Marketing inventory data anomaly analysis methods and related devices

Abstract

This disclosure provides a method and related apparatus for anomaly analysis of marketing stock data. The method includes: acquiring marketing business data from an electricity marketing business system; constructing a marketing stock database based on the marketing business data; identifying anomalies in the marketing stock database to obtain a list of anomalies; constructing a marketing anomaly problem library based on the list of anomalies; and analyzing the marketing business data in the marketing anomaly problem library to trace the root causes of anomalies in the marketing business data. This disclosure enables fully automated scanning of marketing stock data, intelligent anomaly identification, standardized problem management, and accurate tracing of anomaly root causes, thereby improving data governance efficiency and quality.

Description

Technical Field

[0001] This disclosure relates to the field of data analysis and processing technology, and in particular to a method and related apparatus for anomaly analysis of marketing stock data. Background Technology

[0002] This section is intended to provide background or context for the embodiments of this disclosure as set forth in the claims. The description herein is not intended to be a prior art simply because it is included in this section.

[0003] Marketing stock data refers to the historical data accumulated and stored in various business systems by power grid companies in their long-term power marketing business. It mainly includes customer basic profile information, electricity metering data, electricity billing data, line loss statistical analysis data, business expansion application process data, and customer service records.

[0004] However, in the relevant technologies, when conducting anomaly analysis of marketing stock data, there are problems such as low efficiency and high missed detection rate due to reliance on manual sampling and verification, fragmented and unclosed-loop management of anomalies, and difficulty in identifying hidden and complex anomalies. Summary of the Invention

[0005] In view of this, the purpose of this disclosure is to propose a method and related apparatus for anomaly analysis of marketing stock data, which at least to some extent solves one of the technical problems in the related technologies.

[0006] To achieve the above objectives, a first aspect of the exemplary embodiments of this disclosure provides a method for anomaly analysis of marketing stock data, the method comprising:

[0007] Acquire marketing business data from the electricity marketing business system, and construct a marketing stock database based on the marketing business data; Anomaly identification was performed on the marketing inventory database to obtain a list of abnormal issues; A marketing anomaly problem library is constructed based on the aforementioned list of anomalies. The marketing business data in the aforementioned marketing anomaly database is analyzed to enable the tracing of the root causes of anomalies in the marketing business data.

[0008] Based on the same inventive concept, a second aspect of the exemplary embodiments of this disclosure provides a marketing inventory data anomaly analysis apparatus, comprising: The database determination module is configured to acquire marketing business data from the electricity marketing business system, and construct a marketing stock database based on the marketing business data. The problem list determination module is configured to perform anomaly identification on the marketing inventory database to obtain an anomaly problem list; The problem database determination module is configured to construct a marketing anomaly problem database based on the list of anomalies. The problem database analysis module is configured to analyze marketing business data in the marketing anomaly problem database in order to trace the root causes of anomalies in the marketing business data.

[0009] Based on the same inventive concept, a third aspect of the exemplary embodiments of this disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method as described in the first aspect.

[0010] Based on the same inventive concept, a fourth aspect of the exemplary embodiments of this disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described in the first aspect.

[0011] Based on the same inventive concept, a fifth aspect of the exemplary embodiments of this disclosure provides a computer program product including computer program instructions that, when run on a computer, cause the computer to perform the method as described in the first aspect.

[0012] As can be seen from the above, the marketing inventory data anomaly analysis method and related apparatus provided in this disclosure include: This invention discloses a marketing business database. It acquires marketing business data from the electricity marketing business system, constructs a marketing inventory database based on this data, identifies anomalies in the database, generates a list of anomalies, constructs a marketing anomaly database based on this list, and analyzes the marketing business data in the database to trace the root causes of anomalies. This invention enables fully automated scanning of marketing inventory data, intelligent anomaly identification, standardized problem management, and accurate root cause tracing, thereby improving data governance efficiency and quality. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in this disclosure or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A schematic diagram illustrating an application scenario of the marketing stock data anomaly analysis method provided for an exemplary embodiment of this disclosure; Figure 2A flowchart illustrating a marketing stock data anomaly analysis method provided for an exemplary embodiment of this disclosure; Figure 3 A schematic diagram of a marketing stock data anomaly analysis device provided for an exemplary embodiment of this disclosure; Figure 4 A schematic diagram of the hardware structure of an electronic device provided for an exemplary embodiment of this disclosure. Detailed Implementation

[0015] It is understood that before using the technical solutions disclosed in the various embodiments of this application, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this application in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0016] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this application's technical solution, based on the prompt message.

[0017] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0018] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this application. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this application.

[0019] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0020] To make the objectives, technical solutions, and advantages of this disclosure clearer, the principles and spirit of this disclosure will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement this disclosure, and are not intended to limit the scope of this disclosure in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.

[0021] In this article, it is important to understand that any number of elements in the accompanying figures is for illustrative purposes and not for limitation, and any naming is for distinction only and has no limiting meaning.

[0022] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this disclosure should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar words used in the embodiments of this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly. The article "a" or "an" preceding an element does not exclude the existence of multiple such elements.

[0023] The principles and spirit of this disclosure will be explained in detail below with reference to several representative embodiments.

[0024] As described in the background section, related technologies suffer from problems such as low efficiency, high false negative rates, fragmented and unclosed-loop management of anomalies, and difficulty in identifying hidden and complex anomalies when conducting anomaly analysis of existing marketing data. Specifically, with the comprehensive advancement of smart grid construction and the full-scale expansion of electricity marketing business, the number of electricity users served by power grid companies continues to rise, and the coverage of marketing business continues to expand. Various types of existing marketing data, such as customer basic information, electricity metering data, electricity billing data, line loss statistical analysis data, and business expansion application process data, are showing explosive growth, multi-source heterogeneity, and complex dimensions. The data volume is gradually increasing from millions to hundreds of millions, and the data sources cover multiple independent business platforms such as electricity collection systems, marketing business application systems, electricity billing systems, line loss management systems, and customer service systems. The data types include various forms such as structured basic data, semi-structured process data, and unstructured supplementary data, significantly increasing the difficulty of data governance.

[0025] Currently, most domestic power grid companies still generally adopt a crude management model for quality control of existing marketing data, which relies on traditional manual verification, sampling inspection, and post-event rectification. In practical application, this model has many unavoidable technical shortcomings and management loopholes: First, manual verification is extremely inefficient. Faced with massive amounts of existing data, it is impossible to achieve full coverage inspection. Only key data sampling inspections can be carried out, and a large number of hidden data anomalies are missed for a long time, creating potential business risks. Second, data inspection has poor real-time performance. Manual verification relies on fixed cycles and cannot achieve 24 / 7 routine monitoring. The time from the generation of data anomalies to their discovery is long, which can easily lead to a series of business problems such as incorrect electricity billing, abnormal fluctuations in line loss, incorrect customer information, and distorted metering data, and may even affect the quality of power supply services and the company's economic benefits. Third, the management of abnormal issues is not standardized. Problems discovered through manual investigation are mostly recorded in scattered tables and documents, without forming a standardized and structured problem database. Problem classification is chaotic, the responsible parties are unclear, and the rectification process is not tracked, making it easy for problems to recur and for rectification to be inadequate. Fourth, data governance lacks a closed loop. In the manual mode, the various stages of problem investigation, order dispatch and rectification, review and acceptance, and effect evaluation are disconnected and lack a unified process control mechanism. The governance effectiveness cannot be quantified and statistically analyzed, and there is no supporting basis for continuous improvement of data quality. Fifth, the level of intelligence is extremely low. No intelligent identification model has been built in conjunction with the specific business rules of power marketing. It can only rely on human experience to judge simple data errors and cannot identify complex problems such as hidden anomalies, related anomalies, and fluctuation anomalies. The accuracy and depth of data governance are seriously insufficient.

[0026] To address the aforementioned issues, this disclosure provides a method and related apparatus for analyzing anomalies in marketing inventory data. The method specifically includes: This process involves acquiring marketing business data from the power marketing business system, constructing a marketing inventory database based on this data, identifying anomalies in the marketing inventory database to obtain a list of anomalies, constructing a marketing anomaly problem database based on this list, and analyzing the marketing business data in the marketing anomaly problem database to trace the root causes of anomalies in the marketing business data. Specifically, this disclosure uses a B / S architecture to build a visual operation platform, compatible with the existing intranet system environment of the power grid enterprise, achieving seamless integration with multiple source business systems such as the electricity consumption data acquisition system, marketing business system, electricity billing system, and line loss management system, and reserving standardized data interfaces and expansion interfaces; a distributed data processing cluster is built at the bottom layer to adapt to the high-speed storage and parallel computing requirements of massive marketing inventory data, and configures basic functions such as permission management, log recording, and data security encryption, distinguishing different role permissions such as administrators, inspectors, rectification personnel, and reviewers, ensuring stable system operation, data security, and traceable operation, and providing the underlying environment and technical support for the subsequent operation of various modules.

[0027] First, this disclosure acquires marketing data from the electricity marketing business system and constructs a unified marketing inventory database based on this data. This step directly addresses the technical problems of low efficiency and inability to achieve full coverage in traditional manual verification. In the traditional model, marketing data is scattered across multiple independent systems such as electricity consumption collection, electricity billing, and line loss management, and its volume is enormous. Manual verification can only be done through sampling, resulting in a very high rate of missed detections. This disclosure automatically acquires and integrates marketing business data from across the entire domain, constructing a complete and unified marketing inventory database. This lays the data foundation for subsequent full-scale automated scanning, thereby overcoming the limitations of manual sampling and achieving comprehensive and thorough data verification.

[0028] Secondly, this disclosure performs anomaly identification on the constructed marketing inventory database to obtain a list of anomalies. This step primarily addresses the issues of insufficient intelligence and poor accuracy in anomaly identification. Traditional methods rely solely on human experience to judge simple data errors, making it difficult to detect latent anomalies, correlation anomalies, or dynamic fluctuation anomalies. This disclosure employs a combination of a pre-set business rule engine and machine learning algorithms to automatically scan the data in the database. It can simultaneously identify explicit anomalies (such as missing data, field errors, and out-of-bounds values) and latent anomalies (such as fluctuation anomalies and inconsistent correlations), significantly improving the efficiency and accuracy of anomaly identification and achieving a leap from human experience-based judgment to intelligent and precise identification.

[0029] Next, this disclosure constructs a marketing anomaly issue database based on the anomaly issue list. This step solves the problem of fragmented and unstandardized anomaly issue management. Previously, issues discovered through manual verification were often recorded in scattered tables or documents, resulting in chaotic issue classification, unclear responsible parties, and difficulty in traceability and reuse. This disclosure, after reviewing, confirming, classifying, and grading the identified anomalies, uniformly collects and stores them in a structured anomaly issue database, supporting ledger-based management, rapid retrieval, and multi-dimensional statistical analysis. This ensures that every anomaly issue is traceable, categorized, and accountable, completely changing the current fragmented and disordered state of issues and achieving standardized and asset-based management of anomalies.

[0030] Finally, this disclosure analyzes marketing business data in the marketing anomaly database to trace the root causes of anomalies. This step not only directly supports the closed-loop management of the data governance process but also solves the problems of unquantifiable governance effectiveness and difficulty in iterating rules. By analyzing the distribution patterns, high-frequency anomaly types, and root causes in the anomaly database, the system can automatically generate rectification work orders and dispatch them to responsible departments, track rectification progress, and archive them after review and acceptance, forming a complete closed loop of "scanning—database entry—work order dispatch—rectification—review—archiving," preventing inadequate rectification and recurring problems. Simultaneously, based on the statistical analysis and root cause tracing results of the anomaly database, the system can quantify core indicators such as anomaly occurrence rate, rectification completion rate, and problem recurrence rate, and continuously optimize the parameters of business rules and AI recognition models accordingly, forming a virtuous cycle of continuous improvement in data quality. This comprehensively solves the problems of unquantifiable governance effectiveness and difficulty in iterating rules under the traditional model.

[0031] After introducing the basic principles of this disclosure, various non-limiting embodiments of this disclosure will be described in detail below.

[0032] refer to Figure 1 This is a schematic diagram of an application scenario of the marketing stock data anomaly analysis method provided by the exemplary embodiments of this disclosure.

[0033] This application scenario includes a terminal device 101 and a server 102. The terminal device 101 and the server 102 can be connected via a wired or wireless communication network to achieve data interaction.

[0034] Terminal device 101 may be an electronic device located close to the user side, possessing data transmission and multimedia input / output functions, including but not limited to desktop computers, mobile phones, portable computers, tablet computers, media players, smart wearable devices, personal digital assistants (PDAs), or other electronic devices capable of performing the aforementioned functions. This electronic device may include a processor and a display screen with touch input functionality. The display screen is used to present a graphical user interface (GUI), which can display an application interface. The processor is used to process application data, generate the GUI, and control the display of the GUI on the screen.

[0035] Server 102 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0036] In some exemplary embodiments, the marketing stock data anomaly analysis method can run on terminal device 101 or server 102.

[0037] When the marketing stock data anomaly analysis method is running on server 102, server 102 is used to provide marketing stock data anomaly analysis services to users of terminal device 101.

[0038] Server 102 acquires marketing business data from the power marketing business system, and builds a marketing stock database based on the marketing business data. Server 102 performs anomaly identification on the marketing inventory database and obtains a list of abnormal issues; Server 102 constructs a marketing anomaly problem library based on the aforementioned list of anomalies; Server 102 analyzes the marketing business data in the marketing anomaly database to trace the root cause of the anomalies in the marketing business data, and then transmits the analysis results to terminal device 101.

[0039] It should be noted that the above application scenarios are shown only to facilitate understanding of the spirit and principles of this disclosure, and the implementation of this disclosure is not limited in any way. On the contrary, the implementation of this disclosure can be applied to any applicable scenario.

[0040] refer to Figure 2 A method for analyzing anomalies in marketing inventory data, the method comprising the following steps: Step S210: Obtain marketing business data from the electricity marketing business system, and construct a marketing stock database based on the marketing business data.

[0041] In practice, the method for obtaining marketing business data from the electricity marketing business system is as follows: Through standardized API interfaces, database integration, message queue transmission, and other methods, marketing business data from multiple sources, including electricity consumption data collection systems, marketing business application systems, electricity billing systems, line loss management systems, and customer service systems, is collected and accessed across the entire domain.

[0042] In some embodiments, the marketing business data includes: customer basic profile information, metering data, electricity billing data, line loss statistics data, and business expansion and service data.

[0043] In practice, customer basic profile information refers to the relevant data in the customer basic profile of the electricity marketing business, including user name, electricity address, account information, electricity type, etc.

[0044] In practice, metering data refers to electricity metering-related data collected in the electricity marketing business, including voltage, current, power, meter readings, and data collection success rate.

[0045] In practice, electricity billing data refers to electricity marketing data including electricity consumption, electricity price, electricity bill amount, late payment penalties, and accounting information.

[0046] In practice, line loss statistics refer to the relevant data for line loss statistical analysis in the power marketing business, including line loss, transformer area loss, loss rate, and abnormal fluctuation data.

[0047] In practice, the business expansion and service data of the electricity marketing business includes business expansion application and customer service related data such as application process, repair record, and payment information.

[0048] In some embodiments, a marketing inventory database is constructed based on the marketing business data, including: The marketing business data is deduplicated to obtain the deduplicated business data; The deduplicated business data is cleaned to obtain cleaned business data; The cleaned business data is standardized to obtain standardized business data; The standardized business data is correlated and matched to obtain the marketing inventory database.

[0049] In practice, the marketing business data is deduplicated to obtain the deduplicated business data in the following way: Based on unique business identifiers such as customer number, metering point number, and line number, the system automatically compares and identifies redundant data that is repeatedly entered or collected, and removes duplicate records. This ensures that every piece of data in the marketing inventory database is logically unique and free of redundancy, thus obtaining deduplicated business data and providing a clean and compact data foundation for subsequent data cleaning and standardization.

[0050] In specific implementation, the deduplicated business data is cleaned to obtain the cleaned business data in the following way: The system automatically repairs basic issues in the data, such as format errors, garbled characters, and missing fields, while filtering out invalid and abnormal noise data. This results in higher-quality, more structured cleaned business data, providing reliable data input for subsequent data standardization and anomaly identification.

[0051] In practice, the cleaned business data is standardized to obtain standardized business data in the following way: In accordance with the unified marketing data standards and specifications of power grid companies, the cleaned data will have unified field names, data formats, units of measurement and coding rules. For example, voltage and current data from different sources will be converted to the same unit, date fields will be unified to the "YYYY-MM-DD" format, and various business codes will be aligned with the standard coding table. This will eliminate the format and semantic differences between heterogeneous data and form standardized business data that can be directly used for anomaly scanning and identification.

[0052] In specific implementation, the standardized business data is correlated and matched to obtain the marketing inventory database in the following way: Based on unique business identifiers such as customer number, metering point number, and line number, the system automatically associates and aligns standardized data that were originally scattered in different business systems (such as electricity consumption collection system, marketing business system, electricity billing system, line loss management system, etc.) according to these identifiers. This integrates various types of information from the same user or the same device into a marketing inventory database, solving the problem of multi-source data silos and providing a high-quality, correlated data source for subsequent anomaly scanning and identification.

[0053] Step S220: Perform anomaly identification on the marketing inventory database to obtain a list of abnormal issues.

[0054] In some embodiments, anomaly identification is performed on the marketing inventory database to obtain a list of abnormal issues, including: The marketing database is scanned by a preset business rule engine to obtain explicit abnormal data. The explicit abnormalities include missing data, field errors, logical contradictions, out-of-bounds values, and duplicate anomalies. The marketing inventory database is identified using machine learning algorithms to obtain hidden anomalies, including fluctuation anomalies and inconsistency in correlation anomalies. A preliminary list of anomalies is generated based on the explicit and implicit anomalies. The preliminary list of anomalies is then classified into anomaly levels and labeled with anomaly types to obtain the list of anomaly issues.

[0055] In practice, the marketing database is scanned using a preset business rule engine to obtain explicit abnormal data. These explicit anomalies include data loss, field errors, logical contradictions, out-of-bounds values, and duplicate anomalies. In this embodiment, the system incorporates hundreds of electricity marketing-specific business rules, including customer profile specifications, metering data thresholds, electricity billing logic, reasonable range of line loss, and data integrity requirements. The rule engine automatically matches and scans each piece of data in the existing marketing database against these preset rules. When data violates a rule, it is judged as an explicit anomaly, specifically including: missing data (required fields are empty), incorrect fields (field format or content does not conform to specifications), logical contradictions (such as incorrect relationship between electricity consumption and electricity bill calculation), out-of-bounds values ​​(such as voltage, current, power, etc. values ​​exceeding reasonable thresholds), and duplicate anomalies (duplicate records exist based on unique identifiers such as customer number), thereby generating a list of explicit anomaly data.

[0056] In specific implementation, the marketing inventory database is identified using machine learning algorithms to obtain hidden anomalies. These hidden anomalies include fluctuation anomalies and inconsistency anomalies in the following ways: Machine learning anomaly detection models (such as time series analysis, outlier detection, clustering, or association rule learning algorithms) are used to deeply mine marketing stock data. On the one hand, by learning and modeling the fluctuation patterns of historical normal data, the system automatically identifies anomalies in the time series of indicators such as voltage, current, and electricity consumption (such as sudden increases, decreases, or abnormal changes exceeding the historical normal fluctuation range). On the other hand, association learning algorithms are used to cross-validate fields with logical relationships in different business systems or different data tables (such as the correspondence between customer files and metering points, and the consistency between electricity consumption and electricity bill calculation). This detects inconsistencies in association that are difficult for the rule engine to directly determine (such as incorrect matching between customer number and metering point number even though the field format itself is correct), thereby efficiently capturing hidden data problems that traditional rule scanning cannot cover.

[0057] In specific implementation, a preliminary anomaly list is generated based on the explicit and implicit anomaly data. The preliminary anomaly list is then categorized by anomaly level and anomaly type to obtain the anomaly problem list, as follows: After generating a preliminary anomaly list based on explicit and implicit anomaly data, the system categorizes and labels each anomaly in the preliminary list to obtain a list of anomaly issues. This is done by using built-in anomaly level judgment rules (such as classifying anomalies into general, important, and urgent levels based on their impact on business operations like electricity billing accuracy, line loss statistics reliability, and customer service compliance) and anomaly type classification system (labeling anomalies according to their business scenarios, such as customer file anomalies, metering data anomalies, electricity billing anomalies, line loss statistics anomalies, and business expansion service anomalies, while also identifying specific anomaly categories such as missing data, field errors, logical contradictions, out-of-bounds values, duplicate anomalies, fluctuation anomalies, and inconsistent association anomalies). This process automatically determines the level and labels the type of each anomaly in the preliminary anomaly list, thereby generating a list of anomaly issues.

[0058] Step S230: Based on the list of abnormal issues, construct a marketing abnormal issue library.

[0059] In some embodiments, a marketing anomaly issue library is constructed based on the aforementioned list of anomalies, including: The list of abnormal issues is reviewed and confirmed to obtain the approved abnormal data; The approved abnormal data is classified and graded to obtain labeled abnormal data; The labeled abnormal data are uniformly collected and stored to obtain the marketing anomaly problem database.

[0060] In practice, the list of abnormal issues is reviewed and confirmed to obtain the approved abnormal data in the following way: In this embodiment, professional marketing personnel can be assigned to manually review each item on the list of abnormal issues automatically generated by the system. Based on the actual business situation, invalid abnormalities and misjudged issues caused by data noise, rule deviations, or algorithm misjudgments are eliminated. At the same time, valid abnormal data that actually exists is confirmed. Only abnormal issues that have been verified can be used as legitimate data sources for the subsequent construction of a standardized issue database, thereby ensuring the accuracy and reliability of the abnormal issue database.

[0061] In practice, the approved abnormal data is classified and graded to obtain the labeled abnormal data in the following way: Based on the preset classification and grading standards, each approved abnormal data is uniformly labeled and categorized according to multiple dimensions such as business type (e.g., customer files, metering, electricity bills, line loss, business expansion services, etc.), abnormality level (general, important, urgent), risk level, responsible department, and occurrence time. This forms labeled abnormal data, providing an orderly data foundation for the subsequent construction of a standardized problem database and full lifecycle management.

[0062] In specific implementation, the abnormal data after labeling is uniformly collected and stored to obtain the marketing anomaly problem database in the following way: The abnormal data that has been classified and labeled is imported in batches or automatically written into the marketing abnormality problem database according to the preset unified data structure and storage specifications. For each abnormal record, a complete problem description, data details involved, location of occurrence, discovery time, abnormality level, business type and responsible department are indexed. This realizes the centralized, structured and ledger-style storage of abnormal problems, and supports query, retrieval, statistics, export and dynamic update functions, thus forming the marketing abnormality problem database.

[0063] Step S240: Analyze the marketing business data in the marketing anomaly database to trace the root cause of the anomalies in the marketing business data.

[0064] In some embodiments, the marketing business data in the marketing anomaly database is analyzed to enable the tracing of the root causes of anomalies in the marketing business data, including: Based on the abnormal issues in the aforementioned marketing anomaly issue database, rectification work orders are dispatched and closed-loop management is implemented to obtain governance process data. The data from the governance process are statistically analyzed to obtain a data governance report; Based on the data governance report, root cause analysis is performed on high-frequency and recurring anomalies to enable tracing the root causes of anomalies in the marketing business data.

[0065] In practice, rectification work orders are dispatched and closed-loop management is implemented based on the abnormal issues in the aforementioned marketing anomaly issue database. The method for obtaining governance process data is as follows: Based on the responsible department and personnel information marked for each abnormal issue in the abnormal issue database, an electronic rectification work order is automatically generated. The work order clearly marks the issue details, rectification requirements, completion deadline, and reference rectification plan, and is pushed to the corresponding responsible department and rectification personnel through the system. After completing the rectification, the rectification personnel submit supporting materials for rectification in the system and apply for review. The review personnel verify the rectification effect by automatically rescanning the corresponding data through the system and by combining it with manual spot checks. If the acceptance is successful, the issue is archived and closed; if the acceptance is unsuccessful, the reason is noted and the issue is returned for rectification again. Throughout this process, the system tracks and records the entire process information, including issue assignment, rectification progress, review results, acceptance status, and processing time, thereby automatically summarizing and forming governance process data.

[0066] In practice, the data governance report is obtained by statistically analyzing the data from the governance process as follows: The system aggregates and calculates governance process data generated during the closed-loop management of the entire process. It automatically calculates core indicators such as the total number of abnormal issues, the occurrence rate of abnormalities, the rectification completion rate, the rectification timeliness rate, and the problem recurrence rate according to preset statistical dimensions. The system also intuitively displays changes in data quality and governance effectiveness through visual charts (such as trend charts, bar charts, pie charts, etc.). Finally, it automatically generates structured data governance reports on a monthly or quarterly basis, providing quantitative basis for subsequent root cause analysis and rule optimization.

[0067] In specific implementation, the root cause analysis of high-frequency anomalies and recurring anomalies is performed based on the data governance report, so as to achieve the method of tracing the root cause of anomalies in the marketing business data: By conducting in-depth analysis of core indicators such as the occurrence rate, rectification completion rate, and recurrence rate of abnormal issues automatically counted in the data governance report, we can identify high-frequency or recurring anomaly types and their corresponding business scenarios and data locations. Then, by combining the problem details, related data links, and historical rectification records in the marketing anomaly database, we can conduct a comprehensive source tracing analysis from multiple dimensions, including data source quality, business rule logic defects, system interface transmission failures, and the standardization of manual operations. This allows us to accurately pinpoint the root cause of the anomalies and provide a basis for subsequent optimization of scanning rules, adjustment of identification model parameters, and strengthening of weak link governance.

[0068] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0069] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0070] Based on the same inventive concept, corresponding to any of the above embodiments, this disclosure also provides a marketing inventory data anomaly analysis device.

[0071] refer to Figure 3 The marketing inventory data anomaly analysis device includes: The database determination module 310 is configured to acquire marketing business data from the power marketing business system, and construct a marketing stock database based on the marketing business data. The problem list determination module 320 is configured to perform anomaly identification on the marketing inventory database to obtain an anomaly problem list; The problem database determination module 330 is configured to construct a marketing anomaly problem database based on the list of anomalies. The problem database analysis module 340 is configured to analyze the marketing business data in the marketing anomaly problem database in order to trace the root cause of the anomalies in the marketing business data.

[0072] In this exemplary embodiment, the database determination module 310 is specifically configured as follows: Acquire marketing business data from the electricity marketing business system. This marketing business data includes: customer basic profile information, metering data, electricity billing data, line loss statistics, and business expansion and service data. Perform data deduplication on the marketing business data to obtain deduplicated business data. Clean the deduplicated business data to obtain cleaned business data. Standardize the cleaned business data to obtain standardized business data. Perform correlation matching on the standardized business data to obtain the existing marketing database.

[0073] In this exemplary embodiment, the problem list determination module 320 is specifically configured as follows: The marketing database is scanned using a pre-defined business rule engine to obtain explicit abnormal data, including missing data, field errors, logical contradictions, out-of-bounds values, and duplicate anomalies. The marketing database is then analyzed using a machine learning algorithm to identify implicit abnormal data, including fluctuation anomalies and inconsistent correlation anomalies. A preliminary anomaly list is generated based on the explicit and implicit abnormal data, and this list is further categorized by anomaly level and anomaly type to obtain the anomaly problem list.

[0074] In this exemplary embodiment, the problem database determination module 330 is specifically configured as follows: The list of abnormal issues is reviewed and confirmed to obtain approved abnormal data; the approved abnormal data is classified and graded to obtain labeled abnormal data; the labeled abnormal data is uniformly collected and stored to obtain the marketing abnormal issue database.

[0075] In this exemplary embodiment, the problem database analysis module 340 is specifically configured as follows: Based on the abnormal issues in the marketing anomaly issue database, rectification work orders are dispatched and closed-loop management is implemented to obtain governance process data; the governance process data is statistically analyzed to obtain a data governance report; based on the data governance report, root cause analysis is performed on high-frequency anomalies and recurring anomalies to enable the tracing of the root causes of anomalies in the marketing business data.

[0076] For ease of description, the above apparatus is described in terms of its functions, divided into various modules. Of course, in implementing this disclosure, the functions of each module can be implemented in one or more software and / or hardware.

[0077] The apparatus described above is used to implement the corresponding marketing inventory data anomaly analysis method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0078] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the marketing inventory data anomaly analysis method described in any of the above embodiments.

[0079] Figure 4 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0080] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0081] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0082] The input / output interface 1030 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0083] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0084] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0085] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0086] The electronic devices described above are used to implement the corresponding marketing inventory data anomaly analysis method in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0087] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the marketing inventory data anomaly analysis method as described in any of the above embodiments.

[0088] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0089] The aforementioned non-transitory computer-readable storage media can be any available medium or data storage device that a computer can access, including but not limited to magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0090] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the marketing inventory data anomaly analysis method as described in any of the embodiments in the exemplary method section above, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0091] Based on the same inventive concept, corresponding to the marketing inventory data anomaly analysis method described in any of the above embodiments, this disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to execute the marketing inventory data anomaly analysis method. Corresponding to the execution entity for each step in each embodiment of the marketing inventory data anomaly analysis method, the processor executing the corresponding step can belong to the corresponding execution entity.

[0092] The computer program product of the above embodiments is used to cause the computer and / or the processor to execute the marketing inventory data anomaly analysis method as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0093] Those skilled in the art will recognize that embodiments of this disclosure can be implemented as a system, method, or computer program product. Therefore, this disclosure can be implemented as entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this disclosure can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0094] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (not exhaustive) of a computer-readable storage medium may include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0095] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0096] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0097] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0098] It should be understood that each block of a flowchart and / or block diagram, as well as combinations of blocks in a flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine that, when executed by a computer or other programmable data processing device, creates means for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0099] These computer program instructions may also be stored in a computer-readable medium that enables a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce a product comprising an instruction apparatus that implements the functions / operations specified in the boxes of a flowchart and / or block diagram.

[0100] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable apparatus can provide a process for implementing the functions / operations specified in the boxes of a flowchart and / or block diagram.

[0101] Furthermore, although the operations of the methods of this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. Rather, the steps depicted in the flowcharts may be executed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0102] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0103] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0104] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0105] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0106] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0107] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

[0108] While the spirit and principles of this disclosure have been described with reference to several specific embodiments, it should be understood that this disclosure is not limited to the disclosed specific embodiments, and the division of aspects does not imply that features in these aspects cannot be combined for benefit; such division is merely for convenience of expression. This disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. The scope of the appended claims is to be interpreted in the broadest sense, thereby encompassing all such modifications and equivalent structures and functions.

Claims

1. A marketing inventory data anomaly analysis method, characterized by, include: Acquire marketing business data from the electricity marketing business system, and construct a marketing stock database based on the marketing business data; Anomaly identification was performed on the marketing inventory database to obtain a list of abnormal issues; A marketing anomaly problem library is constructed based on the aforementioned list of anomalies. The marketing business data in the aforementioned marketing anomaly database is analyzed to enable the tracing of the root causes of anomalies in the marketing business data.

2. The method of claim 1, wherein, The marketing business data includes: customer basic profile information, metering data, electricity billing data, line loss statistics, and business expansion and service data.

3. The method of claim 1, wherein, The marketing inventory database, constructed based on the marketing business data, includes: The marketing business data is deduplicated to obtain the deduplicated business data; The deduplicated business data is cleaned to obtain cleaned business data; The cleaned business data is standardized to obtain standardized business data; The standardized business data is correlated and matched to obtain the marketing inventory database.

4. The method of claim 1, wherein, The process of identifying anomalies in the marketing inventory database yields a list of anomalies, including: The marketing database is scanned by a preset business rule engine to obtain explicit abnormal data. The explicit abnormalities include missing data, field errors, logical contradictions, out-of-bounds values, and duplicate anomalies. The marketing inventory database is identified using machine learning algorithms to obtain hidden anomalies, including fluctuation anomalies and inconsistency in correlation anomalies. A preliminary list of anomalies is generated based on the explicit and implicit anomalies. The preliminary list of anomalies is then classified into anomaly levels and labeled with anomaly types to obtain the list of anomaly issues.

5. The method of claim 1, wherein, The marketing anomaly database is constructed based on the aforementioned list of anomalies, and includes: The list of abnormal issues is reviewed and confirmed to obtain the approved abnormal data; The approved abnormal data is classified and graded to obtain labeled abnormal data; The labeled abnormal data are uniformly collected and stored to obtain the marketing anomaly problem database.

6. The method of claim 1, wherein, The analysis of marketing business data in the marketing anomaly database to trace the root causes of anomalies in the marketing business data includes: Based on the abnormal issues in the aforementioned marketing anomaly issue database, rectification work orders are dispatched and closed-loop management is implemented to obtain governance process data. The data from the governance process are statistically analyzed to obtain a data governance report; Based on the data governance report, root cause analysis is performed on high-frequency and recurring anomalies to enable tracing the root causes of anomalies in the marketing business data.

7. A marketing inventory data anomaly analysis apparatus characterized by comprising: include: The database determination module is configured to acquire marketing business data from the electricity marketing business system, and construct a marketing stock database based on the marketing business data. The problem list determination module is configured to perform anomaly identification on the marketing inventory database to obtain an anomaly problem list; The problem database determination module is configured to construct a marketing anomaly problem database based on the list of anomalies. The problem database analysis module is configured to analyze marketing business data in the marketing anomaly problem database in order to trace the root causes of anomalies in the marketing business data.

8. An electronic device, comprising: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, comprising: The non-transitory computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 6.

10. A computer program product, characterised in that, It includes computer program instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1 to 6.

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