Intelligent electric meter data extraction and analysis method and system for fire investigation

Abstract

The invention relates to an intelligent electric meter data extraction and analysis method and system for fire investigation, and the method comprises the steps: data collection: automatically generating a data collection scheme according to an investigation demand, and collecting the data of an electric meter; data analysis: analyzing the original data obtained from the intelligent electric meter according to a protocol, converting the original data into readable data information, and extracting the data information; data analysis: according to the analyzed data, drawing an electricity consumption curve and a load curve, identifying abnormal fluctuation, and comparing and analyzing the abnormal fluctuation with historical data; and report generation: generating a detailed analysis report, and providing visual evidence support for fire investigation. Data acquisition is automatic, human intervention is reduced, and the efficiency and accuracy of data acquisition are improved; based on an analysis mode of a protocol, the system can be compatible with various intelligent electric meters; the data analysis process is automatic, a power utilization analysis report can be quickly generated, and investigators are assisted in judging fire reasons related to power.

Description

Technical Field

[0001] The present invention relates to the technical field of fire investigation, and in particular to a method and system for extracting and analyzing smart meter data for fire investigation. Background Art

[0002] After a fire occurs, the investigation and analysis of the cause of the accident requires a large amount of data support. Traditional fire investigations mainly rely on on-site trace analysis and personal testimony, with limited data acquisition channels and uncertainty. In modern fire investigations, power data (such as power consumption, load data, event data, etc.) has become an important reference, which can reflect the power consumption before and after the fire and help investigators identify anomalies. The existing power data acquisition methods are relatively simple, mostly manual operations, low efficiency, and lack of systematic data parsing and analysis tools.

[0003] After the popularization of smart meters, the comprehensiveness and timeliness of meter data provide a new data source for fire investigation. However, there is currently no meter data collection and analysis system designed specifically for fire investigation, which cannot fully play the role of meter data in fire investigation. Summary of the invention

[0004] In view of the above-mentioned defects of the prior art, the present invention proposes a smart meter data extraction and analysis method and system for fire investigation, which can connect the smart meter through an RS485 to USB converter, read and analyze the meter data, generate analysis reports such as power consumption and load curves, and provide strong data support for fire cause investigation.

[0005] To achieve the above object, the present invention adopts the following technical solution: a method for extracting and analyzing smart meter data for fire investigation, comprising the following steps: Data collection: automatically generates a data collection plan based on survey requirements to collect basic data, event data, frozen data and load data of electric meters; Data analysis: parse the raw data obtained from the smart meter according to the protocol, convert it into readable data information, and extract data information including but not limited to voltage, current, power, and power consumption; Data analysis: based on the parsed data, draw the power consumption curve and load curve, identify abnormal fluctuations, and compare and analyze with historical data; Report generation: Generate detailed analysis reports, including but not limited to information summary, chart display and text description, to provide intuitive evidence support for fire investigation.

[0006] Furthermore, the data collection adopts a dynamic adaptation strategy. Before starting formal data collection, the response time of the electric meter is dynamically queried and analyzed with 50 milliseconds as a time unit. By monitoring the response speed of the electric meter at different time points, the communication rhythm and data request frequency can be automatically adjusted to adapt to the communication performance of the electric meter in different environments.

[0007] Furthermore, a data collection plan is automatically generated according to the investigation requirements, and the plan includes specific collection data items, including but not limited to voltage, current, power, total power consumption, frozen data, historical event records, and based on the model of the smart meter, the protocol support scope and the specific needs of the fire investigation, a collection list suitable for the scenario is generated.

[0008] Furthermore, the data collection also includes: Automatic device identification: the data acquisition module automatically identifies the meter model, protocol version, and supported data items; Cyclic data collection and status monitoring: continuously monitor the status of the meter and determine in real time whether the meter is online. If the meter is online, data collection will be performed according to the predetermined collection plan. If communication abnormalities are detected, the data will be automatically recorded and an attempt will be made to reconnect or adjust the collection frequency. Exception handling and retry mechanism, during the collection process, if data error or loss is detected, the retry mechanism is automatically triggered, and the collection request is resent based on the meter's response.

[0009] Furthermore, the specific process of the data analysis is as follows: Data frame analysis: preliminary analysis of the data frame of the received meter data to identify the structure of the data frame, including but not limited to the key fields of the frame start character, address field, control code, data field, and check code; Protocol matching and selection: automatically select DLT645-2007 or DLT645-1997 protocol for parsing according to the meter type and the collected data frame structure, and parse the data in the data frame according to the preset protocol parsing rules; Data domain parsing: after determining the protocol, parse the specific content in the data domain, including but not limited to power consumption, load data, and event records. According to the data format and location definition in the protocol, convert the hexadecimal data into human-readable values ​​or event types, and store them in the corresponding data structure according to the definition of the data item; Verification and confirmation: After parsing is completed, the data is verified, including verification of the checksum and data rationality to ensure the accuracy of the parsed data. If the verification fails, the error handling mechanism is triggered. The specific error handling mechanism is as follows: Error logging: records error information into the system log, including meter address, data frame content, and error type; Retry mechanism: the system attempts to re-request the data frame and performs three retries; if it still fails, the system marks the data as unparseable and notifies the upper-layer application; Alarm triggering: If the number of data parsing failures exceeds the preset threshold, the system will send an alarm to the user to remind them that there may be communication anomalies or equipment failures.

[0010] Furthermore, when data content is abnormal during data parsing, the following mechanisms are adopted: Anomaly detection: the system has a built-in data rationality check mechanism to automatically detect whether the parsed data is within a reasonable range; Data marking: For abnormal data, the system will mark it specially and explain it in the subsequent analysis report to remind investigators to pay attention; Abnormal report generation: the system generates an abnormal data report, which records in detail the project information, abnormal content and occurrence time of the abnormal data to help users conduct further investigation and analysis.

[0011] Furthermore, the data analysis includes: Data preprocessing: preprocessing the raw data obtained from the analysis module, including data cleaning, outlier processing and data format conversion; Curve drawing: draw power consumption curve and load curve according to the preprocessed data; Abnormal fluctuation identification: use a specific algorithm to analyze the drawn curve, identify the abnormal fluctuations, and mark them on the curve; Data comparison analysis compares the current analysis data with historical data to identify significant differences and further help investigators find potential fire causes. The data comparison analysis methods include: Historical comparison: compare the current data curve with the historical data of the same period to identify significant differences; Multi-meter comparison: In scenarios where there are multiple meters, data from different meters can be compared to analyze differences in electricity usage in different areas and identify abnormal areas. Event comparison: compare electricity consumption data with known fire events, analyze the changing patterns of electricity consumption in similar events, and provide reference for the investigation of new events.

[0012] Furthermore, the specific algorithm is used to analyze the drawn curve. Specifically, the specific algorithm is to analyze the curve data in sections by a sliding window algorithm. The size of the sliding window is set by the user or adjusted dynamically. The following indicators are calculated for each window: Mean, calculates the average value of power consumption or load within the window; Standard deviation, which calculates the standard deviation of the data within the window and is used to measure the degree of dispersion of the data; Rate of change, calculates the rate of change of data within a window and is used to identify rapidly changing trends.

[0013] Furthermore, the report generation is specifically as follows: Data aggregation, which aggregates all data obtained from the data parsing module and the data analysis module, including but not limited to power consumption, load curves, and abnormal fluctuation marks; Chart generation: automatically generate a variety of charts based on the aggregated data to display data change trends and abnormal points; the charts include power consumption curves, load curves, event timelines, and comparative analysis charts; Report compilation: compiling data, charts and text descriptions into the final report document according to the established template. During the compilation process, the layout of the content and the key display parts are automatically adjusted according to the survey requirements; Report export: export the compiled report as Word document or PDF document.

[0014] The present invention also proposes a system for realizing the above-mentioned smart meter data extraction and analysis method for fire investigation, comprising: The data collection module is used to automatically generate a data collection plan according to the survey requirements to collect the basic data, event data, frozen data and load data of the electric meter.

[0015] The data analysis module is used to parse the raw data obtained from the smart meter according to the protocol, convert it into readable data information, and extract data information including but not limited to voltage, current, power, and power consumption.

[0016] Data analysis module, used to draw power consumption curve and load curve based on the parsed data, identify abnormal fluctuations, and compare and analyze with historical data; The report generation module is used to generate detailed analysis reports, including but not limited to information summary, chart display and text description, to provide intuitive evidence support for fire investigation.

[0017] The present invention has the following technical effects: 1. The present invention automates data collection, reduces human intervention, and improves the efficiency and accuracy of data acquisition; the parsing method based on the DLT645-2007 / DLT645-1997 protocol enables the system to be compatible with a variety of smart meters; the data analysis process is automated, and power consumption analysis reports can be quickly generated to assist investigators in determining the causes of power-related fires.

[0018] 2. The application of the present invention in fire investigation has the following advantages: High efficiency: Automated data collection and analysis greatly improve investigation efficiency and reduce manual operation errors; Accuracy: Parsing based on standard protocols and multi-level error handling mechanisms ensure the accuracy and completeness of data; Intuitiveness: Graphical analysis results and detailed report layout help investigators quickly understand and apply data; Reliability: The effectiveness of the system is verified through actual cases, proving its applicability and stability in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The present invention is a flow chart of a smart meter data extraction and analysis method for fire investigation. Figure 2 The present invention is a structural block diagram of a smart meter data extraction and analysis system for fire investigation. DETAILED DESCRIPTION

[0020] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings to make the technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0021] like Figure 1 As shown, the present invention proposes a smart meter data extraction and analysis method for fire investigation, comprising the following steps: S1. Data collection: automatically generates a data collection plan based on the survey requirements to collect basic data, event data, frozen data and load data of the electric meter; S2. Data analysis: parse the raw data obtained from the smart meter according to the protocol, convert it into readable data information, and extract data information including but not limited to voltage, current, power, and power consumption; S3. Data analysis: based on the analyzed data, draw the power consumption curve and load curve, identify abnormal fluctuations, and compare and analyze with historical data; S4. Report generation: Generate a detailed analysis report, including but not limited to information summary, chart display and text description, to provide intuitive evidence support for fire investigation.

[0022] Among them, data collection, data parsing, data analysis, and report generation are specifically as follows: Data collection: Connect the 485 interface of the smart meter through the RS485 to USB converter to collect the basic data, event data, frozen data and load data of the meter. Collection process: construct a communication data packet according to the data items collected in the collection plan, send it to the meter, wait for the meter to respond, and obtain the data packet responded by the meter. If the meter responds normally, parse the collected meter data and convert the raw data into readable information; If the data item has a large amount of data and cannot be fully responded to in one time, a communication data packet needs to be constructed to read subsequent data.

[0023] If the meter responds that there is no such data or the response times out, read the next data item.

[0024] The design focus of the data acquisition module is on its efficiency, accuracy and adaptability to various meter models.

[0025] Before data collection, the system will automatically generate a data collection plan based on the investigation requirements. The plan includes specific data items to be collected, such as voltage, current, power, total power consumption, frozen data, historical event records, etc. The system will generate a collection list suitable for the scenario based on the model of the smart meter, the scope of protocol support, and the specific needs of the fire investigation. The system will collect the meter item by item based on this collection plan to ensure that the required data can be obtained completely and accurately.

[0026] Dynamic adaptation strategy: To improve the success rate and efficiency of data collection, the system adopts a dynamic adaptation strategy. Before starting formal data collection, the system will dynamically query and analyze the response time of the meter with a time unit of 50 milliseconds. By monitoring the response speed of the meter at different time points, the system can automatically adjust the communication rhythm and data request frequency to adapt to the communication performance of the meter in different environments. This dynamic adaptation strategy ensures that the system can collect data stably and efficiently under various communication conditions.

[0027] The system's automated data collection solution is designed to reduce human intervention and improve the automation level of the data collection process. Automatic device identification: When the system is connected to a smart meter, the data collection module will automatically identify the meter model, protocol version, and supported data items; cyclic collection and status monitoring: The system will continuously monitor the status of the meter and determine in real time whether the meter is online. If the meter is online, the system will collect data according to the predetermined collection plan; if a communication anomaly is detected, the system will automatically record and try to reconnect or adjust the collection frequency. Exception handling and retry mechanism: During the collection process, if the system detects data errors or loss, the module will automatically trigger the retry mechanism and resend the collection request based on the meter's response to ensure data integrity and accuracy.

[0028] Data analysis The raw data obtained from the smart meter is parsed according to the DLT645-2007 and DLT645-1997 protocols, converted into readable data information, and provides basic data support for subsequent analysis and report generation. The design focus of the module is on compatibility, multi-protocol support, parsing accuracy, and the ability to handle data errors and abnormal situations. The data parsing module follows the following parsing process: (1) Data frame analysis: The meter data received from the data acquisition module is usually presented in the form of data frames. The analysis module will first perform a preliminary analysis of these data frames to identify the structure of the data frames, including key fields such as the frame start character, address field, control code, data field, and check code.

[0029] (2) Protocol matching and selection: The system supports two meter communication protocols, DLT645-2007 and DLT645-1997. The data parsing module will automatically select the corresponding protocol for parsing based on the meter type and the structure of the collected data frame. The module has built-in parsing rules for the two protocols and can correctly parse the data in the data frame according to the protocol definition.

[0030] (3) Data domain analysis: After determining the protocol, the system will analyze the specific content in the data domain, including power consumption, load data, event records, etc. The analysis module converts the hexadecimal data into human-readable values ​​or event types according to the data format and location definition in the protocol, and stores them in the corresponding data structure according to the definition of the data item.

[0031] (4) Verification and confirmation: After parsing is completed, the module will verify the data, including verification of the checksum and data rationality check, to ensure the accuracy of the parsed data. If the verification fails, the system will trigger the error handling mechanism.

[0032] Various errors may occur during data parsing. The data parsing module has designed a multi-level error handling mechanism for these error situations to ensure the robustness of the system and the integrity of the data. During the data frame parsing process, if the checksum verification fails, it means that the data may have an error during the transmission process. At this time, the system will take the following steps: (1) Error logging: The error information is recorded in the system log, including the meter address, data frame content, and error type.

[0033] (2) Retry mechanism: The system will try to re-request the data frame, generally performing three retries. If it still fails, the system will mark the data as unparseable and notify the upper-layer application.

[0034] (3) Alarm triggering: If the number of data parsing failures exceeds the preset threshold, the system will issue an alarm to the user, reminding him that there may be communication anomalies or equipment failures.

[0035] Data exception processing: During the parsing process, data content may be abnormal, such as load data exceeding a reasonable range, negative power consumption, etc. For these abnormal situations, the module provides the following processing mechanisms: (1) Anomaly detection: The system has a built-in data rationality check mechanism that automatically detects whether the parsed data is within a reasonable range.

[0036] (2) Data marking: For abnormal data, the system will specially mark it and explain it in the subsequent analysis report to draw the attention of investigators.

[0037] (3) Abnormal report generation: The system will generate an abnormal data report, which will record in detail the project information, abnormal content and occurrence time of the abnormal data to help users conduct further investigation and analysis.

[0038] 3. Data Analysis Based on the analyzed data, the power consumption curve and load curve are drawn, abnormal fluctuations are identified, and data comparison and analysis are performed. These analysis results provide key data support for fire cause investigation. The design focus of the data analysis module is on efficient algorithm implementation, accurate abnormality identification, and flexible threshold setting.

[0039] The main workflow of data analysis is as follows: (1) Data preprocessing: The system first preprocesses the raw data obtained from the analysis module, including data cleaning, outlier processing, and data format conversion. The preprocessed data is neater and more standardized, which is convenient for subsequent analysis.

[0040] (2) Curve drawing: The system draws power consumption curves and load curves based on the preprocessed data. These curves can intuitively show the changing trend of power usage, especially the power usage before and after the fire.

[0041] (3) Abnormal fluctuation identification: The system uses a specific algorithm to analyze the drawn curve, identify abnormal fluctuations, and mark them on the curve.

[0042] (4) Data comparison and analysis: The system supports comparing the currently analyzed data with historical data to identify significant differences, further helping investigators discover potential fire causes.

[0043] Abnormal fluctuation identification algorithm, the system uses an algorithm based on statistics and machine learning to identify abnormal fluctuations in power consumption and load curves. The specific algorithm includes the following steps: (1) Sliding Window Algorithm The system uses a sliding window algorithm to analyze the curve data in segments. The size of the sliding window can be set by the user or adjusted dynamically. For each window, the system calculates the following indicators: Mean: Calculates the average value of power consumption or load within the window.

[0044] Standard Deviation: Calculates the standard deviation of the data within the window, which is used to measure the degree of dispersion of the data.

[0045] Rate of change: Calculates the rate of change of data within a window to identify rapidly changing trends.

[0046] By calculating these indicators, the system is able to identify areas that differ significantly from the normal range and may represent abnormal fluctuations.

[0047] (2) Threshold setting In order to more accurately identify abnormal fluctuations, the system allows users to customize or automatically generate thresholds. The main thresholds include the following: Static threshold: A fixed threshold set by the user based on experience, used to identify abnormal fluctuations that exceed the threshold. For example, if the power consumption exceeds a certain preset value, the system will mark it as abnormal.

[0048] Dynamic threshold: The system automatically generates thresholds based on historical data and can adapt to different usage environments. For example, the system can dynamically adjust the threshold based on the data fluctuation range of the same period in history.

[0049] (3) Abnormal classification and labeling The system not only identifies abnormal fluctuations, but also categorizes and labels them. For example, based on the amplitude, duration, frequency and other parameters of the fluctuation, the abnormalities are classified into three categories: mild, moderate and severe, making it easier for investigators to quickly locate key issues.

[0050] The data comparison and analysis function can help investigators find the potential reasons behind abnormal fluctuations. The system supports the following comparative analysis methods: (1) Historical comparison: Compare the current data curve with the historical data of the same period to identify significant differences. This function helps to find abnormal changes in electricity consumption before and after a fire occurs.

[0051] (2) Multi-meter comparison: In scenarios where there are multiple meters, the system can compare the data from different meters, analyze the differences in electricity consumption in different areas, and identify abnormal areas.

[0052] (3) Event comparison: The system can compare electricity consumption data with known fire events, analyze the changing patterns of electricity consumption in similar events, and provide reference for the investigation of new events.

[0053] The data analysis module uses a variety of analysis algorithms to ensure the accuracy and reliability of the analysis results: (1) Regression analysis: used to predict future electricity consumption trends and identify data points that differ greatly from the predicted values, which may be abnormal fluctuations.

[0054] (2) Cluster analysis: Classify similar electricity usage patterns and identify abnormal electricity usage behaviors that do not match normal patterns.

[0055] (3) Time series analysis: For load data, the system uses time series analysis technology to identify periodic fluctuations and abnormal peaks.

[0056] 4. Report Generation The system generates data analysis reports, including information summary and chart display, to provide intuitive evidence support for fire investigation. The workflow of the report generation module includes the following steps: (1) Data aggregation: The module first aggregates all data obtained from the data parsing module and the data analysis module, including power consumption, load curve, abnormal fluctuation marks, etc.

[0057] (2) Chart generation: The system automatically generates a variety of charts based on the aggregated data, such as power consumption curves, load curves, event timelines, etc. These charts intuitively display data change trends and anomalies.

[0058] (3) Report layout: The system will compile data, charts and text descriptions into a final report document according to a predetermined template. During the layout process, the module will automatically adjust the layout of the content and the key display parts according to the survey requirements.

[0059] (4) Report export: The compiled report can be exported as a Word document, etc., which is convenient for users to archive and print.

[0060] The first step of the report generation module is to summarize all relevant data in the system. It mainly includes: Basic information: including basic data such as meter number, collection time, and equipment location, as the background information part of the report.

[0061] (1) Electricity consumption data: The system summarizes the collected electricity consumption data, calculates key indicators such as total electricity consumption, average electricity consumption, maximum and minimum electricity consumption, and displays daily and hourly changes in electricity consumption.

[0062] (2) Load data: The system summarizes load data, including average load, peak load, load fluctuation range, etc. The changes in load data before and after the fire may reveal important clues, which will be highlighted in the report.

[0063] (3) Event data: Event data read by the system from the electricity meter, such as voltage anomalies, power overloads, power outage records, etc., will also be summarized in the report as a basis for auxiliary analysis.

[0064] (4) Abnormal fluctuations: The abnormal fluctuations and classification results identified by the data analysis module will be summarized here, and the report will detail the time point, severity and possible impact of the abnormality.

[0065] The report generation module automatically generates a variety of charts to make the analysis results more intuitive. The main charts include: (1) Electricity consumption curve: shows the changing trend of electricity consumption over time, with emphasis on the time points and ranges of abnormal fluctuations.

[0066] (2) Load curve chart: Displays the fluctuation of load data and identifies load anomalies that may be related to fire.

[0067] (3) Event timeline: Arrange the meter event data in chronological order to form a timeline diagram to help investigators understand the sequence of events and possible causal relationships.

[0068] (4) Comparative analysis chart: For data from multiple electricity meters, the system can generate comparative charts that can show the differences in electricity consumption in different areas or time periods, helping to identify abnormal areas or time periods.

[0069] like Figure 2 As shown, the present invention also proposes a system for realizing the above-mentioned smart meter data extraction and analysis method for fire investigation, comprising: The data collection module 101 is used to automatically generate a data collection plan according to the survey requirements, and collect the basic data, event data, frozen data and load data of the electric meter.

[0070] The data analysis module 102 is used to analyze the original data obtained from the smart meter according to the protocol, convert it into readable data information, and extract data information including but not limited to voltage, current, power, and power consumption.

[0071] The data analysis module 103 is used to draw the power consumption curve and the load curve according to the analyzed data, identify abnormal fluctuations, and compare and analyze with historical data; The report generation module 104 is used to generate a detailed analysis report, including but not limited to information summary, chart display and text description, to provide intuitive evidence support for fire investigation.

[0072] The smart meter data collection and analysis system of the present invention has broad application prospects in actual fire investigations, and its main application scenarios include but are not limited to the following: Commercial building fire investigation: After a fire occurs in a large commercial building (such as a shopping mall or office building), investigators need to analyze the power consumption before and after the fire to determine whether the electrical equipment has electrical faults such as overload or short circuit, and then determine the cause of the fire. Residential fire investigation: In residential fires, the system can collect data from smart meters to analyze power consumption fluctuations during the fire period, identify whether there are problems such as abnormal operation of electrical equipment or aging of wires, and assist in determining the cause of the fire. Industrial plant fire investigation: The power system of industrial plants is complex, and fire investigations require detailed power consumption data support. The system can help investigators identify abnormal power consumption of production equipment and analyze whether the fire was caused by electrical equipment failure. Public facility fire investigation: In public facility (such as hospitals and schools) fire investigations, the system can provide comprehensive power consumption data support to help investigators analyze the operating conditions of the power system during the fire and evaluate the impact of electrical equipment on the fire.

[0073] In conjunction with the present invention, a fire accident occurred in a commercial complex in May 2024, which was initially suspected to be caused by electrical equipment failure. Investigators used this system to analyze the smart meter data at the scene to determine the cause of the fire.

[0074] Operation process: Data collection: Connect the smart meter via RS485 to USB converter to collect electricity consumption data within 1 hour before and after the fire.

[0075] Data analysis: The system analyzes data according to the DLT645-2007 protocol and extracts information such as voltage, current, power, and power consumption.

[0076] Data analysis: Draw electricity consumption curves and load curves, identify abnormal fluctuations, and compare with historical data.

[0077] Report generation: Generate detailed analysis reports including charts and text descriptions.

[0078] Analysis results: Electricity consumption curve: Electricity consumption gradually increased in the 30 minutes before the fire, exceeding the historical average by 20%, and then dropped sharply to zero when the fire broke out.

[0079] Load curve: The load increased significantly within 10 minutes before the fire, reached a peak, and then dropped rapidly, indicating that the electrical equipment may fail due to overload.

[0080] Event record: The system recorded an overload alarm 10 minutes before the fire, followed by voltage fluctuations and power outages.

[0081] Comparative analysis: Compared with historical data, the power consumption and load during the fire period were significantly higher than usual, confirming that electrical overload was the main cause of the fire.

[0082] Conclusion: Through the system's comprehensive data collection and analysis, investigators confirmed that the fire was caused by electrical equipment overload. The report generated by the system provides a scientific basis for legal proceedings and subsequent preventive measures.

[0083] In summary, the present invention has the following advantages in fire investigation: Efficiency: Automated data collection and analysis greatly improves survey efficiency and reduces manual operation errors.

[0084] Accuracy: Parsing based on standard protocols and multi-level error handling mechanisms ensures data accuracy and integrity.

[0085] Intuitive: Graphical analysis results and detailed report layout help investigators quickly understand and apply data.

[0086] Reliability: Verify the effectiveness of the system through actual cases and prove its applicability and stability in different scenarios.

[0087] The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary technicians in the field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A smart meter data extraction and analysis method for fire investigation, characterized in that: The following steps are involved: Data collection, automatically generates a data collection plan based on investigation requirements, collects basic data, event data, frozen data and load data of the electricity meter; data analysis, parses the raw data obtained from the smart meter according to the protocol, converts it into readable data information, and extracts data information including but not limited to voltage, current, power, and electricity consumption; data analysis, draws electricity consumption curves and load curves based on the parsed data, identifies abnormal fluctuations, and compares and analyzes with historical data; report generation, generates a detailed analysis report, including but not limited to information summary, chart display and text description, to provide intuitive evidence support for fire investigation.

2. A smart meter data extraction and analysis method for fire investigation according to claim 1, characterized in that: The data collection adopts a dynamic adaptation strategy. Before starting formal data collection, the response time of the electric meter is dynamically queried and analyzed with 50 milliseconds as a time unit. By monitoring the response speed of the electric meter at different time points, the communication rhythm and data request frequency can be automatically adjusted to adapt to the communication performance of the electric meter in different environments.

3. The method for extracting and analyzing smart meter data for fire investigation according to claim 1, characterized in that: The method automatically generates a data collection plan based on the investigation requirements. The plan includes specific collection data items, including but not limited to voltage, current, power, total power consumption, frozen data, and historical event records. A collection list suitable for the scenario is generated based on the model of the smart meter, the scope of protocol support, and the specific needs of the fire investigation.

4. The method for extracting and analyzing smart meter data for fire investigation according to claim 1, characterized in that: The data collection also includes: automatic device identification, the data collection module automatically identifies the model, protocol version and supported data items of the electric meter; cyclic collection and status monitoring, continuously monitoring the status of the electric meter, and judging in real time whether the electric meter is online. If the electric meter is online, data collection will be performed according to the predetermined collection plan; if communication abnormalities are detected, it will automatically record and try to reconnect or adjust the collection frequency; exception handling and retry mechanism, during the collection process, if data errors or losses are detected, the retry mechanism will be automatically triggered, and the collection request will be resent according to the response of the electric meter.

5. The method for extracting and analyzing smart meter data for fire investigation according to claim 1, characterized in that: The specific process of data parsing is as follows: data frame parsing, preliminary parsing of the data frame of the received meter data, identifying the structure of the data frame, including but not limited to the key fields of the frame start character, address field, control code, data field, and check code; protocol matching and selection, automatically selecting DLT645-2007 or DLT645-1997 protocol for parsing according to the meter type and the collected data frame structure, and parsing the data in the data frame according to the preset protocol parsing rules; data field parsing, after determining the protocol, parsing the specific content in the data field, including but not limited to power consumption, load data, event records, converting the hexadecimal data into human-readable numerical values ​​or event types according to the data format and position definition in the protocol, and storing them in the corresponding data structure according to the definition of the data item; verification and confirmation, after the parsing is completed, verifying the data, including check code verification and data rationality check, to ensure the accuracy of the parsed data; If the verification fails, the error handling mechanism is triggered. The specific error handling mechanism is as follows: error logging, which records the error information in the system log, including the meter address, data frame content, and error type; Retry mechanism: the system attempts to re-request the data frame and performs three retries; if it still fails, the system marks the data as unparseable and notifies the upper-layer application; Alarm triggering: If the number of data parsing failures exceeds the preset threshold, the system will send an alarm to the user to remind them that there may be communication anomalies or equipment failures.

6. A method for extracting and analyzing smart meter data for fire investigation according to claim 5, characterized in that: It also includes the following mechanisms when abnormal data content appears during data analysis: anomaly detection, the system has a built-in data rationality check mechanism to automatically detect whether the parsed data is within a reasonable range; data marking, the system will specially mark abnormal data and explain it in the subsequent analysis report to prompt investigators to pay attention; abnormal report generation, the system generates an abnormal data report, which records in detail the project information, abnormal content and occurrence time of the abnormal data, to help users conduct further investigation and analysis.

7. The method for extracting and analyzing smart meter data for fire investigation according to claim 1, characterized in that: The data analysis includes: data preprocessing, preprocessing the original data obtained from the analysis module, including data cleaning, outlier processing and data format conversion; curve drawing, drawing the power consumption curve and load curve according to the preprocessed data; abnormal fluctuation identification, using a specific algorithm to analyze the drawn curve, identify the abnormal fluctuations therein, and mark them on the curve; data comparison analysis, comparing the currently analyzed data with historical data, identifying significant differences, and further helping investigators to discover potential fire causes, wherein the data comparison and analysis methods include: historical comparison, comparing the current data curve with the historical data of the same time period, and identifying significant differences; multi-meter comparison, in the scenario where there are multiple meters, comparing the data of different meters, analyzing the differences in electricity consumption in different areas, and identifying abnormal areas; event comparison, comparing the electricity consumption data with known fire events, analyzing the electricity consumption change patterns in similar events, and providing references for the investigation of new events.

8. The method for extracting and analyzing smart meter data for fire investigation according to claim 7, characterized in that: The specific algorithm is used to analyze the drawn curve. The specific algorithm is specifically to analyze the curve data in sections through a sliding window algorithm. The size of the sliding window is set by the user or adjusted dynamically. The following indicators are calculated for each window: mean, which calculates the average value of the power consumption or load in the window; standard deviation, which calculates the standard deviation of the data in the window, which is used to measure the degree of dispersion of the data; Rate of change, calculates the rate of change of data within a window and is used to identify rapidly changing trends.

9. The method for extracting and analyzing smart meter data for fire investigation according to claim 1, characterized in that: The report generation includes: data aggregation, which aggregates all data obtained from the data parsing module and the data analysis module, including but not limited to power consumption, load curve, and abnormal fluctuation mark content; chart generation, which automatically generates a variety of charts based on the aggregated data to display data change trends and abnormal points; the charts include power consumption curve charts, load curve charts, event timelines, and comparative analysis charts; Report compilation: compiling data, charts and text descriptions into the final report document according to the established template. During the compilation process, the layout of the content and the key display parts are automatically adjusted according to the survey requirements; Report export: export the compiled report as Word document or PDF document.

10. A system for implementing the smart meter data extraction and analysis method for fire investigation according to any one of claims 1 to 9, characterized in that: include: The data acquisition module is used to automatically generate a data acquisition plan according to the investigation requirements, and collect the basic data, event data, frozen data and load data of the meter. The data parsing module is used to parse the raw data obtained from the smart meter according to the protocol, convert it into readable data information, and extract data information including but not limited to voltage, current, power, and power consumption. The data analysis module is used to draw power consumption curves and load curves based on the parsed data, identify abnormal fluctuations, and compare and analyze with historical data; the report generation module is used to generate a detailed analysis report, including but not limited to information summary, chart display and text description, to provide intuitive evidence support for fire investigation.

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