Natural language generation wide table system based on power industry marketing domain data

Abstract

The invention discloses a natural language generation wide table system based on power industry marketing domain data, which relates to the technical field of power industry marketing and comprises a natural language query analysis module, a semantic mapping module, an SQL dynamic conversion module, a data quality verification module and a wide table generation module. And the natural language query analysis module is used for receiving a natural language query input by a user. Through the natural language processing technology of deep learning, the power industry query is accurately analyzed, the SQL is automatically generated, and the query efficiency and accuracy are improved. Meanwhile, SQL optimization and a distributed execution engine are adopted to optimize big data query performance, especially cross-table complex query, and response is accelerated through intelligent fragmentation. The system also introduces an anomaly detection and correction mechanism, automatically repairs data problems, ensures data quality and integrity, reduces human intervention, reduces decision risks, and significantly improves query efficiency, data quality and decision accuracy in the power industry.

Description

Technical Field

[0001] The present invention relates to the field of power industry marketing technology, and in particular to a natural language generation wide table system based on power industry marketing domain data. Background Art

[0002] The natural language wide table generation system based on power industry marketing domain data is an intelligent system that uses natural language processing technology and combines it with the power industry's marketing data to automatically generate wide tables (i.e., multidimensional data tables). Through SQL dynamic conversion technology, the system parses the user's query requirements in natural language into specific SQL query statements, and then extracts, processes, and transforms the data in the power marketing domain to automatically generate wide tables that meet the requirements. Users can automatically obtain and display comprehensive data tables composed of multiple data dimensions (such as user information, electricity usage, bill records, etc.) through natural language query input without having to manually write complex SQL statements. The core advantage of this system is that it simplifies the data processing process and realizes automated table creation through intelligent parsing of natural language queries, greatly improving the efficiency and accuracy of data analysis and decision-making in the power industry.

[0003] SQL (Structured Query Language) is a standard programming language used to manage relational databases. It is primarily used to query, insert, update, and delete data within a database, as well as to create and manage database objects (such as tables, indexes, and views). SQL is a declarative language; users specify desired operations by writing SQL statements without explicitly specifying how to perform these operations. The database management system (DBMS) will optimize and execute the statements.

[0004] The existing technology has the following deficiencies:

[0005] In the operation of a natural language query parsing system for generating wide tables based on power industry marketing data, errors in natural language query parsing can lead to erroneous data generation, potentially misleading decision-making. Power industry marketing data involves complex and sensitive dimensions, such as customer electricity usage, billing rates, and account balances. Any errors or misinterpretations in natural language query parsing can lead to erroneous data generation, resulting in inaccurate or false wide tables. If the system fails to accurately understand user query intent or if errors occur during dynamic SQL conversion, the generated data can be severely distorted, impacting the power company's operational decisions. Inaccurate wide table data not only disrupts critical operations such as customer service, billing, and resource allocation, but can also trigger regulatory scrutiny, leading to legal action and loss of credibility. Ultimately, such errors can have immeasurable negative impacts on a company's financial health and market reputation.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide a natural language generation wide table system based on the marketing domain data of the power industry. Through the natural language processing technology based on deep learning, it can accurately parse complex power industry queries, automatically generate SQL query structures, avoid misunderstandings and omissions in traditional methods, and improve query efficiency and accuracy. At the same time, an SQL optimization module and a distributed execution engine are used to optimize large-scale data and improve query performance, especially in complex cross-table queries, through intelligent sharding and parallel processing to accelerate query responses. In order to ensure data quality, an intelligent anomaly detection and correction mechanism is introduced to automatically identify and repair abnormal data, ensure data integrity, reduce human intervention, and reduce decision-making risks. This system significantly improves the query efficiency, data quality and decision-making accuracy of the power industry to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a natural language wide table generation system based on power industry marketing domain data, comprising a natural language query parsing module, a semantic mapping module, an SQL dynamic conversion module, a data quality verification module, and a wide table generation module;

[0009] The natural language query parsing module receives natural language queries input by users, uses a deep learning-based natural language processing algorithm (such as BERT or GPT) to parse the user's query intent, and generates an intermediate semantic representation;

[0010] The semantic mapping module is used to map the corresponding SQL query structure based on the intermediate semantic representation and the relevant domain knowledge of the power industry marketing data to ensure that the query results are consistent with the user's query intent. Figure 1 To;

[0011] An SQL dynamic conversion module is used to convert the SQL query structure into a valid SQL query statement, and execute the query in the power industry marketing data system to extract target data;

[0012] The data quality verification module is used to evaluate the quality of the generated wide table data and automatically detect and correct potential errors or deviations in the data using rule-based algorithms and machine learning methods;

[0013] The wide table generation module is used to automatically generate the final wide table based on the verified data and provide the results externally for users to conduct decision analysis.

[0014] Preferably, the natural language query parsing module further includes:

[0015] The query intent understanding module is used to identify contextual information and implicit requirements in user input. It uses a long short-term memory network (LSTM) to conduct deep learning on historical queries to better understand the details and potential intent of the query content.

[0016] The user preference modeling module is used to establish a personalized query preference model based on the user's historical query records and interaction habits, and automatically adjust the priority of query parsing, thereby improving the relevance and accuracy of query results.

[0017] The introduction of this module not only optimizes the processing efficiency of individual queries by intelligently understanding and analyzing past user query behavior, but also enhances the system's long-term learning and adaptability. Through deep learning and personalized modeling, the system can better capture common user demand patterns, avoid misinterpretations caused by incomplete information or missing context, and thus reduce the generation of erroneous data caused by natural language parsing errors.

[0018] Preferably, the semantic mapping module includes:

[0019] The industry knowledge base stores various data structures, business rules, and common query templates for the power industry. It combines the experience of domain experts and industry standards to provide reference data required for semantic mapping.

[0020] The semantic reasoning engine is used to infer the best match between query intent and database structure based on different keywords and relationships in the query statement, and dynamically generate an SQL query structure compatible with the database.

[0021] This module leverages business rules and common query templates from industry knowledge bases to improve the accuracy of semantic mapping. The semantic reasoning engine, through reasoning analysis, automatically generates optimized SQL query structures based on complex query requirements (such as multi-dimensional joint queries and time range filtering). This approach more effectively processes natural language queries posed by users and flexibly adjusts query solutions based on business needs, thus avoiding the limitations of traditional fixed templates that cannot handle complex business needs.

[0022] Preferably, the SQL dynamic conversion module further includes:

[0023] The SQL optimization module is used to optimize the performance of SQL statements after they are generated. It uses a query plan generator (such as an optimization algorithm based on a cost model) to select the optimal execution plan, thereby improving query efficiency.

[0024] The distributed execution engine is used to distribute SQL query tasks to multiple servers for parallel processing based on the scale and distribution characteristics of power industry marketing data, reducing query response time and improving big data processing capabilities.

[0025] The SQL optimization module selects appropriate query execution plans to ensure the system avoids performance bottlenecks when processing large amounts of data, while the distributed execution engine ensures efficient concurrent processing of massive amounts of power data. This distributed architecture flexibly responds to data demands of varying scales, supporting both small-scale queries and complex large-scale queries, thereby ensuring system stability and responsiveness.

[0026] Preferably, the data quality verification module includes:

[0027] Anomaly detection module, which performs outlier detection on generated wide table data based on historical data analysis and machine learning algorithms (such as isolation forest or support vector machine), automatically identifying and marking abnormal trends or deviations in the data;

[0028] An intelligent correction module is used to automatically correct or fill in abnormal data based on the business rules and data correlation analysis of the power industry, avoiding manual intervention and improving the automatic correction capability of data quality;

[0029] The data integrity check module is used to analyze the data integrity in wide tables to ensure that each user's various dimensional data (such as electricity consumption, bill information, payment records, etc.) is displayed as needed to prevent missing or inconsistencies.

[0030] This validation module leverages a combination of machine learning and a rules engine to ensure high-quality data generation. Anomaly detection identifies potential errors by comparing data with historical data, preventing business decisions caused by inaccurate or abnormal input data. Intelligent correction automatically processes problematic data using industry rules, significantly improving data processing efficiency and accuracy while avoiding risks associated with human intervention.

[0031] Preferably, the wide table generation module includes:

[0032] Multi-dimensional data merging engine, used to automatically merge data from multiple dimensions (such as user information, electricity consumption data, billing standards, payment records, etc.) according to different query requirements, and automatically sort and filter the data according to its timeliness and relevance;

[0033] The data visualization module is used to present the generated wide table data to users through a visual interface. It supports charts, tables, and other data display formats to help users understand the analysis results more intuitively.

[0034] This module utilizes a multidimensional data merging engine to flexibly handle complex query requirements and consolidate power industry data from various dimensions into an efficient, structured wide table. After generating the wide table, the system presents the data in a user-friendly manner through a data visualization module, helping decision makers make data-driven decisions more quickly. This greatly facilitates the analysis of multi-level, multi-dimensional data.

[0035] Preferably, it also includes:

[0036] The intelligent monitoring module is used to monitor data processing, query execution and other steps during system operation in real time. It uses anomaly detection algorithms (such as trend prediction based on time series analysis) to issue early warnings on system status and ensure the stability and reliability of the query execution process.

[0037] The log audit module is used to record each query request and its corresponding data processing process, and provide detailed log files for later auditing and problem tracing.

[0038] This module monitors system operation status in real time, promptly identifying potential technical issues such as performance degradation or processing failures, ensuring system reliability. The log audit module provides comprehensive query tracking records, ensuring transparency during query execution and providing an effective basis for resolving future technical issues or data errors.

[0039] Preferably, it also includes:

[0040] The multilingual support module is used to support users to conduct natural language queries in multiple languages ​​(such as Chinese and English). It uses multilingual semantic analysis technology to automatically perform cross-language semantic conversion to ensure that the query needs of users in different languages ​​are accurately understood and responded to;

[0041] The cross-platform adaptation module is used to ensure that the system can run stably on different hardware platforms and operating systems, and supports access from multiple platforms such as the cloud, local servers, and mobile devices.

[0042] The multilingual support module enables the system to provide consistent service to electricity users worldwide speaking different languages, expanding its application scope and accessibility. The cross-platform adaptation module addresses compatibility issues across diverse environments, ensuring efficient operation on a variety of terminal devices and enhancing system flexibility and user experience.

[0043] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0044] By adopting a natural language processing algorithm based on deep learning, the present invention can effectively parse complex marketing data queries in the power industry, ensure accurate understanding of the user's query intent, and then generate an accurate SQL query structure. Traditional query methods often rely on manually written SQL statements or use simple keyword matching, which are prone to misunderstandings or omissions, especially when facing a field like the power industry with complex data structures and strong professionalism. Natural language processing based on deep learning can automatically identify contextual relationships, implicit requirements and multi-dimensional information in user queries, thereby avoiding inaccurate data queries caused by parsing errors. Users can directly initiate queries through natural language without having to master complex database structures, which greatly improves query efficiency and accuracy.

[0045] The present invention can optimize queries for large-scale data through the SQL optimization module and distributed execution engine, significantly improving the query performance of the system. The power industry involves a large amount of historical data, such as users' electricity consumption records, billing information, etc. Traditional query methods are often prone to performance bottlenecks when facing big data, with long query response times and even inability to obtain the required information in real time. Through the SQL optimization module, the system can intelligently select the optimal query execution plan, avoid full table scans and repeated calculations, and reduce the load on the database. The distributed execution engine distributes tasks to multiple computing nodes for parallel execution based on the complexity of the query, further speeding up the query response speed. Especially when faced with complex queries that need to span multiple data tables and multiple dimensions, the system can intelligently shard according to data distribution to ensure that query tasks can be efficiently processed in parallel, and ultimately provide accurate query results in a relatively short time. This optimization greatly improves the user experience, reduces the resource consumption of power companies when processing massive amounts of data, and improves the real-time nature of decision-making.

[0046] By introducing intelligent data quality verification and anomaly correction mechanisms, the present invention effectively ensures the accuracy and reliability of generated wide table data. The marketing data of the power industry often involves multiple dimensions and a large amount of real-time data. These data may be affected by factors such as input errors and transmission failures, resulting in anomalies or missing data in some data. If these erroneous data are not discovered and corrected in a timely manner, it may lead to incorrect decisions and business operations. The system can automatically identify and mark abnormal data through anomaly detection modules (such as isolation forest algorithms, support vector machines, etc.), and perform data repair through intelligent correction modules. The data integrity check module further ensures the comprehensiveness and consistency of the data, avoiding erroneous decisions caused by missing or inconsistent data. Through these automated verification and correction mechanisms, the system not only improves the quality of the data, but also reduces the need for human intervention, greatly reduces the decision-making risks caused by data errors, and ensures that power companies make more accurate decisions in operations, bill settlement, resource allocation, etc. Illustrations

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0048] Figure 1 This is a module diagram of the wide table generation system based on natural language of power industry marketing domain data in the present invention. DETAILED DESCRIPTION

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0050] The present invention provides Figure 1 The natural language wide table generation system based on power industry marketing domain data shown in the figure includes a natural language query parsing module, a semantic mapping module, an SQL dynamic conversion module, a data quality verification module, and a wide table generation module;

[0051] The natural language query parsing module receives natural language queries input by users, uses a deep learning-based natural language processing algorithm (such as BERT or GPT) to parse the user's query intent, and generates an intermediate semantic representation;

[0052] The semantic mapping module is used to map the corresponding SQL query structure based on the intermediate semantic representation and the relevant domain knowledge of the power industry marketing data to ensure that the query results are consistent with the user's query intent. Figure 1 To;

[0053] An SQL dynamic conversion module is used to convert the SQL query structure into a valid SQL query statement, and execute the query in the power industry marketing data system to extract target data;

[0054] The data quality verification module is used to evaluate the quality of the generated wide table data and automatically detect and correct potential errors or deviations in the data using rule-based algorithms and machine learning methods;

[0055] The wide table generation module is used to automatically generate the final wide table based on the verified data and provide the results externally for users to conduct decision analysis.

[0056] Specific implementation method 1: Natural language query parsing and multidimensional data query mapping based on deep learning;

[0057] In this implementation, the system uses deep learning-based natural language processing (NLP) technology, specifically pre-trained language models like BERT or GPT, to parse user natural language queries and convert them into directly executable SQL query structures. This implementation primarily involves three steps: user query input, natural language understanding and intent extraction, and query mapping and dynamic SQL generation.

[0058] User query input:

[0059] When users enter natural language queries into the system, the system receives these queries through the front-end interface. These queries can be structured (such as "Query each user's electricity usage and billing information for the past month") or unstructured, containing complex language expressions. For example, a user might enter "I want to know the electricity usage data and bill settlement information for all users in the past month." These queries involve information such as time, dimensions, entities, and relationships.

[0060] Natural language understanding and intent extraction:

[0061] When a user query is entered, the system performs semantic analysis on the query content through the natural language query parsing module. At this time, the system relies on deep learning models (such as BERT, GPT, etc.) to process the syntax and semantics of the query and extract the key elements. The deep learning model is trained on a large amount of power industry corpus, enabling it to understand complex power marketing data and its relationships. For example, the system can identify "the past month" as a time dimension, "electricity consumption" and "bill status" as data dimensions, and "each user" as an entity. During the processing process, models such as BERT will accurately identify the different components in the query through context analysis and word vector matching technology, thereby extracting the user's query intent and avoiding the fuzzy parsing problem of traditional keyword matching.

[0062] Query mapping and SQL dynamic generation:

[0063] Once the system understands the user's query intent, the next step is to map it to the database structure and generate an executable SQL query. To do this, the system needs to rely on a semantic mapping module, which uses an industry knowledge base (including standard data models, common business rules, etc. in the power industry) to accurately map the query intent. The data structure in the power industry usually involves complex multidimensional data relationships, such as user information, electricity consumption, billing history, payment records, etc. Therefore, during the mapping process, the system will ensure that the relationship between different data dimensions is correctly reflected. For example, a user's "electricity consumption" may be associated with "electrical equipment", while "bill status" is related to "bill status" and "payment method".

[0064] This process also involves converting multidimensional data queries. For example, if a user queries "What is the relationship between a user's electricity consumption and bills over the past month?", the system needs to understand that this is a multidimensional query involving time, user, and other data dimensions such as electricity consumption and bills. The correctness of this mapping is crucial for the subsequent generation of SQL statements. By combining deep learning models with domain knowledge of the power industry, the system can convert natural language queries into structured SQL statements.

[0065] Dynamic SQL generation and execution:

[0066] Generated SQL queries are optimized and converted through the SQL dynamic conversion module to ensure they conform to the database schema and can be executed efficiently. The system uses optimization algorithms to automatically select the most appropriate execution plan (such as selecting appropriate indexes and avoiding full table scans) based on the query's complexity and data volume. Furthermore, if the query involves cross-table queries or aggregation operations, the system automatically selects the appropriate SQL syntax for construction, ensuring accurate query results and efficient execution.

[0067] Through this deep learning-based query parsing and dynamic SQL generation technology, the system can quickly and accurately convert users' natural language queries into efficient SQL queries, execute them against power marketing data, and ultimately return a wide table that meets the user's needs. This implementation not only improves the response speed of user queries but also significantly reduces the potential for errors caused by manually entered query statements, especially when dealing with the complex data structures and query requirements of the power industry.

[0068] By introducing deep learning models such as BERT and GPT, the system can deeply understand natural language queries unique to the power industry, and can achieve efficient parsing and accurate processing of both structured and unstructured queries.

[0069] Through intelligent semantic mapping and dynamic SQL generation, the system can accurately convert complex natural language queries into database queries, avoiding language ambiguity or inconsistency problems in traditional query methods.

[0070] The automated query parsing and generation process reduces manual intervention, which not only improves work efficiency but also greatly reduces the risks brought by query parsing errors, ensuring the accuracy and reliability of the generated data.

[0071] Specific implementation method 2: Data query and wide table generation based on SQL optimization and distributed execution;

[0072] In this implementation, the system uses a dynamic SQL conversion module to generate optimized SQL queries, and then uses a distributed execution engine to query and process power marketing data. This implementation addresses the efficiency issues of large-scale data query and processing, ensuring that the system maintains efficient query response speeds when dealing with large amounts of power industry data.

[0073] SQL query optimization:

[0074] After generating the SQL query, the system will optimize the query statement through the SQL optimization module and select the most appropriate query execution plan. SQL optimization is not just about choosing the correct index, but also about optimizing the query execution order, avoiding unnecessary full table scans, optimizing the use of aggregate functions, etc. The system generates the optimal query execution plan through a query optimization algorithm based on a cost model, combined with the characteristics of power industry data, such as the size of the data table, the distribution of indexes, the complexity of the query, etc. For example, a query for "the total electricity consumption and bill amount of each user in the past month" may involve the user information table, electricity consumption table, and bill table. The system will select the optimal JOIN order based on the relationship between the tables and the distribution of the indexes to minimize the amount of data scanning and shorten the query time.

[0075] Distributed execution engine:

[0076] When the scale of query tasks increases and traditional single-machine execution cannot meet the real-time response requirements, the system introduces a distributed execution engine to decompose the query tasks into multiple subtasks and distribute them to multiple servers for parallel processing. For large-scale power marketing data, this distributed computing method greatly improves data processing capabilities, especially when processing queries involving massive user and historical data, it can significantly shorten query response times. The system automatically determines the complexity and data scale of the query task and intelligently chooses whether to enable distributed execution mode. For example, for a complex query involving multiple tables and multiple data dimensions, the system will automatically split the query, select the optimal data sharding strategy based on the data distribution, and execute the subqueries in parallel on multiple computing nodes, and finally merge the results and return them to the user.

[0077] Wide table generation and data return:

[0078] After the query is completed, the system automatically generates a wide table based on the data extracted from the database through the wide table generation module. This wide table consolidates power marketing data according to different dimensions, such as user information, electricity consumption, and billing records. This data is integrated into a multi-dimensional comprehensive data table based on the query time range and user filtering criteria, facilitating subsequent data analysis and decision support. The system also automatically sorts, aggregates, and filters data based on business needs, ensuring that the final data presented best meets user needs.

[0079] The combination of SQL optimization and distributed execution can significantly improve query efficiency, especially when facing large amounts of data. The system can automatically adjust optimization strategies and calculation methods based on query characteristics, thereby effectively avoiding performance bottlenecks.

[0080] Distributed computing not only improves the system's processing capabilities, but also ensures low-latency responses to large-scale queries. This can greatly enhance the user experience, especially in scenarios such as the power industry that require processing massive amounts of data.

[0081] The automated wide table generation process reduces the complexity of manual operations, improves the accuracy and real-time nature of query results, and helps power companies make more efficient and accurate decisions.

[0082] Specific implementation method 3: intelligent data quality verification and abnormal data correction;

[0083] In this implementation, the system uses intelligent data quality verification and anomaly detection mechanisms to ensure the quality of generated wide table data and automatically correct any anomalies found, preventing erroneous data from adversely affecting decision-making. This implementation utilizes machine learning algorithms and rule-based data verification mechanisms to improve data reliability and accuracy.

[0084] Anomaly detection module:

[0085] After generating the wide table data, the system first activates the anomaly detection module. This module uses machine learning algorithms (such as the isolation forest algorithm and support vector machine (SVM)) to detect anomalies in each data item in the query results. These algorithms can identify unusual trends or deviations in the data, such as a user's electricity usage far exceeding typical levels or a significant discrepancy between the bill amount and historical bills. This anomaly is often caused by system errors, input errors, or data loss. By training the model, the system can accurately identify and flag anomalies based on the business rules of the power industry, providing a basis for subsequent corrections.

[0086] Smart correction module:

[0087] When abnormal data is detected, the system triggers an intelligent correction module to correct the abnormal data according to the business rules of the power industry. For example, if a customer's bill is missing, the system automatically estimates the likely bill amount based on the customer's historical billing records and automatically fills in the missing data. If electricity usage data shows abnormalities, the system will make adjustments based on the customer's average electricity usage and historical records. This process relies not only on the rules engine but also incorporates machine learning models to predict the most likely correction data. The intelligent correction module minimizes manual intervention and ensures data accuracy and consistency.

[0088] Data integrity check module:

[0089] After correcting any anomalies, the system also runs a data integrity check module to ensure the integrity of the generated wide table data. For example, within a user's billing information, the system checks whether all user billing records are fully displayed and whether each user's electricity usage data matches the billing data. If any missing or inconsistent data is found, the system reanalyzes the data source and automatically supplements the missing data to ensure data accuracy and consistency.

[0090] Through the combination of machine learning and rule engines, the system can intelligently identify and correct anomalies in data, reducing the risks caused by human errors.

[0091] The automated data quality verification and correction process improves data credibility, ensures that the final generated wide table data meets the business needs and specifications of the power industry, and avoids the negative impact of erroneous data on decision-making and business operations.

[0092] The system can provide comprehensive data integrity checks to ensure that every dimension of the data is accurately filled, making wide table data more complete and consistent with the actual situation.

[0093] By adopting natural language processing algorithms based on deep learning (such as BERT, GPT, etc.), the present invention can effectively parse complex marketing data queries in the power industry, ensure accurate understanding of the user's query intent, and then generate accurate SQL query structures. Traditional query methods often rely on manually written SQL statements or use simple keyword matching, which are prone to misunderstandings or omissions, especially when facing a field like the power industry with complex data structures and strong professionalism. Natural language processing based on deep learning can automatically identify contextual relationships, implicit requirements and multi-dimensional information in user queries, thereby avoiding inaccurate data queries caused by parsing errors. Users can directly initiate queries through natural language without having to master complex database structures, which greatly improves query efficiency and accuracy. Through this technology, power companies can quickly process a large number of query requests while ensuring that each query result is highly consistent with user intent, reducing the risks caused by human errors or query deviations.

[0094] The present invention can optimize queries for large-scale data through the SQL optimization module and distributed execution engine, significantly improving the query performance of the system. The power industry involves a large amount of historical data, such as users' electricity consumption records, billing information, etc. Traditional query methods are often prone to performance bottlenecks when facing big data, with long query response times and even inability to obtain the required information in real time. Through the SQL optimization module, the system can intelligently select the optimal query execution plan, avoid full table scans and repeated calculations, and reduce the load on the database. The distributed execution engine distributes tasks to multiple computing nodes for parallel execution based on the complexity of the query, further speeding up the query response speed. Especially when faced with complex queries that need to span multiple data tables and multiple dimensions, the system can intelligently shard according to data distribution to ensure that query tasks can be efficiently processed in parallel, and ultimately provide accurate query results in a relatively short time. This optimization greatly improves the user experience, reduces the resource consumption of power companies when processing massive amounts of data, and improves the real-time nature of decision-making.

[0095] By introducing intelligent data quality verification and anomaly correction mechanisms, the present invention effectively ensures the accuracy and reliability of generated wide table data. Marketing data in the power industry often involves multiple dimensions and a large amount of real-time data. These data may be affected by factors such as input errors and transmission failures, resulting in anomalies or missing data in some data. If these erroneous data are not discovered and corrected in a timely manner, it may lead to incorrect decisions and business operations. The system can automatically identify and mark abnormal data through anomaly detection modules (such as isolation forest algorithms, support vector machines, etc.), and perform data repair through intelligent correction modules. For example, if there is an obvious error in a user's bill, the system can automatically refer to the user's historical bill records and make automatic adjustments. The data integrity check module further ensures the comprehensiveness and consistency of the data, avoiding erroneous decisions caused by missing or inconsistent data. Through these automated verification and correction mechanisms, the system not only improves the quality of the data, but also reduces the need for human intervention, greatly reducing the decision-making risks caused by data errors, and ensuring that power companies make more accurate decisions in operations, bill settlement, resource allocation, etc.

[0096] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0097] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0098] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0099] Those skilled in the art will appreciate that the units 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 beyond the scope of this application.

[0100] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0101] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0102] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0103] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0104] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as 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.

[0105] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A natural language-generated wide table system based on power industry marketing domain data, characterized by: It includes natural language query parsing module, semantic mapping module, SQL dynamic conversion module, data quality verification module and wide table generation module; The natural language query parsing module is used to receive the natural language query input by the user, parse the user's query intention using a natural language processing algorithm based on deep learning, and generate an intermediate semantic representation; A semantic mapping module is used to map the corresponding SQL query structure according to the intermediate semantic representation and in combination with relevant domain knowledge of power industry marketing data, so as to ensure that the query result is consistent with the user's query intention; An SQL dynamic conversion module is used to convert the SQL query structure into a valid SQL query statement, and execute the query in the power industry marketing data system to extract target data; The data quality verification module is used to evaluate the quality of the generated wide table data and automatically detect and correct potential errors or deviations in the data using rule-based algorithms and machine learning methods; The wide table generation module is used to automatically generate the final wide table based on the verified data and provide the results to the outside for users to make decision analysis.

2. The natural language generation wide table system based on power industry marketing domain data according to claim 1 is characterized in that: The natural language query parsing module also includes: The query intention understanding module is used to identify the contextual information and implicit requirements in the user input, and uses the long short-term memory network to conduct deep learning on historical queries to better understand the details and potential intentions of the query content; The user preference modeling module is used to establish a personalized query preference model based on the user's historical query records and interaction habits, and automatically adjust the priority of query parsing, thereby improving the relevance and accuracy of query results.

3. The natural language generation wide table system based on power industry marketing domain data according to claim 1 is characterized in that: The semantic mapping module includes: The industry knowledge base stores various data structures, business rules, and common query templates for the power industry. It combines the experience of domain experts and industry standards to provide reference data required for semantic mapping. The semantic reasoning engine is used to infer the best match between the query intent and the database structure based on the different keywords and relationships in the query statement, and dynamically generate an SQL query structure that is compatible with the database.

4. The natural language generation wide table system based on power industry marketing domain data according to claim 1 is characterized in that: The SQL dynamic conversion module also includes: SQL optimization module, which is used to optimize the performance of SQL statements after generating them, and use the query plan generator to select the optimal execution plan to improve query efficiency; The distributed execution engine is used to distribute SQL query tasks to multiple servers for parallel processing based on the scale and distribution characteristics of power industry marketing data, thereby reducing query response time and improving big data processing capabilities.

5. The natural language generation wide table system based on power industry marketing domain data according to claim 1 is characterized in that: The data quality verification module includes: The anomaly detection module performs outlier detection on the generated wide table data based on historical data analysis and machine learning algorithms, automatically identifying and marking abnormal trends or deviations in the data; Intelligent correction module, which is used to automatically correct or fill in abnormal data found according to the business rules and data correlation analysis of the power industry, avoiding manual intervention and improving the automatic correction capability of data quality; The data integrity check module is used to analyze the data integrity in the wide table to ensure that each user's data of each dimension is displayed as required to prevent missing or inconsistency.

6. The natural language generation wide table system based on power industry marketing domain data according to claim 1 is characterized in that: The wide table generation module includes: Multidimensional data merging engine, which is used to automatically merge data from multiple dimensions according to different query requirements, and automatically sort and filter data according to its timeliness and relevance; The data visualization module is used to display the generated wide table data to users through a visual interface. It supports charts, tables and other data display formats to help users understand the analysis results more intuitively.

7. The natural language generation wide table system based on power industry marketing domain data according to claim 1 is characterized in that: Also includes: Intelligent monitoring module, which is used to monitor the steps in the system operation process in real time, use anomaly detection algorithms to warn the system status, and ensure the stability and reliability of the query execution process; The log audit module is used to record each query request and its corresponding data processing process, and provide detailed log files for later auditing and problem tracing.

8. The natural language generation wide table system based on power industry marketing domain data according to claim 1 is characterized in that: Also includes: The multi-language support module is used to support users to conduct natural language queries in multiple languages, and use multi-language semantic analysis technology to automatically perform cross-language semantic conversion to ensure that the query needs of users in different languages ​​are accurately understood and responded to; The cross-platform adaptation module is used to ensure that the system can run stably on different hardware platforms and operating systems and support multi-platform access.

Images