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

Abstract

The invention relates to an air energy storage power station information platform data management method and device, equipment and a medium. The method comprises the steps of obtaining a new data set and a current data screening rule corresponding to a current data set; based on the current data screening rule, performing partition processing on a newly added data set to obtain multiple pieces of partition data; based on a distributed computing framework, independently processing the data of each partition to obtain a classification evaluation result corresponding to the data of each partition; and if a result with unqualified classification exists in the plurality of classification evaluation results, correcting the current data screening rule based on the unqualified classification evaluation result and the plurality of partition data to obtain a corrected data screening rule, and providing first target data for the user based on the corrected data screening rule. According to the method and the device, the current data screening rule is dynamically corrected according to the classification evaluation result, so that the data screening rule can be adaptively adjusted to ensure the convenience, the accuracy and the correlation of the screening result.

Description

Technical Field

[0001] The present application relates to the technical field of an air energy storage power station information platform, and in particular to a data management method, device, equipment and medium for an air energy storage power station information platform. Background Art

[0002] With the continuous advancement of energy technology and the growing demand for electricity, air energy storage power stations, as an efficient and environmentally friendly way of energy storage, play an increasingly important role in the power system. However, the operation of air energy storage power stations involves a large number of equipment, complex process flows and strict safety requirements, which poses a great challenge to its operation monitoring and management.

[0003] The traditional method of monitoring power plant operation mainly relies on manual inspections and regular testing. This method is not only inefficient, but also difficult to achieve real-time monitoring and perception of the main operating status and health status of the power plant.

[0004] Currently, there are some power plant operation monitoring systems based on the Internet of Things technology on the market. These systems collect the operation data of power plant equipment in real time by installing sensors and other sensing devices, and upload the data to the cloud platform for processing through the signal transmission system. However, due to the huge amount of data processed by the air energy storage power station information platform, there are many inconveniences in data screening, which further increases the difficulty of operation management. Summary of the invention

[0005] In order to improve the convenience of data screening and reduce the difficulty of operation management, the present application provides an air energy storage power station information platform data management method, device, equipment and medium.

[0006] In a first aspect, the present application provides a data management method for an air energy storage power station information platform, comprising:

[0007] Obtaining current data screening rules corresponding to a newly added data set and a current data set, wherein the newly added data set is preprocessed newly added equipment operation data;

[0008] Based on the current data screening rule, partitioning the newly added data set to obtain a plurality of partitioned data;

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

[0010] If there are unqualified classification results among the multiple classification evaluation results, the current data screening rules are modified based on the unqualified classification evaluation results and the multiple partition data to obtain modified data screening rules, and the newly added data set is stored in the database corresponding to the current data set, so as to filter out the first target data for the user from the database based on the modified data screening rules.

[0011] The beneficial effects of the present application are: dynamically modifying the current data screening rules according to the classification evaluation results, so that the data screening rules can be adaptively adjusted to ensure the convenience, accuracy and relevance of the screening results. According to the modified data screening rules, the first target data corresponding to the user's data screening operation is queried, and through partition processing, the newly added data set can be split into multiple small data sets, which are processed separately, thereby improving processing efficiency.

[0012] Further, before partitioning the newly added data set based on the current data screening rule to obtain a plurality of partitioned data, the method further includes:

[0013] Extracting a first data feature corresponding to the current data set and a second data feature corresponding to the newly added data set;

[0014] Based on the first data feature and the second data feature, obtaining a similarity evaluation result between the current data set and the newly added data set, wherein the similarity evaluation result is a parameter representing a degree of similarity between the two data sets;

[0015] If the similarity evaluation result is lower than the similarity threshold, the step of partitioning the newly added data set based on the current data screening rule to obtain a plurality of partitioned data is executed.

[0016] The beneficial effect of adopting the above further scheme is: through similarity evaluation, when the newly added data set is similar enough to the current data set, there is no need to use the current data screening rules for partition processing, which reduces the amount of data processing operations when the difference between the data sets is too small, reduces the workload of subsequent partition processing and classification evaluation, and improves the efficiency of data processing.

[0017] Furthermore, before judging whether the similarity evaluation result is lower than the similarity threshold, the method further includes:

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

[0019] The beneficial effect of adopting the above further solution is: dynamically adjusting the similarity threshold according to the changes in the platform application scenario, thereby enhancing the flexibility of data management. When the platform application scenario of the newly added data set is different from that of the current data set, re-determining the similarity threshold can ensure that the subsequent data processing process is more in line with the needs of the current second platform application scenario, which helps to improve the efficiency and accuracy of data processing.

[0020] Furthermore, the method further comprises:

[0021] Based on the identity information of the user, obtain the data access permission corresponding to the user, and based on the data access permission, adjust the data access interface corresponding to the air energy storage power station information platform;

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

[0023] The beneficial effect of adopting the above further solution is: by customizing the data access interface and display content according to the user's identity information, historical data access records and preference settings, it is possible to provide users with a more personalized and convenient data access experience. By adjusting the data access interface based on the user's data access rights, it is possible to ensure that users can only access data to which they have permission, thereby protecting sensitive data from being accessed by unauthorized users. By predicting and displaying data sets that users may be interested in, it is possible to help users find the data they need more quickly, thereby improving the efficiency of data utilization.

[0024] Furthermore, after obtaining the identity information of the user, the method further includes:

[0025] Based on the current data set and the newly added data set, the operation 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 operation trend, the historical data access record and the preference setting, the data filtering operation requirement of the user is predicted, and the second target data corresponding to the data filtering operation requirement is acquired.

[0027] The beneficial effect of adopting the above further solution is that by predicting the user's data screening operation needs, the information platform can provide users with more accurate and personalized data display, reducing the time and energy of users to manually screen data. It can provide data support according to the actual needs of users, facilitate better understanding of equipment status, predict potential problems, and make timely decisions and adjustments, thereby improving the overall operational efficiency of the power station.

[0028] Furthermore, after obtaining the identity information of the user, the method further includes:

[0029] If the user is determined to be a non-regular user based on the identity information, a simplified access interface is generated, and prompt information for the simplified access interface is generated based on the user's operating behavior, the prompt information including operation instructions displayed when the mouse is hovered, error prompts and error solutions, and interactive prompt instructions.

[0030] The beneficial effect of adopting the above further solution is: in the simplified access interface, necessary prompt information is provided to users to help them quickly understand how to operate. The prompt information includes operation instructions displayed by hovering the mouse, error prompts and error solutions, interactive prompt instructions, etc. It can only include basic status monitoring of the power station, real-time data viewing of key equipment, simple alarm notifications and other functions. The interface layout should be concise and clear, avoiding excessive text and icon interference.

[0031] Further, after obtaining the current data screening rules corresponding to the newly added data set and the current data set, the method further includes:

[0032] Based on the current data set, obtaining 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 characterizing the importance of the device operation data of the monitoring device, and the display level information is the first level, the second level or the third level.

[0034] The beneficial effects of adopting the above further solution are: through hierarchical display, operators can identify key information more quickly, so as to make faster and more accurate decisions. The interface layout is organized according to the importance of data, making the interface neater and more orderly. By providing different display modes for different levels of data, operators can more easily find the information they need, thereby improving the overall user experience.

[0035] In a second aspect, the present application provides an air energy storage power station information platform data management device, comprising:

[0036] A first acquisition module is used to acquire a new data set and a current data screening rule corresponding to a current data set, wherein the new data set is preprocessed new equipment operation data;

[0037] A partitioning module, used for partitioning the newly added data set based on the current data screening rule to obtain a plurality of partitioned data;

[0038] An evaluation module, for independently processing each partition data based on a distributed computing framework to obtain a classification evaluation result corresponding to each partition data, wherein the classification evaluation result is a qualified classification or a unqualified classification;

[0039] A correction module is used to correct the current data screening rules based on the unqualified classification evaluation results and the multiple partition data when there are unqualified classification results among the multiple classification evaluation results, so as to obtain corrected data screening rules, and store the newly added data set in the database corresponding to the current data set, so as to filter out the first target data for the user from the database based on the corrected data screening rules.

[0040] In a third aspect, the present application provides an electronic device, including a processor and a memory, wherein the processor is coupled to the memory;

[0041] The processor is used to execute the computer program stored in the memory so that the electronic device performs the method as described in any one of the first aspects.

[0042] In a fourth aspect, the present application provides a computer-readable storage medium, comprising a computer program or instructions, which, when executed on a computer, enables the computer to execute the method as described in any one of the first aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of the data management method of the air energy storage power station information platform according to the embodiment of the present application;

[0044] Figure 2 This is a structural block diagram of the data management device of the air energy storage power station information platform according to the embodiment of the present application;

[0045] Figure 3 This is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0046] The present application is further described in detail below in conjunction with the accompanying drawings.

[0047] The embodiment of the present application provides a data management method for an air energy storage power station information platform, which can be executed by a device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a desktop computer, etc., but is not limited thereto.

[0048] like Figure 1As shown, a data management method for an air energy storage power station information platform is performed by an electronic device. The main process of the method is described as follows (steps S101 to S104):

[0049] Step S101: obtaining a new data set and a current data screening rule corresponding to the current data set, wherein the new data set is pre-processed new equipment operation data.

[0050] The electronic device can collect device operation data from various data sources, which may include log files and monitoring sensors, etc. The current data set is stored in the database, and the device operation data in the current data set is the data collected by the historical monitoring step.

[0051] As the number of power plants and equipment operation data increases, new data sets can be collected. Preprocessing includes data cleaning, which includes processing missing values, removing duplicate data, correcting error values, and processing outliers on the collected data to obtain new data sets. By preprocessing the new data sets, you can ensure that they have consistent data quality and format with the current data sets.

[0052] Step S102: Based on the current data screening rule, partition the newly added data set to obtain a plurality of partition data.

[0053] Data screening rules refer to the criteria or conditions used to select or exclude specific data from a data set. In the air energy storage power station information platform, the current data screening rules can be formulated based on multiple dimensions such as data type, time range, and device status. For example, if the current data screening rules are divided by time range and / or by data type.

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

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

[0056] According to business needs and data characteristics, reasonable evaluation indicators are defined to quantify the quality of the partitioning results. For example, it is possible to examine whether the data distribution after partitioning is balanced, whether the processing time is shortened, whether the memory usage is reduced, etc. Based on the distributed computing framework, the partitioning results of each partition are evaluated according to the defined evaluation indicators to obtain the classification evaluation results corresponding to each partition. For example, if the data distribution after partitioning is uneven and / or the processing time is not shortened and / or the memory usage is not reduced, the classification evaluation result is unqualified.

[0057] Step S104: If there are unqualified classification results among the multiple classification evaluation results, the current data screening rules are modified based on the unqualified classification evaluation results and the multiple partition data to obtain modified data screening rules, and the newly added data set is stored in the database corresponding to the current data set, so as to filter out the first target data for the user from the database based on the modified data screening rules.

[0058] According to the evaluation results of each classification, analyze the rationality and deficiencies of the classification results, and identify the problems and deficiencies of 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 data set is good, but the classification effect of the new data set is not good, it means that the current data partitioning rules are not suitable for the new data.

[0059] A specific correction scheme is formulated for the identified problems of the current division rule. The electronic device stores the correspondence between the identified problems of the current division rule and the correction scheme. For example, the correction scheme may include adjusting the parameters of the current division rule.

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

[0061] The air energy storage power station information platform involves a large amount of equipment operation data. Directly processing the newly added data set will lead to low computing efficiency. Through partition processing, the newly added data set can be split into multiple small data sets and processed separately, thereby improving processing efficiency.

[0062] As new data arrives and changes, the current data screening rules are dynamically modified according to the classification evaluation results, so that the data screening rules can be adaptively adjusted to ensure the accuracy and relevance of the screening results. As the amount of data in the database increases, more information can be obtained to optimize the data screening rules, which helps to improve the accuracy and generalization of the data screening rules.

[0063] In this embodiment, before step S102, the following steps are further included:

[0064] Extracting a first data feature corresponding to the current data set and a second data feature corresponding to the newly added data set; based on the first data feature and the second data feature, obtaining a similarity evaluation result between the current data set and the newly added data set, wherein the similarity evaluation result is a parameter characterizing the degree of similarity between the two data sets; if the similarity evaluation result is higher than a similarity threshold, executing the step of partitioning the newly added data set based on the current data screening rule to obtain a plurality of partitioned data.

[0065] Data features refer to specific indicators or attributes extracted from device operation data that can reflect the device's operating status, performance, and relationship with other devices or data sets. The electronic device can extract key data features from the current data set and the newly added data set to obtain the first data feature and the second data feature, respectively.

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

[0067] The second data feature is extracted from a newly collected or newly generated additional data set, and is similar to the first data feature, but represents the characteristics and status of the additional data set. By comparing the first data feature and the second data feature, the similarities and differences between the additional data set and the current data set can be evaluated.

[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 the features are extracted, the similarity between the first data feature and the second data feature can be evaluated by calculating the correlation coefficient between the features, distance measurement (such as Euclidean distance, Manhattan distance, etc.) or similarity measurement (such as cosine similarity, Jaccard similarity, etc.). The similarity evaluation result can be a numerical value indicating the degree of similarity between the two data sets.

[0070] In this embodiment, in addition to evaluating feature similarity, it is also necessary to check whether the category label or feature value in the newly added data set exists in the current data set, so as to analyze whether the newly added data set introduces new categories or features of equipment operation data. If the newly added data set contains new categories or features, the current data screening rules also need to be updated to adapt to the new data distribution, so the processing of step S102 also needs to be performed.

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

[0072] Through similarity evaluation, when the newly added data set is similar enough to the current data set, there is no need to use the current data screening rules for partition processing, which reduces the amount of data processing operations when the difference between the data sets is too small, reduces the workload of subsequent partition processing and classification evaluation, and improves the efficiency of data processing.

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

[0074] The platform application scenario refers to the specific environment in which the air energy storage power station information platform is actually used. Different platform application scenarios have different requirements for similarity. For example, in air quality monitoring, a higher sensitivity is required for changes in pollutant concentrations, so a lower threshold needs to be set; while in energy management, the operating status of the power station may be more stable, so a higher threshold needs to be set. If the application scenario changes, the similarity threshold needs to be adjusted to adapt to new business needs and data characteristics.

[0075] Exemplarily, the application scenario of the first platform is air quality monitoring. In air quality monitoring, the main focus is on changes in the concentration of pollutants in the air, such as PM2.5, PM10, sulfur dioxide, etc. The changes in the concentration of these pollutants have a direct impact on public health and environmental quality, so real-time monitoring and rapid response are required. Air quality monitoring data are usually characterized by high frequency, high precision and real-time. Since the concentration of pollutants may be affected by many factors (such as meteorological conditions, traffic conditions, etc.), the data fluctuates greatly. In order to capture small changes in pollutant concentrations, a lower similarity threshold needs to be set. For example, the similarity threshold between two data sets can be 0.8 (assuming that the value range of the similarity evaluation result is 0 to 1, where 1 means exactly the same).

[0076] The second platform application scenario is energy management. With the development of the business, the air energy storage power station information platform needs to support energy management functions, focusing on the operating status, energy efficiency and stability of the power station. Compared with air quality monitoring, energy management pays more attention to the stability and long-term trends of data. Energy management data is usually characterized by low frequency, low precision but long-term continuity. Since the operating status of the power station is usually relatively stable, the data fluctuation is small, but the long-term accumulated data can reveal energy efficiency changes and potential failures.

[0077] In order to adapt to new business needs and data characteristics, the similarity threshold needs to be adjusted. In energy management, since more attention is paid to the stability and long-term trend of data, a higher similarity threshold can be set. For example, the similarity threshold can be 0.95.

[0078] For the air energy storage power station information platform, different platform application scenarios require different data management and analysis strategies. The similarity threshold is dynamically adjusted according to the changes in the platform application scenario, thereby enhancing the flexibility of data management. When the platform application scenario of the new data set is different from that of the current data set, redefining the similarity threshold can ensure that the subsequent data processing process (such as partition processing, classification evaluation, etc.) is more in line with the needs of the current second platform application scenario, which helps to improve the efficiency and accuracy of data processing.

[0079] In this embodiment, the method also includes the following processing: based on the identity information of the user, obtaining the data access rights corresponding to the user, and based on the data access rights, adjusting the data access interface corresponding to the air energy storage power station information platform; based on the historical data access records and preference settings corresponding to the identity information, predicting the first target data set that meets the data access rights, and based on the data access interface, displaying the first target data set.

[0080] In this embodiment, the user's identity information (such as user name, role, etc.) can be obtained through the user login system, and then the user's data access rights can be determined according to the preset permission rule library, and the access rights can include the data that can be accessed. Through the user login system, the user's login status and activities can be tracked, so that the user change can be automatically detected and processed. When events such as user logout or session timeout are detected, the system can automatically trigger the identity recognition process and require the new user to perform identity authentication.

[0081] According to the user's data access rights, the layout, menu items, buttons, etc. of the data access interface are dynamically adjusted to ensure that users can only see and operate the data for which they have permission. By analyzing the user's historical data access records and preference settings (such as frequently accessed data types, time periods, filtering conditions, etc.), machine learning algorithms are used to predict the data set that the user may be interested in in the current situation, that is, the first target data set. In the adjusted data access interface, the predicted first target data set is displayed first so that users can quickly find the data they are interested in. At the same time, search, filtering and other functions can also be provided to allow users to further refine their data needs.

[0082] By customizing the data access interface and display content based on the user's identity information, historical data access records, and preferences, users can be provided with a more personalized and convenient data access experience. By adjusting the data access interface based on the user's data access permissions, it can ensure that users can only access data to which they have permission, thereby protecting sensitive data from unauthorized users. By predicting and displaying data sets that users may be interested in, users can find the data they need more quickly, thereby improving data utilization efficiency.

[0083] In this embodiment, after obtaining the identity information of the user, the following processing is also included: based on the current data set and the newly added data set, the operation 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 operation trend, the historical data access record and the preference setting, the user's data screening operation requirements are predicted, and the second target data corresponding to the data screening operation requirements are obtained.

[0084] By integrating the current data set and the newly added data set, a complete equipment operation data set can be formed. Then, the data of the target equipment (such as generators, compressors and other key equipment) is analyzed, and the operation trends of the equipment, such as performance degradation and fault warning, are extracted using time series analysis, machine learning and other technologies.

[0085] Combine the user's historical data access records (such as frequently accessed data types, time periods, filtering conditions, etc.) and preference settings (such as preferred data views, alarm thresholds, etc.). Use machine learning algorithms (such as collaborative filtering, decision trees, etc.) to analyze this information and predict the data filtering operation needs that the user may be interested in in the current situation.

[0086] According to the predicted user data screening operation requirements, the corresponding data is automatically screened and extracted to form a second target data set. The second target data set may include real-time operation data, historical performance data, fault warning information, etc. of the equipment to meet the specific needs of users.

[0087] By predicting the user's data screening needs, the information platform can provide users with more accurate and personalized data display, reducing the time and effort of users to manually screen data. It can provide data support based on the user's actual needs, facilitate better understanding of equipment status, predict potential problems, and make timely decisions and adjustments, thereby improving the overall operational efficiency of the power station.

[0088] In this embodiment, after obtaining the identity information of the user, the following processing is also included: if the user is judged to be a non-conventional user based on the identity information, a simplified access interface is generated, and based on the user's operating behavior, prompt information of the simplified access interface is generated, and the prompt information includes operation instructions displayed when the mouse is hovered, error prompts and error solutions, and interactive prompt instructions.

[0089] In the management and application scenarios of the air energy storage power station information platform, users can be divided into different types based on their professional background, job requirements, and frequency of use. Among them, "non-conventional users" refer to those who are not professional operators or do not have a deep professional knowledge background. Non-conventional users can include the following groups of people: temporary visitors, maintenance personnel, and non-professional managers.

[0090] According to the user type field or user permission list in the user identity information, determine whether the user is an unconventional user. If the user is identified as an unconventional user, execute the subsequent steps.

[0091] In this embodiment, a simplified data access interface is designed for non-conventional users, and the interface may include the most commonly used functions and data views. The simplified interface corresponds to a simplified operation process, easy navigation and operation, and avoids excessive options and complex settings.

[0092] Simplifying operational processes 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 answers are relatively fixed, providing a drop-down menu or radio buttons for users to choose from can reduce the possibility of user input errors and speed up operations.

[0094] For multiple selections or switch states, use checkboxes and switch controls to simplify user operations, which are usually more intuitive and easier to use than text input boxes. Automate the user input process as much as possible, such as automatically filling in user information through API calls. For information that needs to be entered repeatedly, provide pre-fill functions to reduce the amount of user input.

[0095] Simplifying form design means optimizing the 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 that guides users to complete complex tasks through step indicators or progress bars, which can help users understand the progress of the current operation and the remaining steps.

[0097] In the simplified access interface, necessary prompt information is provided to help users quickly understand how to operate. Prompt information includes operation instructions displayed when the mouse is hovered, error prompts and error solutions, interactive prompt instructions, etc. It can only include basic status monitoring of the power station, real-time data viewing of key equipment, simple alarm notifications and other functions. The interface layout should be concise and clear, avoiding excessive text and icon interference.

[0098] Mouse hover operation instructions means that when the user hovers the mouse over a button or icon, the operation instructions of the button or icon are displayed. Error prompts and error solutions mean that when the user enters incorrect information or performs an unallowed operation, error prompts are displayed, and simple solutions are provided or the user is guided to perform the correct operation. Interactive prompt instructions mean adding interactive prompts to the user interface, such as guiding the user to click a button to view more information or perform an operation.

[0099] By providing simplified access interfaces and prompt information for non-conventional users, the difficulty and complexity of operation are reduced, allowing users to obtain the required information more quickly and accurately. By providing error prompts and error solutions, users can avoid operational errors and improve the accuracy and reliability of data access. The introduction of simplified access interfaces and prompt information reduces the time cost of users in learning and using the information platform and improves work efficiency.

[0100] In this embodiment, after step S101, the following processing is also included: based on the current data set, the historical operation information corresponding to each monitoring device is obtained; for each monitoring device, based on the historical operation information, the display level information corresponding to the monitoring device is obtained, and 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 the first level, the second level or the third level.

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

[0102] The display level information is divided into the first level, the second level and the third level. The first level represents the most important or urgent data, which requires the operator's immediate attention; the second level represents the more important data or the data that requires regular attention; the third level represents the general or less important data.

[0103] Based on the determined display level information and 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, the device operation data is controlled to be displayed in the form of a chart or icon in the first target area, and the first target area is an independent sub-area in the operation parameter display interface corresponding to the air energy storage power station information platform.

[0105] For each of the monitoring devices, if the display level information corresponding to the monitoring device is the second level, the device operation data is controlled to be displayed in the second target area in text form, and 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 manner, such as using different colors. In addition, a reminder function can also be set for the second-level 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 monitoring devices, if the display level information corresponding to the monitoring device is the third level, the device operation data can be displayed in a broader data browsing interface or report. The data browsing interface or report can contain all the monitoring device operation data of the air energy storage power station, but the third-level device operation data will not be displayed in a particularly prominent manner, but provided as background information. However, this does not mean that the third-level device operation data is not important or does not require attention. On the contrary, the third-level device operation data can be used for more in-depth analysis or long-term trend monitoring. Therefore, when displaying the third-level device operation data, it should be ensured that it is easy to access and query so that operators can find it quickly.

[0107] By displaying data in different levels, operators can more quickly identify key information and make faster and more accurate decisions. The interface layout is organized according to the importance of the data, making it neater 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.

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

[0109] The first acquisition module 201 is used to acquire the current data screening rules corresponding to the newly added data set and the current data set, wherein the newly added data set is the pre-processed newly added equipment operation data;

[0110] A partitioning module 202, configured to partition the newly added data set based on the current data screening rule to obtain a plurality of partitioned data;

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

[0112] The correction module 204 is used to correct the current data screening rules based on the unqualified classification evaluation results and the multiple partition data when there are unqualified classification results among the multiple classification evaluation results, obtain the corrected data screening rules, and store the newly added data set in the database corresponding to the current data set, so as to filter out the first target data for the user from the database based on the corrected data screening rules.

[0113] Optionally, before the partitioning module 202, the following is further included:

[0114] A feature extraction module, used to extract a first data feature corresponding to the current data set and a second data feature corresponding to the newly added data set;

[0115] A similarity acquisition module, configured to acquire a similarity evaluation result between the current data set and the newly added data set based on the first data feature and the second data feature, wherein the similarity evaluation result is a parameter representing a similarity degree between the two data sets;

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

[0117] Optionally, before the similarity comparison module, it also includes:

[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 data set is different from the second platform application scenario corresponding to the newly added data set.

[0119] Optionally, the device further comprises:

[0120] A second acquisition module is used to acquire data access rights corresponding to the user based on the identity information of the user, and adjust the data access interface corresponding to the air energy storage power station information platform based on the data access rights;

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

[0122] Optionally, after the second acquisition module, the method further includes:

[0123] A third acquisition module is used to acquire the operation trend of the target device based on the current data set and the newly added data set. 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 operation trend, the historical data access record and the preference setting, and obtain the second target data corresponding to the data filtering operation demand.

[0125] Optionally, after the second acquisition module, the method further includes:

[0126] A simplified interface production module is used to generate a simplified access interface when the user is judged to be a non-conventional user based on the identity information, and to generate prompt information for the simplified access interface based on the user's operating behavior, wherein the prompt information includes operation instructions displayed when the mouse is hovered, error prompts and error solutions, and interactive prompt instructions.

[0127] Optionally, after the first acquisition module 201, the method further includes:

[0128] A fourth acquisition module, used for acquiring historical operation information corresponding to each monitoring device based on the current data set;

[0129] The fifth acquisition module is used to obtain the display level information corresponding to each 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. The display level information is the first level, the second level or the third level.

[0130] In one example, the module in any of the above devices can 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 another example, when the modules in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0132] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in this application are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from the functions and technical effects embodied / executed in the technical scheme.

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

[0134] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0135] Based on the same technical concept, the present 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 an information input / information output (I / O) interface 303 , one or more of a communication component 304 , and a communication bus 305 .

[0136] Among them, the processor 301 is used to control the overall operation of the electronic device 300 to complete all or part of the steps in the above-mentioned air energy storage power station information platform data management method; the memory 302 is used to store various types of data to support the operation of the electronic device 300, and these data may include, for example, instructions for any application or method used to operate on the electronic device 300, and 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 static random access memory (Static Random Access Memory, SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), programmable read-only memory (Programmable Read-Only Memory, PROM), read-only memory (Read-Only Memory, ROM), magnetic memory, flash memory, disk or optical disk. One or more.

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

[0138] The communication bus 305 may include a path to transmit information between the above 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 may 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 (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processor, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the air energy storage power station information platform data management method given in the above embodiment.

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

[0141] Based on the same technical concept, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned air energy storage power station information platform data management method are implemented.

[0142] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

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

[0144] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0145] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0146] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A data management method for an air energy storage power station information platform, characterized in that: include: Obtaining current data screening rules corresponding to a newly added data set and a current data set, wherein the newly added data set is preprocessed newly added equipment operation data; Based on the current data screening rule, partitioning the newly added data set to obtain a plurality of partitioned data; Based on a distributed computing framework, each partition data is independently processed to obtain a classification evaluation result corresponding to each partition data, wherein the classification evaluation result is a qualified classification or a unqualified classification; If there are unqualified classification results among the multiple classification evaluation results, the current data screening rules are modified based on the unqualified classification evaluation results and the multiple partition data to obtain modified data screening rules, and the newly added data set is stored in the database corresponding to the current data set, so as to filter out the first target data for the user from the database based on the modified data screening rules.

2. According to claim 1, a data management method for an air energy storage power station information platform is characterized in that: Before partitioning the newly added data set based on the current data screening rule to obtain a plurality of partitioned data, the method further includes: Extracting a first data feature corresponding to the current data set and a second data feature corresponding to the newly added data set; Based on the first data feature and the second data feature, obtaining a similarity evaluation result between the current data set and the newly added data set, wherein the similarity evaluation result is a parameter representing a degree of similarity between the two data sets; If the similarity evaluation result is lower than the similarity threshold, the step of partitioning the newly added data set based on the current data screening rule to obtain a plurality of partitioned data is executed.

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

4. The air energy storage power station information platform data management method according to claim 1, characterized in that: The method further comprises: Based on the identity information of the user, obtain the data access permission corresponding to the user, and based on the data access permission, adjust the data access interface corresponding to the air energy storage power station information platform; Based on the historical data access records and preference settings corresponding to the identity information, a first target data set that meets the data access permission is predicted, and the first target data set is displayed based on the data access interface.

5. The air energy storage power station information platform data management method according to claim 4 is characterized in that: After obtaining the identity information of the user, the method further includes: Based on the current data set and the newly added data set, the operation 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 operation trend, the historical data access record and the preference setting, the data filtering operation requirement of the user is predicted, and the second target data corresponding to the data filtering operation requirement is acquired.

6. The air energy storage power station information platform data management method according to claim 4 is characterized in that: After obtaining the identity information of the user, the method further includes: If the user is determined to be a non-regular user based on the identity information, a simplified access interface is generated, and prompt information for the simplified access interface is generated based on the user's operating behavior, the prompt information including operation instructions displayed when the mouse is hovered, error prompts and error solutions, and interactive prompt instructions.

7. The air energy storage power station information platform data management method according to claim 2 is characterized in that: After obtaining the current data screening rules corresponding to the newly added data set and the current data set, the method further includes: Based on the current data set, obtaining 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 characterizing the importance of the device operation data of the monitoring device, and the display level information is the first level, the second level or the third level.

8. An air energy storage power station information platform data management device, characterized in that: include: A first acquisition module is used to acquire a new data set and a current data screening rule corresponding to a current data set, wherein the new data set is preprocessed new equipment operation data; A partitioning module, used for partitioning the newly added data set based on the current data screening rule to obtain a plurality of partitioned data; An evaluation module, for independently processing each partition data based on a distributed computing framework to obtain a classification evaluation result corresponding to each partition data, wherein the classification evaluation result is a qualified classification or a unqualified classification; A correction module is used to correct the current data screening rules based on the unqualified classification evaluation results and the multiple partition data when there are unqualified classification results among the multiple classification evaluation results, so as to obtain corrected data screening rules, and store the newly added data set in the database corresponding to the current data set, so as to filter out the first target data for the user from the database based on the corrected data screening rules.

9. An electronic device, characterized in that: comprising 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, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The method comprises a computer program or an instruction, which, when executed on a computer, causes the computer to execute the method according to any one of claims 1 to 7.

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