, the following may also be included:

[0128] The fourth acquisition module is used to acquire the historical operation information corresponding to each monitoring device based on the current dataset;

[0129] The fifth acquisition module is used to acquire, for each of the monitoring devices, the display level information corresponding to the monitoring device based on the historical operation information. The display level information is a parameter that characterizes the importance of the device operation data of the monitoring device, and the display level information is a first level, a second level, or a third level.

[0130] In one example, the module in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0131] For example, when modules in a device can be implemented via a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling programs. Alternatively, these modules can be integrated together as a system-on-a-chip (SOC).

[0132] In this application, various objects such as messages / information / devices / network elements / systems / devices / actions / operations / processes / concepts may be named. It is understood that these specific names do not constitute a limitation on the relevant objects. The names may be changed depending on the scenario, context, or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from their functions and technical effects embodied / performed in the technical solution.

[0133] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0134] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0135] Based on the same technical concept, this application also provides an electronic device, such as... Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302, and may further include one or more of an information input / output (I / O) interface 303, a communication component 304, and a communication bus 305.

[0136] The processor 301 controls the overall operation of the electronic device 300 to complete all or part of the steps in the aforementioned air energy storage power station information platform data management method. The memory 302 stores various types of data to support the operation of the electronic device 300. This data may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as one or more of Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0137] I / O interface 303 provides an interface between processor 301 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 304 is used to test wired or wireless communication between electronic device 300 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 304 may include a Wi-Fi component, a Bluetooth component, and an NFC component.

[0138] The communication bus 305 may include a path for transmitting information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 can be divided into an address bus, a data bus, a control bus, etc.

[0139] The electronic device 300 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the air energy storage power station information platform data management method given in the above embodiments.

[0140] Electronic device 300 may include, but is not limited to, mobile terminals such as digital broadcast receivers, PDAs (personal digital assistants), and PMPs (portable multimedia players), as well as fixed terminals such as digital TVs and desktop computers, and may also be servers.

[0141] Based on the same technical concept, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described air energy storage power station information platform data management method.

[0142] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0143] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0144] Furthermore, 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 at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0145] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0146] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A data management method for an air-based energy storage power station information platform, characterized in that, include: Obtain the current data filtering rules corresponding to the newly added dataset and the current dataset, wherein the newly added dataset is preprocessed newly added device operation data; Based on the current data filtering rules, the newly added dataset is partitioned to obtain multiple partitioned data. Based on a distributed computing framework, each partition data is processed independently to obtain a classification evaluation result corresponding to each partition data, wherein the classification evaluation result is either qualified or unqualified. If any of the multiple classification evaluation results are unqualified, the current data filtering rule is modified based on the unqualified classification evaluation results and the multiple partition data to obtain a modified data filtering rule. The newly added dataset is then stored in the database corresponding to the current dataset, so as to filter the first target data for the user from the database based on the modified data filtering rule. The method further includes: Based on the user's identity information, obtain the data access permissions corresponding to the user, and adjust the data access interface corresponding to the air energy storage power station information platform based on the data access permissions. Based on the historical data access records and preference settings corresponding to the identity information, a first target dataset that meets the data access permissions is predicted, and the first target dataset is displayed based on the data access interface; After obtaining the user's identity information, the process also includes: Based on the current dataset and the newly added dataset, the operating trend of the target device is obtained. The air energy storage power station information platform monitors multiple monitoring devices, and the target device is any one of the preset multiple monitoring devices. Based on the operating trend, the historical data access records, and the preference settings, the user's data filtering operation needs are predicted, and the second target data corresponding to the data filtering operation needs is obtained.

2. The data management method for an air-based energy storage power station information platform according to claim 1, characterized in that, Before partitioning the newly added dataset based on the current data filtering rules to obtain multiple partitioned data, the method further includes: Extract the first data feature corresponding to the current dataset and the second data feature corresponding to the newly added dataset; Based on the first data feature and the second data feature, a similarity assessment result between the current dataset and the newly added dataset is obtained, wherein the similarity assessment result is a parameter characterizing the degree of similarity between the two datasets; If the similarity assessment result is lower than the similarity threshold, then the step of partitioning the newly added dataset based on the current data filtering rules to obtain multiple partitioned data is executed.

3. The data management method for an air-based energy storage power station information platform according to claim 2, characterized in that, Before determining whether the similarity assessment result is below the similarity threshold, the method further includes: If the first platform application scenario corresponding to the current dataset is different from the second platform application scenario corresponding to the newly added dataset, then the similarity threshold is re-determined based on the second platform application scenario.

4. The data management method for an air-based energy storage power station information platform according to claim 1, characterized in that, After obtaining the user's identity information, the process also includes: If, based on the identity information, the user is determined to be an unregular user, a simplified access interface is generated, and based on the user's operation behavior, prompt information is generated for the simplified access interface. The prompt information includes operation instructions displayed when the mouse hovers over the user, error messages and solutions, and interactive prompts.

5. The data management method for an air-based energy storage power station information platform according to claim 2, characterized in that, After obtaining the current data filtering rules corresponding to the newly added dataset and the current dataset, the following is also included: Based on the current dataset, obtain the historical operation information corresponding to each monitoring device; For each of the monitoring devices, based on the historical operation information, the display level information corresponding to the monitoring device is obtained. The display level information is a parameter that characterizes the importance of the device operation data of the monitoring device. The display level information is a first level, a second level, or a third level.

6. A data management device for an air-based energy storage power station information platform, characterized in that, include: The first acquisition module is used to acquire the current data filtering rules corresponding to the newly added dataset and the current dataset, wherein the newly added dataset is preprocessed newly added device operation data; The partitioning module is used to partition the newly added dataset based on the current data filtering rules to obtain multiple partitioned data. The evaluation module is used to independently process the data of each partition based on a distributed computing framework to obtain the classification evaluation result corresponding to each partition data, wherein the classification evaluation result is either qualified or unqualified. The correction module is used to correct the current data filtering rule based on the unqualified classification evaluation result and the multiple partition data when there is an unqualified classification evaluation result among the multiple classification evaluation results, to obtain a corrected data filtering rule, and to store the new dataset in the database corresponding to the current dataset, so as to filter the first target data for the user from the database based on the corrected data filtering rule; The device further includes: The second acquisition module is used to acquire the data access permissions corresponding to the user based on the user's identity information, and adjust the data access interface corresponding to the air energy storage power station information platform based on the data access permissions. The prediction and display module is used to predict a first target dataset that meets the data access permissions based on the historical data access records and preference settings corresponding to the identity information, and to display the first target dataset based on the data access interface; Following the second acquisition module, it also includes: The third acquisition module is used to acquire the operating trend of the target device based on the current dataset and the newly added dataset. The air energy storage power station information platform monitors multiple monitoring devices, and the target device is any one of the preset multiple monitoring devices. The demand prediction module is used to predict the user's data filtering operation demand based on the operating trend, the historical data access records, and the preference settings, and to obtain the second target data corresponding to the data filtering operation demand.

7. An electronic device, characterized in that, It includes a processor and a memory, wherein the processor is coupled to the memory; The processor is configured to execute a computer program stored in the memory, causing the electronic device to perform the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It includes a computer program or instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1 to 5.

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