Panoramic power failure early warning analysis method based on customer perspective

Abstract

The invention relates to a panoramic power failure early warning analysis method based on a customer perspective, and the method comprises the following steps: obtaining data from different sources, including customer feedback, equipment states and historical power failure records; extracting, converting and loading the data by using an ETL tool, and integrating and storing the data in a data warehouse; performing multi-dimensional analysis on the power failure data by using a data analysis tool, identifying key factors influencing power failure, and extracting a key feature set; feedback and appeal information of clients are obtained and fused into a feature set; key management and control objects are identified through data analysis, and priorities are distributed to different objects according to power failure influence degrees and customer demands; differentiated early warning strategies are designed for different objects, and the early warning strategies are integrated into an existing power failure management process; and developing a visual monitoring board, displaying power failure early warning information and key indexes in real time, and regularly generating a power failure early warning analysis report. The power failure management efficiency and accuracy are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of power outage management, and in particular to a panoramic power outage early warning analysis method based on a customer perspective. Background Art

[0002] In modern power systems, power outage management is a complex and critical task that directly affects the operational efficiency and customer satisfaction of power companies. As power networks continue to expand and become more complex, traditional power outage management methods face many challenges, such as data silos, response delays, and untimely processing of customer feedback. Therefore, power companies need a more intelligent and integrated solution to improve the efficiency and accuracy of power outage management. Summary of the invention

[0003] In order to solve the above problems, the purpose of the present invention is to provide a panoramic power outage warning analysis method based on customer perspective, so as to effectively improve the efficiency and accuracy of power outage management.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for analyzing a power outage warning from a customer's perspective includes the following steps:

[0006] Obtain data from various sources, including customer feedback, equipment status, and historical outage records;

[0007] Use ETL tools to extract, transform and load data, and integrate and store it in the data warehouse;

[0008] Use data analysis tools to conduct multi-dimensional analysis of power outage data, identify key factors affecting power outages, and extract key feature sets;

[0009] Obtain customer feedback and demand information, digitize customer feedback and demand information, and integrate it into feature sets to enhance customer perspective analysis capabilities;

[0010] Identify key control targets through data analysis, including stations, meter boxes, meters with high failure rates, and user groups with greater impact, and assign priorities to different targets based on the impact of the power outage and customer demands;

[0011] Design differentiated early warning strategies for different objects and integrate them into the existing power outage management process to ensure that early warning information can be transmitted and processed in a timely manner;

[0012] Develop a visual monitoring dashboard to display power outage warning information and key indicators in real time, generate power outage warning analysis reports regularly, and notify relevant personnel through automatic distribution functions.

[0013] Furthermore, data analysis tools are used to conduct multi-dimensional analysis of power outage data, including analysis of long-term severe low voltage conditions, analysis of low voltage week-long conditions, distribution analysis of low voltage and frequent power outage users, classification analysis of power outage causes for medium and low voltage users, classification and statistical analysis of frequent power outages, and analysis of power outages in high-fault multi-meter boxes;

[0014] The analysis of the long-term severe low voltage situation: set the HPLC low voltage user caliber and the long-term low voltage user caliber, monitor the HPLC daily low voltage and low voltage continuous points, and the cumulative number of low voltage days in the past 30 days; classify the monitored low voltage users into different levels, forming three voltage levels of 180-198, 160-179, and below 160; calculate the number of long-term severe low voltage users, the proportion of low voltage users, and the month-on-month indicators of the number of low voltage users;

[0015] Analysis of low voltage week duration: monitor the low voltage situation of HPLC users and non-HPLC users on a daily basis, formulate different continuous low voltage rules, calculate the number of users with continuous low voltage in the past 7 days, and classify them according to the longest continuous low voltage days;

[0016] Distribution analysis of low-voltage and frequent power outage users: Monitor HPLC users with power consumption greater than 60 degrees last month, and analyze whether there are at least 4 consecutive points of voltage less than or equal to 198 volts every day. Then trace back 60 days to determine whether these users have had power outage records. If the number of power outages exceeds 2, such users are classified as low-voltage and frequent power outage users, and statistics are collected by city, district, and county to analyze the distribution of low-voltage and frequent power outage users;

[0017] Classification analysis of power outage reasons for medium and low voltage users: Taking months as the time unit, conduct multi-dimensional analysis of each power outage for medium and low voltage customers according to voltage level, plan or fault, fault location, and power outage times. Calculate the indicators of medium voltage fault power outage, planned power outage, and unpublished power outage information per 10,000 households in each city, as well as the indicators of low voltage fault power outage, planned power outage, low voltage distribution network power outage, multi-meter box power outage, and single household power outage per 10,000 households for unpublished power outage information;

[0018] Frequent power outage hierarchical statistical analysis: From the perspectives of users and users with frequent power outages in substations, statistical analysis is conducted on the number of power outages of users in the past 60 days. Power outages of 3 or more times during the period are classified as frequent power outages. Statistics are divided into five levels: provincial, municipal, district, county, power supply station, and grid according to different statistical periods such as day, week, and month. At the same time, the proportion of users with frequent power outages at each level in the unit and the same indicators are calculated;

[0019] Analysis of power outages for multi-meter boxes with high faults: Analyze the power outages for multi-meter boxes of low-voltage users, count the total number of multi-meter boxes and the number of high fault meter boxes by city, calculate the proportion of high fault meter boxes in the total number of multi-meter boxes, and the decline rate of high fault meter boxes; count the number of power outages for meter boxes with 2-5 power outages and more than 6 power outages, as well as the proportion of power outages 6 times or more.

[0020] Furthermore, the key factors affecting power outages are identified and key feature sets are extracted, as follows:

[0021] Normalize the data so that all features have the same scale;

[0022] Support vector machine (SVM) is selected as the base model to evaluate the importance of features. The goal of SVM is to find a hyperplane that maximizes the class interval:

[0023]

[0024] subject to you i (w·x i +b)≥1;

[0025] Among them, w is the feature coefficient vector, which is used to evaluate the importance of the feature;

[0026] Initialize the RFE algorithm using the selected base model and specify the number of features to be selected;

[0027] Train the base model and calculate the importance w of each feature;

[0028] Based on feature importance, recursively remove the least important features;

[0029] The training and elimination process is repeated until the specified number of features is reached.

[0030] Furthermore, customer feedback and demand information is obtained, digitized, and integrated into the feature set to enhance the analysis capability from the customer perspective, as follows:

[0031] Obtain customer feedback and demand information, remove HTML tags, special characters, stop words, and perform word segmentation;

[0032] Use natural language processing toolkits for text cleaning;

[0033] Use sentiment analysis tools to classify customer feedback and use sentiment scores as customer feedback features;

[0034] Use topic modeling algorithms to identify the main topics in the feedback and use topic distribution as customer feedback features;

[0035] Use the bag-of-words model to convert text data into numerical features as customer feedback features;

[0036] Integrate the processed customer feedback features with the existing feature set to form a complete feature set.

[0037] Furthermore, differentiated early warning strategies are designed for different objects, as follows:

[0038] Based on the preliminary analysis, different warning targets are identified, including substations, meter boxes, meters with high failure rates, and user groups with greater impacts;

[0039] According to the failure rate and impact, the warning is divided into different levels, and each level corresponds to different response time and treatment measures;

[0040] For equipment with high failure rates, set up real-time monitoring and automatic early warning mechanisms to detect anomalies in a timely manner and trigger early warnings; for key user groups, establish a rapid response mechanism to ensure that power supply is restored first when a power outage occurs; for general users, provide regular maintenance and inspections to reduce the occurrence of potential failures.

[0041] Define clear warning trigger conditions and trigger warnings based on the trigger conditions.

[0042] Furthermore, different monitoring systems are set up for substations, meter boxes, meters with high failure rates, and user groups that are more affected, as follows: substations, monitor load and equipment status, and warn of overload or failure; meter boxes, monitor temperature and current abnormalities, and warn of equipment damage; meters, monitor abnormal readings, and warn of metering errors or electricity theft; users, warn of service interruptions based on historical records and feedback.

[0043] A panoramic power outage warning analysis system based on customer perspective, including a data acquisition module, a data warehouse, a data analysis module, a text analysis module, a power outage warning module and a visualization module;

[0044] The data acquisition module acquires data from different sources, including customer feedback, equipment status, and historical power outage records; and uses ETL tools to extract, transform, and load the data, and integrate and store it in the data warehouse;

[0045] The data analysis module uses data analysis tools to perform multi-dimensional analysis on the power outage data, identify key factors affecting the power outage, and extract key feature sets;

[0046] The text analysis module obtains customer feedback and demand information, digitizes the customer feedback and demand information, and integrates the customer feedback and demand information into the feature set to enhance the analysis capability from the customer perspective;

[0047] The power outage warning module identifies key control objects through data analysis, including stations, meter boxes, meters with high failure rates, and user groups with greater impact, and assigns priorities to different objects based on the degree of power outage impact and customer demands; designs differentiated warning strategies for different objects, and integrates the warning strategies into the existing power outage management process to ensure that warning information can be transmitted and processed in a timely manner;

[0048] The visualization module displays power outage warning information and key indicators in real time, generates power outage warning analysis reports regularly, and notifies relevant personnel through the automatic distribution function.

[0049] The present invention has the following beneficial effects:

[0050] Based on the customer's perspective, the present invention conducts multi-dimensional analysis of panoramic power outages, realizes the visual monitoring dashboard and automatic report export function of panoramic power outages; and by combing through various links of power outage management and control, integrating customer demand information, identifying key control areas, meter boxes, meters, users and other objects, designs different early warning strategies for different objects, and integrates them into the business process, realizes the active discovery and control of power outage risks, and effectively improves the level of power outage management and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of the method of the present invention;

[0052] Figure 2 FIG. 4 is a system architecture diagram in one embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0054] refer to Figure 1 In this embodiment, a method for analyzing a power outage warning based on a customer's perspective is provided, comprising the following steps:

[0055] Obtain data from various sources, including customer feedback, equipment status, and historical outage records;

[0056] Use ETL tools to extract, transform and load data, and integrate and store it in the data warehouse;

[0057] Use data analysis tools to conduct multi-dimensional analysis of power outage data, identify key factors affecting power outages, and extract key feature sets, including equipment failure rate, load changes, and customer complaint frequency;

[0058] Obtain customer feedback and demand information, digitize customer feedback and demand information, and integrate it into feature sets to enhance customer perspective analysis capabilities;

[0059] Identify key control targets through data analysis, including stations, meter boxes, meters with high failure rates, and user groups with greater impact, and assign priorities to different targets based on the impact of the power outage and customer demands;

[0060] Design differentiated early warning strategies for different objects and integrate them into the existing power outage management process to ensure that early warning information can be transmitted and processed in a timely manner;

[0061] Develop a visual monitoring dashboard to display power outage warning information and key indicators in real time, generate power outage warning analysis reports regularly, and notify relevant personnel through automatic distribution functions.

[0062] In this embodiment, a data analysis tool is used to perform multi-dimensional analysis on the power outage data, including analysis of long-term severe low voltage conditions, analysis of low voltage week-long conditions, distribution analysis of low voltage and frequent power outage users, classification analysis of power outage causes for medium and low voltage users, classification statistical analysis of frequent power outages, and power outage analysis of high-fault multi-meter box;

[0063] Analysis of the long-term severe low voltage situation: set the HPLC low voltage user caliber and the long-term low voltage user caliber, monitor the HPLC daily low voltage and low voltage continuous points, and the cumulative number of low voltage days in the past 30 days; classify the monitored low voltage users into different levels, forming three voltage levels of 180-198, 160-179, and below 160; note that the monitored data range does not include voltage values ​​below 100V and power consumption below 60 degrees last month; calculate the number of long-term severe low voltage users, the proportion of low voltage users, and the month-on-month indicators of low voltage users;

[0064] Analysis of low voltage weekly duration: monitor the low voltage situation of HPLC users and non-HPLC users on a daily basis, formulate different continuous low voltage rules, calculate the number of users with continuous low voltage in the past 7 days, and classify them according to the longest continuous low voltage days (1-7 days); that is, within a week, divide them into 7 categories according to the number of users with low voltage for 7 consecutive days, the number of users with low voltage for 6 consecutive days, etc., and display them in a chart;

[0065] Distribution analysis of low-voltage and frequent power outage users: Monitor HPLC users with power consumption greater than 60 degrees last month, and analyze whether there are at least 4 consecutive points of voltage less than or equal to 198 volts every day. Then trace back 60 days to determine whether these users have had power outage records. If the number of power outages exceeds 2, such users are classified as low-voltage and frequent power outage users, and statistics are collected by city, district, and county to analyze the distribution of low-voltage and frequent power outage users;

[0066] Classification analysis of power outage reasons for medium and low voltage users: Taking months as the time unit, conduct multi-dimensional analysis of each power outage for medium and low voltage customers according to voltage level, plan or fault, fault location, and power outage times. Calculate the indicators of medium voltage fault power outage, planned power outage, and unpublished power outage information per 10,000 households in each city, as well as the indicators of low voltage fault power outage, planned power outage, low voltage distribution network power outage, multi-meter box power outage, and single household power outage per 10,000 households for unpublished power outage information;

[0067] Frequent power outage hierarchical statistical analysis: From the perspectives of users and users with frequent power outages in substations, statistical analysis is conducted on the number of power outages of users in the past 60 days. Power outages of 3 or more times during the period are classified as frequent power outages. Statistics are divided into five levels: provincial, municipal, district, county, power supply station, and grid according to different statistical periods such as day, week, and month. At the same time, the proportion of users with frequent power outages at each level in the unit and the same indicators are calculated;

[0068] Analysis of power outages for multi-meter boxes with high faults: Analyze the power outages for multi-meter boxes of low-voltage users, count the total number of multi-meter boxes and the number of high fault meter boxes by city, calculate the proportion of high fault meter boxes in the total number of multi-meter boxes, and the decline rate of high fault meter boxes; count the number of power outages for meter boxes with 2-5 power outages and more than 6 power outages, as well as the proportion of power outages 6 times or more.

[0069] In this embodiment, the key factors affecting power outages are identified and key feature sets are extracted, as follows:

[0070] Normalize the data so that all features have the same scale;

[0071] Support vector machine (SVM) is selected as the base model to evaluate the importance of features. The goal of SVM is to find a hyperplane that maximizes the class interval:

[0072]

[0073] subject toys i (w·x i +b)≥1;

[0074] Among them, w is the feature coefficient vector, which is used to evaluate the importance of the feature;

[0075] Initialize the RFE algorithm using the selected base model and specify the number of features to be selected;

[0076] Train the base model and calculate the importance w of each feature;

[0077] Based on feature importance, recursively remove the least important features;

[0078] The training and elimination process is repeated until the specified number of features is reached.

[0079] In this embodiment, the feedback and demand information of customers are obtained, digitized, and integrated into the feature set to enhance the analysis ability from the customer perspective, as follows:

[0080] Obtain the feedback and demand information of customers, remove HTML tags, special characters, stop words (such as "de", "shi", etc.), and perform word segmentation;

[0081] Use natural language processing (NLP) toolkits (such as NLTK, spaCy) for text cleaning;

[0082] Use sentiment analysis tools (such as VADER, TextBlob) to classify the sentiment of customer feedback (positive, neutral, negative), and use the sentiment score as a customer feedback feature;

[0083] Use topic modeling algorithms (such as Latent Dirichlet Allocation, LDA) to identify the main topics in the feedback, and use the topic distribution as a customer feedback feature;

[0084] Use the Bag of Words model to convert text data into numerical features as customer feedback features;

[0085] Integrate the processed customer feedback features with the existing feature set to form a complete feature set.

[0086] In this embodiment, different early warning strategies are designed for different objects, as follows:

[0087] Based on the previous analysis, identify different early warning objects, including high-failure-rate substations, meter boxes, meters, and user groups that are more affected;

[0088] Based on the failure rate and impact degree, classify the early warnings into different levels (such as high, medium, low), and each level corresponds to different response times and handling measures;

[0089] For high-failure-rate equipment, set up real-time monitoring and automatic early warning mechanisms to detect abnormalities in a timely manner and trigger early warnings; for key user groups, establish a rapid response mechanism to ensure power restoration priority in the event of a power outage; for general users, provide regular maintenance and inspections to reduce the occurrence of potential failures.

[0090] Define clear early warning trigger conditions, and trigger early warnings according to the trigger conditions, including the number of failures exceeding the threshold, a sharp increase in customer complaints, etc.

[0091] In this embodiment, different monitoring systems are set for substations, meter boxes, meters with high failure rates, and user groups that are more affected, as follows: substations, monitor load and equipment status, and warn of overload or failure; meter boxes, monitor temperature and current abnormalities, and warn of equipment damage; meters, monitor abnormal readings, and warn of metering errors or electricity theft; users, based on historical records and feedback, warn of service interruptions.

[0092] refer to Figure 2 ,In this embodiment, a panoramic power outage warning and analysis system based on customer perspective is also provided, including a data acquisition module, a data warehouse, a data analysis module, a text analysis module, a power outage warning module and a visualization module;

[0093] The data acquisition module acquires data from different sources, including customer feedback, equipment status, and historical power outage records; and uses ETL tools to extract, transform, and load the data, and integrate and store it in the data warehouse;

[0094] The data analysis module uses data analysis tools to perform multi-dimensional analysis on power outage data, identify key factors affecting power outages, and extract key feature sets, including equipment failure rate, load changes, and customer complaint frequency;

[0095] The text analysis module obtains customer feedback and demand information, digitizes the customer feedback and demand information, and integrates the customer feedback and demand information into the feature set to enhance the analysis capability from the customer perspective;

[0096] The power outage warning module identifies key control objects through data analysis, including stations, meter boxes, meters with high failure rates, and user groups with greater impact, and assigns priorities to different objects based on the degree of power outage impact and customer demands; designs differentiated warning strategies for different objects, and integrates the warning strategies into the existing power outage management process to ensure that warning information can be transmitted and processed in a timely manner;

[0097] The visualization module displays power outage warning information and key indicators in real time, generates power outage warning analysis reports regularly, and notifies relevant personnel through the automatic distribution function.

[0098] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A panoramic power outage warning analysis method based on customer perspective, characterized in that: The following steps are involved: Obtain data from various sources, including customer feedback, equipment status, and historical outage records; Use ETL tools to extract, transform and load data, and integrate and store it in the data warehouse; Use data analysis tools to conduct multi-dimensional analysis of power outage data, identify key factors affecting power outages, and extract key feature sets; Obtain customer feedback and demand information, digitize customer feedback and demand information, and integrate it into feature sets to enhance customer perspective analysis capabilities; Identify key control targets through data analysis, including stations, meter boxes, meters with high failure rates, and user groups with greater impact, and assign priorities to different targets based on the impact of the power outage and customer demands; Design differentiated early warning strategies for different objects and integrate them into the existing power outage management process to ensure that early warning information can be transmitted and processed in a timely manner; Develop a visual monitoring dashboard to display power outage warning information and key indicators in real time, generate power outage warning analysis reports regularly, and notify relevant personnel through automatic distribution functions.

2. According to the method for analyzing power outage warning based on customer perspective, it is characterized in that: The data analysis tool is used to perform multi-dimensional analysis on the power outage data, including analysis of long-term severe low voltage conditions, analysis of low voltage week-long conditions, distribution analysis of low voltage and frequent power outage users, classification analysis of power outage causes for medium and low voltage users, classification statistical analysis of frequent power outages, and analysis of power outage conditions for high-fault multi-meter boxes; The analysis of the long-term severe low voltage situation: set the HPLC low voltage user caliber and the long-term low voltage user caliber, monitor the HPLC daily low voltage and low voltage continuous points, and the cumulative number of low voltage days in the past 30 days; classify the monitored low voltage users into different levels, forming three voltage levels of 180-198, 160-179, and below 160; calculate the number of long-term severe low voltage users, the proportion of low voltage users, and the month-on-month indicators of the number of low voltage users; Analysis of low voltage week duration: monitor the low voltage situation of HPLC users and non-HPLC users on a daily basis, formulate different continuous low voltage rules, calculate the number of users with continuous low voltage in the past 7 days, and classify them according to the longest continuous low voltage days; Distribution analysis of low-voltage and frequent power outage users: Monitor HPLC users with power consumption greater than 60 degrees last month, and analyze whether there are at least 4 consecutive points of voltage less than or equal to 198 volts every day. Then trace back 60 days to determine whether these users have had power outage records. If the number of power outages exceeds 2, such users are classified as low-voltage and frequent power outage users, and statistics are collected by city, district, and county to analyze the distribution of low-voltage and frequent power outage users; Classification analysis of power outage reasons for medium and low voltage users: Taking months as the time unit, conduct multi-dimensional analysis of each power outage for medium and low voltage customers according to voltage level, plan or fault, fault location, and power outage times. Calculate the indicators of medium voltage fault power outage, planned power outage, and unpublished power outage information per 10,000 households in each city, as well as the indicators of low voltage fault power outage, planned power outage, low voltage distribution network power outage, multi-meter box power outage, and single household power outage per 10,000 households for unpublished power outage information; Frequent power outage hierarchical statistical analysis: From the perspectives of users and users with frequent power outages in substations, statistical analysis is conducted on the number of power outages of users in the past 60 days. Power outages of 3 or more times during the period are classified as frequent power outages. Statistics are divided into five levels: provincial, municipal, district, county, power supply station, and grid according to different statistical periods such as day, week, and month. At the same time, the proportion of users with frequent power outages at each level in the unit and the same indicators are calculated; Analysis of power outages for multi-meter boxes with high faults: Analyze the power outages for multi-meter boxes of low-voltage users, count the total number of multi-meter boxes and the number of high fault meter boxes by city, calculate the proportion of high fault meter boxes in the total number of multi-meter boxes, and the decline rate of high fault meter boxes; count the number of power outages for meter boxes with 2-5 power outages and more than 6 power outages, as well as the proportion of power outages 6 times or more.

3. The method for analyzing power outage warning based on customer perspective according to claim 2 is characterized in that: The key factors affecting power outages are identified and key feature sets are extracted, as follows: Normalize the data so that all features have the same scale; Support vector machine (SVM) is selected as the base model to evaluate the importance of features. The goal of SVM is to find a hyperplane that maximizes the class interval: subject toyi(w·Xi+b)≥1 Among them, w is the feature coefficient vector, which is used to evaluate the importance of the feature; Initialize the RFE algorithm using the selected base model and specify the number of features to be selected; Train the base model and calculate the importance w of each feature; Based on feature importance, recursively remove the least important features; The training and elimination process is repeated until the specified number of features is reached.

4. The method for analyzing power outage warning based on customer perspective according to claim 1, characterized in that: The customer feedback and demand information is obtained, digitized, and integrated into the feature set to enhance the analysis capability from the customer perspective, as follows: Obtain customer feedback and demand information, remove HTML tags, special characters, stop words, and perform word segmentation; Use natural language processing toolkits for text cleaning; Use sentiment analysis tools to classify customer feedback and use sentiment scores as customer feedback features; Use topic modeling algorithms to identify the main topics in the feedback and use the topic distribution as customer feedback features; Use the bag-of-words model to convert text data into numerical features as customer feedback features; Integrate the processed customer feedback features with the existing feature set to form a complete feature set.

5. The method for analyzing power outage warning based on customer perspective according to claim 1, characterized in that: The differentiated early warning strategies designed for different objects are as follows: Based on the preliminary analysis, different warning targets are identified, including substations, meter boxes, meters with high failure rates, and user groups with greater impact; According to the failure rate and impact, the warning is divided into different levels, and each level corresponds to different response time and treatment measures; For equipment with high failure rates, set up real-time monitoring and automatic warning mechanisms to detect anomalies and trigger warnings in a timely manner; For key user groups, establish a rapid response mechanism to ensure priority restoration of power supply when a power outage occurs; For general users, provide regular maintenance and inspection to reduce the occurrence of potential failures. Define clear warning trigger conditions and trigger warnings based on the trigger conditions.

6. The method for analyzing power outage warning based on customer perspective according to claim 5, characterized in that: Different monitoring systems are set up for the high failure rate areas, meter boxes, meters, and user groups with greater impact, as follows: the area monitors the load and equipment status, and warns of overload or failure; the meter box monitors temperature and current anomalies, and warns of equipment damage; the meter monitors abnormal readings, and warns of metering errors or electricity theft; Users,are warned of service disruptions based on historical records and feedback.

7. A panoramic power outage warning analysis system based on customer perspective, characterized in that: It includes data collection module, data warehouse, data analysis module, text analysis module, power outage warning module and visualization module; The data acquisition module acquires data from different sources, including customer feedback, equipment status, and historical power outage records; and uses ETL tools to extract, transform, and load the data, and integrate and store it in the data warehouse; The data analysis module uses data analysis tools to perform multi-dimensional analysis on the power outage data, identify key factors affecting the power outage, and extract key feature sets; The text analysis module obtains customer feedback and demand information, digitizes the customer feedback and demand information, and integrates the customer feedback and demand information into the feature set to enhance the analysis capability from the customer perspective; The power outage warning module identifies key control objects through data analysis, including areas, meter boxes, meters with high failure rates, and user groups with greater impact, and assigns priorities to different objects based on the degree of power outage impact and customer demands; Design differentiated early warning strategies for different objects and integrate them into the existing power outage management process to ensure that early warning information can be transmitted and processed in a timely manner; The visualization module displays power outage warning information and key indicators in real time, generates power outage warning analysis reports regularly, and notifies relevant personnel through the automatic distribution function.

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